Abstract

This paper examines three keys to successful performance in competitive distance running: maximal oxygen uptake (VO₂MAX), lactate threshold, and running economy. Although these are each influenced by genetics, certain controllable factors affect the extent of each input that a runner can use in a race. This review looked at research on the following seven such factors: hydration, nutrition, sleep and recovery, mental strategy, warm-up and mobility, supplementation and strength training. The findings were then organized into recommendations for: (a) before, (b) during and (c) after a race. First, before a race, a strong body of evidence supports the effectiveness of carbohydrate loading, extra sleep during the taper, a dynamic warm-up and dietary nitrate. Next, during a race, sodium-containing carbohydrate drinks help limit dehydration and maintain blood glucose, previous strength training improves running economy and motivational self-talk reduces perceived exertion. Finally, after a race, carbohydrate and protein intake and adequate sleep support recovery. This paper also identified two limitations: beetroot juice seems to benefit highly trained runners less than the average results in the literature suggest, and sweat-rate testing, while effective, is difficult for most runners to do without laboratory access. Therefore, recommendations should take into account a runner’s training level and the resources available to them.

Introduction

Competitive distance runners perform very close to their metabolic limits. At the highest levels of the sport, a runner’s performance is very much dependent on three physiological factors: maximal oxygen uptake (VO₂MAX), lactate threshold and running economy (Midgley et al., 2007). While genetics play a role in all three, how much of his or her potential a runner actually uses on race day depends to a meaningful extent on factors he or she can control.

Two of the most widely studied of these factors are fluid and fuel intake. We know that when dehydration exceeds about 2% of body mass, plasma volume falls, heart rate rises, and thermoregulation is impaired, all of which have the effect of slowing down the runner (von Duvillard et al., 2004; Casa et al., 2010). In the case of fuel, loading carbohydrate 36 to 48 hours before a marathon increases muscle glycogen stores (Burke, 2019), and runners who take in enough carbohydrate during the race are more likely to maintain their pace (Jiménez-Alfageme et al., 2025).

Strength training also makes a difference. Heavy lifting or plyometrics two to three times a week increases tendon stiffness and improves neuromuscular signaling, which can make runners more efficient without adding much muscle mass (Blagrove et al., 2018). On race day, a proper warm-up prepares these muscles without fatiguing them, and a small number of universally permitted supplements, like beetroot juice and beta-alanine, may provide an additional benefit.

Recovery becomes the final piece. Sleep, along with practices such as compression, easy active recovery, and heat or cold exposure, allows the body to adapt to training. Chronically obtaining poor amounts of sleep reduces the body’s ability to store carbohydrates, raises cortisol, and has been linked to higher injury risk (Fullagar et al., 2015). Getting extra sleep before a long event may even help. In a survey of more than 1,100 mountain ultramarathon finishers, runners who slept more in the week before the race reported fewer falls caused by sleepiness, although it should be noted they did not finish any faster (Kishi et al., 2024).

The research question for this paper is therefore: what are the main performance inputs for competitive distance running, and how does each one contribute before, during, and after a race? To answer it, this paper reviews a range of evidence on hydration, diet, sleep and recovery, mental strategy, warm-up and mobility, supplements, and strength training, and then the paper combines the findings into a practical framework covering the full race cycle.

Literature Review

Hydration Strategies and Fluid Balance

It is known that runners reduce their body temperature through sweating, and that during long races quite a significant amount of fluid is lost this way. One study pointed out that once fluid losses exceed about 2% of body mass, aerobic capacity starts to decline (von Duvillard et al., 2004). The reason for this was due to a drop in plasma volume, which results in lower stroke volume and causes the heart rate to rise to compensate, while less blood is available to carry heat to the skin.

Casa et al. (2010) noticed these effects in a study of runners on a very hot trail course, where dehydrated runners showed faster increases in core temperature and greater physiological strain. The actual beverage consumed during a race must be looked at. In events lasting several hours, simple water does not replace the electrolytes lost in sweat or provide energy, and in fact drinking large amounts of water can actually lead to terrible stomach discomfort or exercise-associated hyponatremia.

Drinks containing 4–8% carbohydrate and some sodium are more effective than plain water, because sodium speeds up fluid absorption in the gut and helps maintain plasma volume, while the carbohydrate works to ensure that blood glucose is kept up (von Duvillard et al., 2004). But, many runners do not follow these guidelines. O’Neal et al. (2011) surveyed half- and full-marathon runners and found that many did not correctly judge how much they needed to drink, followed arbitrary schedules, or waited until they were thirsty. Sweat-rate testing and drinking plans based on race pace could really help correct this.

Nutritional Interventions and Carbohydrate Optimization

Because distance running requires large amounts of energy, people should plan fueling needs before, during and after races. At easy, not-too-intense paces, fat provides much of this energy, but at or above lactate threshold carbohydrate becomes the main fuel. From a biological perspective, because the liver and muscles can store only a limited amount of glycogen, the extent of this storage largely determines when a runner “hits the wall.”

Burke (2019) recommends that fueling strategies be customized directly to race distance and conditions, rather than following a single approach. For shorter races, normal pre-race meals and a carbohydrate mouth rinse, which appears to stimulate the central nervous system, are generally sufficient. In contrast, for the marathon, a carbohydrate-loading phase over the 36 to 48 hours before the race is needed to fully saturate muscle glycogen (Burke, 2019).

Data from actual races support this. In a study of 160 runners in the Seville Marathon, Jiménez-Alfageme et al. (2025) found that runners who consumed the recommended 60–90 g of carbohydrate per hour during the race were more likely to finish in under three hours, although interestingly intake before and after the race was not related to finish time. We should not overlook day-to-day diet as well. Craddock et al. (2025) analyzed food diaries from Australian ultramarathon runners and found that many ate too little fruit, vegetables, and whole grains and too much discretionary food, resulting in only moderate diet quality scores. 

Rest, Recovery, and Sleep Architecture

The recovery phase is when adaptations to training mainly take place. High mileage causes muscle micro-damage, central fatigue, and low-grade inflammation, and a significant amount of the repair process occurs during sleep. In a review of sleep research on marathon and ultramarathon runners, Nikolaidis et al. (2023) reported that good sleep was associated with fewer injuries and infections and that sleep becomes more important to performance in proportion to actual race distance. Short or fragmented sleep has the effect of slowing this recovery.

Fullagar et al. (2015) reviewed research on sleep loss in athletes, and several of their findings apply to distance runners. Sleep restriction impairs glycogen storage, which can reduce the benefit of a carbohydrate load. It also increases cortisol, a hormone that promotes muscle breakdown, and slows reaction time, and in fact both of these have been linked to a higher risk of injury (Fullagar et al., 2015).

Mental Game, Cognitive Pacing, and Strategy

Psychological strategies also can impact endurance performance. McCormick et al. (2015) concluded that motivational self-talk, goal setting and imagery consistently brought down runners’ rating of perceived exertion (RPE). Since the same effort felt easier, it turned out that runners lasted longer before reaching their exhaustion limits and they performed time trials even more effectively, despite no changes to cardiovascular measures (McCormick et al., 2015). Brick et al. (2014) explained how successful endurance athletes are able to switch between monitoring their own bodies and focusing externally on the race while actually in performance mode.

Warm-up Modalities and Mobility Protocols

Ideally, a warm-up should really prepare the body for race pace without using up energy needed for the race itself. A warm-up’s main goals are known to be to raise muscle temperature, increase blood flow to the muscles and improve neuromuscular response while not using up much glycogen at all. Historically speaking. static non-dynamic stretching was used to warm up, but we now know that holding a stretch for more than about 60 seconds per muscle group can actually temporarily reduce muscle-tendon stiffness and force output (Behm & Chaouachi, 2011).

Therefore, now warm-ups are dynamic and include mobility drills and race-specific activation. Exercises like leg swings, high knees, walking lunges and progressive strides involve more motor units and prepare the nervous system, and they also loosen muscles and tendons, which all together has the effect of improving range of motion and stride mechanics. Blazevich et al. (2018) found that when a full dynamic warm-up was used, neither using nor not using short static stretches made a measurable difference in performance.

A warm-up also helps prepare the cardiovascular system. In a study of highly trained middle-distance runners, ending the warm-up with a sustained 200 meter run at race pace improved 800 meter time-trial performance by about one second compared with just using strides alone (Ingham et al., 2013). The most likely explanation is that starting the race with oxygen uptake already raised reduces the oxygen deficit early in the race. 

Ergogenic Aids and Physiological Supplementation

While training volume is the main determinant of aerobic capacity, some supplements can provide a small additional benefit. The IOC consensus statement concludes that only a few supplements have strong evidence of improving performance (Maughan et al., 2018). Of these, dietary nitrate, usually taken as beetroot juice, and beta-alanine, are in fact the most relevant to distance runners. Also, magnesium may be useful for athletes with low magnesium levels.

Beetroot juice is a concentrated source of nitrate, and the body converts it into nitric oxide. What happens is that nitric oxide widens blood vessels, increasing blood flow to working muscles, and reduces the oxygen usage of the body while it works (Jones, 2014). Then, this improves running economy and is most useful in middle- and long-distance events, where oxygen delivery is a main limitation on performance.

Beta-alanine functions through a different mechanism. It is the precursor to carnosine, a buffer inside muscle cells and regular supplementation actually causes an increase of muscle carnosine levels. This helps neutralize hydrogen ions during hard efforts and delays the acidosis that limits a runner’s finishing speed (Saunders et al., 2017). Magnesium, on the other hand, supports muscle relaxation, nerve conduction, and energy production, and supplementation appears to help mainly in athletes who are deficient (Volpe, 2015).

Neuromuscular Strength Training and Biomechanical Efficiency

The conventional wisdom has been that distance runners should avoid strength training because of concerns that extra muscle mass would slow them down. But, more recent research does not support this concern, since strength training combined with high-volume running produces neuromuscular improvements without significant muscle growth.

As noted earlier, endurance performance depends on VO₂MAX, the percentage of it a runner can sustain (lactate threshold), and running economy (Midgley et al., 2007). In a systematic review of strength training studies in middle- and long-distance runners, Blagrove et al. (2018) concluded that heavy, explosive, or plyometric training only two to three times per week actually improved running economy. Because VO₂MAX and blood lactate profiles stayed pretty much the same, the conclusion was that the improvement came from neural and structural changes (Blagrove et al., 2018).

Heavy and explosive training improves signaling between the brain and muscles, and increases motor unit synchronization and makes tendons stiffer. Biology tells us that a stiffer tendon works more like a spring, storing elastic energy when the foot lands and then releasing it during push-off. 

Discussion

To apply these findings, it is useful to divide race preparation into phases, because performance depends on decisions made in three different phases, consisting of before, during and after race day.

In the weeks and more importantly the days before a race, the main priorities must be fueling, sleep and preparation. A carbohydrate load 36 to 48 hours before a race works to fill liver and muscle glycogen stores (Burke, 2019). Long amounts of sleep during the last seven to ten days of the taper period helps offset the poor sleep many runners experience the night before a race (Fullagar et al., 2015), and in ultramarathon runners it was associated with fewer sleep-related falls (Kishi et al., 2024). Right before a race, runners should really avoid long static stretches and instead use dynamic mobility exercises and strides, which raise muscle temperature, involve motor units, and reduce the early oxygen deficit (Behm & Chaouachi, 2011; Blazevich et al., 2018; Ingham et al., 2013). Consuming beetroot juice (for its nitric oxide) two to three hours before the start can also lower the oxygen amount used to maintain race pace (Jones, 2014).

During the race, the focus then shifts to maintaining fluid balance and energy supply and keeping mental focus. Runners should drink a carbohydrate-electrolyte solution containing sodium at planned times instead of relying solely on water (von Duvillard et al., 2004). This beverage strategy helps keep fluid losses below 2% of body mass and maintains the supply of glucose to the muscles (Casa et al., 2010; Jiménez-Alfageme et al., 2025). Strength training completed in the months before the race also helps here, because stiff muscle-tendon units lower the energy cost of each stride (due to the spring mechanism referred to above) and conserve glycogen (Blagrove et al., 2018). Mentally, during the race, runners should make the attempt to switch between monitoring their bodies and focusing on the race around them which has proven to be an effective strategy (Brick et al., 2014), and motivational self-talk can lower perceived exertion, which reduces early pacing errors and the mental fatigue associated with hitting the proverbial wall (McCormick et al., 2015; Meijen et al., 2023).

After the race, recovery becomes a huge priority. Eating high glycemic carbohydrate with lean protein within about 30 to 60 minutes post-race speeds glycogen replacement and begins tissue repair (Burke, 2019). Because a marathon can disrupt a racer’s autonomic regulation and sleep for several nights after a race, getting quite a lot of sleep during this post-race period is vital (Nikolaidis et al., 2023). Easy active recovery, compression and light mobility work increase the flow of blood and help prepare the body for the next training cycle.

Although the research provides fairly clear guidance for each phase, not all runners benefit the same way. Two areas in particular show this limitation.

The first is dietary nitrate. Much of the improvement in running economy attributed to beetroot juice comes from studies of recreational and moderately trained runners, whose lower baseline nitric oxide efficiency leaves more room for improvement (Jones, 2014). In contrast, highly trained runners, whose vascular and mitochondrial systems are already hyper-efficient, tend to show smaller and less consistent responses, which is consistent with the individual variation in supplement response linked to training status, genetics and diet (Maughan et al., 2018). As a result, a plan based on the average effect reported in the literature would probably not yield the same benefit for the well-trained runners this paper focuses on, and coaches should rely on individual trials instead.

The second area of limitation is hydration. Individual sweat-rate testing is the most effective way to close the gap between hydration guidelines and what runners actually do, but it requires detailed body mass measurements before and after exercise and, ideally, sweat sodium analysis (O’Neal et al., 2011), which often requires specialized tools. This is realistic for a professional marathoner with access to sports science staff, but not for most “regular” amateur runners who train on their own. Simpler methods, such as standing on a bathroom scale before and after runs and paying attention to thirst over several weeks of training, are less precise but much more practical.

References

Behm, D. G., & Chaouachi, A. (2011). A review of the acute effects of static and dynamic stretching on performance. European Journal of Applied Physiology, 111(11), 2633–2651.

Blagrove, R. C., Howatson, G., & Hayes, P. R. (2018). Effects of strength training on the physiological determinants of middle- and long-distance running performance: A systematic review. Sports Medicine, 48(5), 1117–1149. https://doi.org/10.1007/s40279-017-0835-7

Blazevich, A. J., et al. (2018). No effect of muscle stretching within a full, dynamic warm-up on athletic performance. Medicine & Science in Sports & Exercise, 50(6), 1258–1266.

Brick, N., MacIntyre, T., & Campbell, M. (2014). Attentional focus in endurance activity: New paradigms and future directions. International Review of Sport and Exercise Psychology, 7(1), 106–134.

Burke, L. M. (2019). Contemporary nutrition strategies to optimize performance in distance runners and race walkers. International Journal of Sport Nutrition and Exercise Metabolism, 29(2), 117–129.

Casa, D. J., Stearns, R. L., Lopez, R. M., Ganio, M. S., McDermott, B. P., Yeargin, S. W., Yamamoto, L. M., Mazerolle, S. M., Roti, M. W., Armstrong, L. E., & Maresh, C. M. (2010). Influence of hydration on physiological function and performance during trail running in the heat. Journal of Athletic Training, 45(2), 147–156. https://doi.org/10.4085/1062-6050-45.2.147

Craddock, J. C., Walker, G., Chapman, M., Lambert, K., & Peoples, G. E. (2025). The diet quality of ultramarathon runners taking part in an Australian event: A cross-sectional explorative study. Nutrients, 17(3), Article 485.

Fullagar, H. H. K., Skorski, S., Duffield, R., Hammes, D., Coutts, A. J., & Meyer, T. (2015). Sleep and athletic performance: The effects of sleep loss on exercise performance, and physiological and cognitive responses to exercise. Sports Medicine, 45(2), 161–186. https://doi.org/10.1007/s40279-014-0260-0

Ingham, S. A., Fudge, B. W., Pringle, J. S., & Jones, A. M. (2013). Improvement of 800-m running performance with prior high-intensity exercise. International Journal of Sports Physiology and Performance, 8(1), 77–83.

Jiménez-Alfageme, R., Pino Garrone, F., Rodriguez-Sanchez, N., Romero-García, D., Sospedra, I., Giménez-Monzó, D., Ayala-Guzmán, C. I., & Martínez-Sanz, J. M. (2025). Nutritional intake and timing of marathon runners: Influence of athlete’s characteristics and fueling practices on finishing time. Sports Medicine – Open, 11, Article 26.

Jones, A. M. (2014). Dietary nitrate supplementation and exercise performance. Sports Medicine, 44(S1), 35–45.

Kishi, A., Millet, G. Y., Desplan, M., Lemarchand, B., & Bouscaren, N. (2024). Sleep and ultramarathon: Exploring patterns, strategies, and repercussions of 1,154 mountain ultramarathons finishers. Sports Medicine – Open, 10, Article 34.

Maughan, R. J., Burke, L. M., Dvorak, J., Larson-Meyer, D. E., Peeling, P., Phillips, S. M., Rawson, E. S., Walsh, N. P., Garthe, I., Geyer, H., Meeusen, R., van Loon, L. J. C., Shirreffs, S. M., Spriet, L. L., Stuart, M., Vernec, A., Currell, K., Ali, V. M., Budgett, R. G., . . . Engebretsen, L. (2018). IOC consensus statement: Dietary supplements and the high-performance athlete. British Journal of Sports Medicine, 52(7), 439–455.

McCormick, A., Meijen, C., & Marcora, S. (2015). Psychological determinants of whole-body endurance performance. Sports Medicine, 45(7), 997–1015.

Meijen, C., Brick, N. E., McCormick, A., Lane, A. M., Marchant, D. C., Marcora, S. M., Micklewright, D., & Robinson, D. T. (2023). Psychological strategies to resist slowing down or stopping during endurance activity: An expert opinion paper. Sport and Exercise Psychology Review, 18(1), 4–37.

Midgley, A. W., McNaughton, L. R., & Jones, A. M. (2007). Training to enhance the physiological determinants of long-distance running performance: Can valid recommendations be given to runners and coaches based on current scientific knowledge? Sports Medicine, 37(10), 857–880. https://doi.org/10.2165/00007256-200737100-00003

Nikolaidis, P. T., Weiss, K., Knechtle, B., & Trakada, G. (2023). Sleep in marathon and ultramarathon runners: A brief narrative review. Frontiers in Neurology, 14, Article 1217788. https://doi.org/10.3389/fneur.2023.1217788

O’Neal, E. K., Wingo, J. E., Richardson, M. T., Leeper, J. C., Neggers, Y. H., & Bishop, P. A. (2011). Half-marathon and full-marathon runners’ hydration practices and perceptions. Journal of Athletic Training, 46(6), 581–591. https://doi.org/10.4085/1062-6050-46.6.581

Saunders, B., et al. (2017). Beta-alanine supplementation to improve exercise capacity and performance: A systematic review and meta-analysis. British Journal of Sports Medicine, 51(8), 658–669.

Volpe, S. L. (2015). Magnesium and the athlete. Current Sports Medicine Reports, 14(4), 279–283.

von Duvillard, S. P., Braun, W. A., Markofski, M., Beneke, R., & Leithäuser, R. (2004). Fluids and hydration in prolonged endurance performance. Nutrition, 20(7-8), 651–656. https://doi.org/10.1016/j.nut.2004.04.011

About the author

Jasper Fine

Jasper is a high school senior focused on the intersection of quantitative tools and exercise science, with a particular interest on the effects of nutrition on performance.

The post Optimizing Competitive Long-Distance Running Performance: An Interdisciplinary Analysis of Physiological, Behavioral, and Nutritional Inputs appeared first on Exploratio Journal.

Abstract

Invasive brain-computer interfaces (BCIs) have the ability to restore deprived functions in people with severe paralysis such as communication and movement. The longevity of said devices hinge on whether implanted electrodes can integrate into the brain’s system and record neural signals over time. However, the brain often walls off these rigid, high-density architectures, which records the best, causing chronic neuroinflammation, degrading signal recordings over time.

In this review, we categorize different neural interfaces into four distinct stages, each with their benefits and drawbacks: Intruder, Neighbor, Partner, and Symbiotic Organ. The Intruder stage offers the highest quality recordings, trading off disturbing tissues locally in the brain due to the rigidity of electrode arrays. The Neighbor stage utilizes flexible and mesh electronics that are more compatible with the softness of brain tissue. The Partner stage employs new materials: graphene, carbon nanotubes, and liquid metals, which constitutes functions beyond basic electrical recording. In the Symbiotic Organ stage, an interface would integrate itself  into the neural tissue and has the ability of self reparation, allowing it to function as a part of the nervous system instead of a separate implant.

This paradigm is not a strict categorization of different device, it offers a way to label different systems to their strengths and limitations. Some devices are often hybridized combinations of different stages, and newer designs are not automatically superior to predecessors. Issues with longevity are not limited to biological reactions such as inflammation or scarring; it can also derive from abiotic failures, such as connector failure, insulation damage, and material degradation. On the other hand, rigid arrays holds some of the strongest long-term human records in signal quality. Overall, optimal systems will come from a combination of co-designing materials as well as adaptive computation together. The goal is not only to record stronger signals, or more electrode channel counts, but also to build interfaces that can remain useful inside the brain for many years.

Fig. 1. The four-stage framework for the evolution of invasive neural interfaces: Intruder, Neighbor, Partner, and Symbiotic Organ. Image generated by the author using ChatGPT (OpenAI).

1. Introduction

Over the past decade, invasive BCIs have become physical experiments. They have restored basic functions, such as control and communication for individuals inflicted with severe paralysis, including cursor operations through neural control (Hochberg et al., 2006; Simeral et al., 2011), high-performance clinical neural prostheses (Gilja et al., 2015), reach-and-grasp with a robotic arm (Hochberg et al., 2012), brain-controlled functional electrical stimulation for reanimating the limb (Ajiboye et al., 2017), and high-bandwidth brain-to-text communication (Willett et al., 2021).

How much of this bottleneck is physical rather than computational remains contested. One line of work treats signal degradation as a hard ceiling: once units are lost to chronic tissue response or device failure, no decoder can recover information absent from the recording (Barrese et al., 2013; Chestek et al., 2011; Salatino et al., 2017). A second line demonstrates that a great deal of apparent degradation is nonstationarity rather than information loss and that closed-loop adaptation (Orsborn et al., 2014) and variability-robust decoder architectures (Sussillo et al., 2016) absorb substantially more drift than the first framing implies. We take the acquisition layer as the primary determinant of longevity while noting that its boundary with computation is precisely what remains unsettled, a point we return to in section 3.3. In the end, how well the systems architecture integrates into the brain is the most important factor: signal quality, longevity, and clinical usefulness all suffer if the connection degrades.

This constraint limits the modality of designs of Brain Computer Interfaces. Non-invasive systems such as electroencephalography (EEG) are safer and more accessible, but volume conduction and the skull’s spatial filtering cap their spatial resolution and signal-to-noise ratio, limiting the neural detail recoverable at the scalp (Srinivasan et al., 1998). Invasive interfaces instead capture local field potentials and spiking activity with millisecond precision and cellular-scale specificity, which is what makes high-performance applications possible (Hochberg et al., 2006; Wolpaw et al., 2002). Rigid intracortical arrays such as the Utah Array (Maynard et al., 1997) and high-density silicon probes of the Neuropixels class (Jun et al., 2017; Steinmetz et al., 2021) are the pinnacle of highest-fidelity approach.

Increased signal fidelity will often sacrifice clinical usefulness after an extended period of time. Many high-density systems that rely on rigid, silicon-based construction, whose elastic modulus are several times bigger in magnitude in comparison to brain tissue. The mechanical mismatch amplifies micromotion-induced strain and chronic injury (Minev et al., 2015; Salatino et al., 2017), which in turn drive persistent inflammation, glial scarring (gliosis), neuronal loss near the implant, and progressive signal decline (Minev et al., 2015; Salatino et al., 2017). This is the paradox at the core of BCI hardware: the structures best suited to high-precision electrophysiology are often the ones most prone to causing long-term biological disruption (Minev et al., 2015; Salatino et al., 2017). The field has therefore started a shift opted in more compatible materials and designs which are less physically disruptive to the brain. As a result, these systems counteract mechanical mismatch and immune response without sacrificing the signal quality needed for useful recording.

This framework is not strictly in a chronological timeline. Some devices hybridize between different stages: Neuralink combines high channel count with flexible materials such as threads and robotic insertion, while mesh electronics may behave as Neighbor or Partner systems depending on whether the emphasis is mechanical integration or multifunctional sensing. The categories are therefore not fixed rules, rather flexible, useful labels.

To organize this response, we introduce a four-stage framework for the evolution of invasive neural interfaces: Intruder, Neighbor, Partner, and Symbiotic Organ (see Fig. 1). The sequence describes a progressive and linear shift from rigid foreign implants toward biologically integrated, adaptive bioelectronic systems, and it gives us a consistent way to compare how each generation navigates the paradox between signal fidelity and biological integration.

2. Evolution of Invasive Neural Interface Technologies

Invasive neural interfaces have developed through distinct paradigms, each a different attempt to reconcile high-fidelity recording with long-term biological compatibility.

2.1 Rigid High-Density Electrode Arrays: The “Intruder”

The Intruder paradigm is often categorized by a pursuit of high-fidelity recording, utilizing rigid, silicon-based microelectrode arrays to receive signals. Devices such as the Utah Array and, more recently, Neuropixels probes are the pinnacle of  spatial resolution, channel counts, and temporal precision, properties that can easily detect single-neuron activity and dynamics in population(Jun et al., 2017; Maynard et al., 1997; Steinmetz et al., 2021). Other devices such as Neuralink have scaled this approach along, further raising the ceiling of channel counts and automating implantation while maintaining signal quality transfusion. (Musk & Neuralink, 2019). However, it must be said that Chronic instability is not an inherent property of rigid devices. Neuropixels 2.0 was designed explicitly around stable long-term recording, pairing a miniaturized probe with methods for tracking the same units across multiple sessions, for an extended period of time in various conditions (Steinmetz et al., 2021), a rigid device pursuing the stability as their objective is usually credited to compliant ones. The paradigms are connecting shared goals from opposite starting points, which is why they are better labeled as design logic instead of chronology. Mechanical rigidity is a key focus: it gives positional stability during implantation and precise targeting between cortical layers, making these arrays the gold standard for short- to medium-term performance in both experimental and clinical settings. The Utah Array and Neuropixels have high signal fidelity, but they often sacrifice biocompatibility.

