Bowdoin Science Journal

Psychology and Neuroscience

A Promising New Treatment for Glioblastoma Patients: Personalized Neoantigen Peptide Vaccines

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Introduction

Glioblastoma and Current Treatment Challenges

Glioblastoma (GBM) is the most aggressive malignant primary brain tumor in adults and is characterized by rapid proliferation, infiltration into surrounding brain tissue, and high rates of recurrence. Despite advances in neuro-oncology, survival outcomes remain poor. The current standard of care is surgical removal of as much tumor tissue as possible, followed by radiotherapy with concurrent Temozolomide chemotherapy, and results in a median overall survival of approximately 15 months (Latzer et al., 2024). Recurrence is nearly universal due to tumor heterogeneity, immune evasion, and resistance to therapy. Because of these limitations, researchers have increasingly explored immunotherapy as a potential treatment strategy for glioblastoma.

Personalized Neoantigen Vaccines

One promising immunotherapeutic approach is the use of personalized neoantigen vaccines. Neoantigens are peptides derived from tumor-specific somatic mutations that can be recognized as foreign by the immune system. Personalized peptide vaccines are developed by sequencing tumor DNA, identifying mutated peptides predicted to bind patient-specific Human Leukocyte Antigens (HLA) molecules, and vaccinating the patient with these peptides to stimulate CD4+ and CD8+ T-cell responses (Latzer et al., 2024). Because neoantigens are unique to tumor cells, they may allow for more targeted immune responses with reduced off-target toxicity. Prior Phase I/Ib work has established early proof of concept for this approach in GBM: Keskin et al. (2019) demonstrated that neoantigen-specific T cells induced by personalized peptide vaccines could migrate into the intracranial tumor itself, and Johanns et al. (2024) showed that multi-region tumor sequencing could expand the targetable neoantigen pool and drive clonal expansion of tumor-directed effector T cells. Latzer et al. (2024) investigates the feasibility and clinical outcomes of using personalized neoantigen peptide vaccines in a large real-world cohort of glioblastoma patients.

Purpose of the Study

The primary goal of the study by Latzer et al. (2024) was to evaluate whether personalized neoantigen-derived peptide vaccines could be feasibly produced and used in clinical practice for patients with glioblastoma. Specifically, the authors sought to assess the feasibility of vaccine production and administration, evaluate the safety of repeated peptide vaccinations, measure immunogenicity by detecting vaccine-induced T-cell responses, analyze clinical outcomes such as overall survival, and compare outcomes with propensity-matched historical control cohorts (Latzer et al., 2024).

Methods

Cohort

The study analyzed 173 patients with IDH-wildtype glioblastoma who received personalized peptide vaccines between 2015 and 2023 (Latzer et al., 2024). The cohort included both newly diagnosed patients treated before disease progression and patients treated after recurrence. Most individuals had previously received standard therapy involving radiotherapy and temozolomide (Latzer et al., 2024).

Vaccine Design

The personalized vaccine development pipeline involved several steps: sequencing tumor tissue to identify somatic mutations, predicting potential neoantigens capable of binding patient HLA molecules using computational algorithms, synthesizing peptides corresponding to predicted neoantigens, and administering repeated peptide vaccinations over several months (Latzer et al., 2024). Across the study cohort, 2,955 peptides were synthesized in total, with each vaccine containing a median of 19 peptides targeting different tumor mutations (Latzer et al., 2024). This multi-epitope strategy is consistent with emerging best practices, as targeting a broader neoantigen repertoire may help counteract immune escape– when the immune system fails to recognize and destroy a pathogen– driven by tumor heterogeneity (Johanns et al., 2024).

Immune Monitoring

Immune responses were evaluated using peripheral blood samples collected during treatment. Flow cytometry and intracellular cytokine staining were used to detect activated CD4+ and CD8+ T cells responding to vaccine peptides (Latzer et al., 2024). Patients were categorized as immunological responders or non-responders depending on the magnitude of the T-cell response observed following vaccination (Latzer et al., 2024).

Key Findings

Safety

The therapy exhibited a favorable safety profile. Most adverse events were mild (grade 1–2), with only four grade-3 reactions reported and no grade-4 toxicities observed (Latzer et al., 2024). These findings suggest that peptide vaccination is generally well tolerated.

Immune Response

Among patients with available immune monitoring data, approximately 90% developed detectable T-cell responses to at least one neoantigen peptide (Latzer et al., 2024). Many patients generated responses against multiple peptides, indicating broad immune activation. This rate of immunogenicity is notable given GBM’s typically immunosuppressive behavior (Keskin et al., 2019).

Survival Outcomes

Median overall survival for the vaccinated cohort was 31.9 months from diagnosis, which is substantially longer than typical survival estimates for glioblastoma (Latzer et al., 2024). Furthermore, patients exhibiting multiple vaccine-induced T-cell responses demonstrated a median survival of approximately 53 months, compared with 27 months for patients with weaker or absent immune responses (Latzer et al., 2024).

Interpretation of Results

The results suggest that personalized neoantigen vaccines are both feasible and capable of eliciting significant immune responses in glioblastoma patients. The strong correlation between vaccine-induced immune responses and survival outcomes indicates that immune activation may contribute to improved clinical outcomes (Latzer et al., 2024). 

However, because the study was observational and not randomized, the authors emphasize that causal relationships cannot be definitively established (Latzer et al., 2024). Improved survival in this cohort could reflect patient selection, concurrent therapies, or other unmeasured confounders rather than a direct effect of vaccination.

Limitations

The study used a retrospective observational design, which limits the ability to attribute survival improvements directly to vaccination (Latzer et al., 2024). The patient population was heterogeneous, including individuals with both newly diagnosed and recurrent tumors. Additionally, many patients received other treatments during the study period, which may have influenced outcomes (Latzer et al., 2024). Finally, immune monitoring data were not available for all participants, introducing the possibility of systematic bias in the immunogenicity findings (Latzer et al., 2024).

A particularly important limitation is the socioeconomic dimension of patient access. Personalized neoantigen vaccines are incredibly expensive, with costs estimated at over $100,000 per patient (Zhang et al., 2024). Patients who can access this treatment outside of a formal clinical trial are, by definition, those with substantial financial resources or exceptional insurance coverage. Wealthier patients tend to have better baseline health, greater access to supportive care, and higher rates of treatment at high-volume academic medical centers– all factors that independently predict better outcomes in GBM. This represents a meaningful form of selection bias that is not accounted for in the analysis and that substantially tempers the interpretability of the survival data.

Future Directions

Despite these limitations, the study by Latzer et al. (2024) represents one of the largest real-world analyses of personalized neoantigen vaccines in glioblastoma. The findings suggest that individualized cancer vaccines are feasible, safe, and capable of inducing tumor-specific immune responses at real-world scale. These results support further investigation of personalized immunotherapy strategies in controlled prospective clinical trials to determine whether neoantigen vaccines can meaningfully improve survival in glioblastoma patients (Latzer et al., 2024).

The study by Latzer et al. (2024) provides encouraging evidence that personalized neoantigen peptide vaccines may represent a promising strategy for treating glioblastoma. By stimulating tumor-specific immune responses, this approach could potentially complement existing therapies and improve patient outcomes. However, randomized clinical trials will be necessary to confirm the therapeutic benefit of this strategy, and future work must also grapple seriously with the equity implications of a treatment that currently remains accessible only to a privileged subset of patients.

 

References

Iamukova, L., Alferova, E. (2026) Personalized Cancer Vaccines in the Clinical Trial Pipeline. Asia-Pacific Journal of Clinical Oncology 22, no. 3: 362–368. https://doi.org/10.1111/ajco.70006 

Johanns, T. M., Garfinkle, E. A. R., Miller, K. E., Livingstone, A. J., Roberts, K. F., Rao Venkata, L. P., Dowling, J. L., Chicoine, M. R., Dacey, R. G., Zipfel, G. J., Kim, A. H., Mardis, E. R., & Dunn, G. P. (2024). Integrating Multisector Molecular Characterization into Personalized Peptide Vaccine Design for Patients with Newly Diagnosed Glioblastoma. Clinical cancer research : an official journal of the American Association for Cancer Research, 30(13), 2729–2742. https://doi.org/10.1158/1078-0432.CCR-23-3077 

Keskin, D.B., Anandappa, A.J., Sun, J. et al. (2019). Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature 565, 234–239. https://doi.org/10.1038/s41586-018-0792-9 

Latzer, P., Zelba, H., Battke, F. et al. (2024). A real-world observation of patients with glioblastoma treated with a personalized peptide vaccine. Nat Commun 15, 6870. https://doi.org/10.1038/s41467-024-51315-8 

Wu, D. W., Jia, S. P., Xing, S. J., Ma, H. L., Wang, X., Tang, Q. Y., Li, Z. W., Wu, Q., Bai, M., Zhang, X. Y., Fu, X. F., Jia, M. M., Tang, Y., Chen, L., & Li, N. (2024). Personalized neoantigen cancer vaccines: current progression, challenges and a bright future. Clinical and experimental medicine, 24(1), 229. https://doi.org/10.1007/s10238-024-01436-7 


The Association Between Tooth Loss and Cognitive Decline

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Imaging of a vertical (coronal) slice through the brain of an Alzheimer patient (left) compared with a normal brain ( right).
“Could Magnetic Brain Stimulation Help People with Alzheimer’s? | Scientific American.” n.d. Accessed May 4, 2026. https://www.scientificamerican.com/article/could-magnetic-brain-stimulation-help-people-with-alzheimer-rsquo-s/.

