In this study, the impact of fluorination of heterocyclic drug Allopurinol on its stability and other chemical properties is investigated.

Wearing face masks has become a common occurrence in everyday life and during athletics due to the spread of diseases. This study tested if masks would affect blood percent saturation of hemoglobin (SpO₂) during treadmill exercise. The data analysis showed that mask type, time, and the interaction of mask type and time were significant results, regardless of physical ability. These results may assist athletes in understanding the differences between training and competing with and without a mask.

The sugar-rich modern diet underlies a suite of metabolic disorders, most common of which is diabetes. Accurately reporting the sugar content of pre-packaged food and drink items can help consumers track their sugar intake better, facilitating more cognisant and, eventually, moderate consumption of high-sugar items. In this article, the authors examine the effect of several variables on the accuracy of Fehling's reaction, a colorimetric reaction used to estimate sugar content.

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

Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules.

The authors examine a relationship between tweet sentiment and stock market behavior during the early weeks of the COVID-19 pandemic.

In this study the authors looked at developing a more efficient particle collision classification method with the goal of being able to more efficiently analyze particle trajectories from large-scale particle collisions without loss of accuracy.

In this study, the authors test whether excess copper exposure has neurobehavioral effects on Hirudo verbana leeches.

Seeking to investigate the effects of ambient pollutants on human respiratory health, here the authors used machine learning to examine asthma in Lost Angeles County, an area with substantial pollution. By using machine learning models and classification techniques, the authors identified that nitrogen dioxide and ozone levels were significantly correlated with asthma hospitalizations. Based on an identified seasonal surge in asthma hospitalizations, the authors suggest future directions to improve machine learning modeling to investigate these relationships.

Research relating to freshwater acidification is minimal, so the impact of aquatic plants, Anubias barteri var. congensis and Anubias barteri var. nana, on minimizing changes in pH was explored in an ecosystem in Northern California. Creek water samples, with and without the aquatic plants, were exposed to dry ice to simulate carbon emissions and the pH was monitored over an eight-hour period. There was a 25% difference in the observed pH based on molar hydrogen ion concentration between the water samples with plants and those without plants, suggesting that aquatic plants have the potential to limit acidification to some extent. These findings can guide future research to explore the viable partial solution of aquatic plants in combating freshwater acidification.