Integration of virtual screening of phytoecdysteroids as androgen receptor inhibitors by 3D-QSAR Model, CoMFA, molecular docking and ADMET analysis: An extensive and interactive machine learning.

Ecdysteroids, a class of naturally isolated polyhydroxylated sterols, stands at a very good place in the pharmaceutical industry from their medicinal point of views like anti-inflammatory, neuroprotective, anti-microbial, anti-diabetic, antioxidant, and anti-tumor effects. Due to their excellent antioxidant and anti-microbial potential, ecdysteroids have extensive use in skin products, especially derma creams. To monitor the best anti-acne phytoecdysteroids, here made use of different computational approaches, by using the rapid, easy, cost-effective and high throughput method to screen and identify ecdysteroids as androgen receptor inhibitors. 3D-QSAR study was carried out on a dataset of ecdysteroids by using comparative molecular field analysis (CoMFA) to determine the factors responsible for the activity of compounds. Statistically a cross-validated (q2) 0.1457 and regression coefficient (r2) 0.9713 indicated the best model. Contour map results showed the influence of steric effect to enhance activity. A molecular docking analysis was done to further find out the binding sites and their anti-acne potential against three crystal structured macromolecules (PDB ID: 2REQ, 2BAC, 4EM0). Docking results were further evaluated by prime MM-GBSA analysis and findings confirmed the accuracy. Toxicity by ADMET assessment was carried out and M2 was found as lead druglike with best anti-acne activity against Propionium acnes GehA lipase bacteria after passing all filters. This research study is novel because it is representing first effort to explore ecdysteroids class for their high therapeutic output as androgen receptor inhibitor by using computational tools and expectedly led to novel scaffold for androgen receptor inhibitor. This is a novel and new approach to investigate the ecdysteroids for first time for their practical applications.

Coronary Stent Infection: A Systematic Review of Literature.

Coronary stent infection is a rare yet serious complication of coronary artery stenting, with potentially significant morbidity and mortality. This systematic review aimed to comprehensively assess the available evidence on the diagnosis, management, and outcomes of coronary stent infection. A comprehensive search of electronic databases, including PubMed, Embase, Cochrane Library, and Scopus, was conducted from inception until March 2023, in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A total of 1 case series and 41 case reports, covering a cumulative sample size of 44 patients, were included in the analysis. The predominant stent types were drug-eluting stents in 22 studies, bare-metal stents in 3 studies, and a combination of drug-eluting stents and bare-metal stents in 4 studies. Staphylococcus aureus was the predominant identified organism in microbiological profiles. Primary outcomes, including mortality, morbidity, and recurrence rates, were evaluated. The aggregate mortality rate across studies was 18%, underscoring the severity of coronary stent infections. Morbidity ranged from 3% to 60%, with a spectrum of complications such as sepsis, heart failure, and embolic events. Recurrence rates varied from 3% to 33%, emphasizing the importance of effective management. Treatment strategies encompassed antibiotics alone, antibiotics with stent removal, and antibiotics with stent retention, with the duration of antibiotic therapy ranging from 2 weeks to 12 months. The optimal management strategy remains uncertain due to limited high-quality evidence. Early diagnosis and treatment were emphasized as critical factors in improving outcomes. Prophylactic antibiotics during stenting procedures and increased awareness among healthcare providers were suggested as preventive measures.