Cognitive decline with age is a major concern in medicine and public health. In 2021, the World Health Organization reported 57 million people were affected by dementia worldwide (World Health Organization, 2023). Well-established risk factors include alcohol intake, lower education level, physical inactivity, obesity, and diabetes, and preventive strategies have developed steadily. However, one potential contributor is often overlooked in major dementia research: tooth loss. Galindo-Moreno et al. (2022) examined this relationship through a large-scale analysis of over 100,000 US Americans, making a case for oral health as an underrecognized factor in cognitive decline. 

Edentulism refers to the partial or complete loss of permanent teeth. Edentulism can be caused by a multitude of factors, including biological processes such as caries (tooth decay) and periodontal disease (infection or inflammation of gums and bone), pulpal pathologies (damage to nerves, tissue, and blood vessels in the center of a tooth), trauma, or oral cancer. In addition to biological causes, edentulism can result from factors affecting dental care: patient preference, access to care, treatment options, and health insurance (Felton 2009). A study found 37% of edentulism cases were due to extraction from caries, 29% from periodontal diseases, and 12% due to trauma (Al-Rafee 2020).  

Although oral health care has developed significantly in the last few decades, edentulism remains a prevalent and irreversible condition (Al-Rafee 2020). It can occur at all ages, but the highest incidence occurs between the ages of 75-79 [Figure 1] (Chen et al. 2025). Those most affected by tooth loss typically have a lower socioeconomic standing, which makes health care less affordable and accessible [Figure 2] (Vemulapalli et al. 2024)  

Graph of global incidence and prevalence of edentulism per 100,000 across all ages. Highest incidence rate at ages 75-79. Prevalence per 100,000 gradually increases as age increases.
Figure 1: Global prevalence and incidence rates of edentulism in 2021. Chen, Hui Min, Kuo Shen, Ling Ji, Colman McGrath, and Hui Chen. 2025. “Global and Regional Patterns in Edentulism (1990-2021) With Predictions to 2040.” International Dental Journal 75 (2): 735–43. https://doi.org/10.1016/j.identj.2024.11.022. December 31, 2024: 738

 

Prevalence rate of complete edentulism in US adults 65 years and older across different socio-economic status'. As income increases, the rate of complete edentulism decreases.
Figure 2: Prevalence rate of complete edentulism in US adults 65 years and older according to demographic characteristics: Behavioral Risk Factor Surveillance System 2012-2020. Income level. Vemulapalli, Abhilash, Surendra Reddy Mandapati, Anusha Kotha, Hemanth Rudraraju, and Subhash Aryal. 2024. “Prevalence of Complete Edentulism among US Adults 65 Years and Older.” The Journal of the American Dental Association 155 (5): 399–408. https://doi.org/10.1016/j.adaj.2024.02.002. May 6, 2024: 407

Galindo-Moreno et al. proposed multiple pathways by which tooth loss can lead to cognitive decline. Two that play directly into known factors are the “diet and nutrition mechanism” and the masticatory mechanism. The number of teeth and which teeth are present affect what we can eat and how we eat. Mastication — chewing of food (Xu et al. 2008) — is directly influenced by edentulism due to the reduced bite force one can exert with missing teeth or dentures (Galindo-Moreno et al. 2022; Weijenberg et al. 2011). Changes to mastication may impact cognition by decreasing sensory input, which would reduce cell growth and development, impairing the cholinergic neurotransmitter system responsible for regulating memory, muscles, and attention, and reducing the generation of new neurons triggered by exercise (Weijenberg et al. 2011). Mastication additionally restricts our diet and therefore directly plays into the diet and nutrition mechanism. Often, with altered dentition, chewing can be an immense hurdle, for which the solution is a softer yet less nutritious diet.Nutrients such as omega-3 fatty acids, B vitamins, and antioxidants have important neuroprotective properties that help preserve the blood brain barrier, an essential layer that prevents toxins from entering the brain,additionally reducing inflammation, lowering the risk of cognitive decline (Power et al. 2019). Both the masticatory and diet and nutrition mechanisms are intertwined with diabetes and obesity, which are known risk factors for cognitive decline (Galindo-Moreno et al. 2022). 

Another pathway this study mentions is the inflammation/infection mechanism. A leading cause of edentulism is periodontitis, a severe gum infection often driven by the bacterium Porphyromonas gingivalis. This bacterium induces the local release of cytokines, proinflammatory proteins (Galindo-Moreno et al. 2022). Once in the bloodstream, cytokines promote the production of amyloid-β, a peptide whose accumulation is associated with Alzheimer’s disease (Leira et al. 2020). Simultaneously, Porphyromonas gingivalis increases the permeability of the blood-brain barrier (Lei et al. 2023). The heightened permeability of the BBB causes accumulation of overproduced amyloid-β in the brain tissue [Figure 3] (Galindo-Moreno et al. 2022; Leira et al. 2020)  

Amyloid PET scan of patient with Alzheimer's Disease (right), and patient without Alzheimer's (left). Patient with Alzheimer's Disease shows higher detection of Amyloid plaques.
Figure 3: Amyloid PET scan comparison of healthy brain and Alzheimer’s disease. Chapleau, Marianne, Leonardo Iaccarino, David Soleimani-Meigooni, and Gil D. Rabinovici. 2022. “The Role of Amyloid PET in Imaging Neurodegenerative Disorders: A Review.” Clinical Investigation. Journal of Nuclear Medicine 63 (Supplement 1): 13S-19S. https://doi.org/10.2967/jnumed.121.263195.

To investigate the relationship between tooth loss and cognitive decline, the researchers analyzed data from over 100,000 Americans drawn from two large national health surveys, NHIS (2014-2017) and NHANES (2005-2018). The NHIS survey was particularly well-suited for assessing cognitive state, as it included four questions on concentration and memory. However, the survey included only one binary dental question asking whether the participants had a complete dentition or had lost ≥1 teeth. The NHANES survey complemented this with a thorough section on dental records. The exact number and location of lost teeth were documented. However, it assessed cognitive state with only one question on memory and confusion (Galindo-Moreno et al. 2022).   

Their primary statistical tool was multinomial logistic regression, a method used when an outcome has more than two categories. In this case, the categories were cognitive difficulty, ranging from “none” to “some” to “a lot.” By using this model, the researchers simultaneously accounted for other factors known to affect cognitive health, including age, income, education level, depression, anxiety, cardiovascular health, and lifestyle habits such as smoking and exercise, which were included in the health surveys. By modeling these variables together, the researchers could estimate the independent contribution of tooth loss to cognitive decline.  

The results were expressed as odds ratios (ORs), which indicate how much more likely a given outcome is in one group than in a reference group. Here, the reference was a fully toothed person reporting no cognitive difficulties. An OR above 1.0 indicated higher odds of cognitive problems among people with missing teeth. This held true even after the other variables were statistically accounted for. The researchers also used a technique called ROC curve analysis on the NHANES data that included exact tooth counts, allowing them to identify a meaningful threshold below which cognitive risk measurably increased (Galindo-Moreno et al. 2022).  

The researchers found that, overall, the presence of teeth was statistically associated with a better cognitive state. The NHIS data showed that people with edentulism (partial or complete) had an OR > 1 across all cognitive categories, especially memory, even after accounting for other risk factors. This trend was also observed across categories of gender, socio-economic status (SES), education, and cardiovascular risk — all of which negatively impact cognition. Notably, socioeconomic status emerged as one of the strongest predictors, alongside edentulism, reflecting how directly financial circumstances shape access to dental care and, through it, long-term cognitive health. 

Using ROC curve analysis of the NHANES data, they determined the threshold for cognitive risk to be 20.5 teeth, indicating that a person with fewer than 21 teeth has an increased risk of cognitive decline compared to a fully dentulous person (Galindo-Moreno et al. 2022). Importantly, the study analyzed the NHANES survey and found a gradient effect: the fewer teeth a person had, the worse their cognitive outcomes tended to be, which strengthens the case that the association is meaningful rather than coincidental. Furthermore, a threshold could be determined for each individual tooth category: 5.5, 5.5, 3.5, 4.5, respectively, for molars, premolars, canines, and incisors. The multinomial regression of the NHANES data determined molars had the highest OR. The researchers linked this to the masseter, an important masticatory muscle supported by molars, which may, through its activity, stimulate the release of neurotrophic factors that support brain health. 

