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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.

This study explores global trends in average total cholesterol levels among adults from 1980 to 2018, finding a significant decline in both men and women, with a sharper decrease among men. While these improvements likely reflect advances in public health and medical treatment, the persistent gender gap highlights the need for more targeted approaches to cardiovascular disease prevention.

This project investigated whether previously identified genetic risk factors in non-Korean populations were associated with high gastric cancer incidence rates in Koreans.

The authors studied the relationship between air quality and weather conditions (wind speed, precipitation, and temperature) in the Washington D.C. area.

Autonomous drone imaging combined with machine learning offers a promising approach for early detection of invasive species. In this study, students built an autonomous drone and compared three models: CNN, SGDC, and XGBoost, to identify Brassica nigra from aerial footage. Their results show that CNNs most effectively recognize key visual features, demonstrating strong potential for supporting conservation and invasive plant management.

The authors used developed a machine learning tool for studying social media discourse surrounding use of tobacco and cannabis.

The main goal of this study is to determine what demographics are related to tuberculosis incidence in the United States populations, particularly if changing demographics are related to differences in tuberculosis risk over two discrete time periods. The major finding is that in the two studied time periods, tuberculosis risk factors were somewhat consistent and may be influenced by things such as immigration, healthcare access, and race or ethnicity, although the top predictor did change.

The authors looked at how injuries sustained during water tubing, that require treatment at an emergency department, differ between males and females.

The authors looked at how vocabulary is impacted in Alzheimer's disease and whether it could be used a predictor of disease onset.

The authors studied the ability of deep learning models to predict droughts in the midwestern United States.