Additionally, the same rigidity of the paradigm’s design focus is their defining limitation. Because the elastic modulus of silicon-based probes are magnitudes bigger than the brain cells, the implant sits in permanent state of conflict with the soft, viscoelastic tissue around it. Over time this provokes a chronic foreign-body immune response, persistent inflammation, glial scarring, neuronal loss at the electrode surface, and micromotion-induced strain (Minev et al., 2015; Salatino et al., 2017), which steadily lowers the quality of recordings over a long period of time (Chestek et al., 2011; Simeral et al., 2011).

This causal story is, however, is an incomplete summary of attempts at correcting these flaws. Barrese et al.’s (2013) failure-mode analysis of silicon arrays in vivo attributed a large fraction of failures not to the foreign-body response but to abiotic modes, including connector and percutaneous pedestal failure, insulation cracking and delamination, and progressive material degradation. Their retrospective analysis of seventy-eight silicon arrays implanted in twenty-seven macaques found that most complete recording failures were not attributable to the foreign-body response at all. Acute mechanical failure was the single largest class at 48 percent, and 83 percent of those traced to the percutaneous connector rather than to anything occurring at the electrode-tissue boundary; material failures, including broken tips, insulation leakage, and parylene cracking and delamination, accounted for most of the remainder. Grossly observable biological failures made up 24 percent, the most common single mechanism being progressive meningeal encapsulation (14.5%). Mechanical and material modes together thus account for roughly three-fifths of failures (Barrese et al., 2013). A large share of array loss therefore occurs in packaging, wiring, and connector hardware, components that compliance-based redesign of the electrode does not touch.

One qualification protects this claim from over-reading. Barrese et al. (2013) define failure as the absence of extractable action potentials across all channels of an array, so their categories register catastrophic device death rather than the gradual attrition of usable units. Progressive, gliosis-driven decline in unit yield, the degradation most consequential for BCI performance and precisely what the Neighbor paradigm targets, would largely not count as a failure under this definition. The 24 percent figure therefore bounds the biological contribution to total array loss, not to signal degradation. This is the sharpest available illustration of why sources in this literature so often appear to disagree: Barrese et al. (2013) measure when a device dies, whereas chronic-yield studies (Chestek et al., 2011; Simeral et al., 2011) measure how well it performs while alive, and conclusions drawn from one cannot be transferred to the other without argument.

The Intruder paradigm’s limitation is not simply that it is too stiff: it fails along at least two partly independent axes, and the more frequent one is not biological. The Intruder paradigm does not resolve the paradox; it accepts biological instability as the price of maximal instantaneous fidelity.

Fig. 2. Five high-density planar silicon probes ordered by shank width (a 32-channel NeuroNexus polytrode, 128- and 255-channel NeuroSeeker probes, and two Neuropixels variants), with edge recording sites shown in green and center sites in red. Reproduced from Fiáth et al. (2021), Figure 1 (CC BY 4.0).

Fig. 3. Scanning electron micrograph of the Utah Intracortical Electrode Array, a 10 × 10 grid of 1.2-mm silicon needles on a 4.2-mm-square base. Reproduced from Maynard et al. (1997), Figure 1.

2.2 Flexible and Mesh Electronics: Transitioning to a “Neighbor”

The Neighbor paradigm softens the interface to better integrate to the needs of neighbouring brain tissues. Work on syringe-injectable electronics and macroporous mesh architectures showed that ultraflexible, polymer-based electrode networks integrate more seamlessly into tissue, attenuating the chronic immune response and enabling stable recording over extended timescales in animal models (Liu et al., 2015; Xie et al., 2015). By lowering bending stiffness to approach the mechanical compliance of brain tissue, these systems reduce the micromotion-induced damage that defines the Intruder problem (Jeong et al., 2015; Zhang et al., 2020).

Bio-integration is just part of the upside. The porous architecture of mesh electronics allows tissue interpenetration and vascularization, so the implant will not clash and compete(Xie et al., 2015). Neighbor designs therefore prioritize long-term integration and stability over recording density, and this comes at a considerable cost. Flexible architecture, however, raises the issue of not hitting the necessary threshold of electrode counts, and because they cannot penetrate tissue unaided, delivery depends on syringe injection (Liu et al., 2015) or temporary stiffening shuttles, approaches that will inevitable complicate the surgical procedure and whose procedures to the human cortex remains untested. Even so, the flexible and mesh electronics architecture serves as proof of principle that long-term neural implants are viable  without defects such as tissue disruption. This decouples one side of the paradox entirely. Recent reviews furthers this idea, arguing that longevity and soft, flexible structures go hand in hand, rather than opting for rigid recording elements alone (E. Song et al., 2020).

Fig. 4. Syringe-injectable mesh electronics: the mesh design and single unit cell, injection into the mouse brain, and a comparison of the open mesh with a conventional flexible thin-film probe. Reproduced from Zhou et al. (2017), Figure 1.

2.3 Novel Material-Based Electrodes and Hybrid Designs: The Functional “Partner”

The Partner paradigm does not further increase along the biocompatibility pathway, instead it diverges. Where mesh electronics optimize a single figure in its metric, longevity of passive recording systems(Xie et al., 2015), transparent graphene arrays, an example of a partner paradigm, are built to increase a different metric altogether, combining electrophysiology with simultaneous optical access to the same circuit (Kuzum et al., 2014). The two paradigms, Partner and Neighbour, are not contrasting solutions; instead, they sought to answer differently about what an interface is for, and who they are designed for. Resulting in the direct performance comparison between them as largely meaningless, arbitrary numbers. This stage is defined by materials such as graphene, carbon nanotubes, conductive polymers, and liquid metals that make up the interface. Strictly chosen for a combination of purposes: conductivity, optical transparency, stretchability, and chemical sensitivity (Chapman et al., 2018; Hong & Lieber, 2019; E. Song et al., 2020). Partner interfaces excel through its multimodal and biomimetic interactions rather than passive electrical functions represented by previous two paradigms.

Materials illustrate this shift. Compounds such as transparent graphene electrodes permit electrical recording and optical access to the same tissue at once, permitting an alternative pathway to “view” the brain. Whilst, also enabling simultaneous electrophysiology and two-photon calcium imaging of a single circuit, a pairing that opaque metal electrodes from previous paradigms physically obstruct (Kuzum et al., 2014). Additionally, carbon nanotube coatings enlarge effective surface area whilst lowering impedance, improving electrode-tissue coupling and recording quality (Keefer et al., 2008). Liquid metal conductors add extreme deformability while maintaining electrical continuity, so the interface can track tissue motion (Wen et al., 2019). Together, these materials support researchers in a multimodal approach to record the brain’s activity. By measuring electrical activity and neurochemical signals at once, recent reviews stress that interface performance now hinges on micro and nanoscale engineering, not just electrode composition alone (Chapman et al., 2018; Hong & Lieber, 2019).

This expansion in multimodal approach introduces its own functional costs. Fabrication is often intricate and less feasible, long-term material stability under chronic in vivo conditions remains under investigation, and integration with conventional electronics presents abnormal challenges. However, it must be said that Partner systems expand what the interface can measure, but at the cost of fabrication, packaging, long-term reliability, and clinical translation.

Fig. 5. A fully flexible Parylene C neural probe whose PEDOT/silica-nanoparticle-coated chemical sites electrically release glutamate (Glu) or GABA while neighboring microelectrodes record the resulting increase or decrease in spike rate in rat cortex. Reproduced from Malekoshoaraie et al. (2024) (CC BY 4.0).

2.4 Emerging Injectable and Self-Adaptive Interfaces: Toward a “Symbiotic Organ”

The current frontier reframes the interface as an adaptive extension of the nervous system rather than a static implant. The Symbiotic Organ paradigm proposes a structure of self-healing bioelectronic materials, injectable or self-deploying architectures, and closed-loop systems that respond to changes in neural and tissue state. This vision inherits the Partner stage’s costs and compounds them. The fabrication complexity and uncertain chronic material stability that constrain graphene, nanotube, and liquid-metal interfaces (Keefer et al., 2008; Kuzum et al., 2014; Wen et al., 2019) do not disappear when those materials are asked to additionally self-heal, self-deploy, and operate under closed-loop control; they multiply, and they are joined by power and telemetry demands that passive recording never faced. Self-healing interfaces, for instance, restore electrical and mechanical integrity after mechanical damage, a route to durability that does not depend on avoiding damage in the first place (K.-I. Song et al., 2020).

Dynamic adaptation is what distinguishes this stage. Where earlier paradigms rely on fixed geometries and static material properties, these systems continuously renegotiate the balance between signal quality and biological stability; coupling adaptive decoders with closed-loop control lets the interface respond to neural variability and drift in real time (Orsborn et al., 2014; Sussillo et al., 2016). In principle, an interface that evolves alongside the tissue it inhabits dissolves the paradox rather than trading around it, and bioinspired neuron-like designs push toward that goal by minimizing the structural distinction between device and neural tissue (Yang et al., 2019).

For now, this paradigm is still only a proof of concept, with unresolved obstacles in power delivery, wireless telemetry, computational load, long-term reliability, and safety. They define a long-term direction for the field rather than a deployable technology, and the gap between experimental demonstration and clinical translation remains wide.

Fig. 6. Conceptual vision of a symbiotic neural interface combining adaptive materials, tissue integration, intelligent on-chip electronics, and a wireless ecosystem. Image generated by the author using ChatGPT (OpenAI).

3. Discussion and Future Directions

The move from rigid intracortical arrays toward adaptive, tissue-integrated systems reflects a shift in the field’s underlying design philosophy. Early BCI work was driven by maximal signal fidelity and spatial resolution, predicated on the assumption that the highest-quality neural data would translate into robust decoding and clinical utility. Decades of chronic-implant experience overturned that assumption: performance depends not on instantaneous signal quality but on the long-term stability of the interface as a coupled biological-technical system. That realization reoriented the field’s priorities from absolute electrophysiological performance toward sustained harmony among tissue, materials, and computation, the transition the Intruder-to-Symbiotic-Organ progression describes.

3.1 Reconciling the Engineering Paradox: A Multi-Dimensional Optimization Problem

The paradox, that high-precision electrophysiology favors rigid, dense structures while long-term biocompatibility demands soft, compliant, adaptive ones has never been solved outright. Each stage handles this trade-off differently.

The Intruder paradigm buys fidelity by absorbing the biological cost: Utah arrays and Neuropixels probes set the benchmark for recording density and temporal resolution, and they accept chronic tissue disruption as the consequence of achieving it. But the accounting is less clean than the paradox implies. If abiotic failure modes contribute as heavily to array loss as Barrese et al. (2013) report, part of what the Intruder paradigm “pays” is not a biocompatibility cost at all, and no amount of movement along the compliance axis will refund it. The Neighbor paradigm moves the balance the opposite way, matching interface mechanics to tissue to lower inflammation and extend recording lifetimes, at the expense of electrode density, targeting precision, and spatial stability over time. The Partner paradigm widens the problem rather than picking a side: by adding optical transparency, neurochemical sensing, or stretchable multimodality, it redefines “performance” beyond electrical metrics, and in doing so exchanges a binary rigidity-versus-compliance choice for a harder question of fabrication and integration. The Symbiotic Organ paradigm attempts to remove the trade-off entirely, letting self-healing materials, self-deploying architectures, and closed-loop control adapt the interface to changing demands, an approach that, for now, exchanges the old paradox for unresolved problems of power, reliability, and safety. Read together, the four stages show the paradox migrating from a fixed constraint into a multi-dimensional optimization problem, not disappearing.

3.2 Insights from Cross-Technology Comparison and Application-Driven Design

No single paradigm dominates across every performance dimension. Rigid arrays lead on fidelity and spatial resolution, flexible and mesh electronics on chronic biocompatibility, material-based hybrids on functional diversity, and symbiotic concepts on adaptability and autonomy. The absence of a universally superior interface indicates that the field is bounded by fundamental trade-offs, not merely by incremental engineering inefficiencies.

Two implications follow. First, progress will come from context-specific optimization rather than a single “best” interface: applications demanding fine motor control or high-bandwidth speech decoding will likely continue to require high-density invasive arrays (Musk & Neuralink, 2019; Pandarinath et al., 2017; Willett et al., 2023), while long-term neuromodulation or monitoring may favor softer, lower-density systems built for chronic stability. Second, this makes application-driven design central: interface architecture should be matched to the physiological, temporal, and functional requirements of the specific use case rather than optimized in the abstract.

3.3 Persistent Field-Wide Challenges and the Need for Co-Design

Several challenges cut across all four paradigms. Long-term biostability is the central one, spanning not only immune response and tissue remodeling but also material degradation, delamination, and electrochemical drift over multi-year timescales. Manufacturing scalability and reproducibility form a parallel obstacle, especially for complex material systems that perform well in the laboratory but resist high yield consistent industrial fabrication. Power delivery and data telemetry impose further limits, particularly for wireless or minimally invasive implants, where energy efficiency and thermal safety become hard constraints.

Computation faces its own version of these problems. Representational drift, non-stationary signal statistics, and user-specific variability complicate decoder design and limit cross-session generalization. Where the ceiling on algorithmic mitigation actually sits is unresolved: results on closed-loop decoder adaptation (Orsborn et al., 2014) and variability-robust architectures (Sussillo et al., 2016) show that computation absorbs more instability than hardware-centric accounts assume, while chronic-yield data (Chestek et al., 2011; Simeral et al., 2011) show it cannot manufacture units that are no longer recorded. The disagreement is not merely academic, because the two positions imply different investments, one in materials, the other in algorithms, and neither literature can settle it alone. This is the strongest argument for co-design: the allocation question is unanswerable unless hardware and computation are developed and evaluated jointly. This interdependence is the argument for co-design, integrating hardware, materials science, and computation from the earliest stages of development rather than optimizing each in isolation. Recent reviews of electronic and flexible neural interfaces reach the same conclusion: future progress depends on tighter integration across materials, device engineering, and system-level signal processing (Hong & Lieber, 2019; E. Song et al., 2020; Zhang et al., 2020).

3.4 Global Perspectives and Collaborative Trajectories

Regional programs bring complementary strengths. In the United States, academic and industrial work often prioritizes high-performance invasive systems and advanced decoding, driven by clinical-translation initiatives and defense funding; Neuralink exemplifies the emphasis on channel count and surgical automation, while others pursue hybrids that balance performance against longevity. In China, research increasingly emphasizes materials science, flexible electronics, and minimally invasive platforms, drawing on strengths in large-scale manufacturing and material synthesis. European groups tend to lead in soft bioelectronics and neuromodulation, prioritizing safety, regulatory compatibility, and long-term implantation. The complementarity suggests that major breakthroughs are more likely to come from cross-border collaboration than from any single region’s dominance.

3.5 Near-Term Breakthroughs: Hybrid Systems at the Interface of Paradigms

A more cautious near-term expectation is that clinically relevant progress will come from partial hybrids rather than fully symbiotic systems. These platforms aim to combine signal quality sufficient for meaningful decoding with improved chronic stability and manageable fabrication complexity. Fully symbiotic systems, though the ultimate goal, still face barriers in power management, reliability, and regulatory approval that make near-term deployment unlikely. The next wave of translational impact is therefore more likely to come from incremental but durable gains in interface longevity, paired with adaptive decoding and closed-loop control.

4. Conclusion

High-performance BCIs could change neuroscience, medicine, and human-machine interaction, but they still face a basic problem: the clearest signals often come from devices that are difficult for brain tissue to tolerate over time. This review traced the field’s response through four stages: rigid Intruder systems, flexible Neighbors, multifunctional Partners, and emerging Symbiotic Organ concepts, each addressing the paradox through a different set of trade-offs. Chronic stability has improved, functional modalities have expanded, and adaptive behaviors have entered the design space, yet no existing technology optimizes every critical dimension at once. Reaching genuinely symbiotic interfaces will require sustained, interdisciplinary work across materials science, system integration, computational neuroscience, and ethical considerations. Four limitations in particular remain unresolved, and each cuts across all stages of the framework. First, an abiotic failure ceiling: because connector, insulation, and material degradation account for a substantial share of chronic array loss (Barrese et al., 2013), improvements in biocompatibility cannot by themselves extend device lifetime. Second, a translation gap: the chronic-stability case for compliant interfaces rests largely on rodent work (Liu et al., 2015; Xie et al., 2015), while the longest human recordings remain those of rigid arrays (Simeral et al., 2011). Third, power delivery and telemetry impose hard energy and thermal constraints on precisely the wireless, minimally invasive systems the Symbiotic Organ concept requires. Fourth, the division of labor between interface and algorithm is still contested (Chestek et al., 2011; Orsborn et al., 2014; Sussillo et al., 2016), leaving open how much instability future systems must engineer away rather than decode around. The clear direction of travel, however, is toward tissue-aware, adaptive design, interfaces that stop behaving as foreign bodies and begin functioning as durable, integrated collaborators within the nervous system.

References

Ajiboye, A. B., et al. (2017). Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: A proof-of-concept demonstration. The Lancet, 389(10081), 1821-1830. https://doi.org/10.1016/S0140-6736(17)30601-3

Barrese, J. C., et al. (2013). Failure mode analysis of silicon-based intracortical microelectrode arrays in non-human primates. Journal of Neural Engineering, 10(6), 066014. https://doi.org/10.1088/1741-2560/10/6/066014

Chapman, C. A. R., et al. (2018). Multifunctional neural interfaces for closed-loop control of neural activity. Advanced Functional Materials, 28(12), 1703523. https://doi.org/10.1002/adfm.201703523

Chestek, C. A., et al. (2011). Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex. Journal of Neural Engineering, 8(4), 045005. https://doi.org/10.1088/1741-2560/8/4/045005

Fiáth, R., et al. (2021). Recording site placement on planar silicon-based probes affects signal quality in acute neuronal recordings. Scientific Reports, 11, 2028. https://doi.org/10.1038/s41598-021-81127-5

Gilja, V., et al. (2015). Clinical translation of a high-performance neural prosthesis. Nature Medicine, 21(10), 1142-1145. https://doi.org/10.1038/nm.3953

Hochberg, L. R., et al. (2006). Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature, 442(7099), 164-171. https://doi.org/10.1038/nature04970

Hochberg, L. R., et al. (2012). Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature, 485(7398), 372-375. https://doi.org/10.1038/nature11076

Hong, G., & Lieber, C. M. (2019). Novel electrode technologies for neural recordings. Nature Reviews Neuroscience, 20(6), 330-345. https://doi.org/10.1038/s41583-019-0140-6

Jeong, J.-W., et al. (2015). Soft materials in neuroengineering for hard problems in neuroscience. Neuron, 86(1), 175-186. https://doi.org/10.1016/j.neuron.2014.12.035

Jun, J. J., et al. (2017). Fully integrated silicon probes for high-density recording of neural activity. Nature, 551(7679), 232-236. https://doi.org/10.1038/nature24636

Keefer, E. W., et al. (2008). Carbon nanotube coating improves neuronal recordings. Nature Nanotechnology, 3(7), 434-439. https://doi.org/10.1038/nnano.2008.174

Kuzum, D., et al. (2014). Transparent and flexible low-noise graphene electrodes for simultaneous electrophysiology and neuroimaging. Nature Communications, 5, 5259. https://doi.org/10.1038/ncomms6259

Liu, J., et al. (2015). Syringe-injectable electronics. Nature Nanotechnology, 10(7), 629-636. https://doi.org/10.1038/nnano.2015.115

Malekoshoaraie, M. H., et al. (2024). Fully flexible implantable neural probes for electrophysiology recording and controlled neurochemical modulation. Microsystems & Nanoengineering, 10, 91. https://doi.org/10.1038/s41378-024-00685-6

Maynard, E. M., et al. (1997). The Utah intracortical electrode array: A recording structure for potential brain-computer interfaces. Electroencephalography and Clinical Neurophysiology, 102(3), 228-239. https://doi.org/10.1016/S0013-4694(96)95176-0

Minev, I. R., et al. (2015). Electronic dura mater for long-term multimodal neural interfaces. Science, 347(6218), 159-163. https://doi.org/10.1126/science.1260318

Musk, E., & Neuralink. (2019). An integrated brain-machine interface platform with thousands of channels. Journal of Medical Internet Research, 21(10), e16194. https://doi.org/10.2196/16194

Orsborn, A. L., et al. (2014). Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control. Neuron, 82(6), 1380-1393. https://doi.org/10.1016/j.neuron.2014.04.048

Pandarinath, C., et al. (2017). High performance communication by people with paralysis using an intracortical brain-computer interface. eLife, 6, e18554. https://doi.org/10.7554/eLife.18554

Salatino, J. W., et al. (2017). Glial responses to implanted electrodes in the brain. Nature Biomedical Engineering, 1, 862-877. https://doi.org/10.1038/s41551-017-0154-1

Simeral, J. D., et al. (2011). Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array. Journal of Neural Engineering, 8(2), 025027. https://doi.org/10.1088/1741-2560/8/2/025027

Song, E., et al. (2020). Materials for flexible bioelectronic systems as chronic neural interfaces. Nature Materials, 19(6), 590-603. https://doi.org/10.1038/s41563-020-0679-7

Song, K.-I., et al. (2020). Adaptive self-healing electronic epineurium for chronic bidirectional neural interfaces. Nature Communications, 11, 4195. https://doi.org/10.1038/s41467-020-18025-3

Srinivasan, R., et al. (1998). Estimating the spatial Nyquist of the human EEG. Behavior Research Methods, Instruments, & Computers, 30(1), 8-19.

Steinmetz, N. A., et al. (2021). Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings. Science, 372(6539), eabf4588. https://doi.org/10.1126/science.abf4588

Sussillo, D., et al. (2016). Making brain-machine interfaces robust to future neural variability. Nature Communications, 7, 13749. https://doi.org/10.1038/ncomms13749

Wen, X., et al. (2019). Flexible, multifunctional neural probe with liquid metal enabled, ultra-large tunable stiffness for deep-brain chemical sensing and agent delivery. Biosensors and Bioelectronics, 131, 37-45. https://doi.org/10.1016/j.bios.2019.01.060

Willett, F. R., et al. (2021). High-performance brain-to-text communication via handwriting. Nature, 593(7858), 249-254. https://doi.org/10.1038/s41586-021-03506-2

Willett, F. R., et al. (2023). A high-performance speech neuroprosthesis. Nature, 620(7976), 1031-1036. https://doi.org/10.1038/s41586-023-06377-x

Wolpaw, J. R., et al. (2002). Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767-791. https://doi.org/10.1016/S1388-2457(02)00057-3

Xie, C., et al. (2015). Three-dimensional macroporous nanoelectronic networks as minimally invasive brain probes. Nature Materials, 14(12), 1286-1292. https://doi.org/10.1038/nmat4427

Yang, X., et al. (2019). Bioinspired neuron-like electronics. Nature Materials, 18(5), 510-517. https://doi.org/10.1038/s41563-019-0292-9

Zhang, M., et al. (2020). Electronic neural interfaces. Nature Electronics, 3(4), 191-200. https://doi.org/10.1038/s41928-020-0390-3

Zhou, T., et al. (2017). Syringe-injectable mesh electronics integrate seamlessly with minimal chronic immune response in the brain. Proceedings of the National Academy of Sciences, 114(23), 5894-5899. https://doi.org/10.1073/pnas.1705509114

About the author

Anthony Qian

Anthony is a senior at Stevenson School with a strong interest in neuroscience, artificial intelligence, and brain-computer interfaces. His academic interests lie at the intersection of biology, computer science, and machine learning, particularly in understanding brain function and developing technologies that interpret neural activity and human behavior.

Through independent research and hands-on computational projects, Anthony has explored machine learning, behavioral data analysis, and neural signal processing. He is passionate about interdisciplinary research, scientific problem-solving, and applying emerging technologies to advance our understanding of the human brain.

The post From Intruders to Symbiotic Organs: The Evolution of Neural Interface Technologies for Invasive Brain–Computer Interfaces appeared first on Exploratio Journal.

Abstract

Clustered regularly interspaced short palindromic repeats, or CRISPR, and its CRISPR-associated proteins together form an adaptive immune system that allows many bacteria and archaea to recognize and defend themselves from invading genetic elements, including bacteriophages.The groundbreaking finding of  the CRISPR-Cas system expanded the understanding of prokaryotic immunity accepted among scientists at the time and unveiled an evolutionary arms race in which bacteriophages have evolved mechanisms to evade or inhibit CRISPR-mediated immunity, such as production of anti-CRISPR proteins. Some of these methods have been utilized in the clinic to engineer phages to target pathogenic bacteria, providing an intriguing strategy for the treatment of antibiotic-resistant infections. This review analyzes the discovery, structure, and mechanism of CRISPR-Cas immunity phage strategies for evading this defense and the translation of these biological interactions into CRISPR-enhanced phage therapy and human genome-editing applications.

Introduction

Bacteria often come across bacteriophages which are viruses that infect bacterial cells and inflict an intense evolutionary pressure on populations of bacteria (Clokie et al., 2011; Labrie et al., 2010). As a result, bacteria have evolved to employ various antiviral defenses, one of which is the CRISPR-Cas system. The CRISPR-Cas complex is a sequence-specific adaptive immune system that seizes fragments of invading genetic material as spacers and utilizes them to recognize subsequent infections (Barrangou et al., 2007; Marraffini, 2015). The discovery of CRISPR-Cas complex resulted in a paradigm shift of the understanding of prokaryotic immunity by proving that bacteria can remember heritable molecular information about past viral encounters and additional mobile genetic elements.

The interaction between CRISPR-protected bacteria and bacteriophages has produced an evolutionary arms race. While bacteria use CRISPR-Cas to recognize and destroy phage genetic material, bacteriophages can escape CRISPR immunity through mechanisms including mutations in targeted sequences and production of anti-CRISPR proteins that inhibit Cas activity (Malone et al., 2021; Stanley & Maxwell, 2018). Understanding this interaction has become particularly relevant to phage therapy because bacterial antiviral defenses can limit therapeutic phage infection. Conversely, researchers can exploit CRISPR biology by engineering bacteriophages to carry CRISPR-Cas systems that selectively target pathogenic bacteria.

This review traces CRISPR from its discovery as an unusual repetitive DNA sequence to its identification as a bacterial adaptive immune system and subsequent development as a therapeutic technology. Particular emphasis is placed on the molecular mechanisms of CRISPR-Cas immunity, bacteriophage evasion of these defenses, the emerging development of CRISPR-enhanced bacteriophages as precision antimicrobials, and the broader application of CRISPR genome editing to human genetic disease.

