Browse Articles

This study used multi-omic analysis to investigate gene expression and DNA methylation patterns associated with hypertrophic cardiomyopathy (HCM). The researchers found significant downregulation of mitochondrial and energy-related pathways, highlighting mitochondrial dysfunction—particularly altered PINK1 expression—as a potential contributor and therapeutic target in HCM.

The authors looked at how the presence of difference antibiotic resistance genes would influence antibiotic resistance in Staphylococcus aureus.

The authors looked at genes and pathways that are enriched in glioblastoma multiforme.

Depression affects millions globally, yet identifying symptoms remains challenging. This study explored detecting depression-related patterns in social media texts using natural language processing and machine learning algorithms, including decision trees and random forests. Our findings suggest that analyzing online text activity can serve as a viable method for screening mental disorders, potentially improving diagnosis accuracy by incorporating both physical and psychological indicators.

The study discusses the relationship between bacterial species and the human gut microbiome, emphasizing the role of quorum sensing molecules in bacterial communication and its implications for health. Authors investigated the impact of bacterial supernatants from Escherichia coli (E. coli) on the growth of new E. coli and Enterobacter aerogenes (E. aerogenes) cultures.

The authors looked at biomarkers in glioblastoma patients they hypothesized to be correlated with survival rate. Ultimately they did not find hMSH2 or hMSH6, genes involved in mismatch repair, to be significantly associated with outcomes related to increased survival.

The authors looked at 5-FU resistant HeLa cells and the ability of an herbal extract to show anti-inflammatory properties.

In this study, the authors developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.

In this study, a deep learning model is used to classify post-traumatic stress disorder patients through novel markers to assist in finding candidate biomarkers for the disorder.

In this study, the authors investigated the time-dependent cytokine secretion ability of phyto-hemagglutinin (PHA)-activated T cells derived from human peripheral (PB) and cord blood (CB). They hypothesized that the anti-inflammatory cytokine, IL-10, and pro-inflammatory cytokine, TNFα, levels would be higher in PHA-activated T cells obtained from PB as compared to the levels obtained from CB and would decrease over time. Upon PHA-activation, the IL-10 levels were relatively high while the TNFα levels decreased, making these findings applicable in therapeutic treatments e.g., rheumatoid arthritis, psoriasis, and organ transplantation.