Unveiling the interplay between soluble guanylate cyclase activation and redox signalling in stroke pathophysiology and treatment.

Soluble guanylate cyclase (sGC) stands as a pivotal regulatory element in intracellular signalling pathways, mediating the formation of cyclic guanosine monophosphate (cGMP) and impacting diverse physiological processes across tissues. Increased formation of reactive oxygen species (ROS) is widely recognized to modulate cGMP signalling. Indeed, oxidatively damaged, and therefore inactive sGC, contributes to poor vascular reactivity and more severe neurological damage upon stroke.

Transcription factor prediction using protein 3D secondary structures.

Motivation: Transcription factors (TFs) are DNA-binding proteins that regulate gene expression. Traditional methods predict a protein as a TF if the protein contains any DNA-binding domains (DBDs) of known TFs. However, this approach fails to identify a novel TF that does not contain any known DBDs.

Transforming literature screening: The emerging role of large language models in systematic reviews.

Systematic reviews (SR) synthesize evidence-based medical literature, but they involve labor-intensive manual article screening. Large language models (LLMs) can select relevant literature, but their quality and efficacy are still being determined compared to humans. We evaluated the overlap between title- and abstract-based selected articles of 18 different LLMs and human-selected articles for three SR.

Comparative evaluation of feature reduction methods for drug response prediction.

Personalized medicine aims to tailor medical treatments to individual patients, and predicting drug responses from molecular profiles using machine learning is crucial for this goal. However, the high dimensionality of the molecular profiles compared to the limited number of samples presents significant challenges. Knowledge-based feature selection methods are particularly suitable for drug response prediction, as they leverage biological insights to reduce dimensionality and improve model interpretability.