The link between edentulism and cognitive decline is still scarcely researched. As of March 2026, there are only 66 results on PubMed, 142 on ScienceDirect, and 148 on Wiley on the correlation between edentulism and cognitive decline. To put this into perspective, there are 2,277 results on PubMed, 18,967 on ScienceDirect, and 10,546 on Wiley on the relationship between diet and cognitive decline. The discussed research article combines two USA national health surveys with diverse samples, NHIS and NHANES, making it one of the largest in scope to date on tooth loss and cognitive decline. Although Galindo-Moreno and his team compellingly demonstrate the correlation, they recognize that their findings cannot answer whether edentulism leads to poorer cognition or rather poor cognition leads to edentulism (Galindo-Moreno et al. 2022, 3498). Some of the issues the researchers faced were the binary assessment of dentition in the NHIS survey, the single question on cognitive condition in the NHANES survey, and the overall lack of records on the reasons for tooth loss (Galindo-Moreno et al. 2022).   

Nevertheless, this study is a step in the right direction. Galindo-Moreno et al. showed that edentulism is correlated with cognition, thereby providing meaningful epidemiological evidence for a relatively young field. Consequently, this study and further research could have great clinical implications for cognitive health, not only in cost-effective treatment and prevention, but also in an important personal impact for those struggling with cognitive impairments and dental hygiene. 

Al-Rafee, Mohammed A. 2020. “The Epidemiology of Edentulism and the Associated Factors: A Literature Review.” Journal of Family Medicine and Primary Care 9 (4): 1841–43. https://doi.org/10.4103/jfmpc.jfmpc_1181_19.  

Chapleau, Marianne, Leonardo Iaccarino, David Soleimani-Meigooni, and Gil D. Rabinovici. 2022. “The Role of Amyloid PET in Imaging Neurodegenerative Disorders: A Review.” Clinical Investigation. Journal of Nuclear Medicine63 (Supplement 1): 13S-19S. https://doi.org/10.2967/jnumed.121.263195.  

Chen, Hui Min, Kuo Shen, Ling Ji, Colman McGrath, and Hui Chen. 2025. “Global and Regional Patterns in Edentulism (1990-2021) With Predictions to 2040.” International Dental Journal 75 (2): 735–43. https://doi.org/10.1016/j.identj.2024.11.022.  

“Dementia.” n.d. Accessed March 27, 2026. https://www.who.int/news-room/fact-sheets/detail/dementia.  

Felton, David A. 2009. “Edentulism and Comorbid Factors.” Journal of Prosthodontics 18 (2): 88–96. https://doi.org/10.1111/j.1532-849X.2009.00437.x.  

Galindo-Moreno, Pablo, Lucia Lopez-Chaichio, Miguel Padial-Molina, et al. 2022. “The Impact of Tooth Loss on Cognitive Function.” Clinical Oral Investigations 26 (4): 3493–500. https://doi.org/10.1007/s00784-021-04318-4.  

Lei, Shuang, Jian Li, Jingjun Yu, et al. 2023. “Porphyromonas Gingivalis Bacteremia Increases the Permeability of the Blood-Brain Barrier via the Mfsd2a/Caveolin-1 Mediated Transcytosis Pathway.” International Journal of Oral Science15 (January): 3. https://doi.org/10.1038/s41368-022-00215-y.  

Leira, Yago, Álvaro Carballo, Marco Orlandi, et al. 2020. “Periodontitis and Systemic Markers of Neurodegeneration: A Case–Control Study.” Journal of Clinical Periodontology 47 (5): 561–71. https://doi.org/10.1111/jcpe.13267.  

Power, Rebecca, Alfonso Prado-Cabrero, Ríona Mulcahy, Alan Howard, and John M. Nolan. 2019. “The Role of Nutrition for the Aging Population: Implications for Cognition and Alzheimer’s Disease.” Annual Review of Food Science and Technology 10 (1): 619–39. https://doi.org/10.1146/annurev-food-030216-030125. 

Vemulapalli, Abhilash, Surendra Reddy Mandapati, Anusha Kotha, Hemanth Rudraraju, and Subhash Aryal. 2024. “Prevalence of Complete Edentulism among US Adults 65 Years and Older.” The Journal of the American Dental Association 155 (5): 399–408. https://doi.org/10.1016/j.adaj.2024.02.002.  

Weijenberg, R. A. F., E. J. A. Scherder, and F. Lobbezoo. 2011. “Mastication for the Mind—The Relationship between Mastication and Cognition in Ageing and Dementia.” Neuroscience & Biobehavioral Reviews 35 (3): 483–97. https://doi.org/10.1016/j.neubiorev.2010.06.002.  

World Health Organization. 2023. “Dementia” Fact Sheets. https://www.who.int/news-room/fact-sheets/detail/dementia 

Xu, W. L., J. E. Bronlund, J. Potgieter, et al. 2008. “Review of the Human Masticatory System and Masticatory Robotics.” Mechanism and Machine Theory 43 (11): 1353–75. https://doi.org/10.1016/j.mechmachtheory.2008.06.003. 

How Epigenetics Dictates the Birth of New Neurons

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One of the most contentious debates in neuroscience has revolved around the question of whether the adult human brain can produce new neurons. Though there is evidence that rodents maintain a population of immature neurons throughout their lives, confirming this phenomenon in humans is troublesome, namely due to post-mortem tissue degradation and the lack of specific molecular markers. A new study by Disouky et al. (2026), published in Nature, carries out a deep dive into this process. Disouky et al. reveal that the “birth” of new neurons not only occurs in the adult human hippocampus but that its decline in Alzheimer’s disease is dictated by changes in the cell’s epigenetic landscape. In other words, while the sequence of the DNA remains the same, the chemical tags and structural packing of the genome changes, effectively deciding which genes are turned on or off.

To settle the debate, researchers analyzed over 355,000 individual cell nuclei from the hippocampi of young adults, healthy seniors, and people with Alzheimer’s. They discovered a clear assembly line in the brain where starter cells, known as neural stem cells, begin a transformation into neuroblasts. These cells then become Immature Neurons before finally graduating into mature granule neurons that are fully integrated into memory circuits. The team used a predictive calculation called RNA velocity to prove that these cells actually move through these stages, confirming that the adult human brain maintains a pool of neural stem cells. RNA velocity, by taking into account the concentration of various RNA populations, can project the dynamics within the cell (La Manno et al., 2018). In other words, a cell’s stage in development can be determined by what types of RNA it is producing.

The study’s most important discovery involves epigenetics, which dictates how the brain’s internal switches are managed. If DNA is like a massive library of books (genes), then epigenetics determines which books are actually open and readable. The researchers found that in Alzheimer’s disease, the problem isn’t just that cells are dying, but that the books for making new neurons are being slammed shut. This is known as a change in chromatin accessibility (Klemm et al., 2019). In Alzheimer’s patients, the number of immature neurons is slashed significantly compared to healthy individuals. Interestingly, in people with preclinical Alzheimer’s—those with early symptoms of Alzheimer’s—these DNA locks are beginning to appear. So, while the DNA itself remains the same, its expression differs.

A diagram depicting chromatin accessibility from various stages, to closed, permissive, and open domains.
Chromatic accessibility allows for differences in DNA expression without directly altering its genetic sequence. Figure from Klemm et al., 2019

When the authors looked at a third population group known as SuperAgers (SA)—people over 80 years old with the memory capacity of someone in their fifties—they found a distinct profile of neurogenesis, new neuron formation. The brains of SuperAgers contained a significantly greater number of immature neurons compared to those with Alzheimer’s. Even after excluding potential outliers, the researchers observed a 2.5-fold increase in immature neurons in the SuperAger group compared to other cohorts. This suggests there is a “resilience signature” of neurogenesis that may play a role in maintaining exceptional memory capacity despite advanced age. This signature is primarily characterized by maintained chromatin accessibility in regions that are typically “locked” or downregulated in the Alzheimer’s brain.

Ultimately, this research shifts the focus of Alzheimer’s study from simple cell death to the underlying gene regulatory networks that govern how cells function and grow. By identifying the specific “activator” and “repressor” switches (transcription factors) that are active in SuperAgers versus those that are shut down in Alzheimer’s, the study provides a roadmap for future medical interventions. For example, targeting the specific chromatin regions that govern synaptic plasticity could potentially prevent or mitigate the deterioration of neurogenesis seen in dementia. While the study notes limitations due to the high variability of human brain samples and limited sample sizes, the findings highlight the critical role of epigenetics as a more definitive indicator of cognitive health than traditional gene expression alone. This suggests that the future of treating cognitive decline may lie in opening up the brain’s internal library in order to restore its natural ability to regenerate and remember.

