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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 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.

Here, based on the identified importance of physical activity in the development of young children, the authors investigated the effects of socioeconomic factors on the amount of physical activity of government-school children in India. They found significant differences between boys and girls, rural and urban, and children who were encouraged to exercise and those who were not. Overall, they suggest that their findings point to the important role of schools and communities in promoting healthy active lifestyles for developing children.

Although no comprehensive characterization of schizophrenia exists, there is a general consensus that patients have electrical dysfunction in the prefrontal cortex. The authors designed a novel piezoelectric silk-based implant and optimized electrical output through the addition of conductive materials zinc oxide (ZnO) and aluminum nitride (AlN). With further research and compatibility studies, this implant could rectify electrical misfiring in the infralimbic prefrontal cortex.

In this study, the authors seek to improve a machine learning algorithm used for image classification: identifying male and female images. In addition to fine-tuning the classification model, they investigate how accuracy is affected by their changes (an important task when developing and updating algorithms). To determine accuracy, a set of images is used to train the model and then a separate set of images is used for validation. They found that the validation accuracy was close to the training accuracy. This study contributes to the expanding areas of machine learning and its applications to image identification.