Browse Articles

This study combines gene expression, mutation profiling, and survival analysis of 17 clinically important genes in breast cancer, utilizing the TCGA-BRCA dataset. Our results show that there are different patterns of oncogene upregulation, different levels of tumor suppressor activity, and complicated survival associations. TP53 was the most frequently mutated gene in this cohort. The results underscore the significance of multidimensional genomic analyses for a comprehensive understanding of breast cancer biology and its therapeutic ramifications.

Content warning: This article discusses the frequency and impact of mass violence incidents, particularly school shootings. Gun violence, threats of gun violence, firearm supply, fatalities among minors, and bullying are discussed but not graphically described.

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 studied the impact of menstrual costs on the gender wage gap.

The authors studied the impact of drying blood as a storage solution for space travel.

The authors looked at how nanosilver particles may negatively impact the pond water environment. They used D. magna as an indicator species to look at the impact of different concentrations of nanosilver particles.

The authors looked at different strategies to extract oil from almonds to determine which gave the best results.

The authors looked at ways that seizures in patients with glioblastoma could be treated using C. elegans as a model system.

The authors looked at the ability of berberine in combination with melatonin to have anticancer effects when tested in an in vitro cell model.

The authors looked the ability of sound sensors to predict clogged pipes when the sound intensity data is run through a machine learning algorithm.