Interplay Between Drug-Induced Liver Injury and Gut Microbiota: A Comprehensive Overview.

Drug-induced liver injury is a prevalent severe adverse event in clinical settings, leading to increased medical burdens for patients and presenting challenges for the development and commercialization of novel pharmaceuticals. Research has revealed a close association between gut microbiota and drug-induced liver injury in recent years. However, there has yet to be a consensus on the specific mechanism by which gut microbiota is involved in drug-induced liver injury. Gut microbiota may contribute to drug-induced liver injury by increasing intestinal permeability, disrupting intestinal metabolite homeostasis, and promoting inflammation and oxidative stress. Alterations in gut microbiota were found in drug-induced liver injury caused by antibiotics, psychotropic drugs, acetaminophen, antituberculosis drugs, and antithyroid drugs. Specific gut microbiota and their abundance are associated closely with the severity of drug-induced liver injury. Therefore, gut microbiota is expected to be a new target for the treatment of drug-induced liver injury. This review focuses on the association of gut microbiota with common hepatotoxic drugs and the potential mechanisms by which gut microbiota may contribute to the pathogenesis of drug-induced liver injury, providing a more comprehensive reference for the interaction between drug-induced liver injury and gut microbiota.

Machine learning techniques for prediction in pregnancy complicated by autoimmune rheumatic diseases: Applications and challenges.

Autoimmune rheumatic diseases are chronic conditions affecting multiple systems and often occurring in young women of childbearing age. The diseases and the physiological characteristics of pregnancy significantly impact maternal-fetal health and pregnancy outcomes. Currently, the integration of big data with healthcare has led to the increasing popularity of using machine learning (ML) to mine clinical data for studying pregnancy complications. In this review, we introduce the basics of ML and the recent advances and trends of ML in different prediction applications for common pregnancy complications by autoimmune rheumatic diseases. Finally, the challenges and future for enhancing the accuracy, reliability, and clinical applicability of ML in prediction have been discussed. This review will provide insights into the utilization of ML in identifying and assisting clinical decision-making for pregnancy complications, while also establishing a foundation for exploring comprehensive management strategies for pregnancy and enhancing maternal and child health.

Carbapenem-resistant Gram-negative bacteria (CR-GNB) in ICUs: resistance genes, therapeutics, and prevention - a comprehensive review.

Intensive care units (ICUs) are specialized environments dedicated to the management of critically ill patients, who are particularly susceptible to drug-resistant bacteria. Among these, carbapenem-resistant Gram-negative bacteria (CR-GNB) pose a significant threat endangering the lives of ICU patients. Carbapenemase production is a key resistance mechanism in CR-GNB, with the transfer of resistance genes contributing to the extensive emergence of antimicrobial resistance (AMR). CR-GNB infections are widespread in ICUs, highlighting an urgent need for prevention and control measures to reduce mortality rates associated with CR-GNB transmission or infection. This review provides an overview of key aspects surrounding CR-GNB within ICUs. We examine the mechanisms of bacterial drug resistance, the resistance genes that frequently occur with CR-GNB infections in ICU, and the therapeutic options against carbapenemase genotypes. Additionally, we highlight crucial preventive measures to impede the transmission and spread of CR-GNB within ICUs, along with reviewing the advances made in the field of clinical predictive modeling research, which hold excellent potential for practical application.

Advances in Traditional Chinese Medicine research in diabetic kidney disease treatment.

CONTEXT: Diabetic kidney disease (DKD) is a prominent complication arising from diabetic microangiopathy, and its prevalence and renal impact have placed it as the primary cause of end-stage renal disease. Traditional Chinese Medicine (TCM) has the distinct advantage of multifaceted and multilevel therapeutic attributes that show efficacy in improving clinical symptoms, reducing proteinuria, protecting renal function, and slowing DKD progression. Over recent decades, extensive research has explored the mechanisms of TCM for preventing and managing DKD, with substantial studies that endorse the therapeutic benefits of TCM compounds and single agents in the medical intervention of DKD. OBJECTIVE: This review lays the foundation for future evidence-based research efforts and provide a reference point for DKD investigation. METHODS: The relevant literature published in Chinese and English up to 30 June 2023, was sourced from PubMed, Cochrane Library, VIP Database for Chinese Technical Periodicals (VIP), Wanfang Data, CNKI, and China Biology Medicine disc (CBM). The process involved examining and summarizing research on TCM laboratory tests and clinical randomized controlled trials for DKD treatment. RESULTS AND CONCLUSIONS: The TCM intervention has shown the potential to inhibit the expression of inflammatory cytokines and various growth factors, lower blood glucose levels, and significantly affect insulin resistance, lipid metabolism, and improved renal function. Furthermore, the efficacy of TCM can be optimized by tailoring personalized treatment regimens based on the unique profiles of individual patients. We anticipate further rigorous and comprehensive clinical and foundational investigations into the mechanisms underlying the role of TCM in treating DKD.