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The authors investigated an important parasite-host relationship between the raccoon roundworm and the raccoon to understand how parasite prevalence is affected by location. They found that the parasite infection was more prevalent in raccoons found closer to human dwellings, though the number of roundworm eggs was not significantly different. These results are important human health, since roundworm infection is lethal to humans and can be transmitted from raccoons to humans - the authors suggest that more research into this parasite and awareness of its prevalence is needed to prevent disease.

Many weight loss supplements contain the stimulant caffeine, but do not disclose the amount. Here, authors measure and compare the amount of caffeine in different dietary supplements. This research gives consumers better understanding of the impact natural supplements may have on their health.

This article discusses Alopecia areata, an autoimmune disorder causing sudden hair loss due to the immune system mistakenly attacking hair follicles. The article introduces the use of deep learning (DL) techniques, particularly convolutional neural networks (CNN), for classifying images of healthy and alopecia-affected hair. The study presents a comparative analysis of newly optimized CNN models with existing ones, trained on datasets containing images of healthy and alopecia-affected hair. The Inception-Resnet-v2 model emerged as the most effective for classifying Alopecia Areata.

Here, the researchers investigated how exposure to active versus passive music affects a teenager's ability to perform a challenging task, namely a Sudoku puzzle, under stressful conditions. Following testing 75 high school teenagers split into two group, the researchers found that singing in a choir (active music) yielded a greater improvement in performance compared to passive listening for brief time periods.

Kalirin is a guanine nucleotide exchange factor (GEF) for the GTPase RAC1, linked to schizophrenia and Alzheimer’s Disease. It plays a crucial role in synaptic plasticity by regulating dendritic spine formation and actin cytoskeleton remodeling, which are essential for creating new synapses. Authors developed two novel compounds targeting kalirin, confirming that predictive modeling can indicate biological activity.

The authors test various machine learning models to improve the accuracy and efficiency of pneumonia diagnosis from X-ray images.

The authors use machine learning and electroencephalogram data to propose a method for improving epilepsy diagnosis.

The authors use the Census Household Pulse Survey issued by the US Census Bureau to examine the prevalence of stress and depression among people across socioeconomic statuses.

It can be challenging for the general public to understand data on medical risk. Weseley-Jones and Mordechai tackle this issue by conducting a survey to assess people's skill and comfort with understanding medical risk information in percentage and frequency formats.

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder in the U.S., second only to Alzheimer’s disease. Current diagnostic methods are often inefficient and dependent on clinical exams. This study explored using machine and deep learning to enhance PD diagnosis by analyzing spiral drawings affected by hand tremors, a common PD symptom.