The Discovery of CRISPR

The discovery of CRISPR began unexpectedly in 1987 when Ishino and colleagues identified an unusual pattern of repetitive DNA sequences while studying the iap gene in Escherichia coli. Near the target gene, the researchers noticed a series of highly conserved DNA repeats separated by unique spacer sequences. Although the organization of these sequences was unusual, their biological function remained unknown. At the time, the repeats were simply reported as an interesting genomic feature, with no identified connection to bacterial immune responses (Ishino et al., 1987). Nevertheless, this observation marked the first documented description of what would later become known as CRISPR.

Over the following decade, similar repeat-spacer arrays were recognized in numerous bacterial and archaeal species (Groenen et al., 1993; Mojica et al., 1993, 1995). Their widespread occurrence suggested that these structures served an important biological function rather than representing random genomic elements. A major advancement occurred in 2002 when Jansen and colleagues systematically characterized these repetitive loci and proposed the term clustered regularly interspaced short palindromic repeats, or CRISPR. In the same study, they identified neighboring genes consistently associated with CRISPR arrays and named them CRISPR-associated, or cas, genes (Jansen et al., 2002). The discovery of these genes suggested that CRISPR arrays functioned as part of a larger molecular system rather than as isolated DNA sequences.

Despite identification of CRISPR and its associated genes, the origin and function of the spacer sequences remained unclear. An important answer emerged in 2005 when three independent groups compared spacer sequences across microorganisms and discovered that many matched fragments of bacteriophage and plasmid DNA. Because bacteriophages infect bacteria, this finding suggested that spacers originated from previously encountered foreign genetic elements. The researchers proposed that CRISPR could function as a form of acquired immunity, allowing microorganisms to retain genetic information from previous encounters with foreign DNA (Bolotin et al., 2005; Mojica et al., 2005; Pourcel et al., 2005).

Direct experimental evidence supporting this hypothesis emerged in 2007 when Barrangou and colleagues studied Streptococcus thermophilus, a bacterial species widely used in yogurt and cheese production. Following exposure to bacteriophages, surviving bacterial strains acquired new spacer sequences derived from the infecting phages. Acquisition of these spacers was associated with resistance to subsequent infection, while removal or addition of particular spacers altered phage resistance accordingly (Barrangou et al., 2007). These experiments provided direct evidence that CRISPR functions as an adaptive antiviral defense system.

Together, studies conducted between 1987 and 2007 transformed CRISPR from an unexplained genomic feature into a major discovery in microbiology. Researchers progressed from identifying repetitive DNA sequences to demonstrating that bacteria can acquire, store, and use genetic information from previous viral encounters. Recognition of this adaptive immune function led researchers to investigate the molecular components and mechanisms through which CRISPR-Cas systems recognize and destroy invading genetic material.

Structure and Components of the CRISPR-Cas System

Following the discovery that CRISPR functions as an adaptive immune system, researchers sought to understand the molecular components that enable bacteria to recognize and defend against invading viruses. CRISPR-Cas systems consist of two major elements: CRISPR arrays and CRISPR-associated genes that encode proteins responsible for immune activity (Wiedenheft et al., 2012).

A CRISPR array contains short repetitive DNA sequences known as repeats separated by variable sequences known as spacers. The spacers frequently originate from foreign genetic elements such as bacteriophages and plasmids and therefore preserve sequence information from previous encounters with these elements (Horvath & Barrangou, 2010). CRISPR arrays are also associated with a leader sequence that contains regulatory elements important for array transcription and spacer acquisition.

Adjacent to CRISPR arrays are cas genes, which encode proteins responsible for different stages of the immune response. Cas1 and Cas2 are broadly conserved proteins involved in acquisition and integration of new spacers into CRISPR arrays. Other Cas proteins participate in processing CRISPR-derived RNAs and targeting invading nucleic acids. For example, Cas9 in type II CRISPR-Cas systems functions as an RNA-guided endonuclease capable of cleaving DNA corresponding to the sequence complementary by its RNA guide (Jinek et al., 2012).

The CRISPR array is transcribed into a precursor CRISPR RNA, or pre-crRNA, that is subsequently processed into shorter CRISPR RNAs (crRNAs) containing individual spacer-derived guide sequences. These crRNAs associate with Cas proteins and direct them toward complementary sequences in invading genetic material. Together, the CRISPR array, crRNAs, and Cas proteins create a sequence-specific surveillance and defense system capable of recognizing foreign nucleic acids (Wiedenheft et al., 2012).

Mechanism of CRISPR-Cas Immunity

CRISPR-Cas immunity can generally be divided into three stages: adaptation, expression, and interference. During adaptation, fragments of foreign genetic material are captured and integrated into the CRISPR array as new spacers, commonly through processes involving Cas1 and Cas2. The corresponding sequence in the invading genome is referred to as a protospacer. Incorporation of a new spacer provides the cell and its descendants with sequence information that can contribute to recognition of the same or closely related foreign genetic element during later encounters (Marraffini, 2015).

During the expression stage, the CRISPR array is transcribed into pre-crRNA and processed into individual crRNAs. Each crRNA contains sequence information derived from a previously acquired spacer and associates with Cas proteins to form an effector complex. These complexes use base-pair complementarity to recognize foreign nucleic acids corresponding to the stored spacer sequence.

During interference, the crRNA guides the CRISPR-Cas effector machinery to complementary sequences in invading genetic material. After appropriate target recognition, Cas proteins cleave or degrade the foreign nucleic acid, limiting successful replication of the invader. The precise molecular machinery differs among CRISPR-Cas types, but the underlying principle of RNA-guided sequence recognition allows CRISPR systems to provide highly specific antiviral protection (Marraffini, 2015; Wiedenheft et al., 2012).

The discovery of this mechanism substantially changed scientific understanding of immunity in unicellular organisms. Bacteria were shown to possess an adaptive defense capable of incorporating genetic information from previous encounters and using that information in subsequent responses. The programmability of this system also provided the biological foundation for later CRISPR-based genome-editing technologies.

Phage Evasion of CRISPR-Cas Immunity

CRISPR-Cas immunity places strong selective pressure on bacteriophages to develop countermeasures that allow them to infect CRISPR-protected bacterial populations. The resulting interactions form an evolutionary arms race in which bacterial defenses select for phage escape mechanisms, while successful phage variants can subsequently drive diversification of bacterial immunity (Malone et al., 2021).

One of the most direct methods of CRISPR evasion is alteration of the phage sequences recognized by the bacterial CRISPR system. CRISPR interference relies on sequence recognition between a crRNA and a corresponding target within the phage genome. Mutations in the targeted protospacer or important recognition sequences, including protospacer-adjacent motifs in systems that require them, can reduce effective recognition and allow some phages to escape existing CRISPR immunity (Malone et al., 2021). This demonstrates an important limitation of sequence-specific immunity: changes to the targeted viral sequence can reduce the effectiveness of previously acquired protection.

A particularly important phage counter-defense is the production of anti-CRISPR (Acr) proteins. Anti-CRISPRs are phage-encoded proteins that inhibit components of CRISPR-Cas systems and thereby permit infection of bacteria that would otherwise possess effective CRISPR immunity (Stanley & Maxwell, 2018). Different Acr proteins act through distinct mechanisms. Some interfere with DNA recognition or binding by CRISPR-Cas complexes, whereas others inhibit the nuclease activity required to destroy the invading genetic material. By disrupting critical stages of CRISPR-mediated interference, these proteins can provide phages with an opportunity to replicate despite bacterial adaptive immunity.

Anti-CRISPR activity can also affect infection at the level of a phage population. Borges et al. (2018) demonstrated that anti-CRISPR-producing phages can cooperate to overcome CRISPR-Cas immunity. An initial infection may fail to produce a complete phage replication cycle but can leave behind anti-CRISPR proteins that temporarily weaken bacterial immunity. Subsequent phages then have a greater probability of successfully replicating within the partially immunosuppressed bacterial population. This finding demonstrates that phage evasion can involve population-level interactions rather than solely mutations or proteins acting within an individual virus.

These evasion mechanisms are particularly relevant to phage therapy because a therapeutic phage must overcome the defenses of its bacterial target before it can successfully replicate and kill that cell. CRISPR-Cas immunity can therefore contribute to bacterial resistance against therapeutic phages, whereas understanding phage escape mechanisms can help researchers predict and potentially overcome barriers to successful treatment (Malone et al., 2021). Importantly, the relationship between CRISPR and phages has also been exploited in the opposite direction: researchers can engineer bacteriophages to carry CRISPR-Cas systems that increase their ability to selectively kill target bacteria.

CRISPR-Enhanced Phage Therapy

The growing incidence of antimicrobial-resistant bacterial infections has resulted in a rekindled focus in bacteriophage therapy. Traditional phage therapy incorporates bacteriophages with the ability to infect and kill vulnerable pathogenic bacteria. Nevertheless, bacterial defenses against phages, including CRISPR-Cas systems, can hinder effective infection, while the customary restricted scope of hosts of individual phages can limit the amount of bacterial strains that they recognize (Malone et al., 2021). Developments in the field of genetic engineering have led to the invention of an additional approach in which phages are modified to improve bacterial targeting or carry additional antimicrobial genetic components.

CRISPR-enhanced phage therapy integrates the natural host specificity of bacteriophages with the sequence specificity of CRISPR-Cas systems. In this approach, engineered phages transport CRISPR-Cas elements aimed at particular sequences within the target bacterium. In oppose to altering the patient’s genome, the phage distributes the antimicrobial system among bacterial cells. After inside a suitable bacterial host, CRISPR-mediated identification and degradation of bacterial DNA amplifies bacterial elimination. This method overturns the natural link among the two systems. To explain further, CRISPR initially evolved to protect bacteria from bacteriophages, whereas modified phages can employ CRISPR machinery against bacteria themselves.

One CRISPR-enhanced phage therapeutic that has begun its usage in human clinical testing is LBP-EC01, a genetically enhanced six-bacteriophage cocktail developed to target E. coli associated with urinary tract infections. LBP-EC01 uses a CRISPR-Cas3 system. In type I CRISPR-Cas systems, target recognition recruits Cas3, a nuclease-helicase capable of processive degradation of target DNA. This mechanism allows CRISPR-Cas3-enhanced phages to supplement the natural antibacterial activity of the phage with targeted destruction of bacterial DNA.

Kim et al. (2024) evaluated LBP-EC01 in the first portion of the phase 2 ELIMINATE clinical trial in adults with uncomplicated E. coli urinary tract infections and a recent history of drug-resistant E. coli UTI. Thirty-nine participants were included in the safety population. Different combinations of intraurethral and intravenous LBP-EC01 were evaluated, with participants also receiving the antibiotic trimethoprim-sulfamethoxazole. No serious adverse events were observed. Among 16 pharmacodynamically evaluable participants, investigators observed a rapid reduction of E. coli in urine after treatment that was maintained through day 10, and these participants had complete resolution of UTI symptoms by day 10 (Kim et al., 2024).

These results require careful interpretation. This portion of the ELIMINATE study was an open-label, uncontrolled dose-finding component, and LBP-EC01 was administered with an antibiotic. Consequently, the study cannot establish that the CRISPR-enhanced phages alone caused bacterial clearance or symptom resolution. Instead, it provides important early human evidence regarding the safety, pharmacokinetics, and pharmacodynamic activity of a CRISPR-Cas3-enhanced phage therapeutic and supports further controlled investigation (Kim et al., 2024).

Another CRISPR-enhanced phage therapeutic, SNIPR001, was developed to selectively target E. coli in the gastrointestinal tract. SNIPR001 consists of four complementary CRISPR-Cas-armed bacteriophages. During its development, selected phages were engineered with CRISPR-Cas systems containing E. coli-specific sequences, and phage engineering was also used to improve bacterial targeting (Gencay et al., 2024). The therapeutic is being investigated as a method of reducing intestinal E. coli, which can be an important source of bloodstream infection in vulnerable patients with hematological cancers.

The first-in-human study of SNIPR001 was reported by Petersen et al. (2026). This phase 1 trial was randomized, double-blind, placebo-controlled, and dose-escalating and enrolled 36 healthy adults. Participants received oral SNIPR001 or placebo for seven days. No serious adverse events or grade 3–4 adverse events occurred in participants receiving SNIPR001, and investigators did not observe a significant overall disruption of gut microbiome composition. Functional phages were recovered from stool in a dose-dependent manner, demonstrating that the therapeutic phages could survive passage through the gastrointestinal tract (Petersen et al., 2026).

The highest SNIPR001 dose was associated with an estimated 78% reduction in E. coli relative to placebo at day 14. However, the difference was not statistically significant following correction for multiple testing (Petersen et al., 2026). Therefore, the trial should primarily be interpreted as evidence supporting the safety, tolerability, recovery, and biological feasibility of CRISPR-Cas-armed phages rather than proof of clinical efficacy. Furthermore, the participants were healthy adults rather than patients being treated for active bacterial infections.

Together, the LBP-EC01 and SNIPR001 trials represent important early steps toward translating CRISPR-enhanced phage therapy into humans. The approach directly connects several stages in the history of CRISPR research. CRISPR was first identified as a bacterial defense against bacteriophages; investigation of phage evolution revealed mechanisms for overcoming bacterial CRISPR immunity; and researchers are now engineering phages to carry CRISPR machinery that can be directed back against bacterial cells. Although larger controlled studies are necessary to establish therapeutic efficacy, these first human investigations demonstrate the potential for CRISPR and bacteriophages to function together as precision antimicrobial technologies.

Other Clinical Applications of CRISPR: Genetic Diseases

Despite CRISPR-enhanced phage therapy continuing to be an emerging field, CRISPR genome editing has already been utilized clinically to treat hereditary human diseases. One of the most notable examples is exagamglogene autotemcel (exa-cel; Casgevy), engineered for sickle cell disease and transfusion-dependent β-thalassemia. Instead of  directly repairing disease-causing HBB variants, CRISPR-Cas9 is utilized ex vivo to disturb an erythroid-specific enhancer of BCL11A in the patient’s hematopoietic stem and progenitor cells. Decreasing BCL11A activity in the erythroid lineage amplifies fetal hemoglobin production, which can neutralize the effect of deficient adult hemoglobin. The edited cells are then reinfused into the patient after conditioning (Frangoul et al., 2021).

Additionally, CRISPR editing has been executed within the human body. NTLA-2001 employs lipid nanoparticles to transfer Cas9 messenger RNA and a guide RNA targeting TTR to hepatocytes in patients with transthyretin amyloidosis. A human study displayed dose-dependent reductions in circulating transthyretin succeeding a single treatment (Gillmore et al., 2021). Collectively with the clinical advancement of CRISPR-enhanced bacteriophages, these practices emphasize how molecular machinery beginning as a bacterial antiviral defense has been repurposed both to modify human cells and to selectively target pathogenic bacteria.

Conclusion

The discovery of  the CRISPR-Cas system revolutionized the understanding of prokaryotic immunity among scientists as it proved that bacteria and archaea can obtain sequence information from foreign genetic elements and apply it to employ targeted defense mechanisms in following confrontations (Barrangou et al., 2007; Marraffini, 2015). Originating as a mere observation of unexplained repetitive sequences in E. coli, it later would result in the depiction of a complex adaptive immune system which in turn provided the molecular basis for one of the most significant tools in biotechnology developed over the past few decades.

The dynamic present among bacteria with CRISPR incorporated immune systems and bacteriophages highlights the fast-paced and constantly changing nature of microbial evolution. CRISPR-Cas immunity inflicts evolutionary pressure on phages, which can react with mechanisms such as modification of targeted sequences along with creation of anti-CRISPR proteins that inhibit CRISPR-Cas activity (Malone et al., 2021; Stanley & Maxwell, 2018). These counteractive measures are not only essential for understanding bacterial-phage coevolution but are also applicable in phage therapy as bacterial antiviral immunity can impact whether a therapeutic phage effectively infects its target.

Researchers have recently begun harnessing the same biological interactions for antimicrobial treatment. Instead of functioning exclusively as the target of bacterial CRISPR immunity, bacteriophages can be engineered to include CRISPR-Cas systems targeted at pathogenic bacteria. Human studies of LBP-EC01 and SNIPR001 supply early indications that CRISPR-enhanced phage therapeutics can be given to humans and reinforce continued examination of their safety and therapeutic potential (Kim et al., 2024; Petersen et al., 2026). However, these procedures stay experimental, and larger controlled studies are required in order to establish clinical potency and determine how obstacles such as bacterial resistance, phage host range, delivery, and interactions with the microbiome influence their therapeutic use.

The expansion of CRISPR-based treatments for human genetic disease further illustrates the remarkable translation of fundamental microbiological research into medicine. From bacterial acquisition of viral sequences, to phage counter-defense, to engineered antimicrobial phages and human genome editing, the history of CRISPR demonstrates how understanding interactions between bacteria and viruses can generate fundamentally new approaches to disease treatment.

References

Barrangou, R., Fremaux, C., Deveau, H., Richards, M., Boyaval, P., Moineau, S., Romero, D. A., & Horvath, P. (2007). CRISPR provides acquired resistance against viruses in prokaryotes. Science, 315(5819), 1709–1712. https://doi.org/10.1126/science.1138140

Bolotin, A., Quinquis, B., Sorokin, A., & Ehrlich, S. D. (2005). Clustered regularly interspaced short palindrome repeats (CRISPRs) have spacers of extrachromosomal origin. Microbiology, 151(8), 2551–2561. https://doi.org/10.1099/mic.0.28048-0

Borges, A. L., Zhang, J. Y., Rollins, M. F., Osuna, B. A., Wiedenheft, B., & Bondy-Denomy, J. (2018). Bacteriophage cooperation suppresses CRISPR-Cas3 and Cas9 immunity. Cell, 174(4), 917–925.e10. https://doi.org/10.1016/j.cell.2018.06.013

Clokie, M. R. J., Millard, A. D., Letarov, A. V., & Heaphy, S. (2011). Phages in nature. Bacteriophage, 1(1), 31–45. https://doi.org/10.4161/bact.1.1.14942

Frangoul, H., Altshuler, D., Cappellini, M. D., Chen, Y.-S., Domm, J., Eustace, B. K., Foell, J., de la Fuente, J., Grupp, S., Handgretinger, R., Ho, T. W., Kattamis, A., Kernytsky, A., Lekstrom-Himes, J., Li, A. M., Locatelli, F., Mapara, M. Y., de Montalembert, M., Rondelli, D., . . . Corbacioglu, S. (2021). CRISPR-Cas9 gene editing for sickle cell disease and β-thalassemia. The New England Journal of Medicine, 384(3), 252–260. https://doi.org/10.1056/NEJMoa2031054

Gencay, Y. E., Jasinskytė, D., Robert, C., Semsey, S., Martínez, V., Petersen, A. Ø., Brunner, K., de Santiago Torio, A., Salazar, A., Turcu, I. C., Eriksen, M. K., Koval, L., Takos, A., Pascal, R., Schou, T. S., Bayer, L., Bryde, T., Johansen, K. C., Bak, E. G., . . . Sommer, M. O. A. (2024). Engineered phage with antibacterial CRISPR-Cas selectively reduce E. coli burden in mice. Nature Biotechnology, 42(2), 265–274. https://doi.org/10.1038/s41587-023-01759-y

Gillmore, J. D., Gane, E., Taubel, J., Kao, J., Fontana, M., Maitland, M. L., Seitzer, J., O’Connell, D., Walsh, K. R., Wood, K., Phillips, J., Xu, Y., Amaral, A., Boyd, A. P., Cehelsky, J. E., McKee, M. D., Schiermeier, A., Harari, O., Murphy, A., . . . Lebwohl, D. (2021). CRISPR-Cas9 in vivo gene editing for transthyretin amyloidosis. The New England Journal of Medicine, 385(6), 493–502. https://doi.org/10.1056/NEJMoa2107454

Groenen, P. M. A., Bunschoten, A. E., van Soolingen, D., & van Embden, J. D. A. (1993). Nature of DNA polymorphism in the direct repeat cluster of Mycobacterium tuberculosis: Application for strain differentiation by a novel typing method. Molecular Microbiology, 10(5), 1057–1065. https://doi.org/10.1111/j.1365-2958.1993.tb00976.x

Horvath, P., & Barrangou, R. (2010). CRISPR/Cas, the immune system of bacteria and archaea. Science, 327(5962), 167–170. https://doi.org/10.1126/science.1179555

Ishino, Y., Shinagawa, H., Makino, K., Amemura, M., & Nakata, A. (1987). Nucleotide sequence of the iap gene, responsible for alkaline phosphatase isozyme conversion in Escherichia coli, and identification of the gene product. Journal of Bacteriology, 169(12), 5429–5433. https://doi.org/10.1128/JB.169.12.5429-5433.1987

Jansen, R., van Embden, J. D. A., Gaastra, W., & Schouls, L. M. (2002). Identification of genes that are associated with DNA repeats in prokaryotes. Molecular Microbiology, 43(6), 1565–1575. https://doi.org/10.1046/j.1365-2958.2002.02839.x

Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., & Charpentier, E. (2012). A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science, 337(6096), 816–821. https://doi.org/10.1126/science.1225829

Kim, P., Sanchez, A. M., Penke, T. J. R., Tuson, H. H., Kime, J. C., McKee, R. W., Slone, W. L., Conley, N. R., McMillan, L. J., Prybol, C. J., & Garofolo, P. M. (2024). Safety, pharmacokinetics, and pharmacodynamics of LBP-EC01, a CRISPR-Cas3-enhanced bacteriophage cocktail, in uncomplicated urinary tract infections due to Escherichia coli (ELIMINATE): The randomised, open-label, first part of a two-part phase 2 trial. The Lancet Infectious Diseases, 24(12), 1319–1332. https://doi.org/10.1016/S1473-3099(24)00424-9

Labrie, S. J., Samson, J. E., & Moineau, S. (2010). Bacteriophage resistance mechanisms. Nature Reviews Microbiology, 8(5), 317–327. https://doi.org/10.1038/nrmicro2315

Malone, L. M., Birkholz, N., & Fineran, P. C. (2021). Conquering CRISPR: How phages overcome bacterial adaptive immunity. Current Opinion in Biotechnology, 68, 30–36. https://doi.org/10.1016/j.copbio.2020.09.008

Marraffini, L. A. (2015). CRISPR-Cas immunity in prokaryotes. Nature, 526(7571), 55–61. https://doi.org/10.1038/nature15386

Mojica, F. J. M., Juez, G., & Rodríguez-Valera, F. (1993). Transcription at different salinities of Haloferax mediterranei sequences adjacent to partially modified PstI sites. Molecular Microbiology, 9(3), 613–621. https://doi.org/10.1111/j.1365-2958.1993.tb01721.x

Mojica, F. J. M., Ferrer, C., Juez, G., & Rodríguez-Valera, F. (1995). Long stretches of short tandem repeats are present in the largest replicons of the Archaea Haloferax mediterranei and Haloferax volcanii and could be involved in replicon partitioning. Molecular Microbiology, 17(1), 85–93. https://doi.org/10.1111/j.1365-2958.1995.mmi_17010085.x

Mojica, F. J. M., Díez-Villaseñor, C., García-Martínez, J., & Soria, E. (2005). Intervening sequences of regularly spaced prokaryotic repeats derive from foreign genetic elements. Journal of Molecular Evolution, 60(2), 174–182. https://doi.org/10.1007/s00239-004-0046-3

Petersen, A. Ø., Damholt, B., Grove, M., Hink, J., Marotte-Hurbon, T., Söderqvist, J., Troy, A., Zdravkovic, M., Bayer, L., Brunner, K., Bryde, T., Clube, J., Gencay, Y. E., Gram, A., Haaber, J. K., Hallström, B., Jasinskytė, D., Pascal, R., Petersen, M., . . . Grøndahl, C. (2026). Safety, recovery, and pharmacodynamics of CRISPR-Cas therapeutic SNIPR001: A phase 1, randomised, double-blind, first-in-human, dose-escalation study. The Lancet Microbe, 7(4), 101257. https://doi.org/10.1016/j.lanmic.2025.101257

Pourcel, C., Salvignol, G., & Vergnaud, G. (2005). CRISPR elements in Yersinia pestis acquire new repeats by preferential uptake of bacteriophage DNA, and provide additional tools for evolutionary studies. Microbiology, 151(3), 653–663. https://doi.org/10.1099/mic.0.27437-0

Stanley, S. Y., & Maxwell, K. L. (2018). Phage-encoded anti-CRISPR defenses. Annual Review of Genetics, 52, 445–464. https://doi.org/10.1146/annurev-genet-120417-031321

Wiedenheft, B., Sternberg, S. H., & Doudna, J. A. (2012). RNA-guided genetic silencing systems in bacteria and archaea. Nature, 482(7385), 331–338. https://doi.org/10.1038/nature10886

About the author

Ishani Anand

Ishani is a high school Senior in Georgia. She has a strong interest in biology, genetics, and medicine, particularly the applications of biomedical research to improving patient care. Her academic interests have led her to explore topics such as CRISPR-Cas systems, genetic disorders, and cardiovascular health through independent research and scientific writing.

Beyond research, Ishani is actively involved in community service through the American Red Cross and other healthcare-related initiatives. She hopes to pursue a career in medicine while continuing to explore the intersection of scientific research, clinical practice, and public health.

The post The Discovery of the CRISPR-Cas System: How Unexpected Bacterial Immunity Transformed the Field of Genetics appeared first on Exploratio Journal.

Abstract

Western dietary patterns, characterized by high intake of saturated fats, refined carbohydrates, and added sugars and low intake of dietary fiber, have been associated with metabolic dysfunction and changes in the gut microbiome. This study investigated whether Western diet consumption was associated with alterations in the cecal metabolome of mice using publicly available metabolomics data from the NIH Common Fund Metabolomics Workbench.

Cecal metabolite profiles from mice fed Chow, high-sugar, high-fat, or high-fat high-sugar diets were analyzed using principal component analysis (PCA), one-way analysis of variance (ANOVA), Tukey’s honestly significant difference test, and linear discriminant analysis (LDA). PCA revealed separation between Chow and Western diet samples, with the first two principal components explaining 42.7% of total metabolic variation. 

Multiple metabolites differed significantly among dietary groups, including riboflavin, methionine, and pyruvate. These three metabolites were selected for further interpretation because they represent vitamin, amino acid, and cellular energy metabolism, respectively. Riboflavin, methionine, and pyruvate were 4.1-, 2.9-, and 6.8-fold more abundant in Western diet samples than in Chow samples. Overall, the findings demonstrate that Western diet consumption is associated with substantial alterations in the murine cecal metabolome and suggest changes across multiple metabolic pathways. 

