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The authors test the ability of six ayurvedic plants to reduce levels of uric acid in solution.

The authors looked at how acetaminophen may cause liver damage by looking at both serum level markers of liver damage as well as liver pathology.

The authors studied the effect of potassium bromate, a common food additive, to cell viability.

The authors examined the impact of mutations to CDK2 on protein expression.

The authors created a 1D model to diagnose hepatomegaly and cirrhosis via ultrasound of the liver.

Here the authors aimed to understand factors influencing adolescent fentanyl exposure, hypothesizing a positive association between social media usage, socioeconomic factors, and fentanyl abuse among U.S. teens. Their analysis of the Monitoring the Future dataset revealed that a history of suspension and use of marijuana or alcohol were linked to higher fentanyl use, and while not statistically significant, a notable positive correlation between social media use and fentanyl frequency was observed.

In this study the authors looked at the ability to navigate planes in space between randomly placed planets. They used machine and reinforcement learning to run simulations and found that they were able to identify optimal paths for travel. In the future these techniques may allow for safer travel in unknown spaces.

This study explores the factors that influence coffee quality ratings using data from the Coffee Quality Institute. Through a regression model based on gradient descent, the authors aimed to predict coffee ratings (total cup points) and hypothesized that sweetness and the coffee producer would be the most influential factors.

This study explores auxin signaling in Chlorella vulgaris, a green alga with potential for sustainable biofuel and food production. Evidence from protoplast swelling experiments suggests that C. vulgaris secretes auxin and possesses auxin import proteins, highlighting previously uncharacterized signaling pathways. These findings could support more efficient cultivation and resource extraction strategies.

The authors use machine learning to analyze electroencephalogram data and identify slowing patterns that can indicate undetected disorders like epilepsy or dementia