 

References

Disouky, A., Sanborn, M. A., Sabitha, K. R., Mostafa, M. M., Ayala, I. A., Bennett, D. A., Lu, Y., Zhou, Y., Keene, C. D., Weintraub, S., Gefen, T., Mesulam, M., Geula, C., Maienschein-Cline, M., Rehman, J., & Lazarov, O. (2026). Human hippocampal neurogenesis in adulthood, ageing and Alzheimer’s disease. Nature, 652(8112), 1264–1273. https://doi.org/10.1038/s41586-026-10169-4

Klemm, S. L., Shipony, Z., & Greenleaf, W. J. (2019). Chromatin accessibility and the regulatory epigenome. Nature Reviews Genetics, 20(4), 207–220. https://doi.org/10.1038/s41576-018-0089-8

La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., Lönnerberg, P., Furlan, A., Fan, J., Borm, L. E., Liu, Z., Van Bruggen, D., Guo, J., He, X., Barker, R., Sundström, E., Castelo-Branco, G., . . . Kharchenko, P. V. (2018). RNA velocity of single cells. Nature, 560(7719), 494–498. https://doi.org/10.1038/s41586-018-0414-6

Venom As Medicine: Novel Pathways for Dravet Syndrome Treatment Using Modulatory Peptides from Scorpion Venom

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Dravet Syndrome (DS) is a form of pediatric epilepsy that produces prolonged seizures that cannot be prevented or stopped by available medications (Dravet Syndrome Foundation 2025). These seizures cause a wide range of severe health effects, ranging from cognitive impairment to infection and premature death (Dravet Syndrome Foundation 2025). Dravet syndrome is a disease that typically begins between 2 and 15 months of age and it affects 1:15,700 infants born (Dravet Syndrome Foundation 2025). While rare, this disease has a high mortality rate, with 15-20% of patients passing due to Sudden Unexpected Death in Epilepsy (SUDEP) (Dravet Syndrome Foundation 2025).

On the molecular level, DS is caused by a mutation in the SCN1A gene, which encodes a voltage gated sodium channel (Chow et al. 2019). This channel plays an important role in generating action potentials, which are the observable changes in cell voltage that conduct signals between nerves (Fig. 1). These electrical signals are transmitted by the movement of ions into and out of nerve cells, a transition that changes the charge of the cells. Typically, nerve cells have a resting potential of -70mV. When nerve cells reach the threshold potential, typically by allowing a small number of cations into the cell, the voltage gated sodium channels open, and a large number Na+ ions move into the cell, raising the voltage near +40mV. Once this new threshold is reached, the voltage gated sodium channels close and other channels open, allowing potassium ions to leave the cell. This removal of potassium from the cells drives the voltage back to a negative value. This process is vital to the correct transmission of electrical signals throughout the body to drive movement.

The mutation in the SCN1A gene alters the specific voltage-gated sodium channel 1.1 (Chow et al. 2019). Voltage-gated sodium channels are built from specifically folded proteins. In a normally functioning channel, there are two types of subunits: 1-2 β subunits and an ɑ subunit consisting of 4 distinct domains, or regions of the subunit (Chow et al. 2019). Each domain includes a voltage-sensing region, which detects alterations in cell voltage in order to signal for channel opening (Chow et al. 2019). A pore region is also present, which helps control the channel’s permeability (Chow et al. 2019). When SCN1A is mutated, several alterations to function can occur, including increased channel opening or channel mutations that prevent the influx of ions (Escayg and Goldin 2010). Unfortunately, even if only one copy of this gene is mutated, the functional copy is unable to overcome the deficit caused by the mutated gene, a principle known as haploinsufficiency.

Thus, new approaches are needed to control when this dysfunctional channel opens and closes. Venom peptides are a rich source of what are known as channel modulators (Chow et al. 2019). These short molecules, formed from the same molecular building blocks as larger proteins, can assume unique shapes. These folded conformations are stabilized by the presence of numerous cysteine residues, which can form strong disulfide bridges to “lock” a peptide in place. The CSɑβ fold describes one such disulfide bridge which forms between the beta sheets and alpha helix in the protein structure (Fig. 2). Venom peptides containing these folds can act as both alpha and beta toxins, with alpha toxins causing inhibition of channel inactivation and beta toxins causing direct activation. Chow et al. (2019) investigated two of these dual modulatory venom peptides, Hj1a and Hj2a, both found in scorpion venom.

The researchers studied the activation and inactivation of both of the venom peptides (Fig. 3). For Hj1a, the activation threshold increases, making activation harder, and the inactivation threshold decreases, also making inactivation easier (Chow et al. 2019). This change limits the range of functioning for the channels and thus increases its resistance to changes to voltage. This same pattern is seen for Hj2a, but to a lesser extent, as emphasized by the more limited distance between the blue and gray lines (Chow et al. 2019). The researchers then decided to explore the specificity of activation/inactivation using a technique known as patch/clamp electrophysiology. In this method, the researchers attach a small glass pipette to the membrane of a nerve cell and use this connection to pass signals through the nerve cell (Molecular Devices 2025). The researchers found that the inactivation was subtype specific, with Hj1a primarily impacting channels 1.4 and 1.5, and to a lesser extent, 1.1 and 1.6. Hj2a also showed favorability for channels 1.4 and 1.6, and to a lesser extent, channel 1.1 (Chow et al. 2019). Thus, while both have some impact on channel 1.1, the target channel for DS, they also impact other sodium channels, which could have adverse impacts.

These venom peptides are attractive drug candidates due to their stability and potency, but they lack what is known as subtype specificity (Chow et al. 2019). Essentially, they cannot reliably interact with the specific channel involved in DS without impacting the function of other biologically critical channels. This subtype specificity is one of the biggest features of antiepileptic drugs (AEDs). This, evidently, imposes a key problem in the use of Hj1a and Hj2a as AEDs. However, impacts on other channels may be ameliorated. Both Hj1a and Hj2a show agonistic activity on channels 1.4 and 1.5, which impact the smooth and skeletal muscle and the cardiac system respectively – these application sites are restricted to the peripheral nervous system, and impacts can be avoided by targeting the AED to the central nervous system (Chow et al. 2019). Channel 1.2 poses more problems as it is part of neuronal cells, thus pharmacological activation of this could cause similar symptoms to a gain-of-function mutation, including the early onset of severe epileptic encephalopathies (a disease that impacts the brain) (Chow et al. 2019). Since Hj2a does not affect channel 1.2, researchers are evaluating this peptide further and will need to conduct additional structural analyses.

Ultimately, while neither are truly effective AEDs for DS in their present form, venom peptides provide a basis for the investigation of dual modulatory scorpion peptides. Based on the study by Chow et al. (2019), there is a possibility that, with further modification and future study, venom peptides may be able to help ameliorate the symptoms of DS, which have resisted current treatment methods.

A graph showing the action potential curve and the molecular mechanisms which occur at different stages.
Figure 1. Action Potential Generation. The cell spends most of its time negatively charged, until the voltage gated sodium channels open.

 

An image showing the three dimensional protein structure of the peptide including showing two dimensional representation of the disulfide bridges.
Figure Two. The structure of the CSɑβ fold as it appears in the Hj1a and Hj2a scorpion peptides. The pink and blue lines represent key disulfide bridges, critical for maintaining structure.
(Adapted from Chow et al. 2019)

 

An image with two graphs showing the activation and inactivation effects of the two scorpion venom peptides.
Figure Three. Results of the subtype specificity experiments by Chow et al. (2020) showing increased voltage of channel activation and decreased voltage of channel inactivation. (Adapted from Chow et al. 2019)

References:

Dravet Syndrome Foundation: “What is Dravet Syndrome?”; no date [accessed December 15, 2025]. https://dravetfoundation.org/what-is-dravet-syndrome/.

Molecular Devices: “Patch Clamp Electrophysiology”; no date [accessed December 15, 2025]. https://www.moleculardevices.com/applications/patch-clamp-electrophysiology.

Chow, C. Y., et al. (2019). “Venom Peptides with Dual Modulatory Activity on the Voltage-Gated Sodium Channel NaV1.1 Provide Novel Leads for Development of Antiepileptic Drugs.” ACS Pharmacology & Translational Science 3(1): 119-134.

Escayg, Andrew, and Alan L Goldin. “Sodium channel SCN1A and epilepsy: mutations and mechanisms.” Epilepsia vol. 51,9 (2010): 1650-8.

Ethical ramifications of AI-powered medical diagnoses

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Incredible advancements in artificial intelligence (AI) have recently paved the way for the use of AI in healthcare settings. Implementation of AI has the potential to address worker shortages in the medical field, lead to discovery of new drugs, or improve diagnoses (Bajwa et al., 2021). A writer for the American Medical Association, Benji Feldheim applauds AI for restoring the “human side” in medicine. For example, AI scribes in particular ease the documentation burden doctors face—reducing burnout and improving doctors’ interactions with patients as a result (Feldheim, 2025). Another example is the AI model developed by Shmatko et al. (2025), known as Delphi-2M, which is capable of accurately predicting a patient’s next 20 years of disease burden (i.e., what diseases they would contract and when). Evidently, AI is a very promising technology already capable of improving lives, however, there are reasons to be skeptical. While these advances are promising, these uses of AI also raise concerns about fairness and clinical safety. After a brief synopsis of Shmatko et al.’s Delphi-2M, I evaluate the ethical ramifications of AI-powered diagnoses and related clinical tools.

