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The authors looked how different working conditions impacted the microbiome of the human skin.

This study investigates the effects of the PROTAC compound A1874 on CT26 colon carcinoma cells, focusing on its ability to degrade the protein BRD4 and reduce cell viability. While A1874 had previously shown effectiveness in other colon cancer cell lines, its impact on CT26 cells was unknown.

Default opt-ins and social proof tags effect on decision making in an e-commerce context

The authors looked at different combinations of risk-adjusted return measures to determine which combination would provide an optimal return for investors. They found that different combinations performed better dependent on investment timeframe.

This study explored the use of environmental DNA (eDNA) methods to detect native Hawaiian decapod species (‘opae), which are difficult to observe manually due to their low density.

Pay is a widely debated factor in workplace motivation, influencing both incentives and job satisfaction. This work analyzed employee reviews across various industries to examine the relationship between pay importance and job satisfaction. Findings suggest that job satisfaction decreases as the importance of pay increases, particularly in construction, food, and finance industries, as well as among entry-level and experienced workers, though the results were not statistically significant.

The authors investigated whether the physician-patient relationship affected patient perceptions and treatment adherence.

The authors combine chemical, enzymatic, and microbial-based methods to optimize degradation of plastics within a surgical facemask.

This study examines how anchoring—providing numerical suggestions like "2 for $4"—can influence consumer purchasing decisions and increase revenue. The researchers tested three types of price anchors on 29 high school students shopping in a mock store.

Pediatric cancers pose unique challenges due to their rarity and distinct biological factors, emphasizing the need for accurate survival prediction to guide treatment. This study integrated generative AI and machine learning, including synthetic data, to analyze 9,184 pediatric cancer patients, identifying age at diagnosis, cancer types, and anatomical sites as significant survival predictors. The findings highlight the potential of AI-driven approaches to improve survival prediction and inform personalized treatment strategies, with broader implications for innovative healthcare applications.