Browse Articles

This study investigated how 18 commonly used cosmetic products affect the growth of two skin-resident bacteria, Staphylococcus epidermidis and Micrococcus luteus. At higher concentrations, half of the tested products inhibited at least one bacterial species, suggesting that some cosmetics may disrupt the skin microbiome and its natural balance.

This study used single-cell RNA sequencing to examine whether B cells can predict response to pembrolizumab and radiation therapy in patients with triple-negative breast cancer.

Here the authors investigate the relationship between environmental pollutants and cancer incidence rates across various geographical clusters in the United States from 2018 to 2020. By calculating Pearson correlation coefficients and t-statistics on CDC and EPA data, they demonstrated a strong correlation between specific pollutants and various cancers, offering insights that could help explain regional disparities in cancer rates and aid in preventing premature deaths.

Here the authors investigated the effectiveness of algal pretreatment in reducing nitrate toxicity and extending the survival of fish in contaminated river water. Their findings demonstrate that treating nitrate-polluted water with algae extended fish survival by up to 50%, suggesting that algae could serve as a valuable tool for mitigating the destructive impacts of agricultural and industrial runoff on aquatic ecosystems.

This study investigates the dynamic reaction between liquid gallium and aluminum, which poses a challenge when using high-performance gallium-rich thermal interface materials for electronic cooling. Through the usage of in-situ microscopy, we show that while aluminum oxide coatings slow gallium-induced damage, nanometer-thick iridium coatings effectively prevent reaction and surface degradation at device-operating temperatures. These findings highlight a promising thermal-cooling architecture for extending the lifespan and reliability of high-power electronic heat dissipation systems.

Here the authors investigated whether frequent use of generative AI tools triggers upward social comparison and negatively impacts users' performance self-esteem. Based on a survey of 121 adults, they found no significant relationship between AI usage and self-esteem, concluding that current chatbot interactions do not pose the same psychological threat as peer-based comparisons.

This paper develops a gelatin-based hydrogel dressing infused with bromothymol blue dye that visibly changes from yellow to blue as wound pH rises from 5.0 to 8.0, signaling possible infection. The color shift was visually detectable and statistically significant across all six Fitzpatrick skin tones, suggesting a low-cost ($0.40/dressing) tool for early infection monitoring in settings with limited access to care.

Machine learning systems are often expected to make fair decisions, yet the most widely used fairness criteria—independence, separation, and sufficiency—cannot generally be satisfied at the same time. In this study, students tested these criteria using a logistic regression model on a real-world student performance dataset and found that each criterion was met only at different prediction thresholds, with no threshold satisfying all three simultaneously. These results illustrate the inherent trade-offs in algorithmic fairness and highlight why achieving perfectly fair machine learning models is often impossible in practice.

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.

The authors looked at whether retinal features, specifically lens clarity, optic nerve cupping, and hyperreflective foci, can serve as biomarkers for neuro-ophthalmological diseases, which share pathological mechanisms with neurodegeneration and may indicate broader neurological risk. Using multimodal clinical data and machine learning, they investigated the potential of retinal imaging as a complementary diagnostic tool for more accurate and accessible detection of neurological disorders.