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Physics‑Informed Neural Networks (PINNs) offer a promising way to estimate blood‑flow behavior in arteries, especially near the vessel wall where traditional methods struggle. In this study, students tested how sensor density and placement affect PINN accuracy in modeling carotid artery flow and found that accuracy improves up to a moderate sensor density and depends strongly on how close sensors are placed to the arterial wall. Applying the optimized configuration to a patient‑specific carotid model produced velocity predictions closely matching computational fluid dynamics results, highlighting PINNs’ potential for future personalized cardiovascular assessment.

This study investigates skin tone bias in artificial intelligence models used for dermatological disease classification and evaluates a CycleGAN-based data augmentation approach to improve diagnostic performance on darker skin types. We generated synthetic dark-skinned images to enhance dataset diversity and compared model performance before and after augmentation. The results demonstrate that augmentation with synthetic dermatological images can help reduce disparities in diagnostic performance across skin tones, highlighting a practical strategy for improving fairness in dermatology AI systems.

The authors demonstrate computationally that two types of nanoplastics can bind to estrogen receptor beta and potentially disrupt endocrine systems.

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

In an extensive study of gene mutations, and their resulting effect on protein-protein interactions, Desai and Stork found that HTT-PRPF40B-MECP2 interactions are weakened with progression of Lopes-Maciel-Rodan syndrome.

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.

Arabadopsis, “the fruit fly of plants”, is an easy to grow plant system for genetic manipulation. Here, researchers tested the effects of varied light conditions on plants with specific mutations in the light sensing pathways.

Unprocessed water from hand-pressed wells is still commonly used as a source of drinking water in Chenzhou, the “Nonferrous Metal Village” of China. Long et al. conducted a study to measure the heavy metal contamination levels and potential health effects in this area. Water samples were analyzed through Inductively Coupled Plasma Optical Emission Spectroscopy (ICPOES) and the concentrations of 20 metal elements. Results showed that although none of the samples had dangerous levels of heavy metals, the concentrations of Al, Fe, and Mn in many locations substantially exceeded those suggested in the Chinese Drinking Water Standard and the maximum contaminant levels of Environmental Protection Agency (EPA). The authors have made an important discovery regarding the water safety in HuNan and their suggestions to install water treatment systems would greatly benefit the community.

Here, the authors characterize how silver ions nucleate a star-block copolymer to generate nano-sized silver particles.

This study uses neuroimaging to investigate cognitive set-shifting, a type of executive function that involves shifting from one task to another. This study tested whether cortical gray-white matter contrast in subregions of the prefrontal cortex (PFC) was associated with set-shifting abilities in adults.