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The authors test the effectiveness of machine learning to predict onset of cardiovascular disease.

In the quest to understand dark matter, scientists face a profound mystery. Two compelling candidates, Massive Compact Halo Objects (MACHOs) and Weakly Interacting Massive Particles (WIMPs), have emerged as potential sources. By analyzing gravitational waves from binary mergers involving these black holes, authors sought to determine if MACHOs could be the elusive dark matter.

The study explored the role of testosterone beyond its well-established effects on male sex characteristics, focusing on its association with non-communicable diseases (NCDs) like obesity and type 2 diabetes (T2D), using Mendelian randomization (MR) analysis on genomic data.

COVID-19 has officially been downgraded from the status of a global health emergency, but have COVID-19 safety practices become a new way of life for students? The authors collected survey data on COVID-19-related knowledge and behaviors of high-school students in Punjab, Pakistan and Santa Clara County, California, USA, so see where high-schoolers stand on pandemic safety today.

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.

Sadly, around 800,000 people die by suicide worldwide each year. Dong and Pearce analyze health survey data to identify associations between suicidal ideation and relevant variables, such as sleep quality, hopelessness, and anxious behavior.

In this study, the authors looked at a proto-oncogene, KRAS, and searched for molecules that are predicted to be able to bind to the inactive form of KRAS. They found that a modified version of Irbesartan, a derivative of benzimidazole, showed the best binding to inactive KRAS.

Here, in an effort to develop a model to predict future groundwater levels, the authors tested a tree-based automated artificial intelligence (AI) model against other methods. Through their analysis they found that groundwater levels in Texas aquifers are down significantly, and found that tree-based AI models most accurately predicted future levels.

The authors looked at using genetic algorithms to look at the Indian labor market and what features might best explain any variation seen. They found that features such as economic growth and household consumption, among others, best explained variation.

The authors test the ability of planarians to remember conditioned stimuli following regeneration.