Introduction

Diet impacts significantly more than the amount of energy a person consumes. The types of nutrients regularly consumed influence metabolic pathways, immune activity, inflammation, and the microorganisms inhabiting the gastrointestinal tract (GIT) (Hills et al., 2019). The Western diet (WD) is characterized by high consumption of saturated fats, refined carbohydrates, red meat, and added sugars, accompanied by low intake of fruits, vegetables, whole grains, and dietary fiber. This dietary pattern is strongly linked to obesity, insulin resistance, cardiovascular disease, metabolic syndrome, and type 2 diabetes (David et al., 2014). Although excessive caloric intake contributes to these conditions, previous studies show that Western diets affect immune function, intestinal barrier integrity, mitochondrial activity, and systemic inflammation through interactions between the host and the gut microbiome (Mills et al., 2019).

 Figure 1. The figure illustrates the standard composition of a Western Diet, characterized by high intake of saturated fats, refined carbohydrates, processed meats, and added sugars, with relatively low consumption of fruits, vegetables, whole grains, and dietary fiber. This dietary pattern has been associated with obesity, insulin resistance, cardiovascular disease, and metabolic dysfunction. 

The gut microbiome is the community of all microbes and their genes that live in the GIT. The human GIT houses an estimated 100 trillion microorganisms, including bacteria, viruses, yeasts, fungi, and several other microbes. A variety of factors influence the gut microbiome, including diet, age, genetics, lifestyle, and environmental exposures, making its composition and function highly personalized (Hills et al., 2019). The community of microbes in the gut plays a crucial role in several bodily functions. Predominantly, the gut microbiome aids in nutrient and drug metabolism. 

Metabolism is defined by the biological reactions responsible for converting nutrients into energy required for cellular function (Lamichhane et al., 2018). Metabolites are defined as products created when the body breaks down food, drugs, or chemicals, or its own tissue. Metabolomics is the study of metabolites present within biological systems (Yang et al., 2019). Because metabolites represent the end products of metabolism, changes in metabolite abundance provide insight into the effects of dietary patterns on host health (Yang et al., 2019). High-throughput techniques such as liquid chromatography-mass spectrometry (LC-MS) allow hundreds to thousands of metabolites to be quantified simultaneously (Yang et al., 2019).

 Figure 2. This figure provides a summary of metabolomics, the study of small molecules, or metabolites, found in biological samples. Biological and environmental factors influence the metabolites found in biological samples. These metabolites can be measured using techniques such as LC-MS and analyzed to identify differences in metabolic pathways and physiological processes. 

The gut microbiome and host metabolism are connected through the production of microbial metabolites (Li et al., 2026). Gut microbes convert nutrients from food into bioactive compounds that can influence a variety of host processes, including glucose homeostasis, lipid metabolism, amino acid metabolism, immune signaling, and energy production (Lamichhane et al., 2018). Consequently, dietary changes that alter microbial composition also alter the host metabolome.

Figure 3. The figure illustrates the multiple ways in which gut microorganisms influence host metabolism. Gut microbes can directly transform dietary compounds into active or inactive metabolites, indirectly alter host metabolic pathways, or accumulate metabolites that affect physiological function. These interactions link dietary composition to metabolic health. 

Although previous research has demonstrated that Western diets alter both gut microbial communities and host metabolism, the specific metabolic consequences are not completely understood. The objective of this study was to investigate whether consumption of a Western diet significantly alters cecal metabolite profiles in mice using publicly available metabolomics data. Principal component analysis, one-way analysis of variance (ANOVA), Tukey’s honestly significant difference testing, and linear discriminant analysis (LDA) were used to identify metabolites that contribute most strongly to metabolic differences among dietary groups. We hypothesized that Western diet consumption would produce a distinct cecal metabolite profile compared with the Chow diet 

Methods

Metabolomics data were obtained from the publicly available NIH Common Fund Metabolomics Workbench (Study ID: ST003695). The dataset included cecal metabolite profiles from mice fed one of four diets: Chow (control), High Sugar Diet (HSD), High Fat Diet (HFD), and High Fat, High Sugar Diet (HFHSD), also referred to as a Western diet (WD). Metabolites were measured using liquid chromatography-mass spectrometry (LC-MS) in both positive and negative ionization modes. Each sample represented one mouse.

Data preprocessing and statistical analyses were performed in RStudio. Positive and negative ion mode datasets were imported separately and merged into a single metabolite matrix. Missing values were replaced with the median value for each metabolite, negative values were replaced with zero, and metabolite abundances were transformed using log₂(x + 1) to reduce the influence of extreme values. Metabolites with no variation across samples were removed before analysis.

Principal component analysis (PCA) was used to visualize the overarching patterns in the metabolomics data. Because the focus of this study was the effect of a Western diet, PCA was performed using only the Chow and WD samples. PCA loadings were used to identify metabolites that contributed most strongly to sample separation. A PCA biplot displaying the ten strongest loading vectors was generated, and cos² values were calculated to evaluate how well each metabolite was represented by the first two principal components.

One-way analysis of variance (ANOVA) was performed for each metabolite using all four dietary groups (Chow, HSD, HFD, and WD) to identify metabolites whose abundances differed significantly among diets. To reduce false-positive results caused by multiple testing, p-values were adjusted using the Benjamini–Hochberg false discovery rate (FDR) method. For metabolites with significant ANOVA results, Tukey’s Honestly Significant Difference (HSD) test was performed to identify which pairs of diet groups differed significantly.

Linear discriminant analysis (LDA) was used to identify metabolites that differed most significantly between the Chow and WD groups. Metabolites were first ranked using the ANOVA results, and the highest-ranking metabolites were selected as predictors for the LDA model. Standardized LD1 coefficients were examined to determine which metabolites contributed most strongly to the observed separation.

Results

Figure 4. PCA score plot of cecal metabolite profiles from Chow and WD mice. PC1 and PC2 explain 24.5% and 18.2% of total variation, respectively, for 42.7% combined. The separation between groups indicates broad differences in cecal metabolite profiles associated with diet.

To identify the metabolites responsible for this separation, PCA loading scores were examined using a biplot. Metabolites including riboflavin, methionine, phenylalanine, tyrosine, valine, isoleucine, threonine, urate, and MetSul contributed strongly to the first two principal components (Figure 5).

Figure 5. PCA biplot showing the strongest metabolite loading vectors for Chow and WD samples. Vector direction and length indicate how strongly individual metabolites contribute to the first two principal components. Metabolites including riboflavin, methionine, phenylalanine, and urate contributed prominently to the observed separation. 

To more closely examine which metabolites differed significantly between diet groups, one-way analysis of variance (ANOVA) followed by Tukey’s honestly significant difference (HSD) test was performed. Several metabolites differed significantly among mice fed Chow, WD, HFD, and HSD, including riboflavin, thiamine, methionine, pyruvate, uracil, succinate, adenosine, serotonin, citrulline, and aspartate. These results indicate that statistical comparisons of individual metabolites also supported the metabolic differences observed in the PCA. 

Riboflavin showed one of the biggest differences among dietary groups (F = 26.418, FDR-adjusted p = 1.13 × 10⁻⁶). Methionine also differed significantly among groups (F = 8.151, FDR-adjusted p = 4.28 × 10⁻³), as did pyruvate (F = 13.367, FDR-adjusted p = 8.30 × 10⁻⁴). The other statistical analyses also supported the importance of these three metabolites. Riboflavin and methionine were among the strongest contributors to the first two principal components, while riboflavin, methionine, and pyruvate were all among the metabolites contributing most strongly to LDA separation between Chow and Western Diet samples. These findings guided the selection of the three metabolites for further biological interpretation.

While PCA identifies the largest patterns in the data without using group labels, linear discriminant analysis (LDA) uses the known diet groups to determine which metabolites best distinguish between them. LDA identified riboflavin, thiamine, methionine, pyruvate, alanine, phenylpyruvate, phenylalanine, and uracil as the strongest contributors to separating the Chow and Western Diet samples (Figure 7).

Figure 6. Standardized LD1 coefficients from LDA comparing Chow and WD samples. Riboflavin and thiamine had the largest positive coefficients, while methionine had the largest negative coefficient. Together with pyruvate and other metabolites, these predictors contributed most strongly to separation between the two diet groups. 

Discussion

Box 1

The statistical test results demonstrate significant changes in the cecal metabolome of mice fed a Western Diet. PCA revealed separation between the WD and Chow dietary groups, while ANOVA and LDA identified several metabolites that strongly contributed to these differences. Many of the statistically different metabolites are involved in vitamin metabolism, amino acid metabolism, and cellular energy production, suggesting that Western Diet consumption is associated with changes across multiple metabolic pathways.

Riboflavin, or vitamin B₂, was 4.1 times more abundant in Western diet samples than in Chow samples. Riboflavin is used to form the coenzymes FAD and FMN, which play an important role in cellular energetics. In addition to dietary sources, many gut microorganisms possess pathways for B-vitamin synthesis, including riboflavin production (Saedisomeolia & Ashoori, 2018). Therefore, the increased cecal riboflavin observed in this study could reflect differences in dietary composition, microbial metabolism, host metabolism, or interactions among these factors. 

Methionine also possessed greater abundance in Western Diet samples, being 2.9 times higher than in Chow. Methionine is an amino acid involved in protein production and methylation and the production of antioxidants (Hoare et al., 2024). High dietary methionine turns into homocysteine, which in large amounts is linked to vascular and neurological stress (Nuru et al., 2018). 

Pyruvate showed the largest change of the three metabolites, with levels 6.8 times higher in Western Diet samples. Pyruvate is primarily responsible for aiding in glucose metabolism and cellular energy production (Echlin et al., 2020). Its increase suggests that a Western Diet may disrupt how the body processes glucose for energy. Because problems with glucose metabolism are linked to insulin resistance and type 2 diabetes, this finding may provide an explanation for how Western dietary patterns contribute to metabolic disease. 

Together, the increases in riboflavin, methionine, and pyruvate demonstrate that the metabolic differences associated with Western Diet consumption disrupt several biological pathways, including shifts in vitamins, amino acids, and cellular energy production. These findings demonstrate the value of metabolomics research because it allows changes across several pathways to be examined simultaneously, providing a comprehensive understanding of the biological effects of dietary patterns.

Several limitations should also be considered. The study used an existing metabolomics dataset with a relatively small number of 36 samples compared with the 200 metabolites measured. Analyzing many variables with a small sample increases uncertainty and the possibility that some observed differences may not generalize to other populations. In addition, this study measured metabolites in mouse cecal contents. Therefore, the findings cannot be assumed to represent metabolite concentrations in the blood, other tissues, or humans. Metabolomics alone also cannot determine why riboflavin, methionine, and pyruvate were more abundant in Western Diet samples. Because the LDA predictors were selected from the same ANOVA results used to identify group differences, the LDA should be interpreted as exploratory rather than as an independent validation of those metabolites. The changes could result directly from dietary composition, alterations in the gut microbiome, changes in host metabolism, or interactions among these factors. Future research combining metabolomics with microbiome sequencing and transcriptomics could help determine which biological mechanisms are responsible for these changes.

These findings also emphasize the importance of overall diet quality in supporting metabolic health. A well-balanced dietary pattern that includes fruits, vegetables, whole grains, fiber, and a variety of nutrient sources provides compounds needed for normal cellular function while also supporting a diverse gut microbiome. In contrast, Western dietary patterns that are high in saturated fats and refined carbohydrates and low in fiber have been associated with changes in microbial composition, inflammation, and metabolic function (David et al., 2014; Mills et al., 2019). This message is consistent with the Dietary Guidelines for Americans, 2025-2030, which emphasize whole, nutrient-dense foods and reducing reliance on highly processed and refined foods (U.S. Department of Health and Human Services [HHS] & U.S. Department of Agriculture [USDA], 2026). One practical takeaway is to build eating patterns around fruits and vegetables, fiber-rich whole grains, and minimally processed foods more often. 

Conclusion

Overall, Western Diet consumption was associated with significant alterations in the cecal metabolome of mice. The increased abundance of riboflavin, methionine, and pyruvate illustrates the changes involving vitamins, amino acids, and glucose and energy metabolism induced by the Western Diet. These findings demonstrate how a Western Diet is associated with substantial alterations across several metabolic pathways. This emphasizes why making healthy, balanced food choices over time is important for supporting the body’s metabolism and overall health. Studying these early metabolic differences may help researchers better understand the biological processes linking long-term dietary patterns with insulin resistance, obesity, and other metabolic diseases.

Acknowledgements

I would like to thank Dr. Hong Pan at Boston University for his mentorship throughout my research process. 

References

David, L. A., Maurice, C. F., Carmody, R. N., Gootenberg, D. B., Button, J. E., Wolfe, B. E., Ling, A. V., Devlin, A. S., Varma, Y., Fischbach, M. A., Biddinger, S. B., Dutton, R. J., & Turnbaugh, P. J. (2014). Diet rapidly and reproducibly alters the human gut microbiome. Nature, 505(7484), 559–563. https://doi.org/10.1038/nature12820

Echlin, H., Frank, M., Rock, C., & Rosch, J. W. (2020). Role of the pyruvate metabolic network on carbohydrate metabolism and virulence in Streptococcus pneumoniae. Molecular Microbiology, 114(4), 536–552. https://doi.org/10.1111/mmi.14557

Hoare, M., Tan, R., Welle, K. A., Swovick, K., Hryhorenko, J. R., & Ghaemmaghami, S. (2024). Methionine Alkylation as an Approach to Quantify Methionine Oxidation Using Mass Spectrometry. Journal of the American Society for Mass Spectrometry, 35(3), 433–440. https://doi.org/10.1021/jasms.3c00337

Hills, R. D., Jr., Pontefract, B. A., Mishcon, H. R., Black, C. A., Sutton, S. C., & Theberge, C. R. (2019). Gut microbiome: Profound implications for diet and disease. Nutrients, 11(7), 1613. https://doi.org/10.3390/nu11071613 

Lamichhane, S., Sen, P., Dickens, A. M., Orešič, M., & Bertram, H. C. (2018). Gut metabolome meets microbiome: A methodological perspective to understand the relationship between host and microbe. Methods (San Diego, Calif.), 149, 3–12. https://doi.org/10.1016/j.ymeth.2018.04.029

Li, Z., Samui, S., Liu, J., Yang, Y., Liu, X., Chen, Q., Li, J., Gopinath, D., Luo, P., & Shan, D. (2026). Gut microbiome and metabolic health: Mechanisms and precision interventions. Gut Microbes, 18(1), 2644677. https://doi.org/10.1080/19490976.2026.2644677

Mills, S., Stanton, C., Lane, J. A., Smith, G. J., & Ross, R. P. (2019). Precision Nutrition and the Microbiome, Part I: Current State of the Science. Nutrients, 11(4), 923. https://doi.org/10.3390/nu11040923

Nuru, M., Muradashvili, N., Kalani, A., Lominadze, D., & Tyagi, N. (2018). High methionine, low folate and low vitamin B6/B12 (HM-LF-LV) diet causes neurodegeneration and subsequent short-term memory loss. Metabolic Brain Disease, 33(6), 1923–1934. https://doi.org/10.1007/s11011-018-0298-z 

Saedisomeolia, A., & Ashoori, M. (2018). Riboflavin in Human Health: A Review of Current Evidences. Advances in Food and Nutrition Research, 83, 57–81. https://doi.org/10.1016/bs.afnr.2017.11.002

Yang, Q., Zhang, A., Miao, J., Sun, H., Han, Y., Yan, G., Wu, F., & Wang, X. (2019). Metabolomics biotechnology, applications, and future trends: A systematic review. RSC Advances, 9(64), 37245–37257. https://doi.org/10.1039/c9ra06697g

About the author

Maggie McIntyre

Maggie is a senior at Glenbard South High School with a passion for math and computer science. She teaches coding and robotics classes to elementary and middle school students, competes on her school’s math team, and enjoys baking and cooking in her free time.

The post Examining Associations Between a Western Diet and Metabolism in Mice appeared first on Exploratio Journal.

Abstract

Have you ever studied for hours and still failed a test, while someone else barely studied and aced it? This paper explores why this occurs. It turns out grades depend a lot less on how “smart” someone is, and a lot more on habits, mindset, and even the environment around them. This paper looks at personality traits, motivation, and study strategies like quizzing oneself and spacing out studying over many days, along with the brain science behind memory and forgetting. It also examines how classrooms and family income can affect learning, and how AI is starting to change education — for better and worse.

Key Terms

Introduction

It’s 9 a.m. on a Monday, and I am sitting in my first period Calculus class. We just got our tests back, and all I can hear is students whispering to each other, “I studied for hours, and still bombed it” or “How did you do so well? You must be naturally smart.” I couldn’t stop thinking about it: Why did some students who barely studied do fine, while others who studied all night struggled? That question is what motivated me to research how studying actually works and what really helps students succeed. 

Most of us grow up assuming grades are just a reflection of intelligence, and that studying more automatically means doing better. Turns out, that’s not actually true. Here’s what’s really happening in a student’s brain: Every time they learn something new, neurons form tiny connections with each other across gaps called synapses. Every time those neurons fire together, the connection gets a little bit stronger. So, when the student goes back over information, especially trying to force themself to remember it, instead of passively rereading it, the connection sharpens. On the other hand, if they never revisit it, the connection weakens, and the memory fades away. This is the problem with rereading something for hours: it can feel productive, but it doesn’t actually build a solid and lasting memory. Real learning happens when your brain has to work to pull information out instead of just looking at it again. 

Once I understood this, it changed how I saw the whole “naturally smart” thing. Grades have a lot less to do with raw intelligence than people assume, and more to do with study habits, mindsets, and the environment around them. These are all personal habits on which every student can improve. 

The focus of this paper will be on academic success in an institutional environment. First, the paper will examine personality traits and mindset — traits like staying organized and having confidence — which can drastically improve grades. Next, the paper will cover study strategies backed by science, like quizzing yourself instead of rereading and studying a bit over time instead of cramming. Then, the paper will look at the classroom itself, and even things like family income, which can affect how the brain works and learns. Finally, it will examine the future of learning and how AI is changing education for the good and bad. All of this builds towards one main idea: Doing well in school isn’t something a student has to be born with; it’s something they can learn how to do. 

Box 1

Bloom’s Taxonomy 

The basics of how students obtain mastery of a concept are widely known as Bloom’s Taxonomy. This diagram gives a general summary of what a student must be able to do before they can move up the pyramid to being a professional or expert in a subject.  

The pyramid begins with a base of remembering basic facts and concepts. Then, once the learner has achieved that level, they can progress upwards to explain or teach those ideas. Next is applying the information in different situations. The next level would be being able to connect those ideas to new things. Second to last is justifying a decision, defending, or supporting something. The final level is producing new work with the concepts the student has learned. The entire pyramid shows how a student can expand their learning and achieve mastery on a topic. 

GPA as a fixed measure of intelligence 

In past years, the intelligence of a student has typically been measured by their GPA. It helps decide where a student can get into college, how well a student is expected to perform in a specific class, and can end up limiting a student’s learning experience because of what they are told they are capable of. 

The term for factors like GPA and SAT or ACT scores as a measurement for intelligence is called intellective factors. These ways of ranking a student are important and often close to accurate, but there are a lot more factors called non-intellective factors that play a key role in a student’s intelligence. 

Personality and motivation

Personality and motivation, which are both non-intellective factors, are very important predictors of a student’s intelligence, and once a student understands how to improve these aspects of themselves, they can improve their performance in school (Richardson). 

The first graph shows that performance self-efficacy is the leading factor and can help improve a student the most. Intellective predictors, such as high school GPA and ACT score, are next on the list. The rest of the non-intellective predictors, like effort regulation, academic self-efficacy, and grade goals, are still very important but play a smaller role. 

Similarly, other personality traits can lead to an increased GPA. The most critical is conscientiousness, then openness, agreeableness, extraversion, and finally neuroticism. None of these will decrease the GPA; some just assist it more than others.

These graphs help to put it into perspective that having confidence, staying organized, and setting goals can help a student achieve their goals and produce a higher GPA.

Personality traits and their effect

If a student wants to succeed in school, one of the simplest ways is to have the personality traits to do it. These don’t just have to be something they’re born with, but a character trait they can adapt to and improve upon. The biggest factor is being organized, disciplined, and achievement-motivated. This can look like students staying on top of homework, keeping their school bag clean, keeping track of when they have assignments due or when an assessment is, dedicating time to study or do work, and setting goals. 

Another super important trait is openness. This is the curiosity and willingness to try new ideas and learn. If a student constantly lives in a state of never trying new things and sticking to what they know, they will never learn and never get better at anything. This can look like stepping out of their comfort zone, trying something they’ve never done before, failing, asking for help, and working with others. 

There are also bad traits to have, like procrastination and emotional instability. These can be bad habits or things to work on; however, if a student wants to improve their learning, they should stick to the most important traits. 

Motivational Factors and their effect 

One of the best ways to change a student’s actions and take accountability for how they will perform in school is to work on motivation. 

The best way to do this is to have performance self-efficacy, which is having confidence in one’s ability to complete a task or generally do well in school. It’s also good to set grade goals for oneself. Goals are really important to keep on track and motivated towards success. 

Intrinsic motivation and need for cognition can motivate a student for the right reasons — not just for the outcome but for the love of learning. If a student loves what they’re learning, it will automatically become so much easier. This is why some students might perform well in one class and not as well in another; It’s not necessarily because one’s harder than the other, but because they love and care about one class more than the other, and therefore it makes it easier. If one loves to learn, they will learn well.

 Finally, having a good locus of control and optimism can shape a student’s mindset to be able to do hard things and have positive outcomes. 

Self-regulated learning and study strategies 

In the top left is a diagram created by Noel Burch of Gordon Training International. This cycle describes the very basics of learning and helps remind students where they might be in the process of learning anything. 

A learner begins in unconscious incompetence, meaning they are unaware of what they don’t know and are comfortable. The second stage is conscious incompetence. This is marked by insecurity because they see what they need to learn. The third level is conscious competence, which is reached after practice. At this point, the learner can perform the skill correctly, but it still takes effort and contemplation. The final stage is unconscious competence, where the skill becomes automatic to the learner. 

Next, in the cycle diagram in the top right by Zimmerman (1989), is the process for self-regulated learning, which is important to every individual student. It shows how accomplishing a goal begins with planning and setting goals to gather ideas and create a study strategy. The learner then uses those strategies and carries out their goals while monitoring their performance. In the final stage, the learner self-reflects and attributes the success or failure of the work to a controllable cause like effort or strategy choice rather than luck. 

Next, in the bottom left corner is the difference between a growth versus fixed mindset. It was taken from Carol Dweck’s book: Mindset: The New Psychology of Success. It shows how people with growth mindsets learn from their mistakes, take on challenges, and work with inspiration from others to reach higher levels of achievement than their peers who don’t. People with fixed mindsets might instead give up easily, avoid hard things, and feel threatened by the success of their peers, and as a result, they will never achieve their full potential. 

Lastly, in the bottom right corner is a graph describing the best ways to study. The figure from the Association for Psychological Science by Roediger and Karpicke (2006) shows the percent of facts recalled by students who studied more versus students who took a practice test. It shows that students who took practice tests were able to remember more facts long-term because they learned better. Those who studied more did better after five minutes, but then their percent of facts recalled dramatically declined because they weren’t able to remember the information long-term. 

All of these figures tell a story about how students can most effectively learn things and make progress with studying (Xu). 

Research on memory has shown that memory can fade fast unless it’s actively and consistently practiced. That’s why it’s better to space out studying than cram the night before a test. Reviewing material once, then again a couple of days later, and then again a week later is forcing a student’s brain to retrieve the information from scratch each time, which is exactly the purpose of active recall. Every time one struggles to remember something and then figure it out, they are making that memory stronger and stronger, which makes it last even longer. 

This is also why elaboration is so helpful. It allows the brain to make pathways to that memory and find even more reasons to remember it. The best learner isn’t necessarily the one who studies for the most hours, but it’s the one who studies the best, and planning out your studying as described is the best way to retain information, understand its importance, and end up performing your best on an exam. 

A huge part of learning is failing. Without failing and without making mistakes, one will not learn. The brain remembers things better when one fails multiple times. It takes harder work to teach oneself why one got it wrong and what the real answer is, and therefore remembering it is easier because it took more energy to process it. 

Another strong piece of learning is concentration and focus in class. It seems obvious, but the less a student pays attention in class, the less they’re going to know. If one actively engages, asks questions, debates responses, and works with peers, they are building the muscles in the brain to actively learn the information and remember it.

The Forgetting Curve

The forgetting curve discovered by Ebbinghaus and proven time and time again by other researchers has shown that right when a student learns something, they remember close to 100% of it. Within the first 20 minutes, they have already forgotten a good portion of the information. By the next day, they might only remember 30-40% of the information, and then after a week without review, the number drops even further, continuously declining by just a little more slowly every week (Murre, Dros). 

This proves that most of the forgetting actually happens very soon after one learns the information, so the best time to review anything is quickly after learning it, and again that night, and then the student can slowly start reviewing it less because the forgetting curve slows down. 

There was an interesting finding that memory actually increases when one sleeps. This shows the importance of sleep in memory consolidation, and without sleep, it’s even harder to learn and remember things. 

The Physical Learning Environment

Most classrooms in the past have consisted of rows of desks with uncomfortable seating. Students sit in classrooms for hours listening to their professors lecture to them. There was little communication between the student and the teacher and from student to student. This raises many issues in a student’s learning (Kariippanon). 

First of all, students learn better when applying their knowledge to something, working out problems with peers, and making mistakes and learning from those mistakes. Without communication and problem-solving, students are just passively hearing information. 

Because students are stuck in their seats for so many hours of the day, being forced to sit still and stay quiet, they can become restless and even more distracted by small things, having a hard time focusing on the learning at hand. 

Sitting down for long periods of time can actually decrease a student’s GPA. On the other hand, standing for longer periods can increase a student’s learning. There are also many more health benefits related to standing over sitting. 

Stepping can also mildly increase a student’s GPA, but not as much as standing. This graph helps to prove that flexible learning spaces are a really positive environment for students in which to learn. 

However, there is another side of the argument. Flexible learning spaces can be a distraction. Being able to choose one’s seating, some students might choose to sit next to their friends, and instead of paying attention or getting work done, they might be more inclined to talk about outside topics and be off task. 

Perhaps there is also a limit to how “fun” the chairs should be. It’s good to have movement in the classroom to help with learning; however, too much can turn into playing around or goofing off and can become another distraction that will take away from learning. 

