Advances in AI-Driven Retention Prediction for Different Chromatographic Techniques: Unraveling the Complexity.

Retention prediction through Artificial intelligence (AI)-based techniques has gained exponential growth due to their abilities to process complex sets of data and ease the crucial task of identification and separation of compounds in most employed chromatographic techniques. Numerous approaches were reported for retention prediction in different chromatographic techniques, and consistent results demonstrated that the accuracy and effectiveness of deep learning models outclassed the linear machine learning models, mainly in liquid and gas chromatography, as ML algorithms use fewer complex data to train and predict information. Support Vector machine-based neural networks were found to be most utilized for the prediction of retention factors of different compounds in thin-layer chromatography.

Current trends in chromatographic prediction using artificial intelligence and machine learning.

Artificial intelligence (AI) and machine learning (ML) gained tremendous growth and are rapidly becoming popular in various fields of prediction due to their potential abilities, accuracy, and speed. Machine learning algorithms employ historical data to analyze or predict information using patterns or trends. AI and ML were most employed in chromatographic predictions and particularly attractive options for liquid chromatography method development, as they can help achieve desired results faster, more accurately, and more efficiently.

Sodium-Glucose Cotransporter-2 (SGLT-2) Inhibitors and Genital Infections in Patients With Diabetic Mellitus and Concomitant Coronary Artery Disease: A Single-Center Experience.

Objective To investigate the incidence of genital infection due to the use of sodium-glucose cotransporter-2 (SGLT-2) inhibitors in patients with type 2 diabetes mellitus (T2DM) concomitant coronary artery diseases (CAD). Methods A single-center, physician-initiated study was conducted at a tertiary-care center in India. The study enrolled patients with T2DM who were taking SGLT-2 inhibitors for at least two months and divided them into two groups: patients with concomitant CAD as the case group and without CAD as the control group.