Browse Articles

This study evaluates the potential of natural language processing (NLP) models in an emotion-driven bibliotherapy framework to improve mental health challenges.

Elevated acetylcholinesterase (AChE) activity contributes to cognitive decline and neurodegenerative diseases such as Alzheimer’s, motivating the search for more effective inhibitors with better bioavailability. This study used computational methods to design novel, non-toxic AChE inhibitors.

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

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 examined the effect of varying glucose concentrations on cut rose longevity.

The authors survey adults to assess how childhood adversity may impact adult relationships and ways of giving or receiving affection.

The authors used machine learning to predict the presence of anxiety and burnout in students based on facial expressions and demographic information.

The authors looked at the ability of different deep learning models to predict the presence of lung cancer from chest CT scans. They found that a pre-trained CNN model performed better than an autoencoder model.

This study investigates the effects of the PROTAC compound A1874 on CT26 colon carcinoma cells, focusing on its ability to degrade the protein BRD4 and reduce cell viability. While A1874 had previously shown effectiveness in other colon cancer cell lines, its impact on CT26 cells was unknown.

Building on previous work by earlier astronomers, the authors investigate the correlation between the density and size of celestial objects in the universe, including neutron stars, galaxies, and galaxy clusters.