Perhaps there needs to be a balance in what is considered a flexible learning space, such as standing tables, which tend to result in better learning and are generally better for the body. And in terms of seating arrangements, it can be helpful for students to sit near someone they feel comfortable having discussions with and truly deciphering information, but the second it turns into distracting behavior, the seating should change.  

Socioeconomic factors

Another part of the physical learning environment that has more of an impact than people have realized is socioeconomic factors. Socioeconomic factors such as household income, education, and neighborhood can be clearly seen in MRI scans of children’s brains (Hamilton). 

This is because in these types of circumstances there can be less social support, more stress, and less sleep. This leads to stress hormone signaling and arousal, which can affect a child’s motor and sensory parts of the brain. 

Sleep is also a very important component of memory consolidation. When you sleep, all of your memories get organized and locked into your brain. With less sleep, students remember less and aren’t considered as smart as other students. This is then seen in their MRI scans and has now been linked to how well these students perform in school. 

They were not necessarily born with a lower IQ, yet they have suffered disadvantages that have affected the way their brain will work forever. 

Future (AI as a tool and with risks)

Benefits

Moving forward in education, AI is taking over some of the traditionally human-performed roles. It is also becoming a learning assistant to students. 

In one study, AI feedback was rated more helpful than human tutor feedback (70% vs 45%). This is a great tool for students and doesn’t necessarily out-compete teachers or make them useless, but instead provides extra assistance and more personalized learning for students (Burnett). 

This also ties back to the issue with socioeconomic factors. Underprivileged students don’t have access to tutors or private education, and with the implementation of free AI, students can get the extra help that they deserve. 

Another source found that AI automated grading was more reliable than human grading. AI scoring reached 97.21% reliability, and human graders had 95.23%. This statistically proves the future of where education should be heading and why (Blanchard). 

Many people spend a lot of time worrying about others’ moods and maintaining relationships, and often this gets in the way of having good conversations, giving meaningful feedback, and getting a task done more efficiently.  But one doesn’t need to worry about maintaining a relationship with AI; one can give it honest feedback, share deep and personal information that one might not share with a person, and get a true, honest answer. Nobody’s feelings are being hurt, and more time can be spent on the job at hand. 

Philosophers Simone Weil and Iris Murdoch say that the core of being a good, ethical person is giving someone your full, true attention, and humans are generally horrible at this, but AI is good at it. Now that we have the resources to really excel at a task, why not use it?

Risks

There are tons of benefits to the future of education; however, there are equally as many risks. 

The most obvious risk is how students might abuse AI and have the “illusion of understanding”. Many students use AI to complete things for them and rarely ask follow-up questions, so instead of learning in school, they are having AI do the learning for them, and they are passively turning in information they have no understanding of. When AI makes help too easy, students may stop choosing engagement and effort, which are crucial elements to learning as described in Bloom’s Taxonomy, having a growth mindset, and self-regulated learning. 

There is a balance that needs to be created. The future is moving in the direction of AI, and students should be able to use it because they will be able to for the rest of their lives, but to what extent is it fair, helpful, or creating issues that will impact society later? 

The truth is, learning is changing. Some people believe we don’t need to know some of the basics anymore because AI can do it for us. And it’s important to move with the times and adapt to new things. 

But perhaps we need to figure out what makes humans different from AI. Perhaps we no longer need to collect facts and write textbooks for people to read, but instead we need to discover the answers to questions that only humans can discover through living. 

With the addition of AI into our lives, we can often feel scared and intimidated; however, it is a really good thing in some ways. We are being forced to become newly conscious of what it means to be human, live our lives, and what makes us exactly human.

Conclusion and Discussion 

The research in this paper shows a lot about how personality, motivation, study habits, and environment can affect how well students do in school. However, these studies aren’t always perfect. Many of them used small groups of students and only checked results for a short period of time. This makes it hard to know if things like feeling more motivated resulted in better grades in the long run. The research on flexible seating is still pretty new, so one can’t be sure how much it really helps. Even GPA isn’t a perfect measure because all schools grade things differently, and grade inflation is becoming increasingly prominent. 

Neuroscience adds important input as well. Studies have recently proven that socioeconomic factors resulting in more stress and lack of sleep can change how a brain develops. This means that some of what people consider motivation issues are actually caused by unfair life circumstances, instead of just being lazy. In the future, it would be helpful to combine brain scans with studies on study habits and mindsets, so we can better understand not just what helps students learn but why some students have a harder time than others learning. 

One of the biggest questions for the future is about AI. Recent studies have shown that feedback from AI can be much more helpful than feedback from a human tutor, and AI grading systems can be just as, if not more, accurate than a teacher grading by hand. AI can also be assistance to students who don’t have access to tutors or extra help in school because of their socioeconomic status. But there are still risks. If students let AI think for them instead of using it to support their own effort and learning, they could end up in a similar trap as passive rereading: feeling like they understand something, but they aren’t actually learning it. With the rapid increase of AI and technology, future research needs to figure out the best way for students to use AI to think harder and remember things better instead of as a shortcut. 

About the author

Sloane McDowell

Sloane is a senior in high school in Orinda, California. She plays lacrosse, is a part of numerous clubs, and loves to volunteer at her local memory care nursing home. Sloane has always had a passion for science, and specifically the brain. She would love to study neuroscience and become a neuroscientist.

The post Hacking Your Own Brain: What Neuroscience Says About Getting Better Grades appeared first on Exploratio Journal.

Abstract      

Agitation, such as verbal outbursts and physical aggression, is one of the most challenging symptoms of dementia in patients with Alzheimer’s disease. Non-pharmacological interventions, such as pet therapy, have emerged as a potential treatment for agitation symptoms. This literature review evaluates the application of  pet therapy and its efficacy on mitigating agitation in patients with Alzheimer’s. Synthesizing clinical trials, this paper focuses on the physiological and neurobiological mechanisms through which Animal Assisted Interactions (AAI) can reduce agitated behaviors. Mechanically, this paper explores how through observable decreases in physical stress indicators like pacing and verbal outbursts, as well as increases in measured biomarkers like oxytocin, AAI shows itself as a promising non-pharmacological alternative to treating agitation in patients. That being said, methodological limitations across current literature such as brief intervention timelines highlight the need for larger and highly controlled randomized controlled trials to establish long-term clinical protocols.

Introduction

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of individuals above the age of 40 globally and represents a major public health challenge. When most consider AD, cognitive decline is recognized as the hallmark symptom of the disorder. Despite this, it is often the non-cognitive manifestations—known as Behavioral and Psychological Symptoms of Dementia (BPSD)—that place the greatest burden on caregiving facilities and families of patients (Cerejeira, Lagarto, and Mukaetova-Ladinska 2012). Historically, agitation in AD patients has been managed through psychotropic medications; however, these pharmacological approaches carry significant risks, including sedative side effects and accelerated cognitive decline. Consequently, there is a clinical focus on identifying effective, non-pharmacological interventions (Cerejeira, Lagarto, and Mukaetova-Ladinska 2012).

 Animal-Assisted Interactions (AAI) have emerged as a compelling non-pharmacological therapy for use in geriatric care. AAI uses the human-animal bond to improve emotional, physiological, and social well-being (Filan and Llewellyn-Jones 2006). Therapy animals provide a non-verbal and non-judgmental companion whose contact with patients triggers a release of oxytocin along with a simultaneous decrease in cortisol levels and blood pressure (Kim et al. 2021).

Unfortunately, data on AAI for Alzheimer’s patients is severely hampered by methodological limitations, such as small sample sizes and insufficient follow-up tracking (Filan and Llewellyn-Jones 2006; Yakimicki et al. 2019).

Background

Alzheimer’s disease is a degenerative and irreversible neurological disorder that was first discovered in 1906 by German psychiatrist and neuropathologist Dr. Alois Alzheimer. It began when Dr. Alzheimer examined a middle-aged female patient, August Deter, who was exhibiting extreme and abnormal paranoia, memory loss, and agitation. Unable to prevent Deter’s death, Dr. Alzheimer performed a post-mortem histological examination hoping to find the cause for the observed deterioration of Deter’s physical, cognitive, and behavioral functions. Upon the examination, Dr. Alzheimer discovered the now hallmark signs of Alzheimer’s disease such as amyloid plaques and neurofibrillary tangles of brain fibers (Alzheimer 1907; Graeber and Mehraein 1999). These findings were presented to Dr. Alzheimer’s colleagues and the disease was coined “Alzheimer’s disease” nearly 4 years later in 1910 by Dr. Emil Kraepelin in his textbook Psychiatrie (Kraepelin 1910). At first, Alzheimer’s disease was believed to be a rare condition with an age barrier dictated by Deter who had developed the disease in her fifties (Graeber and Mehraein 1999; Kraepelin 1910). However, around the 1960s and 70s, what was originally thought to be “senile dementia” (cognitive decline that came with age) was found to be pathologically identical to Alzheimer’s disease (Blessed, Tomlinson, and Roth 1968). This discovery led to Alzheimer’s disease and dementia being interchangeable names for this cognitive condition that affects an overall population of around 55 million people worldwide (World Health Organization 2023) including an estimated 7.4 million Americans (Alzheimer’s Association 2024). The disease can show symptoms as early as in the patient’s 40s in rare cases of early-onset. However, most cases see a typical late onset of Alzheimer’s disease on individuals 65 or older (Alzheimer’s Association 2024). These diagnoses were gathered through measuring a patient’s cognitive decline as well as an objective framework based on living biomarkers (Jack et al. 2018). As part of that framework, amyloid-beta testing (brain imaging) and tau pathology testing (blood or cerebrospinal fluid tests) are used to establish the existence of abnormal amyloid plaques and the spread of toxic neurofibrillary tangles, respectively. The presence of these signs and symptoms are required to establish an Alzheimer’s diagnosis (Jack et al. 2018; Hansson et al. 2021).

Pharmacological intervention has been used for many years in an attempt to combat Alzheimer’s disease. Medicinal drugs, while impermanent, hold many benefits for patients (Cummings et al. 2022; Birks and Harvey 2018). Along with boosting neurotransmitters and managing behavior, some medicines can provide a temporary stabilization for Alzheimer’s patients that can help reduce caregiver burnout (Cummings et al. 2022; Birks and Harvey 2018). Even so, pharmacological limitations continued to halt scientists and doctors in developing a cure for Alzheimer’s (Cummings et al. 2022). Furthermore, pharmacological treatment can have adverse side effects such as gastrointestinal (GI) distress and increased risk of falling (Birks and Harvey 2018). Most of all, regular use of Alzheimer’s medication reduces its efficacy over time as more neurons die off (Cummings et al. 2022; Birks and Harvey 2018). As a result, modern medicine bifurcated its approach to the disease into two distinct focuses (Winblad et al. 2016). One track being long-term prevention and early detection of Alzheimer’s and the other being a focus on the management and comfort of patients already suffering from the disease (Winblad et al. 2016). With how progressive Alzheimer’s disease is, it became important to identify the warning signs of cognitive impairment before it developed into a near complete incapability of self-regulation (Jack et al. 2018, 535; Winblad et al. 2016). For those with Alzheimer’s disease, the primary clinical target of dementia care is quality of life (Winblad et al. 2016; Gitlin et al. 2012).

As such, there have been a wide variety of methods attempted ranging from pharmacological interventions to familial and personal caregivers (Gitlin et al. 2012, 2020). And while there are pharmaceutical treatments that manage to slightly slow cognitive decline, they often come with side effects and limited efficacy for behavioral symptoms (Cummings et al. 2022; Birks and Harvey 2018). This has prompted researchers to look for non-pharmacological therapies (Gitlin et al. 2012; Park et al. 2020). One such method that has recently gained more traction is pet therapy or animal assisted therapy (AAI) (Park et al. 2020). Pet therapy has already been proven to be effective in treating general mental health conditions like anxiety and depression (Charry-Sánchez et al. 2018). The presence of an animal companion helps many individuals with such afflictions to feel more grounded and calmer; and in hope of seeing a similar effect in patients with Alzheimer’s disease, pet therapy has gradually been introduced as a possible option for maintaining a reasonably high quality of life for individuals with Alzheimer’s (Park et al. 2020; Charry-Sánchez et al. 2018).

Effect of Pet Therapy on Agitation

The effectiveness of pet therapy in mitigating agitation is rooted in distinct psychological and physiological mechanisms. From a sensory perspective, the experience of stroking an animal’s fur returns the individual to the functions of the parasympathetic nervous system, increasing release of chemicals like acetylcholine and allowing calming hormones like oxytocin to take over. Furthermore, these positive interactions are associated with reduced production of the stress hormone cortisol, which was detected in saliva samples from patients (Sheikh et al., 2021). Additionally, what Sheikh’s literature review found that is especially promising about AAI is the bi-directional aspect that can be seen with pets such as dogs that dynamically react to human emotion — for example, when a dog holds eye contact with a human, or when a dog knows to lean into a person to help them feel accepted by the animal.

Managing the behavioral and psychological symptoms of dementia (BPSD)—particularly agitation, aggression, and late-afternoon distress known as sundowning—presents a profound clinical challenge in Alzheimer’s care for both the caregivers and those with Alzheimer’s. Clinical trials within high-stress medical settings demonstrate that the introduction of a trained therapy dog yields statistically significant, immediate reductions in patient agitation compared to standard care routines (Krüger et al., 2021). Kruger’s study was a systematic review of 51 studies concerning AAI with patients with Alzheimer’s disease. The study found that there was a correlation between animal intervention and a decrease in agitation, while oppositely finding no significant relationship between AAI and depression/quality of life.  Even so, this behavioral shift suggests that the physical presence of an animal provides a grounding, non-threatening stimulus capable of redirecting a patient’s attention away from environmental stressors or internal confusion.

Despite these positive outcomes, a comprehensive analysis of the literature reveals critical methodological limitations regarding the sustainability of these interventions. While the immediate de-escalation of agitation during a pet therapy session is well-documented, evidence tracking the long-term or residual effects after the animal leaves remains scarce and often low-certainty (Park et al. 2020). Park performed a systematic review of 9 randomized controlled trials published between 2000 and 2019. It was found that there is a statistically significant amount of evidence that AAI reduces depression in dementia patients. However, with broader outcomes like sustained agitation reduction and long-term behavioral changes, the overall evidence base was limited by small sample sizes and a lack of follow-up tracking. Additionally, individual patient histories must be considered; for individuals with pre-existing cynophobia (fear of dogs) or animal allergies, the introduction of a therapy animal can inadvertently exacerbate psychological distress rather than soothe it. Consequently, current research positions pet therapy as a highly potent, acute tool for situational agitation management, though further standardized, longitudinal studies are required to determine how to extend these calming effects into a patient’s broader daily routine.

Research Gaps 

Most studies concerning AAI in use for treating stress and agitation in dementia patients all share similar research limitations. Those limitations being that oftentimes the studies lack sufficient sample sizes or long-term follow-up for the realistic viability of using therapy animals in caregiving settings — to address this, prospective longitudinal studies should be conducted. Another variable these studies fail to capture is the maintenance aspect of using therapy animals to help treat dementia patients. The care for the therapy animals adds another layer of work to be done for patients that already require around the clock care. Even if there were to be specific people assigned to taking care of the therapy animals, it would require more people in the caregiving/nursing profession which is already short staffed.

Conclusion

The synthesis of current literature indicates that pet therapy can serve as a valuable non-pharmacological strategy for reducing agitation and behavioral distress in individuals living with Alzheimer’s disease. The presence of therapy animals consistently yields reductions in acute agitation, accompanied by increased positive emotional engagement. The physical presence provided by animals appears to mitigate anxiety without the adverse risks often associated with psychotropic medications.

However, while short-term behavioral benefits during active sessions are well-supported, long-term durability remains questionable. Future research should prioritize larger sample sizes and designs that allow researchers to follow up with patients from the sample long after the end of the initial experiment. In this way, the research on AAI with Alzheimer’s patients can hopefully be expanded and possibly conformed into a standardized protocol in caregiving facilities.

References

Alzheimer, Alois. 1907. “Über eine eigenartige Erkrankung der Hirnrinde.” Allgemeine Zeitschrift für Psychiatrie und Psychisch-Gerichtliche Medizin 64: 146–148.

Alzheimer’s Association. 2024. “2024 Alzheimer’s disease facts and figures.” Alzheimer’s & Dementia 20 (5): 3708–3821.

Birks, Jacqueline S., and Richard J. Harvey. 2018. “Donepezil for dementia due to Alzheimer’s disease.” Cochrane Database of Systematic Reviews, no. 6.

Blessed, G., B. E. Tomlinson, and M. Roth. 1968. “The association between quantitative measures of dementia and senile change in the cerebral grey matter of elderly subjects.” The British Journal of Psychiatry 114 (512): 797–811.

Cerejeira, Joaquim, Luis Lagarto, and Elka B. Mukaetova-Ladinska. 2012. “Behavioral and psychological symptoms of dementia.” Frontiers in Neurology 3: 73. https://doi.org/10.3389/fneur.2012.00073.

Charry-Sánchez, Juan D., et al. 2018. “Animal-assisted therapy in adults: A systematic review.” Complementary Therapies in Clinical Practice 32: 169–180.

Cummings, Jeffrey, et al. 2022. “Alzheimer’s disease drug development pipeline: 2022.” Alzheimer’s & Dementia: Translational Research & Clinical Interventions 8 (1): e12295.

Filan, S. L., and R. H. Llewellyn-Jones. 2006. “Animal-assisted therapy for dementia: A review of the literature.” International Psychogeriatrics 18 (4): 597–611. https://doi.org/10.1017/s1041610206003322.

Gitlin, Laura N., et al. 2012. “Nonpharmacologic management of behavioral symptoms in dementia.” JAMA 308 (19): 2020–2029.

Graeber, Manuel B., and P. Mehraein. 1999. “Reanalysis of the first case of Alzheimer’s disease.” European Archives of Psychiatry and Clinical Neuroscience 249 (3): S10–S13.

Hansson, Oskar, et al. 2021. “The Alzheimer’s Association appropriate use recommendations for blood biomarkers in clinical practice.” Alzheimer’s & Dementia 17 (10): 1675–1693.

Jack, Clifford R., et al. 2018. “NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease.” Alzheimer’s & Dementia 14 (4): 535–562.

Kim, S., Y. Nam, M.-J. Ham, C. Park, M. Moon, and D.-H. Yoo. 2021. “Neurological mechanisms of animal-assisted intervention in Alzheimer’s disease: A hypothetical review.” Frontiers in Aging Neuroscience 13: 682308. https://doi.org/10.3389/fnagi.2021.682308.

Kraepelin, Emil. 1910. Psychiatrie: Ein Lehrbuch für Studierende und Ärzte. 8th ed. Johann Ambrosius Barth.

Kruger, Katelyn, et al. 2021. “Animal-Assisted Intervention in Dementia.” ResearchGate. https://www.researchgate.net/publication/315461366_Animal-Assisted_Intervention_in_Dementia. 

Park, A. L., et al. 2020. “Animal-assisted interventions for people with dementia: A systematic review and meta-analysis.” Journal of Clinical Nursing 29 (17-18): 3121–3133.

Sbrizzi, C., and W. Sapuppo. 2021. “Effects of pet therapy in elderly patients with neurocognitive disorders: A brief review.” Dementia and Geriatric Cognitive Disorders Extra 11 (3): 198–206. https://doi.org/10.1159/000518469.

Sheikh, Saba, et al. 2021. “Pet-Assisted Therapy for Delirium and Agitation in Hospitalized Patients with Neurocognitive Impairment: A Review of Literature.” Geriatrics 6 (4): 96. doi.org.

Winblad, Bengt, et al. 2016. “Defeating Alzheimer’s disease and other dementias: a priority for European science and society.” The Lancet Neurology 15 (5): 455–532.

World Health Organization. 2023. Dementia key facts. World Health Organization.

Yakimicki, Mary L., Nancy E. Edwards, Elizabeth Richards, and Alan Beck. 2019. “Animal-assisted intervention and dementia: A systematic review.” Clinical Nursing Research 28 (1): 9–29. https://doi.org/10.1177/1054773818756987.

About the author

Colin Weindel

Colin is a senior at Carmel High School who is hoping to pursue a career in veterinary medicine after graduating high school starting with attending university to major in Animal Science.

The post A Literature Review of the Effect of Animal-Assisted Therapy in Reducing Agitation in Patients with Alzheimer’s Disease appeared first on Exploratio Journal.

Abstract

It has been known that pregnancy is a profound biological transformation where the maternal body can reshape its cardiovascular, physiological, and immune system to sustain the developing fetus.

Such intense anatomical restructuring, especially neurologically, can leave women highly vulnerable to medical complications both during gestation and in the immediate postpartum state.
Tremendous surges of estrogen, progesterone, and placental cortisol actively contort and restructure maternal neural networks to prepare the brain for the postnatal period.

Postpartum psychosis is a severe mental health condition which affects people after they give birth. This condition is rare and dangerous. People with postpartum psychosis have a much higher risk of harming themselves, dying by suicide or harming their children.

However, the hypothesis that a sudden postpartum crash in these chemical messengers can cause maladaptive neuroplasticity, severely disrupting circuits to trigger postpartum depression or postpartum psychosis is worth investigating to  control any such cases. The hormonal shifts in the mother’s brain, as well as the extent of altered neuroplasticity, would have to be monitored to ensure the right amount of adaptive restructuring after pregnancy.

Keywords: hormones, brain alteration, structural changes, functional consequences, maternal maladaptation, Post-partum psychosis

Introduction

During the eighteenth century, first found records of Friedrich Hoffman, citing the appearance of a condition same as postpartum psychosis was observed in his book

Medicinae Rationalis Systematicae (Systematic Rational Medicine). Almost 400 non-organic episodes in the literature started more than 3 weeks after the birth, some of them being recurrent.

Hoffmann described post-partum psychosis symptoms in a young woman as following (1721): [Brockington I, 2017]

“Four weeks after childbirth a 20-year old, rather melancholy and anxious by temperament, had a severe fright – she ‘saw’ the ghost of her long-dead mother. Three days later she became confused and started to rave. She was restless, talked day and night (mainly in rhyme), and ate and drank little, but had no fever. The infant was given to the wet-nurse, leeches were applied to her feet, and medicines given to bring on the menses; but her raving increased more and more, and she had to be restrained. It was 2 months before she calmed down. She immediately became pregnant again and remained well.”

The first case series were seen in the 19th century out of which Esquirol’s statements were the most detailed. Postpartum psychosis symptoms were observed in 92 women of which around 53% of the women predominantly contained manic symptoms, 38% showed depressive symptoms, and 9% of them displayed nonaffective psychosis. [Bergink V et al., 2016]

The connections between parturition and post-partum psychosis has been overlooked historically, despite them having hypothesized a definite pathophysiology path. Although there is a connection observed, the cellular mechanisms and the fundamental basics of the molecular changes that occur during post-partum psychosis still remain quite a mystery.

During pregnancy, the maternal placenta and the fetus’s endocrine system work together to  manage the endocrine changes that are required to maintain and promote pregnancy and also to prepare women for childbirth, breastfeeding, and other important processes post-partum. [Stern C et al., 2021] Majority of the endocrine hormones are sex hormones, example- estrogens and progesterone, that steadily increase throughout pregnancy having peaks during certain months and drastically fall at parturition. Progesterone is largely produced by the corpus luteum until about 10 weeks of gestation. The powerful influence of estrogens and progesterone (along with other hormones) on the neuroanatomical state of the brain is demonstrated during puberty, menopause, the menstrual cycle and pregnancy. Therefore, consequential changes in the brain’s physical state are also inevitable during pregnancy and after giving birth. However, research in this field is relatively scarce. [Kumar P et al., 2012]

Some symptoms of postpartum psychosis are insomnia, irritability and mood shifts, with an increase in mania, depression, or even a mixed state. Rapid mood fluctuations are a characteristic visual of the disease, however women suffering from postpartum psychosis have symptoms that are often atypical in patients with bipolar disorder. [Osborne LM et al., 2018] Disorganized, unusual behavior and obsessive thoughts with relation to the newborn occur frequently. Various cognitive symptoms such as disorientation, confusion, derealization, and depersonalization may also come along with post-partum psychosis. Delusions of altruistic homicide (seen with maternal suicide at times) to “save them both” from a fate apparently worse than death may occur and are an important exploration within the clinical examination. PPP is attached with an increased risk of both suicide and infanticide. [Bergink V et al., 2016]

Although many longitudinal neuroimaging studies have established that pregnancy is accompanied by extensive neuroanatomical remodeling, the functional importance and adaptive extent of these changes remain incompletely understood. 

The question comes whether the preservation of adaptive maternal neuroplasticity through modulation of perinatal stress and early identification of aberrant neural trajectories will reduce vulnerability to maladaptive maternal behavior and peripartum mental disorders. [ L Pritschet et al., 2024]

Neuroanatomical Remodeling During Pregnancy

Grey matter volume and cortical thickness change

Reductions have been observed in the volume of grey matter in the brain (GMV) post childbirth, especially in the regions of the brain central to theory-of-mind- processing. Such changes could be visible even after 6 years postpartum and can be traced decades later.[Losse EM et al., 2026] These findings depict that pregnancy is an increasingly vigorous stage for neural restructuring, and currently neuroscientists lack a detailed map of how exactly the human brain shifts its state throughout the period of gestation. [ L Pritschet et al., 2024]

Both the cortical thickness and grey matter decrease has been observed throughout pregnancy, and after birth has partially jumped back. The cerebral cortex and other large-scale brain networks experienced grey matter volume increase across their regions. Even in certain parts of the brain deep within, there was a decrease in grey matter volume. All these changes were substantially larger in pregnant women, as compared to the women who were not pregnant over a similar period of observation.

