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

The authors developed and tested machine learning methods to diagnose tuberculosis from pulmonary X-ray scans.

In this study, locally produced fall and spring honeys were tested to determine whether there was a significant difference in their abilities to limit or prevent bacterial growth of E. coli, S. aureus, P. aeruginosa, and S. epidermidis.

In this study, preparations of ginger were tested for an effect on the growth of four common bacterial species.

The authors looked at the potential of different phenolic compounds to reduce oxidative stress (i.e., act as antioxidants).

The authors looked at how the presence of difference antibiotic resistance genes would influence antibiotic resistance in Staphylococcus aureus.

The authors evaluated the effect of concentration and variant of N-Acetylcysteine in hydrolyzing mucus.

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.

The authors compare nutritional content of foods found in Western versus Asian grocery stores to determine whether one cultural diet is healthier than the other.

Poor air quality is associated with negative effects on human health but can be difficult to measure in an accurate and cost-effective manner. The authors design and test a monitor for measuring indoor air quality using low-cost components.