Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.

The COVID-19 pandemic has caused disruption in social interactions. In this study, the authors tested if walking a dog will change human interactions and found that walking with a dog increased social interaction.

Reimagize, a role-playing with decision-making, was conjured, implementing social psychological concepts like counter-stereotyping and perspective-taking. As the game works implicitly to influence body image, it even counters image issues beyond personal body dissatisfaction. This study explored whether a digital role-playing card game, incorporating some of the most common prejudices of body image (like size prejudice, prejudices from the media, etc.) as identified by a digital survey/questionnaire completed by Indian girls aged 11-21, could counter these issues and reduce personal body dissatisfaction.

Here, the authors sought to better understand how and why people experience beauty in music. They explored this fundamental aesthetic response by considering numerous emotional responses of participants to diverse musical excerpts using a 42-item Aesthetic Emotions Scale assessment. They found that the highly nuanced emotional experience of beauty in music includes positive, negative, and knowledge-related feelings.

This study examined the effects of stress and selective serotonin reuptake inhibitors (SSRIs) on a measure of astrocyte reactivity in nonhuman primate (NHP) models of stress. Results showed that chronic separation stress in NHPs leads to increased signs of astrogliosis in the NHP hippocampus. The findings were consistent with the hypotheses that hippocampal astrogliosis is an important mechanism in stress-induced cognitive and behavioral deficits.

Machine learning and deep learning techniques can be used to predict the early onset of breast cancer. The main objective of this analysis was to determine whether machine learning algorithms can be used to predict the onset of breast cancer with more than 90% accuracy. Based on research with supervised machine learning algorithms, Gaussian Naïve Bayes, K Nearest Algorithm, Random Forest, and Logistic Regression were considered because they offer a wide variety of classification methods and also provide high accuracy and performance. We hypothesized that all these algorithms would provide accurate results, and Random Forest and Logistic Regression would provide better accuracy and performance than Naïve Bayes and K Nearest Neighbor.

The degeneration of nerve cells in the brain can lead to pathologies such as Parkinson’s disease. It has been suggested that neurons in humans may regenerate. In this study, the effect of different doses of caffeine on regeneration was explored in the planeria model. Caffeine has been shown to enhance dopamine production, and dopamine is found in high concentrations in regenerating planeria tissues. Higher doses of caffeine accelerated planeria regeneration following decapitation, indicating a potential role for caffeine as a treatment to stimulate regeneration.

Dental resins can be a source of reactive oxygen species (ROS) which in unruly amounts can be toxic to cellular and overall health. In this report, the authors test whether the consumption of antioxidant rich foods like avocado and asparagus can protect against the effect of dental resin-derived ROS. However, rather than testing humans, they use fruit flies and their climbing abilities as an experimental readout.

In this study, the authors investigate associations between use of histamine H₂ receptor antagonists and suicidal ideation in different age groups.

Breast cancer is the most common cancer in women, with approximately 300,000 diagnosed with breast cancer in 2023. It ranks second in cancer-related deaths for women, after lung cancer with nearly 50,000 deaths. Scientists have identified important genetic mutations in genes like BRCA1 and BRCA2 that lead to the development of breast cancer, but previous studies were limited as they focused on specific populations. To overcome limitations, diverse populations and powerful statistical methods like genome-wide association studies and whole-genome sequencing are needed. Explainable artificial intelligence (XAI) can be used in oncology and breast cancer research to overcome these limitations of specificity as it can analyze datasets of diagnosed patients by providing interpretable explanations for identified patterns and predictions. This project aims to achieve technological and medicinal goals by using advanced algorithms to identify breast cancer subtypes for faster diagnoses. Multiple methods were utilized to develop an efficient algorithm. We hypothesized that an XAI approach would be best as it can assign scores to genes, specifically with a 90% success rate. To test that, we ran multiple trials utilizing XAI methods through the identification of class-specific and patient-specific key genes. We found that the study demonstrated a pipeline that combines multiple XAI techniques to identify potential biomarker genes for breast cancer with a 95% success rate.