White matter is another integral tissue in the brain consisting mainly of nerve fiber tracts which carry signals between the regions of grey matter in the brain. The fine-scale health and packing density of white matter here increased the entirety of the first two trimesters of pregnancy. This increase returned to its baseline level after birth of the child. Volumes of cerebrospinal fluid in the lateral ventricles (C-shaped cavities) had also increased during the second and third trimesters of pregnancy, which then dropped sharply after birth. These brain changes observed were associated with shifts in steroid hormone levels. This supports the idea that pregnancy is a period of intensive neuroplasticity, a capacity of various networks of neurons to adapt and reorganize fast. [ L Pritschet et al., 2024]

Subcortical changes

In tandem with reductions in broader cortical regions in GMV, various subcortical regions showed significant reduction in volume across gestation(fig 1, left). This included the bilateral ventral diencephalon ( right hemisphere values depicted in fig.1, right; compromising hypothalamus, substantia nigra, mammillary body, lateral geniculate nucleus and red nucleus among others), caudate, hippocampus and thalamus, along with left putamen and brain stem. [Pritschet L et al., 2024]

^{Fig. 1. Subcortical GMV changed throughout gestation, adapted from ( Pritschet L, 2024)}

Mechanisms of Maternal Neuroplasticity

Hormone action

Pregnancy activates intense physiological and neurobiological shifts in maternal body and brain, including a sharp rise in movement of sex steroid hormones, especially estrogen and progesterone. [Losse EM et al., 2026]

Due to the lipid-like nature of steroid hormones, certain hormones like progesterone and estradiol passively cross the blood brain barrier which lets them bind to the estrogen and progesterone receptors present in the brain. Such steroids act on a network of hormone-sensitive regions of the brain which are identified as maternal caregiving networks. [Catenaccio E et al., 2016]  These shifts play an important role in making the maternal brain fit for childcare, regulating neurotransmission and neuroplasticity by interactions with the key receptor systems, including primary inhibitory and excitatory neurotransmitters.

GABA Synthesis and working

Gamma-aminobutyric acid (GABA) and glutamate (N-methyl-D-aspartate receptor; NMDA), are principal to neuroplasticity processes. Estrogen catalyses glutamatergic neurotransmission by increasing the NMDA receptor subunit expression, the number of binding sites, and the neuronal sensitivity to synaptic input. [Sears SM et al., 2021]

Allopregnanolone is a potent positive modulator of  GABA_A receptors and it helps with the excitatory/inhibitory balance (E-I Balance) which is required for maintaining neural stability. The simultaneous release of GABA_A and glutamate is essential for brain functioning, particularly in regions like the prefrontal cortex, amygdala and the hippocampus. It plays a vital role here in cognitive and emotional regulation. In certain animal models, GABA_A and glutamate have been observed to get synchronously upregulated during postpartum, depicting a coordinated neurochemical adaptation for motherhood. In mice, dysregulation or imbalance in E-I after a simulated pregnancy was associated with depression-like behaviour, and restoration of the E-I balance mitigated the condition. [Losse EM et al., 2026]

Oxytocin

Beside these neurotransmitter systems, oxytocin comes out as another important modulator of maternal brain working. It is a neuropeptide which enhances the synaptic excitability and promotes neuroplasticity, fortifying maternal behavior. [Froemke RC et al., 2021]Oxytocin levels increase progressively with mother-infant interactions, further improving their attachment.[Scatliffe N et al., 2019] It also interacts closely with the GABAergic system, implying its broader role in regulating mood regulations and showing resilience to stress in the postpartum period. [Walter MH et al., 2021]

HPA-Axis

The hypothalamic-pituitary-adrenal (HPA) axis adapts during pregnancy and postpartum to remake the maternal brain. Glucocorticoid and corticotropin-releasing hormone (CRH) signaling undergo alterations due to HPA-axis. These changes facilitate structural and functional neuroplasticity in brain regions which control anxiety, reward, and maternal caregiving. Altered fetal HPA axis ultimately results in dysregulation of the brain stress-response system long after birth and possibly lifelong. [O’Keane V et al., 2011]

Functional Significance of Maternal Brain Remodeling

Mother-infant interactions based on neural changes

The structural and functional plasticity in the human maternal brain is associated with individual differences in maternal thoughts and behaviors. With reference to structural plasticity, it is seen that the greater the growth in the midbrain region, the higher the positive emotions a mother reports about her baby, which could indicate positive adaptation to parenthood in the third and fourth months postpartum. [Kim P , 2016]

At 4 to 6 months postpartum, neural activation patterns associated with sensitive and synchronous maternal behaviors (for example: increased coordination of gaze, touch, and vocalization with infants) were examined. In response to a mother’s own baby video stimuli as compared to control baby video stimuli, synchronous mothers had greater activation in the nucleus accumbens, which is a key reward/motivation region. This functionally correlated with activity in the inferior frontal gyrus as well as medial frontal gyrus. Thus, reward-related neural responses to one’s own infant were associated with magnified neural connectivity for social information processing, which may further support mother-infant interactions.

Maternal neural responses to infants at the time of early postpartum can play a crucial role to predict positive infant socioemotional results. Among a few new mothers, high levels of anxious thoughts in the mother about their own infants and their competency as a parent at the first month postpartum showed lower socioemotional competencies among their infants at 18-24 months of age. High anxiety about parenting and infants was further associated with decrease in activation in the substantia nigra,, in response to their own infant cry compared to other infant cry sounds at the first month postpartum, suggesting reduced neural sensitivity to own infants. [Kim P, 2016]

These understandings also suggest that abnormality in the neural plasticity of a maternal brain could increase vulnerability for a difficult transition to parenting among some mothers. Mothers may be vulnerable to severe difficulties in mood regulation and stress. New mothers are exposed to increased levels of risk for mood disorders like postpartum depression and anxiety disorders. [Garapati J et al., 2023]

Together, the findings from these studies show a structural increase in many maternal brain regions that are involved in parenting, from immediately after childbirth to 3–4 months postpartum. 

Dysregulated Neuroplasticity and Maladaptive Outcomes  

The decrease observed in GMV, brain volume, neurogenesis and glial density can actually be part of the normal optimization of maternal behaviour, rather than deterioration (JL. Pawluski 2022). So the matter to understand is how the same broad phenomenon of neuroplastic remodeling is adaptive in one context but potentially maladaptive in another.

Some possible agents that could deviate the normal optimal working of the maternal brain due to its changes are harmful stressors, hormonal dysregulations and the most underscored one yet, E/I imbalance in the brain.

Stress

Stress has been introduced by Hans Selye as the GAS, which is a process in which the body confronts noxious agents. The mechanisms in which external stressors alter brain function have been studied extensively over the past 60 years. [Rochette L et al., 2023] Corticotropin-releasing hormone (CRH), the stress hormone, has become the focus of the study. In both animals and humans, CRH stimulates norepinephrine (NE) synthesis and release. CRH release activates the hypothalamic-pituitary-adrenal (HPA) axis, which acts as one of the main mechanisms involved in stress. The CRH induced synthesis of ACTH (adrenocorticotropic hormone) in turn alters the function of the neural network by changing the building blocks in the networks and by restructuring the integrative properties, finally causing the behavioral or emotional changes. [Contoreggi C et al., 2015] This neural plasticity underlying stress, undoubtedly impacts the brain function and can cause functional alterations in mental disorders. Thus, stress-induced neuroplasticity plays a pivotal role in almost all of the mental disorders, and has become a synonym for  displaying certain negative emotions, such as depression and anxiety.

Hormone dysregulation

The hormonal changes attached with pregnancy can attain levels which could mimic diseases like cushing disease, diabetes mellitus, hyperthyroidism and more, these themselves being associated with psychiatric disturbances. Such hormonal shifts can also alter the circadian rhythm of the body causing negative impact on health and leading to insomnia. Further, Insomnia is known to be associated with depression and suicidal ideation. [Trifu S et al., 2019]

The sudden fall in progesterone post-partum can have a role in postpartum depression. An explanation hypothesised is that progesterone and its cerebral metabolite called allopregnanolone decrease irritability. Ovaries don’t secrete progesterone again until the first menstrual cycle occurs, which can cause a temporary imbalance. The brain and body adapts to low levels of estradiol which promote lactation. Estradiol works to facilitate serotonergic transmission by catalysing synthesis of serotonin and/or reducing reuptake of serotonin, thus reducing depressive symptoms. The reduced estradiol levels in the maternal body have the opposite effect, depriving it of its natural protection against depression. [Hernández-Hernández OT et al., 2019]

Testosterone is known to have a relation to greater mood disturbances after birth.
Plasma corticosteroids reach a high during labor and reduce gradually within 4 hours postpartum, creating an emotionally vulnerable period for the mother.In the presence of low estradiol, potentially lower dopamine and variations in thyroid hormones, it is not uncommon that such imbalances can cause an important psychological impact. [Trifu S et al., 2019]

E/I Imbalance

Persistent E/I imbalance could be one mechanism distinguishing normal postpartum neuroplasticity from maladaptive neuroplasticity. The E/I balance is impactful enough to cause the shift from normal neural stability to depression like behaviour. [ Yin, Y. Y., et al., 2021] If in certain cases, this balance is not restored due to insufficient GABA receptors or excess glutamate, deregulation of the E-I balance could cause a number of nervous system disorders, such as epilepsy, mental retardation and autism. Prolonged imbalance could alter the extent of neuroplasticity of the maternal brain, and cause episodes similar to those seen in postpartum-psychosis.

Preventive and Therapeutic Implications

If stress-related dysregulation contributes to abnormal neuroplasticity, interventions that improve regulation of the stress system might indirectly protect adaptive brain remodeling.
As normal plasticity is adaptive and dysregulated plasticity becomes potentially harmful, then a possible control for preventing maladaptive outcomes is by controlling either the extent of plasticity or the imbalance that can occur in the maternal brain before parturition occurs. During normal pregnancy, the maternal stress response system itself changes. Severe stress can disrupt this adaptation. [Coussons-Read ME, 2013]

Other possibilities that can be used to scope out treatments include finding the abnormal neural trajectories. Longitudinal MRI’s of the maternal brain that get monitored during and after parturition and landscaping the paths and changes of the brain that shift from the normal trajectories are ways post-partum disorders can be prevented and controlled. [Kim P et al., 2010] Mothers who are giving birth and have any previous exposure to certain substances, stressors, or early psychological trauma must be checked regularly to ensure that slight changes don’t alter the makeup of the brain, potentially causing post-partum psychosis or post-partum depression. If any such patients are observed then reducing the possibilities of any exposure to such stressors must be eliminated.

E/I Imbalance is an underlying problem, which if not restored, can create maladaptive pathways to brain abnormalities which cause manic and mood shift behaviours. This is still largely a future-research proposition, not an established clinical screening method. Postpartum psychosis is a medical emergency which requires immediate psychiatric hospitalization. The best current approach involves fast-acting antipsychotics and benzodiazepines for acute symptom control, the mood stabilizer lithium as the gold standard for long-term recovery and prevention, and adopting electroconvulsive therapy (ECT) for severe or extremely resistant cases. [Jairaj C et al., 2023] However, prevention is much better than draining the body out with cures, so before ppp occurs, for any female with the chance of having post-partum disorders or facing immense stress, close monitoring is the best safety measure.

Abbreviations

1. GMV- Grey matter volume
   
2. PPP- Post-partum psychosis
   
3. GABA- Gamma amino butyric acid
   
4. NMDA- N-methyl-D-Aspartate receptor
   
5. E/I- Excitatory/Inhibitory
   
6. HPA- Hypothalamic-pituitary-axis
   
7. CRH- Corticotropin-releasing hormone
   
8. GAS- General adaptation syndrome
   
9. NE- Norepinephrine
   
10. ACTH- Adrenocorticotropic hormone
    
11. MRI- Magnetic resonance imaging
    
12. ECT- Electroconvulsive therapy
    

References and Citations 

Altemus, M., Redwine, L. S., Leong, Y.-M., Frye, C. A., Porges, S. W., & Carter, C. S. (2001). Responses to laboratory psychosocial stress in postpartum women. Psychosomatic Medicine, 63, 814–821. https://doi.org/10.1097/00006842-200109000-00015

Atzil, S., Hendler, T., & Feldman, R. (2011). Specifying the neurobiological basis of human attachment: Brain, hormones, and behavior in synchronous and intrusive mothers. Neuropsychopharmacology, 36(13), 2603–2615. https://doi.org/10.1038/npp.2011.172

Been, L. E., Sheppard, P. A. S., Galea, L. A. M., & Glasper, E. R. (2022). Hormones and neuroplasticity: A lifetime of adaptive responses. Neuroscience & Biobehavioral Reviews, 132, 679–690. https://doi.org/10.1016/j.neubiorev.2021.11.029

Bergink, V., Rasgon, N., & Wisner, K. L. (2016). Postpartum psychosis: Madness, mania, and melancholia in motherhood. American Journal of Psychiatry, 173(12), 1179–1188. https://doi.org/10.1176/appi.ajp.2016.16040454

Brockington, I. (2017). Late onset postpartum psychoses. Archives of Women’s Mental Health, 20(1), 87–92. https://doi.org/10.1007/s00737-016-0680-y

Catenaccio, E., Mu, W., & Lipton, M. L. (2016). Estrogen- and progesterone-mediated structural neuroplasticity in women: Evidence from neuroimaging. Brain Structure and Function, 221(8), 3845–3867. https://doi.org/10.1007/s00429-016-1197-x

Contoreggi, C. (2015). Corticotropin releasing hormones and imaging, rethinking the stress axis. Nuclear Medicine and Biology, 42(4), 323–339. https://doi.org/10.1016/j.nucmedbio.2014.11.008

Coussons-Read, M. E. (2013). Effects of prenatal stress on pregnancy and human development: Mechanisms and pathways. Obstetric Medicine, 6(2), 52–57. https://doi.org/10.1177/1753495X12473751

Del Río, J. P., Alliende, M. I., Molina, N., Serrano, F. G., Molina, S., & Vigil, P. (2018). Steroid hormones and their action in women’s brains: The importance of hormonal balance. Frontiers in Public Health, 6, 141. https://doi.org/10.3389/fpubh.2018.00141

Eichler, S. A., & Meier, J. C. (2008). E-I balance and human diseases—from molecules to networking. Frontiers in Molecular Neuroscience, 1, 2. https://doi.org/10.3389/neuro.02.002.2008

Elyada, Y. M., & Mizrahi, A. (2015). Becoming a mother—Circuit plasticity underlying maternal behavior. Current Opinion in Neurobiology, 35, 49–56. https://doi.org/10.1016/j.conb.2015.06.007

Florio, A. D., Munk-Olsen, T., & Bergink, V. (2016). The birth of a psychiatric orphan disorder: Postpartum psychosis. The Lancet Psychiatry, 3(6), 502. https://doi.org/10.1016/S2215-0366(16)30037-2

Froemke, R. C., & Young, L. J. (2021). Oxytocin, neural plasticity, and social behavior. Annual Review of Neuroscience, 44, 359–381. https://doi.org/10.1146/annurev-neuro-102320-102847

Garapati, J., Jajoo, S., Aradhya, D., Reddy, L. S., Dahiphale, S. M., & Patel, D. J. (2023). Postpartum mood disorders: Insights into diagnosis, prevention, and treatment. Cureus, 15(7), e42107. https://doi.org/10.7759/cureus.42107

Hernández-Hernández, O. T., Martínez-Mota, L., Herrera-Pérez, J. J., & Jiménez-Rubio, G. (2019). Role of estradiol in the expression of genes involved in serotonin neurotransmission: Implications for female depression. Current Neuropharmacology, 17(5), 459–471. https://doi.org/10.2174/1570159X16666180628165107

Jairaj, C., Seneviratne, G., Bergink, V., Sommer, I. E., & Dazzan, P. (2023). Postpartum psychosis: A proposed treatment algorithm. Journal of Psychopharmacology, 37(10), 960–970. https://doi.org/10.1177/02698811231181573

Kim, P., Leckman, J. F., Mayes, L. C., Feldman, R., Wang, X., & Swain, J. E. (2010). The plasticity of human maternal brain: Longitudinal changes in brain anatomy during the early postpartum period. Behavioral Neuroscience, 124(5), 695–700. https://doi.org/10.1037/a0020884

Kim, P. (2016). Human maternal brain plasticity: Adaptation to parenting. New Directions for Child and Adolescent Development, 2016(153), 47–58. https://doi.org/10.1002/cad.20168

Kim, P. (2021). How stress can influence brain adaptations to motherhood. Frontiers in Neuroendocrinology, 60, 100875. https://doi.org/10.1016/j.yfrne.2020.100875

Kumar, P., & Magon, N. (2012). Hormones in pregnancy. Nigerian Medical Journal, 53(4), 179–183. https://doi.org/10.4103/0300-1652.107549

Losse, E. M., Daneshnia, N., Dukart, J., Nehls, S., & Chechko, N. (2026). Maternal brain alterations based on neurotransmitter and hormone receptor distributions over six months postpartum. Translational Psychiatry, 16(1), 280. https://doi.org/10.1038/s41398-026-04104-4

Lucia, M., Viviana, M., Alba, C., Giulia, D., Carlo, D. R., Porpora, M. G., Luca, T., Federica, V. M., Immacolata, V. A., & Grazia, P. M. (2023). Neurological complications in pregnancy and the puerperium: Methodology for a clinical diagnosis. Journal of Clinical Medicine, 12(8), 2994. https://doi.org/10.3390/jcm12082994

Luders, E., Kurth, F., & Sundström Poromaa, I. (2022). The neuroanatomy of pregnancy and postpartum. NeuroImage, 263, 119646. https://doi.org/10.1016/j.neuroimage.2022.119646

Martínez-García, M., et al. (2021). Do pregnancy-induced brain changes reverse? The brain of a mother six years after parturition. Brain Sciences, 11, 168.

O’Keane, V., Lightman, S., Patrick, K., Marsh, M., Papadopoulos, A. S., Pawlby, S., Seneviratne, G., Taylor, A., & Moore, R. (2011). Changes in the maternal hypothalamic-pituitary-adrenal axis during the early puerperium may be related to the postpartum “blues.” Journal of Neuroendocrinology, 23(11), 1149–1155. https://doi.org/10.1111/j.1365-2826.2011.02139.x

Osborne, L. M. (2018). Recognizing and managing postpartum psychosis: A clinical guide for obstetric providers. Obstetrics and Gynecology Clinics of North America, 45(3), 455–468. https://doi.org/10.1016/j.ogc.2018.04.005

Pawluski, J. L., Hoekzema, E., Leuner, B., & Lonstein, J. S. (2022). Less can be more: Fine tuning the maternal brain. Neuroscience & Biobehavioral Reviews, 133, 104475. https://doi.org/10.1016/j.neubiorev.2021.11.045

Rochette, L., Dogon, G., & Vergely, C. (2023). Stress: Eight decades after its definition by Hans Selye: “Stress is the spice of life.” Brain Sciences, 13(2), 310. https://doi.org/10.3390/brainsci13020310

Ruffaner-Hanson, C., Noor, S., Sun, M. S., Solomon, E., Marquez, L. E., Rodriguez, D. E., Allan, A. M., Caldwell, K. K., Bakhireva, L. N., & Milligan, E. D. (2022). The maternal-placental-fetal interface: Adaptations of the HPA axis and immune mediators following maternal stress and prenatal alcohol exposure. Experimental Neurology, 355, 114121. https://doi.org/10.1016/j.expneurol.2022.114121

Scatliffe, N., Casavant, S., Vittner, D., & Cong, X. (2019). Oxytocin and early parent-infant interactions: A systematic review. International Journal of Nursing Sciences, 6(4), 445–453. https://doi.org/10.1016/j.ijnss.2019.09.009

Sears, S. M., & Hewett, S. J. (2021). Influence of glutamate and GABA transport on brain excitatory/inhibitory balance. Experimental Biology and Medicine, 246(9), 1069–1083. https://doi.org/10.1177/1535370221989263

Sit, D., Rothschild, A. J., & Wisner, K. L. (2006). A review of postpartum psychosis. Journal of Women’s Health, 15(4), 352–368. https://doi.org/10.1089/jwh.2006.15.352

Spalek, K., et al. (2024). Pregnancy renders anatomical changes in hypothalamic substructures of the human brain that relate to aspects of maternal behavior. Psychoneuroendocrinology, 164, 107021.

Stern, C., Schwarz, S., Moser, G., Cvitic, S., Jantscher-Krenn, E., Gauster, M., & Hiden, U. (2021). Placental endocrine activity: Adaptation and disruption of maternal glucose metabolism in pregnancy and the influence of fetal sex. International Journal of Molecular Sciences, 22(23), 12722. https://doi.org/10.3390/ijms222312722

Trede, K., Baldessarini, R. J., Viguera, A. C., & Bottero, A. (2009). Treatise on insanity in pregnant, postpartum, and lactating women (1858) by Louis-Victor Marcé: A commentary. Harvard Review of Psychiatry, 17(2), 157–165. https://doi.org/10.1080/10673220902891802

Trifu, S., Vladuti, A., & Popescu, A. (2019). The neuroendocrinological aspects of pregnancy and postpartum depression. Acta Endocrinologica (Bucharest), 15(3), 410–415. https://doi.org/10.4183/aeb.2019.410

Walter, M. H., Abele, H., & Plappert, C. F. (2021). The role of oxytocin and the effect of stress during childbirth: Neurobiological basics and implications for mother and child. Frontiers in Endocrinology, 12, 742236. https://doi.org/10.3389/fendo.2021.742236

Wang, F., Pan, F., Shapiro, L. A., & Huang, J. H. (2017). Stress induced neuroplasticity and mental disorders. Neural Plasticity, 2017, 9634501. https://doi.org/10.1155/2017/9634501

Xu, K., Song, D., Li, H., Lu, Y., & Zhu, Z. (2025). Advance in the mechanisms underlying prenatal stress-induced depressive-like behavior in offspring. Brain, Behavior, & Immunity—Integrative, 9, 100108. https://doi.org/10.1016/j.bbii.2025.100108

Yin, Y. Y., Wang, Y. H., Liu, W. G., Yao, J. Q., Yuan, J., Li, Z. H., Ran, Y. H., Zhang, L. M., & Li, Y. F. (2021). The role of the excitation–inhibition functional balance in the mPFC in the onset of antidepressants. Neuropharmacology, 191, 108573. https://doi.org/10.1016/j.neuropharm.2021.108573

Zoubovsky, S. P., Hoseus, S., Tumukuntala, S., Schulkin, J. O., Williams, M. T., Vorhees, C. V., & Muglia, L. J. (2020). Chronic psychosocial stress during pregnancy affects maternal behavior and neuroendocrine function and modulates hypothalamic CRH and nuclear steroid receptor expression. Translational Psychiatry, 10(1), 6. https://doi.org/10.1038/s41398-020-0704-2

About the author

Diva John

Diva is an incoming medical student from India with a strong interest in neuroscience, psychology, and the biological mechanisms underlying human health and behaviour. Her research interests particularly lie in neuroplasticity and emerging areas of neuroscience that remain relatively underexplored.

She has also previously gained an understanding of clinical exposure through a psychology internship at Fortis Hospital. Through her research, Diva hopes to continue exploring questions at the intersection of neuroscience, psychology, and medicine.

The post Maladaptive Significance of Neuroanatomical Changes in Pregnancy Leading to Postpartum Psychosis appeared first on Exploratio Journal.

Abstract

Participation in martial arts like karate has risen globally and therefore, interest in how training, kata, and kumite affects human physiology has also increased.  Understanding how is vital for both optimizing performance and benefits and ensuring long-term health. Practicing karate produces beneficial effects on the human body, including increased left ventricular mass under normal function, enhanced hamstring and quadriceps flexibility and micro-fractures in bone tissue that stimulates and encourages osteogenesis, which can help protect the bones against osteoporosis and other bone diseases in the future. However, the exact impact of karate on the skeletal system remains very controversial regarding posture, and whether the effects are positive or negative. Studies demonstrate karate corrects existing posture defects in children while causing a decrease in ROM of the hip for elite-adult practitioners. The exact biomechanical trade-offs explaining these conflicting structural and physiological results have not been thoroughly synthesized. In this paper, we will show how karate has both beneficial and negative effects on the human body, where training can enhance internal health while also causing muscle fatigue and skeletal system problems. We found that karate training does benefit the cardiovascular system while increasing bone density, but permanent lumbar hypolordosis, a posterior pelvic tilt, and decreased ROM in hip rotation occur due to the low stance of elite kata practitioners. This contradicts the claim that karate is a purely corrective exercise modality. Our analysis shows that karate can improve internal health, yet compromise lower-body and spinal mechanics. Ultimately, these findings provide insight on the critical trade offs between athletic performance and structural health that must be balanced during training. These insights help redefine our understanding of the physiological effects of martial arts, combining mechanical strain with improved internal organ health. By proving this connection, it can eventually lead to the advancement of prevention techniques and advanced sports medicine protocols to help combat long-term musculoskeletal degeneration. 

Introduction

Martial artists are a subset of athletes who practice self-defense and combat. All trained martial artists, regardless of their practice, should be proficient in both attacking and defending (Su et al., 2024). Today, martial arts is considered a part of the fitness industry with schools in every town. It’s estimated that 100 million people practice martial arts worldwide (Corcoran, 1997). In that group, approximately 73% of practitioners are male while the remaining are female. 

Karate has one of the highest female participation rates at around 30 – 40%. Also, the largest age group practicing is adults ages 25 – 34 at about 22.1%, with children ages 6 – 11 close behind at 17.5%. The smallest age group of practitioners is ages 65 and up at less than 2.6% (Sports & Fitness Industry Association, 2024). Karate in particular is a martial art with a wide variety of styles and techniques that have evolved over time. The core physical and mental practices of karate also increase success in combat situations (Clements, 2011). Karate has mainly three components: kihon – which translates to basic karate techniques, kata – the sequence of offensive and defensive techniques and kumite – sparring/fighting an opponent. Kata is performed without interruption by the judges, as it is a predetermined sequence of karate techniques performed at maximal intensity. This, in turn, leads to kata having a great effect on body posture with kyphosis, lordosis, and increased range of motion (ROM) (Gaweł & Zwierzchowska, 2024). Kumite, on the other hand, requires a higher level of metabolic power than kata, as practitioners need explosive, powerful kicks to score points. Also, they are constantly interrupted between techniques to announce point penalties; therefore, kumite focuses on high-intensity anaerobic recovery rather than purely continuous aerobic endurance (Doria et al., 2009). By the 20th century, master practitioners were teaching karate worldwide (Piepiora, 2025). Also, karate practitioners require correct posture and body position for correct technique execution, which is essential to the art. This entails correct spinal posture, shoulder, head and hip placement in the physiological system. (Bălăiasa et al., 2024). 

While karate is beneficial for all ages, with factors like bone mass, it has a greater effect on children, as they are still growing, and it provides preventive benefits as they grow older (Barbeta et al., 2019). Regarding gender, males do gain more bone mass, but both genders do gain bone mass (Barbeta et al., 2017). Also, practitioners of more elite levels and older than 19 often experience more hip pain than those of lower levels and younger (Mack et al., 2022). As karate becomes increasingly popular, it’s essential to understand the risks and benefits for the athlete’s body. The effects of karate on the human body can be varied (Figure 1). In this paper, I will discuss how karate affects bone mass and health; the cardiovascular system; the skeletal structure; and the muscular system. 