Delphi-2M is an AI model trained on over 400,000 patient histories from a UK database to forecast an individual’s 20-year disease trajectory. Similar to chatbots like ChatGPT, Delphi-2M is a large language model (LLM), a type of AI that can recognize and reproduce patterns from large amounts of data. Similar to how chatbots pick up on what words are likely to appear with other words in order to form sentences, Delphi-2M learns from its vast training set of medical records to predict a patient’s disease trajectory from realworld patterns. As Yonghui Wu puts it in her summary of Shmatko et al.’s work, it’s just how becoming a smoker may be followed by a future diagnosis of lung cancer—these are patterns Delphi-2M recognize. To do this, Delphi-2M is fed “tokens” that link diseases or health factors to specific times in a person’s life, like chickenpox at age 2 or smoking at age 41 (Figure 1). Then, Delphi-2M outputs new tokens that predict what diseases and when they will occur in an individual’s life, like the onset of respiratory disorders at age 71 as a result of smoking. Delphi-2M, after being trained, was tested by predicting the medical histories of 1.9 million patients not included in the original training set. Shmatko et al. demonstrate this AI to have great success in accurately predicting disease trajectory, as it partially predicts patterns in individuals’ diagnoses in 97% of cases.

Visualization of Delphi-2M input and output (Wu, 2025).

Nonetheless, we must hold AI used to diagnose patients to a higher level of scrutiny compared to AI used commercially. LLMs are not perfect as they are subject to algorithmic bias and misuse, beginning before their creation. Shmatko et al. (2025), for example, address some shortcomings of the training data used for Delphi-2M. Notably, they explain the data from a mostly-white, older subset of the UK population isn’t entirely generalizable to very different demographics. Though Shmatko et al. found successes testing the model against a Danish database after training it on UK patients, I’m still concerned how Delphi-2M would perform on non-European and younger demographics, or those underrepresented in training data. Facial recognition is a prime example of where AI underperforms when training datasets lack diverse representation. AI designed to recognize faces historically underperform on individuals with feminine features or darker skin due to unrepresentative training data (Hardesty, 2018). With this in mind, it’s important that training data for diagnostic AI is representative of all demographics prior to widespread implementation.

Furthermore, Cabitza et al. (2017) wrote on some of the unintended consequences of machine learning in healthcare, postulating that widespread implementation of these tools also has the potential to reduce the skill of physicians. Though convenient in the short run, Cabitza et al. raise concerns with overreliance on AI—as studies show physicians aided by AI were less sensitive and accurate in diagnosing patients. Mammogram readers, for instance, were 14% less sensitive in their diagnostics when presented with images marked by computer-aided detection (Povyakalo et al., 2013). Though this study focused on image diagnoses, it’s clear how widespread use of Delphi-2M would lead to the same problems of deskilling in physicians. Delphi-2M is also exclusively a text-based model, which as Cabitza et al. detail, means that these diagnosis algorithms do not incorporate crucial contextual elements that are “psychological, relational, social, and organizational” in nature. A realworld example that Cabitza et al. described was an instance in which an AI model predicted a lower mortality risk for patients with pneumonia and asthma compared to those with pneumonia and without asthma. Understanding that asthma is not a protective factor for pneumonia patients, the involved researchers found the discrepant AI output was the result of hospital procedures that admitted pneumonia patients with asthma directly to intensive care, giving them better health outcomes. This missing piece of crucial information, which was difficult to represent in these prognostic models, led to an error a physician would not make. Thus, AI is limited in what information it can train on.

Though these new advancements in healthcare AI are promising, they have their limits. Tools like Delphi-2M spot patterns across vast clinical histories that no single clinician could feasibly track, yet the benefits depend on who is represented in the data, how predictions are explained and used, and whether safeguards are in place when they fail. Before AI is implemented in healthcare, we must demand representative training sets, validation across diverse populations, clear disclosures of uncertainty and limitations, and constant human involvement in the process that resists automation bias and deskilling. In short, diagnostic AI should supplmenent—not replace—clinical judgment, and it should be developed with privacy, equity, and patient trust at the forefront. Only then will these systems reliably improve care rather than merely appear to.

 

References

Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthcare Journal, 8(2), e188–e194. https://doi.org/10.7861/fhj.2021-0095

Cabitza, F., Rasoini, R., & Gensini, G. F. (2017). Unintended consequences of machine learning in medicine. JAMA, 318(6), 517. https://doi.org/10.1001/jama.2017.7797

Feldheim, B. (2025, June 12). AI scribes save 15,000 hours—and restore the human side of medicine. American Medical Association. https://www.ama-assn.org/practice-management/digital-health/ai-scribes-save-15000-hours-and-restore-human-side-medicine

Hardesty, L. (2018, February 11). Study finds gender and skin-type bias in commercial artificial-intelligence systems. MIT News. https://news.mit.edu/2018/study-finds-gender-skin-type-bias-artificial-intelligence-systems-0212

Povyakalo, A. A., Alberdi, E., Strigini, L., & Ayton, P. (2013). How to Discriminate between Computer-Aided and Computer-Hindered Decisions. Medical Decision Making, 33(1), 98–107. https://doi.org/10.1177/0272989×12465490

Wu, Y. (2025). AI uses medical records to accurately predict onset of disease 20 years into the future. Nature, 647(8088), 44–45. https://doi.org/10.1038/d41586-025-02971-3

Motor Brain-Computer Interface Reanimates Paralyzed Hand

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Over five million people in the United States live with paralysis (Armour et al., 2016), representing a large portion of the US population. Though the extent of paralysis varies from person-to-person, most with paralysis experience unmet needs that subtract from their overall life satisfaction. A survey of those with paralysis revealed “peer support, support for family caregivers, [and] sports activities” as domains where individuals with paralysis experienced less fulfillment—with lower household income predicting a higher likelihood of unmet needs (Trezzini et al., 2019). Consequently, individuals with sufficient motor function have turned to video games as a means to meet some of these needs, as video games are sources of recreation, artistic expression, social connectedness, and enablement (Cairns et al., 2019). Oftentimes, however, these individuals are limited by what games they are able to engage with—as they often “avoid multiplayer games with able-bodied players” (Willsey et al., 2025). Thus, Willsey and colleagues (2025) explore brain-computer interfaces as a valuable potential solution for restoring more sophisticated motor control of not just video games, but of digital interfaces used for social networking or remote work.

Brain-computer interfaces (BCIs) are devices that read and analyze brain activity in order to produce commands that are then relayed to output devices, with the intent of restoring useful bodily function (Shih et al., 2012). Willsey et al. explain how current motor BCIs are unable to distinguish between the brain activity corresponding to the movement of different fingers, so BCIs have instead relied on detecting the more general movement of grasping a hand (where the fingers are treated as one group). This limits BCIs to controlling fewer dimensions of an instrument: just being able to control a computer’s point-and-click cursor control as compared to typing on a computer. Hence, Willsey et al. seek to expand BCIs to allow for greater object manipulation—implementing finger decoding that will differentiate the brain output signals for different fingers, allowing for “typing, playing a musical instrument or manipulating a multieffector digital interface such as a video game controller.” Improving BCIs would also involve continuous finger decoding, as finger decoding has mostly been done retrospectively, where finger signals are not classified and read until after the brain data is analyzed. 

Willsey et al. developed a BCI system that is capable of decoding three independent finger groups (with the thumb decoded into two dimensions), allowing for four total dimensions of control. By training on the participant’s brain over nine days as they attempt to move individual fingers, the BCI can learn to distinguish brain regions that correspond to finger movements. These four dimensions of control are well reflected in a quadcopter simulation, where a patient with an implemented BCI is able to manipulate a virtual hand to fly a quadcopter drone through various hoops of an obstacle course. Many applications, even beyond video games, are apparent. These finger controls can be extended to a robotic hand or could reanimate the paralyzed limb. 

Finger movement is decoded into three distinct groups (differentiated by color).
Finger movement is decoded into three distinct groups (differentiated by color; Willsey et al., 2025).
Participant navigates quadcopter through a hoop through decoded finger movements.
Participant navigates quadcopter through a hoop through decoded finger movements (Willsey et al., 2025).

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The patient’s feelings of social connectedness, enablement and recreation were greatly improved. Willsey et al. note how the patient often looked forward to the quadcopter sessions, frequently “[asking] when the next quadcopter session was.” Not only did the patient find enjoyment in controlling the quadcopter, but they found training not to be tedious and the controls intuitive. To date, this finger BCI proves to be the most capable kind of motor BCI, and will serve as a valuable model for non-motor BCIs, like Brain2Char, a system for decoding text from brain recordings.

However, BCIs raise significant ethical considerations that must be addressed alongside their development. Are users responsible for all outputs from a BCI, even with outputs unintended? Given that BCIs decode brain signaling and train on data from a very controlled setting, there is always the potential for natural “noise” that may upset a delicate BCI model. Ideally, BCIs are trained on a participant’s brain in a variety of different circumstances to mitigate these errors. Furthermore, BCIs may further stigmatize motor disabilities by encouraging individuals toward restoring “normal” abilities. I am particularly concerned about the cost of this technology. As with most new clinical technologies, implementation is expensive and ends up pricing out individuals with lower socioeconomic statuses. These are often the individuals that face the greatest need for technologies like BCI. As mentioned earlier, lower household income predicts more unmet needs for individuals with paralysis. Nonetheless, so long as they are developed responsibly and efforts are made to ensure their affordability, there is great promise in motor BCIs.

