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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.

The authors test the effect of petroleum-based and bio-based microplastics on the growth of two microalgae species.

The authors looked at the importance of different point breakdowns needed to win in a game of tennis.

The study explores how music and sports impact cognitive development in young children, particularly in relation to learning disorders like ADHD and dyslexia.

The authors investigate how well AI-detection machine learning models can detect real versus AI-generated art across different art styles.

In this study, the authors investigate an alternative way to kill bacteria other than the use of antibiotics, which is useful when considering antibiotic-resistance bacteria. They use bacteriophages, which are are viruses that can infect bacteria, and measure cell lysis. They make some important findings that these bacteriophage can lyse both antibiotic-resistant and non-resistant bacteria.

This study evaluates the potential of natural language processing (NLP) models in an emotion-driven bibliotherapy framework to improve mental health challenges.

The authors test the effects of pH level on different kinds of dental cement to model the long-term effects of reflux-induced stomach acid exposure.

The authors looked at unionization petitions from Starbucks workers between August 2021 and July 2024 to determine what factors influence votes for or against unionization.

The authors use machine learning on MRI images of brain tissue to predict tumor onset as an avenue for early detection of brain cancer.