The Fundamental Role of Dietary Habit, Physical Activity, and Sleep Pattern on Amyloid-Beta and Tau Pathology.

Abstract

Alzheimer’s disease (AD) is a debilitating neurodegenerative disease that affects an estimated 24 million adults over the age of 65 worldwide, with symptoms including memory loss, cognitive decline, and behavioral changes. Currently, there is no cure, although there is a 100% mortality rate once the disease has progressed. Age is the most significant risk factor for developing AD; however, diet, physical activity, and disrupted sleep patterns contribute to disease progression. An improper diet in particular can contribute to inflammation and increase reactive oxygen species leading to AD progression. Physical activity has many benefits on overall health but has a unique positive impact on AD through improved cognitive outcomes. Sleep disturbances are regularly reported by AD patients. There is now evidence to support sleep disruption as an early marker of AD while also significantly contributing to the progression of AD. Improving diet, physical activity, and sleep patterns may have positive outcomes for AD patients and reduce the burden on their families and caretakers, promoting a healthier family environment. In this review, we will explore how diet, physical activity, and sleep patterns contribute to AD progression and highlight how changes in these areas can improve the quality of life of AD patients.

The Effects of Stress on Alzheimer’s Disease Development and Progression

Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder that profoundly impacts patients’ memory, cognition, and overall quality of life. Chronic stress is a substantial contributor to Alzheimer's patients' cognitive deterioration. The effects of stress on hippocampus function and dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis can lead to higher cortisol levels and is the link between stress and memory dysregulations. Moreover, acute and chronic stress have been linked to increased production of Amyloid Beta plaques and Tau Tangles, the primary pathological hallmarks of Alzheimer’s disease. As a result, the cellular mechanisms of memory consolidation and storage are disrupted. This paper reviews ongoing research of how cellular, network and endocrine pathways in the brain are impeded by stress and links these findings to Alzheimer’s disease development and progression. Understanding the interplay between stress and the progression of Alzheimer’s disease can play a significant role in further development of research, disease-modifying treatments, and potential cures.

The Role Of Cross-Sector Collaboration In Reducing Racial And Social Disparities In Healthcare

Abstract

Despite the presence of a robust healthcare system, racial disparity and social inequalities remain prevalent in the United States. These disparities in healthcare are persistent challenges, disproportionately affecting marginalized communities and contributing to unequal health outcomes. This study investigates the role of cross-sector collaboration among healthcare providers, public health agencies, community-based organizations, and policy makers in mitigating these disparities. Using a mixed-methods approach that includes case studies, stakeholder interviews, and data analysis from collaborative health initiatives across five urban regions, the research identifies key drivers of success in cross-sector partnerships. Findings highlight the importance of community engagement, shared data systems, culturally competent care models, and sustained funding in reducing barriers to access and improving health equity. The study concludes that cross-sector collaboration is not only essential but also highly effective in addressing the root causes of health disparities, and it offers policy recommendations to support the development and scalability of such partnerships nationwide.

A Neural Network Model in Identifying Non-Small-Cell Lung Cancer through CT Scans

Abstract

Lung cancer is the leading cause of cancer-related deaths worldwide, causing approximately 1.8 million deaths in 2022 alone. Lung cancer is often detected through the use of Low-Dose Computed Tomography (LDCT) scans, which use small amounts of radiation to construct detailed pictures of regions in the body. This study aims to explore the ability of Artificial Intelligence, particularly Convolutional Neural Networks (CNNs), to detect lung cancer from CT scans. Using an online dataset consisting of 1000 images, a CNN was developed with ResNet50 as the base model used for feature extraction. The model achieved a validation accuracy of 98.78% and a testing accuracy of 97.53%. This showcases the proficiency of the model in detecting lung cancer. However, this was only when a binary classification system was implemented, where the model was made to simply determine the presence of cancer. The model faced great difficulty in distinguishing between the types of lung cancer: Adenocarcinoma, Squamous Cell Carcinoma, and Large Cell Carcinoma. Additionally, the presence of a small number of false negatives while testing shows the danger of relying on AI and demonstrates the necessity of further fine-tuning before practical use.

Exploring The Neurocognitive Effects Of Magic Intervention On Mild Cognitive Impairment And Alzheimer’s Disease

Abstract

Magic, the art of conjuring, has fascinated people around the world for millennia. Magicians have mastered manipulating attention (misdirection) and exploiting the human mind to create seemingly impossible tricks and illusions. In the past few decades, neuroscientists and psychologists have researched the methods of magic and produced a significant amount of literature relating to how magicians manipulate people. With this surge of new knowledge, it is worthwhile to investigate whether magic can be used in treatments for mental disorders and diseases. Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) are two of the most common neurological conditions among elderly, where behavioral interventions are crucial for slowing down the progression of neurocognitive impairment. This paper analyses and proposes how magic intervention can impact neurocognitive function in patients with MCI or AD, by reviewing current literature in the related areas of magic and the science behind it. In particular, it is found that magic intervention can positively affect executive functioning, learning, and memory in individuals with MCI and AD. Furthermore, emerging areas of research in the field indicate that magic intervention may promote curiosity and engage sensory systems, further improving neurocognitive function in these individuals. Therefore, the paper shows that magic intervention is effective in enhancing neurocognitive function in patients with MCI and AD, and in slowing down the progression from MCI to AD.

Underlying Similarities Between Psychedelic and Schizophrenia Hallucinations

Abstract

Though distinctly different in their effects, hallucinations occurring from schizophrenia (SCZ) symptoms and psychedelic drug action can appear similar. To investigate how different mechanisms underlie these effects, this paper highlights similarities at the molecular and anatomical network level and identifies potential new therapeutic applications and directions for research. This paper explores the 1950s-1960s studies involving schizophrenic patients, hallucinogenic drugs, and their historical significance. Next, the paper dives into overlaps between schizophrenic and psychedelic hallucinations– from molecular to cellular levels. The paper’s overlaps section starts with Neuroplasticity overlaps between SCZ and Psychedelics; then is followed by the cortico-striatal-thalamo-cortical (CSTC) theory in psychedelics and schizophrenia’s hallucinations; serotonin (5-HT) mediated hallucinations and psychotic effects; triple network disconnectivity in psychopathology and schizophrenia pathology; and finally, synaptic disconnections role in neuropharmacology of hallucinations seen by computational modeling. The paper includes a final section of how the drug, nicotine, impacts SCZ patients. This paper aims to clarify the neuropharmacological overlap between these phenomena to call for further research in developing targeted treatments.

Classifying Classical Music Genres with Neural Networks

Abstract

Current neural network models can process and interpret music for tasks such as melody completion and genre or style classification. However, previous classification tasks do not account specifically for distinct composition styles of different classical music periods, often focusing instead on modern genres. To bridge this gap, this project investigates the use of natural language processing techniques to classify musical excerpts from the Baroque, Classical, and Romantic periods. A curated dataset of samples representative of the three eras, converted to the OctupleMIDI format, was used to train a Sentence Transformers model to complete the classification task with maximum accuracy—62.5% when trained on all three categories and 90.5% when the Classical and Romantic labels were merged. These results indicate that the model was most effective at distinguishing Baroque music, suggesting clearer stylistic separation. These findings demonstrate the feasibility of using sentence-level embeddings for symbolic music classification, offering potential applications in musicological analysis, genre tagging for recommendation systems, and quantitative exploration of musical style beyond human perception.