A systematic search using Google Scholar and MDPI Open Access Journals using keywords such as ‘karate physiology’, ‘posture’, ‘bone mass’ and ‘cardiovascular’. Review articles were also considered during the selection process. Studies were included if they focused explicitly on the physiological effects of karate training on the human body.

Bone Mass and Density

Given the high impact nature of karate, its effects on bone health are highly dependent on the nature of contact. Controlled mechanical impact from karate training can cause microfractures in bone tissue, which stimulates osteogenesis and promotes beneficial effects on bone mass. This can help prevent osteoporosis. Acquiring bone mass during childhood and adolescence is vital for preventing osteopenia (low bone density) and future diseases such as osteoporosis and bone fractures in adulthood (Barbeta et al., 2019). But also, an incorrectly executed block during sparring can result in a direct collision between the arm and the incoming leg, causing fractures in the weaker bones of the arm (Su et al., 2024). Furthermore, while combat sports such as karate benefit all ages of people regarding bone mass, it’s also important to note there are other factors that could be the cause, like vitamins, calories and mineral intake and daily habits like smoking, alcohol and volume of exercise (Barbeta et al., 2019).

Research indicates a demographic debate on whether age or training volume controls the magnitude of skeletal benefits. On one hand, karate is beneficial for all ages with factors like bone mass, but it has a greater effect on children, as they are still growing, and it provides preventive measures as they grow older. In regard to gender, males do gain more bone mass, but both genders gain bone mass (Barbeta et al., 2017). On the other hand another study indicates an increase in bone mass primarily benefits adults due to longer training, higher impact forces from increased muscle mass, and fully matured skeletons. They argue it has a small effect on children because they are still growing, and skeletal energy supports growth rather than bone density (Drozdzowska et al., 2011). Despite this debate on age in relation to effects, studies confirm karate practice fundamentally alters bone remodeling profiles at a cellular level.  A study conducted on 17 year old male practitioners also shows they have lower amount of C-terminal telopeptideotype I collagen (CTX) and osteocalcin; N-terminal propeptide procollagenase type 1 (P1NP) (bone resorption markers) in their system than a group of sedentary group of the same demographic. This means that the martial artist’s bones won’t break down as fast as the sedentary group (Nasri et al., 2015). In the end, while external life habits can affect individual outcome, the over consensus is that karate training acts as a powerful mechanical stimulus for bone mass optimization, though its exact effect on different age groups needs to be further studied.

Cardiovascular system 

While Karate had mixed effects on bone mass and density, literature suggests only beneficial effects on the cardiovascular system. These systemic enhancements directly translate to a valid indicator of the cardiovascular system being a cooperative function is the rate called VO2max, which means the highest rate your body can consume oxygen in exercise (Impellizzeri & Marcora, 2007).  It has also been shown that six months of karate classes increase the mass of the left ventricle, which pumps oxygenated blood throughout the body, while maintaining optimal function. This shows a safe increase in heart muscle in new practitioners and benefits heart parameters specifically in first-year students (K.k et al., 2024). Furthermore, a study conducted with 70 obese people split into 2 groups, 35 teenagers practicing 3 times a week for 12 weeks and the other 35 not practicing and receiving basic psychological and nutritional care, showed the karate group having a lower resting heart rate (beneficial), increased high-density lipoprotein cholesterol, as well as a reduction of low-density lipoprotein, and triglycerides. These findings suggest that karate training can improve cardiometabolic parameters and inflammation in overweight adolescents (de Souza, 2022). 

Research consistently demonstrates baseline training benefits apply to sedentary individuals and accommodate the extreme, dynamic demands of competitive matches. A study showed that sedentary collegiate women who completed 10 weeks of 30 minutes of karate training could reach the minimal threshold for increasing cardiovascular fitness in that demographic. Showing how karate can enhance cardiovascular fitness, which most likely extends to other groups of people as well (Yoshimura & Imamura, 2010). While these cumulative adaptations lower overall strain at rest, the cardiovascular system remains highly responsive to acute athletic demands. It’s also important to note that karate has a significant effect on heart rate, with resting rate being 55.73 ± 5.17 beats/min rising to 144.20 ± 9.16 beats/min during a karate match and lower to 69.33 ± 5.96 beats/min 10 minutes after the match (Bhattacharya & Chatterjee, 2020). Finally, the consensus from the articles is that karate is excellent for the cardiovascular system; whether to the youth, adults, weight and intensity. 

Skeletal structure

Karate has shown mixed effects on skeletal structure, with outcomes heavily influenced by the practitioner’s age, skill level, and training sub-discipline. Research strongly supports karate being highly corrective in the bodies of young, developing practitioners. A study with 20 practitioners ages 10-12 who exhibited postural defects, such as kyphotic attitude, nonstructural scoliosis and spinal straightness,  were tested for 7 months. During this time, participants did exercises focusing on the trunk and limbs for 20 minutes during their regular training sessions. Afterward, their posture was measured and 60% of athletes displayed no postural defects while 40% only displayed minor deviations, proving how karate is beneficial to posture (Mischenko et al., 2020). Similarly, another study showed that 57 children aged 9-12 who practiced karate for 11 months reduced trunk rotation, thoracic kyphosis, lumbar lordosis angles, and shoulder asymmetry, therefore improving their posture (Brzęk et al., 2022). According to Bălăiasa et al., indicates that individuals experience major gains in body posture, decreased lower and upper spinal curves, and more balanced shoulder and hip alignment (Bălăiasa et al., 2024).

However, a contrast appears when examining elite, adult athletes, where positive adaptations for athletic performance can translate into chronic mechanical strain on the body. In karate, a flattened lower back (decreased lordosis) creates a highly stable, impact-resistant base for karate stances, which is great for performance (Bălăiasa et al., 2024).Yet, maintaining these adaptations require physiological strain. For example, a posterior pelvic tilt may be beneficial from a technical standpoint, as it allows the body to be in the correct position for stance and hips. But excessive posterior pelvic tilt can shift your head forward, cause lumbar hypolordosis, and disrupt the balance between the pelvis and spine, weakening core strength and placing extra stress on the knees and lower body (Gaweł & Zwierzchowska, 2024). One study of elite adult athletes found that elite-level karate training for 225 minutes induces acute changes, such as a decrease in lumbar lordosis (lumbar hypolordosis) and pelvic tilt, in the short term immediately after the session. Long-term, the body’s positioning changes with permanent lumbar hypolordosis, a posterior pelvic tilt, and a decreased ROM (range of motion) in internal and external hip rotation. This permanent loss of the natural lower back curve (lumbar hypolordosis) strains the knees and forces the head forward (Gaweł & Zwierzchowska, 2024). 

This structural degradation is primarily driven by the constant contraction of the hip flexors required to maintain deep stances, a requirement that varies heavily by competitive style (Khorasani et al., 2020). For example, study involving 18 kata practitioners and 20 kumite practitioners revealed distinct structural differences based on training style: while kata athletes—who rely heavily on deep, static stances—showed a higher prevalence of flat feet, kumite practitioners displayed a greater tendency for increased lumbar lordosis (Hosseinzadeh et al., 2019).

As you can see, many studies contradict one another regarding how karate affects the human body. But that is due to the fact that karate cannot be generalized as a singular influence on the skeletal system. It’s clear that while training has a corrective and stabilizing effect for developing skeletal structures, elite-level mastery demands localized repetitive strain that compromises adult spinal and lower-body mechanics.

Muscular system

Karate is shown to enhance muscle strength, flexibility and core stability, yet can cause the risk of muscle fatigue and over use. Research supports that, to punch correctly, it’s important to rely on the whole-body momentum generated by the core muscle groups from a stable stance rather than on the muscles in the shoulder and elbow. Doing this and bracing the core muscles is beneficial as an “internal brace” for the spine (Su et al., 2024.). But in contrast, the extensions of the humeral joint of that limb during a basic punch could overload the muscular force deficits in the body’s segments and joints, which can cause musculoskeletal pain (Gaweł & Zwierzchowska, 2024). Independent of technique, karate is shown to have beneficial effects on the musculoskeletal system by reducing muscle tension, work-related overload syndromes, musculoskeletal diseases, occupation overuse syndromes while enhancing musculoskeletal strength and improve muscle tone (Mastnak, 2017; Piepiora, 2025). 

Research reveals that effects rely heavily on the amount of training and the age of the practitioner. In youth development, karate acts as a powerful preventative tool against the natural loss of flexibility that typically occurs during pre-adolescence. For example, hamstring flexibility increased from 29° to 40.2°  (11.2°  gain) as well as increasing quadricep flexibility from 130.4°  to 143.1°  (12.7° increase) in a study of 14 boys ages 8-13. This showed a positive correlation between right knee extensor strength (Concentric Peak Extension) and knee flexor strength (Concentric Peak Flexion) (Violan et al., 1997). Yet, training in high volume with multiple repetitions of the same movements can also cause changes in the muscular system through external compensation: producing musculoskeletal pain in certain areas (Gaweł & Zwierzchowska, 2024). Elite karate practitioners also have greater strength in their quadriceps and hamstrings, but it’s worth noting that most people suggest that karate practitioners engage in additional training to improve muscular fitness (Shaw, 2020). Motion capture (MoCap), camera and sensor technology to analyze human movement patterns, revealed that experienced martial artists usually have a lower center of body mass, due to low stances and the large core muscles. But MoCap also revealed that repeat kicking can cause muscle fatigue and loss of proper form, which can result in higher injury risk. Specifically, they found that the primary hamstring muscle–the bicep femoris (BF)- is where the fatigue occurred rather than the quadriceps where the vastus lateralis (VL) and rectus femoris (RF) didn’t show any sign of peripheral fatigue (Quinzi et al., 2016). Ultimately, the muscular research reveals that karate is excellent for structural stability, core action and strength while causing muscle fatigue and  localized overuse injuries due to repetitive movements and the large volume of training. 

Conclusion

In conclusion, karate affects the physiological system is both positive and negative as it’s seen as a highly effective conditioning modality but also a source of mechanical stress. On one hand, karate has shown many physiological benefits across many body systems. Cardiovascularly, training is seen to safely increase left ventricle mass, while maintaining optimal pumping efficiency and improving oxygen consumption capacity (VO2max). In relation to bone mass and density, the high-impact nature of karate downregulates bone resorption markers like CTX and P1NP and creates microfractures in bones which encourages osteogenesis. These cellular changes permanently enhance adult bone density and significantly reduce long-term risks for age-related conditions like osteoporosis and others. Furthermore, karate can be a corrective tool in youth for successfully rectifying pre-exist postural defects like thoracic kyphosis and nonstructural scoliosis while increasing hamstring, quadriceps flexibility, musculoskeletal strength and improving muscle tone.

However, these physiological benefits exist alongside mechanical trade offs, specifically seen with competitive combat, high training volume and advanced age. The intense, repetitive movements of martial arts practice create specialized damaging effects on the musculoskeletal system. Maintaining the deep, static stances required in elite-level kata training can cause the constant contraction of the hip flexors, long-term permanent lumbar hypolordosis, a posterior pelvic tilt, and a severe reduction in internal and external hip rotation. While a flattened lower back provides a highly stable and impact resistant structure that is helpful for athletic performance, it can create a forward head posture, cause lumbar hypolordosis, lead to weakening core strength and placing extra stress on the knees and lower body.  Additionally, high-frequency repetitive movements—such as continuous kicking— can cause muscle fatigue and more stress on the knees. 

With these findings, karate cannot be classified simply as a purely corrective or an inherent structural hazard. Rather, its physiological outcomes are heavily dictated by age, training volume, and structural positioning. As karate gains popularity, it is essential to study its effects on practitioners’ health to practice safely and derive optimal benefits.  

Works Cited

Amiri Khorasani, A., Amiri Khorasani, M. T., & Mohammadipour, F. (2020). Comparison of the maximum angular velocity of lower limb joints in different methods of Zenkutsu-dachi in karate. Journal of Sport Biomechanics, 6(1), 12–21. https://doi.org/10.32598/biomechanics.6.1.3

Bălăiasa, D.-G., Cojocariu, A., Betarice Aurelia, A., Neculaes, M., & Ungurean, B.-C. (2024). THE IMPACT OF KARATE ON BODY POSTURE: A SYNTHESIS OF SCIENTIFIC EVIDENCE. SPORT AND SOCIETY, 1–7.https://doi.org/10.36836/2024/02/18

Barbeta, C. J. D. O., Krahenbühl, T., Gonçalves, E. M., & Guerra-Júnior, G. (2019). EFFECTS OF COMBAT SPORTS ON BONE MASS: SYSTEMATIC REVIEW. Revista Brasileira de Medicina Do Esporte, 25(3), 240–244.https://doi.org/10.1590/1517-869220192503163185

Barbeta, C. J. de O., Gonçalves, E. M., Ribeiro, K. D. S., Ribeiro, R., Roman, E. P., & Guerra-Júnior, G. (2017). BONE MASS BY QUANTITATIVE ULTRASOUND OF FINGER PHALANGES IN YOUNG KARATE PRACTITIONERS. Revista Paulista De Pediatria: Orgao Oficial Da Sociedade De Pediatria De Sao Paulo, 35(4), 436–442.https://doi.org/10.1590/1984-0462/;2017;35;4;00003

Bhattacharya, P., & Chatterjee, S. (2020). Immediate Effect of Single Bout of Karate Exercise on Heart Rate. In M. H. A. Hassan, A. M. Che Muhamed, N. F. Mohd Ali, D. K. C. Lian, K. L. Yee, N. S. Safii, S. M. Yusof, & N. F. M. Fauzi (Eds.), Enhancing Health and Sports Performance by Design (pp. 223–234). Springer Singapore.https://doi.org/10.1007/978-981-15-3270-2_25

Brzęk, A., Knapik, A., Brzęk, B., Niemiec, T., Przygodzki, P., Plinta, R., & Szyluk, K. (2022). Evaluation of Posturometric Parameters in Children and Youth Who Practice Karate: Prospective Cross-Sectional Study. International Journal of Environmental Research and Public Health (or BioMed Research International), 19(13), 7781 (or Art. ID 5432743).https://doi.org/10.3390/ijerph19137781

Clements, J. (2011). The Humanist Component within Renaissance Martial Arts Teachings—Ido Movement for Culture. Journal of Martial Arts Anthropology.https://imcjournal.com/index.php/en/volume-xi-2011/contents-number-2/432-the-humanist-component-within-renaissance-martial-arts-teachings

Corcoran, J. (with Graden, J.). (1997). The American Council on Martial Arts Instructor Certification Manual. Graden Media Group.

de Souza, R. A. (2022). Effects of Karate Training on Cardiovascular Risk Factors and Oxidative Stress in Obese Adolescents. Pediatric Exercise Science, 34(2), 85–92.https://doi.org/10.1123/pes.2021-0074

Doria, C., Veicsteinas, A., Limonta, E., Aschieri, P., Maggioni, M. A., Eusebi, F., Fanò, G., & Pietrangelo, T. (2009). Energetics of karate (kata and kumite techniques) in top-level athletes. European Journal of Applied Physiology, 107(5), 603–610.https://doi.org/10.1007/s00421-009-1154-1

Drozdzowska, B., Pluskiewicz, W., & Skiba, M. (2011). Skeletal status assessed by quantitative ultrasound at hand phalanges in karate practitioners. Journal of Clinical Densitometry, 14(3), 312–317.

Gaweł, E., & Zwierzchowska, A. (2024). The Acute and Long-Term Effects of Olympic Karate Kata Training on Structural and Functional Changes in the Body Posture of Polish National Team Athletes. Sports, 12(2), 55.https://doi.org/10.3390/sports12020055

Hosseinzadeh, B., Zandi, S., & Baban, K. (2019). Common postures and sport injuries in elite karate athletes. Journal for Research in Sport Rehabilitation, 7(13), 111–123.https://doi.org/10.22084/rsr.2019.19156.1455

Impellizzeri, F. M., & Marcora, S. M. (2007). The Physiology of Mountain Biking: Sports Medicine, 37(1), 59–71.https://doi.org/10.2165/00007256-200737010-00005

K.k, S., A.v, D., I.n, M., & O.a, R. (2024). Influence of karate training lessons on functional parameters of the heart in first-year students. Theory and Practice of Physical Culture, (1), 28–30.http://tpfk.ru/index.php/TPPC/article/view/924

Mack, A. Q., Ankem, H. K., Kyin, C., Jimenez, A. E., Saks, B. R., Sabetian, P. W., Knott, P., Lall, A. C., & Domb, B. G. (2022). Hip Pain Increases With Age and Experience Level in Adult Karate Athletes: A Statewide Survey Study. Arthroscopy, Sports Medicine, and Rehabilitation, 4(2), e325–e333.https://doi.org/10.1016/j.asmr.2021.10.003

Mastnak, W. (2017). Karate-based prevention of work-related musculoskeletal syndromes: A study on the possible benefits of martial arts in sports medicine and for occupational health. Sciences for Health, 13(1), 1–8.https://doi.org/10.1007/s11332-016-0331-5

Mischenko, N., Kolokoltsev, M., Romanova, E., Ilyina, G., Kotlyar, N., Tsapov, E., & Ustselemova, N. (2020). Posture correction methods and physical qualities development in 10–12-year-old karate athletes. Journal of Physical Education and Sport (JPES), 20(Supplement issue 6), 3146–3152.https://efsupit.ro/images/stories/noiembrie2020/Art%20426.pdf

Nasri, R., Hassen-Zrour, S., & Rebai, H. (2015). Combat sports practice favors bone mineral density among adolescent male athletes. Journal of Clinical Densitometry, 18(1), 54–59.https://doi.org/10.1016/j.jocd.2013.08.016

Piepiora, P. A. (2025). The Health Effects of Karate Training: A Review of 21st Century Research. Healthcare, 13(2), 118.https://doi.org/10.3390/healthcare13020118

Quinzi, F., Camomilla, V., Di Mario, A., Felici, F., & Sbriccoli, P. (2016). Repeated Kicking Actions in Karate: Effect on Technical Execution in Elite Practitioners. International Journal of Sports Physiology and Performance, 11(3), 363–369.https://doi.org/10.1123/ijspp.2015-0162

Shaw, I. (2020). Lower-body strength, power and flexibility in karateka: Implications for musculoskeletal health. Archives of Budo, 16.https://archbudo.eu/index.php/latest-publication/2020-volume-16

Sports & Fitness Industry Association. (2024). 2024 U.S. Martial Arts Participation and Demographic Trends Report (Single Sport Report Series). SFIA Research Core.https://sfia.org

Su, A. W., Johns, W. L., & Bansal, S. (2024). Martial Arts: Orthopaedic Injuries and Related Biomechanics. The Journal of the American Academy of Orthopaedic Surgeons, 32(1), e1–e12.https://doi.org/10.5435/JAAOS-D-23-00261

Violan, M. A., Small, E. W., Zetaruk, M. N., & Micheli, L. J. (1997). The Effect of Karate Training on Flexibility, Muscle Strength, and Balance in 8- to 13-Year-Old Boys. Pediatric Exercise Science, 9(1), 55–64.https://doi.org/10.1123/pes.9.1.55

Yoshimura, Y., & Imamura, H. (2010). Effects of Basic Karate Exercises on Maximal Oxygen Uptake in Sedentary Collegiate Women. Journal of Health Science, 56(6), 721–726.https://doi.org/10.1248/jhs.56.721

About the author

Kiley Wang

Kiley Wang is a senior at Manhasset High School in Long Island, New York. She is drawn to the medical field and hopes to pursue a career as a doctor. A dedicated musician, Kiley sings in the school’s Select Ensemble, plays flute in her school’s marching and holiday bands and recently earned All-State Flute selection in her home state New York.

She has also trained in a Korean martial art since she was 5 years old and now holds a 2nd-degree black belt. She now teaches karate to children at her home studio while also doing academic tutoring in her free time.

The post Karate Effects On the Physiological System appeared first on Exploratio Journal.

Abstract

Dementia is an umbrella term for symptoms that are caused by underlying brain disorders that impair everyday functioning, including memory, thinking, and social abilities. Dementia can arise from a combination of genetic, biological, environmental, and lifestyle factors and is most commonly associated with Alzheimer’s disease (AD). Although available treatments may help manage symptoms and improve quality of life, there is currently no cure for most forms of dementia. As the number of dementia cases increases, understanding not only its causes and treatments but also strategies that may reduce disease risk has become increasingly important. In particular, preventative measures may help reduce the risk of developing the disorder. In this manuscript, I discuss current advances in dementia prevention, with a particular focus on cognitive stimulation and its potential role in promoting cognitive resilience, while offering my perspective on how these approaches could contribute to future prevention strategies. This narrative review evaluates evidence linking cognitive stimulation and structured cognitive training to cognitive resilience and considers whether accessible games could be studied as tools for supporting brain health.

Keywords: Dementia, Alzheimer’s Disease, Preventative Care, Treatment, Cognitive Resilience

Introduction

When I was younger, I did not fully understand what Alzheimer’s disease (AD) truly was. I only knew that my grandmother, Vilma, was gradually changing. She had grown up in Italy and spoke limited English. Although she could still remember episodic memories from her childhood with her two sisters back in Italy and with her husband, my grandfather, she increasingly struggled to remember recent events and routine experiences. At first, the symptoms appeared subtle. She would misplace familiar household items (like putting her undergarments in the freezer), and my family dismissed these episodes as a normal part of the aging process. One day, however, my father and I found her walking alongside the road carrying Stop & Shop plastic bags full of her clothes because she believed she was traveling back to Italy. It was at that moment that I recognized something much more serious than normal aging was occurring.

My grandmother lived with AD for more than a decade. At the time of her diagnosis, my grandmother was in her early-seventies, placing her in the age group at the highest risk for developing AD. Although advanced age is the greatest known risk factor, genetics and family history also play an important role in an individual’s susceptibility (Tenchov et al., 2024). Then I learned that several of my grandmother’s relatives had also been diagnosed with Alzheimer’s disease, showing a possible inherited predisposition. While she was never genetically tested, both her age and family history likely contributed to her risk of developing the disease.

AD is by far the most common cause of dementia, representing about 60-80% of all dementia cases. In the United States alone, an estimated 7.4 million adults in 2026 currently living with Alzheimer’s dementia (Eugene et al., 2026). As life expectancy continues to increase around the world, the number of people living with dementia is expected to rise dramatically, making it one of the greatest public health challenges of the twenty-first century. While researchers have made significant progress in understanding the biological mechanisms responsible for dementia, developing treatments capable of stopping the disease has remained an ongoing challenge.

As the disease progressed, her cognitive function continued to deteriorate, as is usually characterized (Tenchov et al., 2024). She gradually lost her memory and eventually became unable to ambulate independently, dress herself, feed herself, or communicate effectively. Her speech became limited to mumbled words and the occasional repetition of familiar names (my name was one of the main ones she would repeat). As her functional decline became more severe, my mother and aunt could no longer provide the level of care she required, leading to her admission into a long-term care facility where she received around-the-clock supervision and support. During the later stages of the disease, she was prescribed Donepezil (Winblad, 2009), an acetylcholinesterase inhibitor commonly used to improve cognitive function and slow symptom progression temporarily (Dooley & Lamb, 2000). Although the medication modestly delayed aspects of her cognitive decline, it could not stop or reverse the underlying neurodegenerative process responsible for AD. More recently, the anti-amyloid antibodies lecanemab (Li et al., 2026) and donanemab (Sims et al., 2023) were approved for selected patients with early Alzheimer’s disease. These treatments can modestly slow clinical decline but do not cure the disease and carry important risks, including brain swelling and bleeding.

To better understand AD, it is important to understand the areas of the brain responsible for the formation of memory. The brain consists of four major lobes (frontal, temporal, parietal, occipital). Within the temporal lobe lies the hippocampus, a structure essential for learning, memory formation, and spatial navigation (Lee, 2023). The hippocampus works closely with the limbic system, which helps control emotions, motivation, and the formation of long-term memories. As AD progresses, neurons within these regions gradually become damaged and die, causing the hippocampus to shrink over time (Rao et al., 2022). This loss of brain tissue contributes to the progressive decline in memory, learning, and other cognitive abilities experienced by patients with dementia (Fogwe et al., 2026). Understanding how these structures change throughout the disease has become a major focus of modern neuroscience and has provided researchers with valuable insight with respect to potential treatment methods (Simpson et al., 2021).

Modern medicine has transformed the treatment of countless diseases through advances in pharmaceuticals, biotechnology, and medical research. In spite of these breakthroughs, AD remains one of the few major neurodegenerative disorders for which there is still no cure. Current medications for Alzheimer’s disease include donepezil (Buck et al., 2024), rivastigmine (Gao et al., 2024), and galantamine (Gao et al., 2024), targeting the cholinergic deficits characteristic of Alzheimer’s disease by inhibiting acetylcholinesterase and increasing the availability of acetylcholine in the brain (Saud et al., 2024). These may briefly improve symptoms or slow cognitive decline, but they cannot prevent the disease from progressing.

Watching my grandmother’s decline and seeing its effects on my family motivated me to ask whether cognitive decline might be delayed before symptoms become severe.

Preventative care

Preventative care is the use of proactive healthcare strategies to lower the likelihood of disease, identify health conditions before symptoms become severe, and reduce the long-term effects of chronic illnesses through methods that promote overall health and well-being (Hensrud, 2000). Preventative care strategies for Alzheimer’s disease include regular cognitive stimulation, physical exercise, proper sleep, and a balanced diet, all of which have been associated with improved brain health and a reduced risk of cognitive decline (Livingston et al., 2024). Among these strategies, cognitive stimulation has emerged as one of the leading promising areas of research (Woods et al., 2023).

Cognitive Training

Cognitive stimulation broadly includes activities that engage memory, attention, language, or reasoning. Cognitive training is more specific: it involves structured, repeated practice designed to improve particular cognitive skills. Reading, writing, puzzles, and games may be mentally stimulating, but they should not automatically be considered validated cognitive-training interventions (Gates et al., 2019). This may include reading, writing, learning a new language, solving puzzles, or even something as simple as playing a cell phone game. By actively using your brain, these activities strengthen the connections between neurons and keep different areas of the brain engaged (Jahan et al., 2026). This is called neuroplasticity (Margetis & Vadakekut, 2026).

To understand neuroplasticity, think of your phone updating. Your phone requires updates every so often to keep up with new improvements and improve your experience while using it. Neuroplasticity works similarly. When your brain learns something new, it creates and strengthens connections to store that information. It allows you to learn new skills and accommodate new situations. In a way, you’re working out your brain. Just as lifting weights strengthens muscles, repeatedly challenging your brain strengthens the pathways between neurons, allowing it to compensate for damage longer before symptoms begin to appear. This introduces another important concept called cognitive reserve.