 

References

Armour, B. S., Courtney-Long, E. A., Fox, M. H., Fredine, H., & Cahill, A. (2016). Prevalence and Causes of Paralysis—United States, 2013. American Journal of Public Health, 106(10), 1855–1857. https://doi.org/10.2105/ajph.2016.303270

Cairns, P., Power, C., Barlet, M., Haynes, G., Kaufman, C., & Beeston, J. (2019). Enabled players: The value of accessible digital games. Games and Culture, 16(2), 262–282. https://doi.org/10.1177/1555412019893877

Shih, J. J., Krusienski, D. J., & Wolpaw, J. R. (2012). Brain-Computer interfaces in medicine. Mayo Clinic Proceedings, 87(3), 268–279. https://doi.org/10.1016/j.mayocp.2011.12.008

Trezzini, B., Brach, M., Post, M., & Gemperli, A. (2019). Prevalence of and factors associated with expressed and unmet service needs reported by persons with spinal cord injury living in the community. Spinal Cord, 57(6), 490–500. https://doi.org/10.1038/s41393-019-0243-y

Willsey, M. S., Shah, N. P., Avansino, D. T., Hahn, N. V., Jamiolkowski, R. M., Kamdar, F. B., Hochberg, L. R., Willett, F. R., & Henderson, J. M. (2025). A high-performance brain–computer interface for finger decoding and quadcopter game control in an individual with paralysis. Nature Medicine. https://doi.org/10.1038/s41591-024-03341-8

Sex-Specific Brain Responses: How Chronic Stress Reshapes Astrocytes Differently in Males and Females

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Chronic stress is one of the major precursors to numerous neuropsychiatric disorders, such as depression. Women are twice as likely to be affected by mood disorders and respond differently than men to current available treatments. Nonetheless, many preclinical studies are conducted only in male rodents. Investigating the sex-specific responses to stress is critical to identifying mechanisms underlying mood disorders and moving towards developing treatments suitable for sex differences (Zhang et al. 2024). 

Chronic stress is associated with increased inflammation in the brain. The astrocyte is a type of cell in the nervous system that regulates inflammation in the brain. Astrocytes are important for keeping neurons alive, maintaining homeostasis, and secreting cytokines that regulate proinflammatory factors in the brain. The morphological changes of reactive astrocytes can tell us about the inflammation in the brain. These changes include the number of branches the astrocyte has; the more branches it has, the more reactive the astrocyte is, which is a sign of increased stress and inflammation in the brain. Chronic stress can lead to hyperactivation of astrocytes, impairing their ability to control and limit the spread of inflammation. Remodeling of astrocytes through changes in their cellular branching (more cellular branching) has been observed in suicide victims and preclinical chronic stress models. A recent study at Rowan University investigated sex-specific astrocyte responses to chronic stress in brain regions associated with mood disorders. 

Figure 1. Non-reactive vs. Reactive astrocytes. Reactive astrocytes have thicker cell bodies and processes. Astrocytes become reactive in response to injury, as well as to chronic stress. Adapted from: Pekny M, Wilhelmsson U, Pekna M. 2014. The dual role of astrocyte activation and reactive gliosis. Neuroscience Letters. 565:30–38. 

The study used the unpredictable chronic mild stress (UCMS) model to model chronic stress or a lipopolysaccharide (LPS) injection to model systemic inflammation. The UCMS model in rodents induces behavioral symptoms commonly associated with clinical depression as well as physiological and neurological changes that are associated with depression, such as hypertension and learned helplessness. The protocol involves randomized, daily exposures to different stressors, such as removal or bedding, social stresses, or predator sounds/smells. This model allows for an in-depth investigation of changes associated with chronic-stress induced depression (Frisbee et al. 2015). The LPS injection leads to neuroinflammation, sickness behavior, and cognitive impairment; it is a model often used to study neuroinflammation-associated diseases in mice. LPS causes inflammation because it activates microglia, which are the immune cells in the nervous system and play a large role in neuroinflammation (Zhao et al. 2019). 

Male and female mice were randomly assigned to the control group (saline), a 4 hour LPS injection (4LPS), a 24 LPS injection (24LPS), no stress (NS), or stress (UCMS). They measured GFAP (a biological marker of astrocyte reactivity) fluorescent intensity for astrocyte expression and quantified branch points to assess astrocyte complexity in different brain regions. 

The astrocyte complexity was investigated in the hippocampus, amygdala, and hypothalamus. The hippocampus and amygdala are critical brain regions in regulating physiological and behavioral stress processes, which can be useful in the short-term but detrimental in the long-term. In the short term, they help the body respond to stressors in order to maintain homeostasis. However, these stress mechanisms can lead to long-term dysregulation of this process as they promote maladaptive damage on the body and brain under chronically stressful conditions, which compromises resiliency and health. The amygdala is involved in detecting and responding to threats in the environment; the hippocampus is important for memory. These regions work together to make emotional and salient memories strong and long-lasting. Inflammation may hinder learning and memory through structural remodeling of the hippocampus (McEwen and Gianaros 2010). The hypothalamus, which is part of the hypothalamic-pituitary-adrenal (HPA) axis, is essential for mediating the stress response, primarily through the release of stress hormones (Bao et al. 2008). 

Figure 2.  The hypothalamus, hippocampus, and amygdala. Adapted from: https://www.brainframe-kids.com/emotions/facts-brain.htm.

The study found that chronic stress-induced morphological changes in astrocytes occurred in all brain regions that were looked at, and that the effects of chronic stress were both region and sex specific. Females had greater stress or inflammation-induced astrocyte activation in the hypothalamus, hippocampus, and amygdala than males. This indicates that chronic stress induces astrocyte activation that could drive sex-specific differences, which may contribute to the sex differences of mood disorders and disease. 

To better assess astrocyte reactivity, they used the ramification index, which is the ratio between the total number of primary branches and the branch maximum. It indicates how ramified the astrocytes are, as more branches (more ramified) indicates more reactive astrocytes. The ramification index indicated that astrocytes were significantly ramified due to chronic stress or LPS injection. They also conducted analysis of morphological changes, which provides the strongest evidence of astrocyte reactivity. The following results demonstrate the sex differences due to stress in branch point and terminal point morphology measurements. 

In the chronic-stress induced inflammatory environment (UCMS), there was higher astrocyte activation in the female hippocampus and hypothalamus, as demonstrated by increased branch points (Figure 3). 

Figure 3. UCMS and LPS activate astrocytes in the hypothalamus by inducing morphological changes. A. Representative images of female astrocytes in the hypothalamus in each condition (saline, no stress, 4 hours post-LPS, 24 hours post-LPS, and UCMS). D. Branching points in the hypothalamus. There were significant differences between treatment and between sex. There were significantly more branch points for females in the UCMS condition than for males. Adapted from: Zhang AY, Elias E, Manners MT. 2024. Sex-dependent astrocyte reactivity: Unveiling chronic stress-induced morphological changes across multiple brain regions. Neurobiology of Disease. 200:106610.

In the amygdala and hippocampus in the 24LPS condition, there was increased astrocyte reactivity in females compared to males. This indicates that females are more susceptible to chronic systemic inflammation than males in these brain regions (Figure 4). 

Figure 4. UCMS and LPS activate astrocytes in the amygdala by inducing morphological changes. A. Representative images of female astrocytes in the amygdala in the saline, no stress, 4LPS, 24LPS, and UCMS groups. D. Analysis of branch points in the amygdala. There were significant differences between treatment and between sex. Females had significantly more branching points in the 24LPS condition than males. Adapted from: Zhang AY, Elias E, Manners MT. 2024. Sex-dependent astrocyte reactivity: Unveiling chronic stress-induced morphological changes across multiple brain regions. Neurobiology of Disease. 200:106610.

Astrocytes work in tandem with other cells in the nervous system, including microglia, to regulate various processes. Microglia, the cells in the nervous system important for immune response, are also important in the inflammatory response in the brain. Reactive microglia can activate astrocytes by secreting cytokines. Blocking microglia may be able to decrease the number of reactive astrocytes and apply a protective effect against inflammation in the brain. Future studies can look at the activation of microglia and how microglia and astrocytes interact in the chronic stress model. 

Overall, it is important that this study looked at sex differences since there is such a disparity among mental health disorders and treatment in women and men. Understanding the mechanisms behind the sex differences can improve the development of new medications for stress-related disorders so that both men and women can be correctly treated. 

