An Integrative Migraine Polygenic Risk Score Is Associated with Age at Onset But Not Chronification.

: Genome-wide association studies (GWASs) demonstrate a complex genetic landscape for migraine risk. Migraine polygenic risk scores (PRSs) developed from GWAS data may have utility for predicting disease course. We analyzed the strength of association between an integrative migraine PRS and age at onset and chronification.

Dihydroethidium-derived fluorescence in electrically stressed cells indicates intracellular microenvironment modifications independent of ROS.

Intracellular reactive oxygen species (ROS) generation is widely suggested as a trigger for biological consequences of electric field exposures, such as those in electroporation applications. ROS are linked with membrane barrier function degradation, genetic damage, and complex events like immunological cell death. Dihydroethidium (DHE) is commonly used to monitor ROS in cells.

Quantum-assisted fragment-based automated structure generator (QFASG) for small molecule design: an study.

The significance of automated drug design using virtual generative models has steadily grown in recent years. While deep learning-driven solutions have received growing attention, only a few modern AI-assisted generative chemistry platforms have demonstrated the ability to produce valuable structures. At the same time, virtual fragment-based drug design, which was previously less popular due to the high computational costs, has become more attractive with the development of new chemoinformatic techniques and powerful computing technologies.

MFA-DTI: Drug-target interaction prediction based on multi-feature fusion adopted framework.

The identification of drug-target interactions (DTI) is a valuable step in the drug discovery and repositioning process. However, traditional laboratory experiments are time-consuming and expensive. Computational methods have streamlined research to determine DTIs.

An effective framework for predicting drug-drug interactions based on molecular substructures and knowledge graph neural network.

Drug-drug interactions (DDIs) play a central role in drug research, as the simultaneous administration of multiple drugs can have harmful or beneficial effects. Harmful interactions lead to adverse reactions, some of which can be life-threatening, while beneficial interactions can promote efficacy. Therefore, it is crucial for physicians, patients, and the research community to identify potential DDIs.