Cognitive reserve refers to the brain’s skill to adapt, improvise, and find alternative ways to function despite physical damage or age-related changes. You can think of it as mental padding. Individuals with a higher cognitive reserve can withstand more disease-related damage before showing noticeable symptoms of cognitive decline. Although cognitive reserve cannot stop aging or cure AD, it may significantly delay the onset of symptoms, allowing people to maintain their memory, independence, and quality of life for a longer period.

To improve neuroplasticity and cognitive reserve, researchers have shown that reading is one of the most accessible ways (Liu et al., 2024; Perdue et al., 2022; Simfukwe et al., 2025). These studies show that, as you read, your brain is constantly interpreting the words your eyes are following while also connecting them to language, comprehension, attention, and memory (Landi et al., 2013). Instead of relying on just one area of the brain, reading activates several regions at the same time, strengthening communication between neurons; because of this, reading acts as a full workout for the brain rather than exercising only one cognitive skill (Turker et al., 2025). Over time, these repeated mental challenges may help build cognitive reserve and make the brain more resistant to age-related decline.

Writing is another way to activate the brain (Hajikarim-Hamedani et al., 2025). Although it uses many of the same thinking skills as reading, writing requires even more active participation (Tindle & Longstaff, 2015). It forces the brain to arrange thoughts, retrieve memories, choose language, and communicate ideas in a meaningful way. Even something as simple as keeping a journal or writing about your day before bed encourages the brain to recall experiences and put them into words. Rather than allowing memories to pass by, writing requires you to actively process them, giving your brain another opportunity to strengthen its pathways.

The previous two activities may sound like a bore to many, so how about something enjoyable? It has been shown that phone games that involve puzzles and reasoning can also stimulate the brain (Urwyler et al., 2023).

Commercial games such as Candy Crush and Block Blast require visual attention, pattern recognition, planning, and rapid decision-making. However, practicing these games may primarily improve performance on the games themselves. There is currently insufficient evidence that either game improves everyday cognition or reduces dementia risk. Their accessibility and popularity nevertheless make them interesting candidates for controlled research.

Growing research surrounding cognitive training suggests that keeping the brain active throughout life may delay when symptoms begin to appear (Coe et al., 2026). However, cognitive training focuses on strengthening the brain before significant damage develops. This shift from treating dementia to preventing or delaying it has become one of the most encouraging fields of Alzheimer’s research. While scientists continue searching for a cure, something as simple as picking up a book, writing in a journal, or solving a puzzle may help protect the memories that make us who we are.

Limitations

Although research on cognitive training and dementia prevention has produced encouraging results, several limitations still exist. One of the greatest challenges is that AD develops very slowly. Amyloid-beta plaques and tau tangles can begin accumulating in the brain decades before any symptoms appear (Gordon et al., 2018). Because of this, researchers often need to follow participants for many years before determining whether a potential risk-reduction strategytruly reduces the risk of developing dementia. Long-term studies are expensive, time-consuming, and difficult to complete, causing it to be difficult to gather conclusive evidence.

Another limitation is that dementia does not develop the same way in every individual. Genetics, age, cardiovascular health, education level, lifestyle, and environmental elements all influence a person’s risk. Certain individuals may have large amounts of amyloid-beta plaques and tau tangles yet never develop noticeable symptoms because of a high cognitive reserve. In contrast, others experience cognitive decline with much less brain damage. In addition, Alzheimer’s disease is only one cause of dementia. Other conditions, such as vascular dementia, Lewy body dementia, and frontotemporal dementia, involve different biological mechanisms, meaning that a potential risk-reduction strategy effective for one type may not be equally effective for another.

An important limitation is the problem of transfer. A person may improve on a practiced puzzle or game without showing improvement in untrained abilities, everyday functioning, or dementia risk. Observational studies also cannot fully exclude confounding: people who read, write, or solve puzzles regularly may differ in education, health, income, and social engagement from people who do not.

Finally, it is impossible to predict with certainty who will eventually develop dementia. As a result, preventative strategies such as cognitive training, exercise, and healthy lifestyle habits would need to be practiced by everyone rather than only those considered high risk. Even then, keeping these habits over an entire lifetime can be difficult. Although the current evidence strongly suggests that keeping the brain mentally active is beneficial, additional long-term research is needed to determine which activities provide the greatest protection, how often they should be performed, and how to incorporate them into everyday life.

Although researchers have made significant progress in understanding Alzheimer’s disease, there is still a long way to go before a cure is found.. Rather than relying on one medication to treat every patient, many researchers now believe that future treatment will involve a combination of therapies based on each person’s biology, genetics, and stage of the disease. In other words, Alzheimer’s treatment may become much increasingly personalized instead of using the same approach for everyone. While these advances are encouraging, they are still being studied in clinical trials. Until more effective treatments become available, prevention through healthy lifestyle habits and keeping the brain active will continue to be one of the best tools we have for decreasing the risk of dementia.

Conclusion

Watching my grandmother slowly lose the memories that made her who she was showed me that Alzheimer’s disease affects far more than memory itself alone. It changes a person’s independence, relationships, and identity while deeply affecting the families who care for them. My goal is to delay the progression as much as possible for myself and others who are worried they may develop this symptom.

Evidence supports lifelong cognitive engagement as one component of a broader brain-health strategy that also includes physical activity, cardiovascular risk management, sufficient sleep, education, and social connection. These behaviors may support cognitive resilience, but none can guarantee that dementia will be prevented.

Candy Crush should therefore be viewed as a testable example, not a proven intervention. Future randomized, long-term studies should compare accessible commercial games with established cognitive-training programs and determine whether any benefits transfer beyond gameplay to everyday cognition and dementia risk. Even familiar activities may inspire useful scientific questions, but those questions must be tested before conclusions are drawn.

References

Buck, A., Rezaei, K., Quazi, A., Goldmeier, G., Silverglate, B., & Grossberg, G. T. (2024). The donepezil transdermal system for the treatment of patients with mild, moderate, or severe Alzheimer’s disease: a critical review. Expert Rev Neurother, 24(6), 607-614. https://doi.org/10.1080/14737175.2024.2355981

Coe, N. B., Miller, K. E. M., Sun, C., Taggert, E., Gross, A. L., Jones, R. N., Felix, C., Albert, M. S., Rebok, G. W., Marsiske, M., Ball, K. K., & Willis, S. L. (2026). Impact of cognitive training on claims-based diagnosed dementia over 20 years: evidence from the ACTIVE study. Alzheimers Dement (N Y), 12(1), e70197. https://doi.org/10.1002/trc2.70197

Dooley, M., & Lamb, H. M. (2000). Donepezil: a review of its use in Alzheimer’s disease. Drugs Aging, 16(3), 199-226. https://doi.org/10.2165/00002512-200016030-00005

Eugene, D. Y., Kilmer, G., Ali, A. R., Claxton, J. S., Woodruff, R., Coronado, F., & Strahan, A. E. (2026). Subjective Cognitive Decline Among Adults Aged >/=45 Years-United States, 2023. J Public Health Manag Pract, 32(3), E105-E110. https://doi.org/10.1097/PHH.0000000000002338

Fogwe, L. A., Reddy, V., & Mesfin, F. B. (2026). Neuroanatomy, Hippocampus. In StatPearls. https://www.ncbi.nlm.nih.gov/pubmed/29489273

Gao, Y., Liu, Y., & Li, Y. (2024). Safety and efficacy of acetylcholinesterase inhibitors for Alzheimer’s disease: A systematic review and meta-analysis. Adv Clin Exp Med, 33(11), 1179-1187. https://doi.org/10.17219/acem/176051

Gates, N. J., Rutjes, A. W., Di Nisio, M., Karim, S., Chong, L. Y., March, E., Martinez, G., & Vernooij, R. W. (2019). Computerised cognitive training for maintaining cognitive function in cognitively healthy people in late life. Cochrane Database Syst Rev, 3(3), CD012277. https://doi.org/10.1002/14651858.CD012277.pub2

Gordon, B. A., Blazey, T. M., Su, Y., Hari-Raj, A., Dincer, A., Flores, S., Christensen, J., McDade, E., Wang, G., Xiong, C., Cairns, N. J., Hassenstab, J., Marcus, D. S., Fagan, A. M., Jack, C. R., Jr., Hornbeck, R. C., Paumier, K. L., Ances, B. M., Berman, S. B., . . . Benzinger, T. L. S. (2018). Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer’s disease: a longitudinal study. Lancet Neurol, 17(3), 241-250. https://doi.org/10.1016/S1474-4422(18)30028-0

Hajikarim-Hamedani, A., Rassa, S., Noroozian, M., & Jafari, D. (2025). Writing as cognitive rehabilitation in MCI and dementia: a systematic review of therapeutic benefits and applications. Front Neurol, 16, 1568336. https://doi.org/10.3389/fneur.2025.1568336

Hensrud, D. D. (2000). Clinical preventive medicine in primary care: background and practice: 1. Rationale and current preventive practices. Mayo Clin Proc, 75(2), 165-172. https://doi.org/10.4065/75.2.165

Jahan, I., Harun-Ur-Rashid, M., Islam, M. A., Sharmin, F., Al Jaouni, S. K., Kaki, A. M., & Selim, S. (2026). Neuronal plasticity and its role in Alzheimer’s disease and Parkinson’s disease. Neural Regen Res, 21(1), 107-125. https://doi.org/10.4103/NRR.NRR-D-24-01019

Landi, N., Frost, S. J., Menc, W. E., Sandak, R., & Pugh, K. R. (2013). Neurobiological bases of reading comprehension: Insights from neuroimaging studies of word level and text level processing in skilled and impaired readers. Read Writ Q, 29(2), 145-167. https://doi.org/10.1080/10573569.2013.758566

Lee, S. A. (2023). Navigational roots of spatial and temporal memory structure. Anim Cogn, 26(1), 87-95. https://doi.org/10.1007/s10071-022-01726-1

Li, L. L., Wang, R. Z., Wang, Z., Hu, H., Xu, W., Zhu, L., Sun, Y., Chen, K. L., Chen, S. F., He, X. Y., Yuan, M. Y., Huang, Y. Y., Liu, X. Y., Liu, P., Ye, Q. Y., Wang, J., Ju, Z. Z., Zhang, W., Hu, B., . . . Yu, J. T. (2026). Safety and short-term outcomes of lecanemab for Alzheimer’s disease in China: a multicentre study. Brain, 149(6), 1973-1985. https://doi.org/10.1093/brain/awaf427

Liu, Y., Lu, G., Liu, L., He, Y., & Gong, W. (2024). Cognitive reserve over the life course and risk of dementia: a systematic review and meta-analysis. Front Aging Neurosci, 16, 1358992. https://doi.org/10.3389/fnagi.2024.1358992

Livingston, G., Huntley, J., Liu, K. Y., Costafreda, S. G., Selbaek, G., Alladi, S., Ames, D., Banerjee, S., Burns, A., Brayne, C., Fox, N. C., Ferri, C. P., Gitlin, L. N., Howard, R., Kales, H. C., Kivimaki, M., Larson, E. B., Nakasujja, N., Rockwood, K., . . . Mukadam, N. (2024). Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. Lancet, 404(10452), 572-628. https://doi.org/10.1016/S0140-6736(24)01296-0

Margetis, K., & Vadakekut, E. S. (2026). Neuroplasticity. In StatPearls. https://www.ncbi.nlm.nih.gov/pubmed/32491743

Perdue, M. V., Mahaffy, K., Vlahcevic, K., Wolfman, E., Erbeli, F., Richlan, F., & Landi, N. (2022). Reading intervention and neuroplasticity: A systematic review and meta-analysis of brain changes associated with reading intervention. Neurosci Biobehav Rev, 132, 465-494. https://doi.org/10.1016/j.neubiorev.2021.11.011

Rao, Y. L., Ganaraja, B., Murlimanju, B. V., Joy, T., Krishnamurthy, A., & Agrawal, A. (2022). Hippocampus and its involvement in Alzheimer’s disease: a review. 3 Biotech, 12(2), 55. https://doi.org/10.1007/s13205-022-03123-4

Saud, A., Krishnaraju, V., Taha, A., Kalpana, K., Malarkodi, V., Durgaramani, S., Vinoth Prabhu, V., Saleh, F. A., & Ezhilarasan, S. (2024). Potential acetylcholinesterase inhibitors to treat Alzheimer’s disease. Eur Rev Med Pharmacol Sci, 28(6), 2522-2537. https://doi.org/10.26355/eurrev_202403_35759

Simfukwe, C., An, S. S. A., & Youn, Y. C. (2025). Mechanisms and Impact of Cognitive Reserve in Normal Aging and Alzheimer’s Disease. Diagnostics (Basel), 15(23). https://doi.org/10.3390/diagnostics15233068

Simpson, S., Chen, Y., Wellmeyer, E., Smith, L. C., Aragon Montes, B., George, O., & Kimbrough, A. (2021). The Hidden Brain: Uncovering Previously Overlooked Brain Regions by Employing Novel Preclinical Unbiased Network Approaches. Front Syst Neurosci, 15, 595507. https://doi.org/10.3389/fnsys.2021.595507

Sims, J. R., Zimmer, J. A., Evans, C. D., Lu, M., Ardayfio, P., Sparks, J., Wessels, A. M., Shcherbinin, S., Wang, H., Monkul Nery, E. S., Collins, E. C., Solomon, P., Salloway, S., Apostolova, L. G., Hansson, O., Ritchie, C., Brooks, D. A., Mintun, M., Skovronsky, D. M., & Investigators, T.-A. (2023). Donanemab in Early Symptomatic Alzheimer Disease: The TRAILBLAZER-ALZ 2 Randomized Clinical Trial. JAMA, 330(6), 512-527. https://doi.org/10.1001/jama.2023.13239

Tenchov, R., Sasso, J. M., & Zhou, Q. A. (2024). Alzheimer’s Disease: Exploring the Landscape of Cognitive Decline. ACS Chem Neurosci, 15(21), 3800-3827. https://doi.org/10.1021/acschemneuro.4c00339

Tindle, R., & Longstaff, M. G. (2015). Writing, Reading, and Listening Differentially Overload Working Memory Performance Across the Serial Position Curve. Adv Cogn Psychol, 11(4), 147-155. https://doi.org/10.5709/acp-0179-6

Turker, S., Fumagalli, B., Kuhnke, P., & Hartwigsen, G. (2025). The ‘reading’ brain: Meta-analytic insight into functional activation during reading in adults. Neurosci Biobehav Rev, 173, 106166. https://doi.org/10.1016/j.neubiorev.2025.106166

Urwyler, P., Gupta, R. K., Falkner, M., Niklaus, J., Muri, R. M., & Nef, T. (2023). Tablet-Based Puzzle Game Intervention for Cognitive Function and Well-Being in Healthy Adults: Pilot Feasibility Randomized Controlled Trial. JMIR Aging, 6, e46177. https://doi.org/10.2196/46177

Winblad, B. (2009). Donepezil in severe Alzheimer’s disease. Am J Alzheimers Dis Other Demen, 24(3), 185-192. https://doi.org/10.1177/1533317509332094

Woods, B., Rai, H. K., Elliott, E., Aguirre, E., Orrell, M., & Spector, A. (2023). Cognitive stimulation to improve cognitive functioning in people with dementia. Cochrane Database Syst Rev, 1(1), CD005562. https://doi.org/10.1002/14651858.CD005562.pub3

About the author

Emma Iasevoli

Emma is a high school student from Watertown, Connecticut. She is involved in Interact, FBLA, National Honor Society, and several other honor societies and clubs. Science has always been her greatest passion since she was young. She has also volunteered at Saint Mary’s Hospital and Yale New Haven Hospital, which has helped grow her interest in medicine. In the future, she hopes to study neuroscience, attend medical school, and become a neurologist.

The post (Candy) Crushing Dementia? Exploring Cognitive Training as Strategy for Cognitive Health appeared first on Exploratio Journal.

Abstract

Cancer is a collection of diseases characterized by uncontrolled cellular proliferation driven by genetic and epigenetic alterations that disrupt DNA repair, cell cycle regulation, and apoptosis. These changes allow cancer cells to grow and divide without immune detection, making cancer one of the leading causes of mortality worldwide. Although treatments such as chemotherapy and radiation therapy can eliminate malignant cells, they often cause significant damage to healthy tissues, particularly the bone marrow, and fail to restore normal biological function after treatment. Hematopoietic stem cell transplantation addresses this limitation by restoring blood and immune cell production following high-dose cancer therapy. The focus is on how this approach compensates for treatment-induced damage while supporting long-term disease control. The discussion includes the biological role of hematopoietic stem cells, the different types of transplantation, and the process of immune system reconstitution after therapy. It also examines how transplanted stem cells migrate to the bone marrow, reestablish hematopoiesis, and restore essential physiological functions. Key points include the immune-mediated anticancer effect observed in allogeneic transplantation, the extended time required for immune recovery, and the procedure’s associated risks, including graft-versus-host disease. Stem cell transplantation is unique because it not only eliminates cancer but also restores the biological systems necessary for long-term survival, making it a critical strategy for treating malignancies that affect the hematopoietic and immune systems.

Cancer and its Impact on the Body

Cancer is a collection of diseases characterized by uncontrolled cellular proliferation driven by genetic and epigenetic alterations that disrupt DNA repair, cell cycle regulation, and apoptosis. These changes let cancer cells grow and divide without being detected by the immune system, which is why cancer is one of the major causes of mortality around the world. The disease is particularly severe because cancer cells can spread through the bloodstream and lymphatic system to other parts of the body, where they can damage vital organs and cause systemic effects that make the immune system less effective, change metabolism, and make it harder for the body to handle stress (Gil et al., 2008). These effects are particularly significant in hematologic malignancies, which arise from blood-forming tissues critical for immune defense and oxygen transport (Bair et al., 2020). Even though oncology has come a long way, many treatments we have now cause significant damage to healthy tissues, especially the bone marrow, and do not restore biological function to normal levels after treatment (National Cancer Institute, 2023). 

Hematopoietic stem cell transplantation offers a potential solution to this limitation by restoring blood and immune cell production following high-dose cancer therapy. In allogeneic contexts, it can also provide an immune-mediated anticancer effect by enabling donor cells to detect residual malignant cells. Stem cell transplantation differs from other treatments because it can both regenerate the hematopoietic system and contribute to cancer control, making it a significant approach in the treatment of blood malignancies. 

This paper explores the biological mechanisms, clinical applications, and therapeutic significance of hematopoietic stem cell transplantation as a strategy to address the limitations of conventional cancer treatment.

Problems with Current Cancer Treatments 

Chemotherapy, radiation therapy, surgery, and, more recently, targeted therapy and immunotherapy are all essential aspects of how cancer is treated today (National Cancer Institute, 2023). In some cases, these strategies work well, but they all have huge problems. Chemotherapy stops cells that are quickly dividing from making copies of their DNA or going through mitosis. But this lack of specificity harms the proliferation of healthy cells, especially hematopoietic stem cells in the bone marrow (National Cancer Institute, 2023). This is why patients have anemia, neutropenia, thrombocytopenia, and very weak immune systems, which makes them more likely to get sick and hemorrhage (Bair et al., 2020). Chemotherapy can have long-term complications, including making it hard to get pregnant, hurting the heart, and helping other tumors grow. Radiation therapy affects DNA by using high-energy particles or electromagnetic radiation. Radiation is effective for tumors that are only in one place, but it can also injure healthy tissues nearby, leading to fibrosis, persistent inflammation, and organ malfunction (National Cancer Institute, 2023). Radiation is harmful to bone marrow, which makes hematologic toxicity worse. Surgery is effective for numerous solid tumors, but it is ineffective for cancers that disseminate throughout the body, such as leukemia and lymphoma.

When tumors have spread or are close to essential organs, surgery may not be able to help with solid tumors. Targeted therapies and immunotherapies are major advances, as they aim to block specific oncogenic pathways or enhance the immune system’s ability to identify neoplastic cells. Some individuals respond well to these medicines, but if the tumor becomes resistant to them, they may stop working as effectively. Immunotherapies can make the immune system exceedingly sick. Damage to the hematopoietic system remains a major problem despite various treatments. People can’t create new blood and immune cells when their bone marrow isn’t working well (Appelbaum, 2007). This fundamental issue is what makes stem cell transplantation a beneficial approach in medicine.

Stem Cells and Their Role in Hematopoietic Recovery 

Stem cells are undifferentiated cells that have two crucial biological traits:

• The power to keep renewing themselves

• The capacity to differentiate into several cell types.

Hematopoietic stem cells produce blood cells throughout a person’s life through a process called hematopoiesis. To be more specific, they make:

• Red blood cells that deliver oxygen

• White blood cells that keep the body healthy

• Platelets that help repair tissue and make clots

Most of the time, these stem cells live in the bone marrow microenvironment. However, they can also be transported into the peripheral circulation or retrieved from the umbilical cord blood (Appelbaum, 2007). They are invaluable in cancer treatment because they can fully restore the blood and immune systems. This is especially true when chemotherapy or radiation has killed off the body’s own stem cells.

Using Stem Cell Transplants to Treat Blood Malignancies 

Leukemia, lymphoma, multiple myeloma, and myelodysplastic syndromes are the most frequent types of blood cancer that stem cell transplantation is used to treat. Since these malignancies arise in blood-forming organs, it is typically necessary to replace the damaged hematopoietic system to achieve long-term remission. There are three primary types of stem cell transplantation. In autologous transplantation, stem cells are taken from the patient before rigorous therapy and then returned to the patient after treatment. This approach prevents the immune system from rejecting the cells, but it could also allow cancer cells to return. In allogeneic transplantation, stem cells are obtained from a donor whose human leukocyte antigen markers closely match those of the recipient (Appelbaum, 2007). This allows for the complete replacement of the diseased marrow and induces an immune-mediated anticancer effect. Syngeneic transplantation, which uses an identical twin as a donor, is the best way to match, but it doesn’t happen very often. You can obtain stem cells from the blood in your bone marrow, peripheral blood, or umbilical cord. Peripheral blood stem cell transplantation is currently the most popular procedure because it speeds engraftment and immunological recovery (National Cancer Institute, 2023).

How Transplantation and Immune Reconstitution Work 

Before the transplant, patients go through a conditioning program that may or may not include high-dose chemotherapy (Appelbaum, 2007). This treatment kills off existing bone marrow and gets rid of any cancer cells that are still there. After training, healthy stem cells are released into the bloodstream and travel to the bone marrow, where they connect and begin making new blood cells.

The biological steps of transplantation are as follows:

• Conditioning-induced marrow ablation

• Infusion of stem cells and their migration to marrow niches

• Engraftment and the first restoration of blood cells

• Rebuilding the immune system over time

Blood cell counts usually return to normal within weeks, but it might take months to years for the immune system to recover, especially following a fully allogeneic transplant. One of the best things about allogeneic transplantation is that it can help fight leukemia. Donor immune cells recognize remaining cancer cells as foreign and actively destroy them (Appelbaum, 2007). This lowers the likelihood of relapse and improves long-term outcomes. But this same immune response can cause graft-versus-host disease, in which donor immune cells attack healthy tissues in the recipient, most often the skin, liver, and digestive tract (National Cancer Institute, 2023).

Issues and Plans for the Future

Even if stem cell transplantation could heal people, it is still an extremely risky and complicated surgery. Patients endure prolonged immune suppression, increased susceptibility to infections, organ damage resulting from conditioning treatments, and protracted recovery periods. Transplantation incurs significant psychological and financial burdens. Current research seeks to optimize donor matching, promote stem cell engraftment, alleviate immune-mediated problems, and develop reduced-intensity conditioning regimens (Aljagthmi et al., 2025). Advancements in stem cell biology and immune control present opportunities to improve safety while preserving therapeutic effectiveness.

Conclusion 

Stem cell transplantation is one of the most advanced biological treatments for cancer, especially for malignancies that develop in the blood system. Stem cell transplantation brings back the important biological systems needed for long-term life, unlike standard treatments that just destroy cancer cells. This medication involves a lot of hazards, but scientists are constantly finding new ways to improve it and make it more useful. Stem cell transplantation will remain an essential aspect of cancer treatment and a promising area for future medical advances as we learn more about how stem cells and the immune system function.

References

Gil, J., Stembalska, A., Pesz, K. A., & Sasiadek, M. M. (2008). Cancer stem cells: The theory and perspectives in cancer therapy. Journal of Applied Genetics, 49(2), 193–199.

Liu, P., & Hu, Q. (2024). Engineering cells for cancer therapy. Accounts of Chemical Research, 57, 2358–2371.

Zhang, C. L., Huang, T., Wu, B. L., He, W. X., & Liu, D. (2017). Stem cells in cancer therapy: Opportunities and challenges. Oncotarget, 8(43), 75756–75766.

Zhang, Y., et al. (2023). Intracavity generation of glioma stem cell–specific CAR macrophages primes locoregional immunity for postoperative glioblastoma therapy. Nature Biomedical Engineering.

Emerging strategies for engineering Escherichia coli Nissle 1917-based therapeutics. (2014). Wiley Interdisciplinary Reviews: Systems Biology and Medicine.

National Cancer Institute. (2023). Stem cell transplants in cancer treatment. https://www.cancer.gov/about-cancer/treatment/types/stem-cell-transplant

Appelbaum, F. R. (2007). Hematopoietic cell transplantation. New England Journal of Medicine, 357(15), 1472–1485.

Bair, S. M., Brandstadter, J. D., Ayers, E. C., & Stadtmauer, E. A. (2020). Hematopoietic stem cell transplantation for blood cancers in the era of precision medicine and immunotherapy. Cancer, 126(9), 1837–1855. https://share.google/rA0DvxNWjRkbf1Gjq

Aljagthmi, A. A., & Abdel-Aziz, A. K. (2025). Hematopoietic stem cells: Understanding the mechanisms to unleash the therapeutic potential of hematopoietic stem cell transplantation. Stem Cell Research & Therapy, 16(1), 60. https://share.google/tsfuAPVczotv1G5lr

About the author

Sophia Zheng

Sophia is a junior, currently interested in biology and various areas of medical research. She really enjoys reading scientific papers on cells, diseases, genes, and multiple treatments. In the future, She hopes to be a part of the medical system and help society find more ways to treat diseases.

The post Stem Cell Transplantation: An Emerging Therapeutic Approach for Cancer appeared first on Exploratio Journal.