Moreover, female susceptibility to chronic stress may mediate the increased risk for Alzheimer’s Disease. The different biochemical responses to stress, such as activity of the hypothalamic-pituitary-adrenal (HPA) axis and female-biased increases in molecules associated with AD, between females and males could be a sex-dependent risk factor for AD. Female-specific alterations in inflammation and microglial function are proposed to be one reason, but this needs more investigation (Yan et al. 2018). Understanding sex-specific disease mechanisms is essential for the development of personalized medicine, which is the use of an individual’s genetic profile to prevent, diagnose, and treat disease. Differences in mechanisms of disease between sexes will likely require different drugs for men and women to treat a variety of psychiatric and neurological disorders (Bangasser and Wicks 2017). 

 

References. 

Bangasser DA, Wicks B. 2017. Sex-specific mechanisms for responding to stress. Journal of Neuroscience Research. 95(1–2):75–82. 

Bao A-M, Meynen G, Swaab DF. 2008. The stress system in depression and neurodegeneration: Focus on the human hypothalamus. Brain Research Reviews. 57(2):531–553. 

Emotion Facts: Emotions in the Brain. [accessed 2024 Oct 29]. https://www.brainframe-kids.com/emotions/facts-brain.htm.

Frisbee JC, Brooks SD, Stanley SC, d’Audiffret AC. 2015. An Unpredictable Chronic Mild Stress Protocol for Instigating Depressive Symptoms, Behavioral Changes and Negative Health Outcomes in Rodents. J Vis Exp.(106):53109. 

McEwen BS, Gianaros PJ. 2010. Central role of the brain in stress and adaptation: Links to socioeconomic status, health, and disease. Annals of the New York Academy of Sciences. 1186:190. 

Pekny M, Wilhelmsson U, Pekna M. 2014. The dual role of astrocyte activation and reactive gliosis. Neuroscience Letters. 565:30–38. 

Tynan RJ, Naicker S, Hinwood M, Nalivaiko E, Buller KM, Pow DV, Day TA, Walker FR. 2010. Chronic stress alters the density and morphology of microglia in a subset of stress-responsive brain regions. Brain, Behavior, and Immunity. 24(7):1058–1068. 

Yan Y, Dominguez S, Fisher DW, Dong H. 2018. Sex differences in chronic stress responses and Alzheimer’s disease. Neurobiology of Stress. 8:120–126. 

Zhang AY, Elias E, Manners MT. 2024. Sex-dependent astrocyte reactivity: Unveiling chronic stress-induced morphological changes across multiple brain regions. Neurobiology of Disease. 200:106610. 

Zhao J, Bi W, Xiao S, Lan X, Cheng X, Zhang J, Lu D, Wei W, Wang Y, Li H, et al. 2019. Neuroinflammation induced by lipopolysaccharide causes cognitive impairment in mice. Sci Rep. 9(1):5790. 

Asthma and ADHD: How do Pediatricians Approach This Intersection?

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According to the CDC, 11.4% of children aged 3-17 in the USA are diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) (Data and Statistics on ADHD | Attention-Deficit / Hyperactivity Disorder (ADHD), 2024). ADHD is a developmental disorder characterized by symptoms of hyperactivity, impulsivity, and inattention, as the name suggests. Treating this disorder often requires a variety of approaches including medication, psychotherapy, and workplace or school-based accommodations (Attention-Deficit/Hyperactivity Disorder – National Institute of Mental Health (NIMH), n.d.).  Comorbidities are very common in people with ADHD, this makes it so that it is rarely the only concern during a primary care visit (Silver, 2024). Sleath et al. discuss the communication primary physicians held with families with children that have both ADHD and asthma. There has been found to be a correlation between the severity of ADHD and asthma symptoms (Blackman & Gurka, 2007). In turn, balancing treatment for both primary care visits was a driver for the paper. Asthma is a chronic lung condition that results in the narrowing of the lung pathways. Medication to alleviate symptoms of both ADHD and asthma is often prescribed at primary care visits hence the study of their intersection. 

Figure 1. Happy little girl and pediatrician doing high five after medical checkup. AAP Schedule of Well-Child Care Visits. (2023). Healthy Children.org. https://www.healthychildren.org/English/family-life/health-management/Pages/Well-Child-Care-A-Check-Up-for-Success.aspx

Sleath et al. approach this balance by studying the communication between patients with ADHD and asthma and pediatricians. The study focuses on the communication breakdown when the patient has ADHD during an asthma visit. All of these were pediatric visits. To measure the effectiveness of communication, the American Association of Pediatrics (AAP) guidelines for discussing ADHD were used. The percentage of adherence was measured through the visits using recordings. 

Before data collection eligibility tests were conducted. This made sure that all participants in the study were 8-16 years of age, could speak English, was capable of filling out an assent form, had had at least one prior visit to the clinic, had persistent asthma, and had a guardian present who is over the age of 18 and is competent in English. After the visits concluded, guardians were provided with questionnaires, and children were interviewed. These were used to supplement the recordings. 

The audio taping and coding are the backbone of the data. The audio tapes were transcribed by a coding tool that was flagged for AAP guidelines. To ensure accuracy two research assistants met twice a month to review and refine criteria. The other important aspect of the collection was a thorough socio-demographic data set: gender, age, race, insurance, and tears of asthma. All demographic data but asthma status was also recorded from guardians. 

The results yielded from this were extreme. Throughout the visits 23% of the 296 children had ADHD noted in their medical chart. It was found that boys were more likely to have ADHD diagnoses. It is important to note that it is not because ADHD affects males more, but women are less likely to get diagnosed or are diagnosed later in life due to inattentive presentations (Attoe & Climie, 2023). When understanding the extent of utilization of AAP guidelines, categories were formed; functioning, outcomes, treatment plan, ADHD asthma relationship, chronic and follow-up visits. In all of these categories, the percentage of providers that used AAP guidelines never rose above 40%. In the adherence to medication, only one provider out of the 35 discusses the topic (41 providers participated, but recording forms only 35 were usable). Overall, it was shown that AAP guidelines were more likely to be followed if the visit was unrelated to asthma, highlighting providers’ tendency to neglect proper ADHD management in patients with comorbidities. 

The aim was to highlight the need for better communication practices in the pediatric setting. Particularly in cases where comorbid conditions are present. Future development in this field would be understanding the reason behind the present communication pattern. Approaching the issue from the physician and patient perspective. On the other hand, research on how to remedy the disparity in guideline adherence. 

 

Article based on ‘Communication about ADHD and its treatment during pediatric asthma visits’

Sleath, B., Sulzer, S. H., Carpenter, D. M., Slota, C., Gillette, C., Sayner, R., Davis, S., & Sandler, A. (2014, Feb). Communication about ADHD and its treatment during pediatric asthma visits. Community Ment Health J ., 50(2), 185-192. 10.1007/s10597-013-9678-3

References

AAP Schedule of Well-Child Care Visits. (2023). Healthy Children.org. https://www.healthychildren.org/English/family-life/health-management/Pages/Well-Child-Care-A-Check-Up-for-Success.aspx

Attention-Deficit/Hyperactivity Disorder – National Institute of Mental Health (NIMH). (n.d.). National Institute of Mental Health. Retrieved November 1, 2024, from https://www.nimh.nih.gov/health/topics/attention-deficit-hyperactivity-disorder-adhd

Attoe, D. E., & Climie, E. A. (2023, March 30). Miss. Diagnosis: A Systematic Review of ADHD in Adult Women. J Atten Disord, 27(7), 645–657. 10.1177/10870547231161533

Blackman, J. A., & Gurka, M. J. (2007). Developmental and Behavioral Comorbidities of Asthma in Children. Journal of Developmental & Behavioral Pediatrics, 28(2), 92-99. 10.1097/01.DBP.0000267557.80834.e

Data and Statistics on ADHD | Attention-Deficit / Hyperactivity Disorder (ADHD). (2024, May 22). CDC. Retrieved November 1, 2024, from https://www.cdc.gov/adhd/data/index.html

Silver, L. (2024, April 3). ADHD Symptoms Or ADHD Comorbidity? Diagnosing Related Conditions. ADDitude. Retrieved November 1, 2024, from https://www.additudemag.com/when-its-not-just-adhd/

Sleath, B., Sulzer, S. H., Carpenter, D. M., Slota, C., Gillette, C., Sayner, R., Davis, S., & Sandler, A. (2014, Feb). Communication about ADHD and its treatment during pediatric asthma visits. Community Ment Health J ., 50(2), 185-192. 10.1007/s10597-013-9678-3

Genomics of severe and treatment-resistant obsessive-compulsive disorder treated with deep brain stimulation: a preliminary investigation

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Obsessive-compulsive disorder (OCD) can be severely disabling, and some patients do not respond to standard treatments like medication and therapy. Deep brain stimulation (DBS), an invasive neurosurgical intervention where thin electrodes are connected to a neuro-pacemaker and introduced into subcortical central structures of the brain to modulate pathological neuronal activity with electrical current, has shown promise for these treatment-resistant cases. However, responses to DBS vary widely, prompting a need to identify genetic factors that might predict which patients will benefit. Understanding these genetic markers may ultimately lead to more personalized, effective approaches for treatment-resistant OCD.

This study (Chen et al, 2023) conducted a preliminary genomic analysis on a small cohort of patients with severe, treatment-resistant OCD who received DBS. Researchers sequenced the patients’ DNA to examine specific genetic variants. These included instances where a single nucleotide in a genomic sequence was altered in a phenomenon known as single nucleotide variants and among other genetic markers previously associated with psychiatric disorders and traits related to treatment resistance. Statistical analysis was then applied to explore any associations between these genetic markers and the clinical outcomes of DBS in these patients.

The results identified several genetic markers such as missense variants in the gene KNCB1 that seemed to correlate with positive or negative DBS responses. However, because the study involved a small number of participants, these findings are considered preliminary. Certain genetic variants showed potential as predictors for treatment outcomes, but further research with a larger sample size is needed to validate these associations and understand the mechanisms by which they influence DBS response.

This study provides initial evidence that genetics may play a role in how patients with treatment-resistant OCD respond to DBS. If validated by larger studies, these findings could pave the way for genetically-informed approaches to selecting and optimizing DBS candidates, contributing to more precise, personalized treatment strategies for severe OCD cases.

References:

Long Long Chen, Matilda Naesström, Matthew Halvorsen, Anders Fytagoridis, David Mataix-Cols, Christian Rück, James J Crowley, Diana Pascal (2023) Genomics of severe and treatment-resistant obsessive-compulsive disorder treated with deep brain stimulation: a preliminary investigation, medRxiv , https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10153313/

The Dark Side of Antibiotics

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Antibiotics are medications that fight infections caused by bacteria. But they can also lead to mental health issues, such as anxiety and depression, later in life. 

The discovery of antibiotics was one of the greatest medical advances of the 20th century. Antibiotics have significantly reduced mortality from infectious diseases and increased average life expectancy. However, they have a variety of side effects, including cognitive impairment and emotional disorders in adulthood (Adedji 2016). 

Antibiotics destroy bacteria in the gut, which can disrupt brain function. Gut microbiota are an important component of the gut-brain axis–the two-way line of communication between the gastrointestinal tract and the central nervous system. Gut microbiota produce neurotransmitters that regulate mood, such as dopamine, norepinephrine, and serotonin, which travel through the vagus nerve to the brain (Karakan et al 2021). Previous studies have found that antibiotic-induced gut microbiota depletion causes dysfunction of the gut-brain axis, increasing anxiety and depression-related behaviors (Mosaferi et al 2021).

Aging plays an important role in the development of both gut microbiota and the brain. Microbiota first appear at birth and rapidly colonize the intestinal tract. The composition and diversity of gut microbiota resembles adult level by 2 years of age and remains stable throughout adulthood before decreasing in old age. The brain develops until the mid-to-late 20s and starts to decline in middle age. It has been shown that antibiotic-induced gut microbiota depletion has negative effects on the brain (Li et al 2022). However, no prior research has been done on this relationship during the different stages of life. This study aimed to determine the connection between gut microbiota and cognitive and emotional function during the different life stages. 

In this experiment, the researchers used mice as models for human subjects, randomly assigning 75 mice to five groups. One group served as the control (Veh group) and was given distilled water from birth to death. The other four groups were given an antibiotic cocktail at different life stages: birth to death (Abx group), birth to postnatal day 21 (Abx infant group), postnatal day 21 to 56 (Abx adolescence group), and postnatal day 57 to 84 (Abx adulthood group).  

At postnatal day 85, the researchers randomly selected thirteen mice from each group for testing. They measured the cognitive function and emotion of the mice by using four traditional behavioral tests: open-field test (OFT), passive avoidance test (PAT), morris water maze test (MWM), and tail suspension test (TST). The OFT measured anxiety level by observing how long the mice moved in an open field for 5 minutes. The PAT measured short-term memory, which was defined as the difference in latency–the time it took mice to reenter a room that delivered an electric shock–between day 1 and day 2 (Jahn-Eimermacher et al 2011). The MWM measured spatial memory, which was defined as incubation time, or how long it took mice to find a submerged platform in a pool after a 5-day training period. The TST measured depression state by observing the duration of quiescence (motionless state) of the mice while they were suspended upside down for 4 minutes.

Figure 1 | Results of OFT, PAT, MVM, and TST tests. Researchers conducted four traditional behavioral tests on mice given water, as well as mice given antibiotics at different stages of life: birth to death, infancy, adolescence, and adulthood. They found that exposure to antibiotics from birth to death and in infancy led to the most severe cognitive and emotional dysfunction, followed by exposure in adolescence and adulthood (Li et al 2022).

The results of this study suggested that life cycle stages influence the relationship between gut microbiota and cognitive and emotional function. For the OFT test, the total movement time of the Abx, Abx infant, and Abx adolescent groups was significantly lower than the Veh group, indicating they were more anxious than the Veh group (Figure 1). In other words, exposure to antibiotics from birth to death, in infancy, and in adolescence caused anxiety-related behaviors. For the PAT test, the difference in latency for every Abx group was significantly lower than the Veh group, meaning all Abx groups reentered the room with the electric shock more quickly after training. These results signaled that short-term memory loss was greater in the Abx groups than the Veh group; exposure to antibiotics at any stage of life caused short-term memory loss. The MWM test found that the incubation time after the 5-day training period was significantly higher for the Abx and Abx infant groups than the Veh group, so they experienced more spatial memory loss than the Veh group; exposure to antibiotics from birth to death and in infancy caused spatial memory loss. The TST test found that the duration of quiescence in the Abx and Abx infant groups was significantly higher than the Veh group, implying they were more depressed than the Veh group. In other words, exposure to antibiotics from birth to death and in infancy caused depression-related behaviors.

The researchers’ findings align with previous work showing that depletion of gut microbiota causes cognitive impairment and emotional problems (Lach et al 2020). Furthermore, the researchers demonstrated that life cycle stages are an important factor in the relationship between gut microbiota and cognitive and emotional function. In particular, their findings strengthened the idea that infancy is a crucial stage of development of gut microbiota and the brain (Hunter et al 2023). Gut microbiota lost in infancy recovers over time; however, this depletion has lasting cognitive effects. In this study, mice given antibiotics in infancy exhibited similar behaviors in adulthood as mice given antibiotics from birth to death: anxiety, depression, memory loss, and learning ability decline. Exposure to antibiotics in infancy and in the long term led to the most severe cognitive and emotional dysfunction, followed by exposure in adolescence and adulthood.

The researchers’ findings also have implications for the treatment of mental health illnesses. Previous studies have shown that probiotics replace depleted gut microbiota, alleviating symptoms of anxiety and depression. Antidepressants and anxiolytics–current medications for anxiety and depression–cause side effects such as nausea, weight gain, insomnia, constipation, dizziness, agitation, and restlessness. Probiotics are associated with milder side effects, including gas and bloating (Bistas et al 2023). Probiotics are unlikely to treat severe depression and anxiety, but they are promising treatments for people with milder conditions. The next step is to identify and manufacture effective probiotics, which would revolutionize the field of psychiatry and improve the lives of people around the world.

 

References

Adedji, W.A. 2016. THE TREASURE CALLED ANTIBIOTICS. Annals of Ibadan Postgraduate Medicine. 14(2):56-57. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5354621/.

Bistas KG, Tabet JP. 2023. The Benefits of Prebiotics and Probiotics on Mental Health. Cureus Journal of Medical Science. 15(8):e43217. doi:10.7759/cureus.43217.

Hunter S, Flaten E, Petersen C, Gervain J, Werker JF, Trainor LJ, Finlay BB. 2023. Babies, bugs and brains: How the early microbiome associates with infant brain and behavior development. PLOS One. 18(8):e0288689. doi:10.1371/journal.pone.0288689.

Jahn-Eimermacher A, Lasarzik I, Raber J. 2011. Statistical analysis of latency outcomes in behavioral experiments. Behavioural Brain Research. 221(1):271-275. doi:10.1016/j.bbr.2011.03.007.

Karakan T, Ozkul C, Akkol EK, Bilici S, Sobarzo-Sánchez E, Capasso R. 2021. Gut-Brain Microbiota Axis: Antibiotics and Functional Gastrointestinal Disorders. Nutrients. 13(2):389. doi:10.3390/nu13020389.

Lach G, Fülling C, Bastiaanssen TFS, Fouhy F, O’Donovan AN, Ventura-Silva AP, Stanton C, Dinan TG, Cryan JF. 2020. Translational Psychiatry. 10(1):382. doi:10.1038/s41398-020-01073-0.

Li J, Pu F, Peng C, Wang Y, Zhang Y, Wu S, Wang S, Shen X, Li Y, Cheng R, He F. 2022. Antibiotic cocktail-induced gut microbiota depletion in different stages could cause host cognitive impairment and emotional disorders in adulthood in different manners. Neurobiology of Disease. 170:105757. doi:10.1016/j.nbd.2022.105757.

Mosaferi B, Jand Y, Salari AA. 2021. Gut microbiota depletion from early adolescence alters anxiety and depression-related behaviors in male mice with Alzheimer-like disease. Scientific Reports. 11:22941. doi:10.1038/s41598-021-02231-0.