Bacteroides and related species: The keystone taxa of the human gut microbiota.

Microbial communities play a significant role in maintaining ecosystems in a healthy homeostasis. Presently, in the human gastrointestinal tract, there are certain taxonomic groups of importance, though there is no single species that plays a keystone role. Bacteroides spp. are known to be major players in the maintenance of eubiosis in the human gastrointestinal tract. Here we review the critical role that Bacteroides play in the human gut, their potential pathogenic role outside of the gut, and their various methods of adapting to the environment, with a focus on data for B. fragilis and B. thetaiotaomicron. Bacteroides are anaerobic non-sporing Gram negative organisms that are also resistant to bile acids, generally thriving in the gut and having a beneficial relationship with the host. While they are generally commensal organisms, some Bacteroides spp. can be opportunistic pathogens in scenarios of GI disease, trauma, cancer, or GI surgery, and cause infection, most commonly intra-abdominal infection. B. fragilis can develop antimicrobial resistance through multiple mechanisms in large part due to its plasticity and fluid genome. Bacteroidota (formerly, Bacteroidetes) have a very broad metabolic potential in the GI microbiota and can rapidly adapt their carbohydrate metabolism to the available nutrients. Gastrointestinal Bacteroidota species produce short-chain fatty acids such as succinate, acetate, butyrate, and occasionally propionate, as the major end-products, which have wide-ranging and many beneficial influences on the host. Bacteroidota, via bile acid metabolism, also play a role in in colonization-resistance of other organisms, including Clostridioides difficile, and maintenance of gut integrity.

Alanyl-glutamine supplementation for Clostridioides difficile infection treatment (ACT): a double-blind randomised controlled trial study protocol.

INTRODUCTION: Clostridioides difficile is the leading cause of healthcare-associated infections in the USA, with an estimated 1 billion dollars in excess cost to the healthcare system annually. C. difficile infection (CDI) has high recurrence rate, up to 25% after first episode and up to 60% for succeeding episodes. Preliminary in vitro and in vivo studies indicate that alanyl-glutamine (AQ) may be beneficial in treating CDI by its effect on restoring intestinal integrity in the epithelial barrier, ameliorating inflammation and decreasing relapse. METHODS AND ANALYSIS: This study is a randomised, placebo-controlled, double-blind, phase II clinical trial. The trial is designed to determine optimal dose and safety of oral AQ at 4, 24 and 44 g doses administered daily for 10 days concurrent with standard treatment of non-severe or severe uncomplicated CDI in persons age 18 and older. The primary outcome of interest is CDI recurrence during 60 days post-treatment follow-up, with the secondary outcome of mortality during 60 days post-treatment follow-up. Exploratory analysis will be done to determine the impact of AQ supplementation on intestinal and systemic inflammation, as well as intestinal microbial and metabolic profiles. ETHICS AND DISSEMINATION: The study has received University of Virginia Institutional Review Board approval (HSR200046, Protocol v9, April 2023). Findings will be disseminated via conference presentations, lectures and peer-reviewed publications. TRIAL REGISTRATION NUMBER: NCT04305769.

Artificial intelligence assessment for early detection and prediction of renal impairment using electrocardiography.

PURPOSE: Although renal failure is a major healthcare burden globally and the cornerstone for preventing its irreversible progression is an early diagnosis, an adequate and noninvasive tool to screen renal impairment (RI) reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance. METHODS: This retrospective cohort study included two hospitals. We included 115,361 patients who had at least one ECG taken with an estimated glomerular filtration rate measurement within 30 min of the index ECG. A DLM was developed using 96,549 ECGs of 55,222 patients. The internal validation included 22,949 ECGs of 22,949 patients. Furthermore, we conducted an external validation with 37,190 ECGs of 37,190 patients from another hospital. The endpoint was to detect a moderate to severe RI (estimated glomerular filtration rate < 45 ml/min/1.73m2). RESULTS: The area under the receiver operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting RI during the internal and external validation was 0.858 (95% confidence interval 0.851-0.866) and 0.906 (0.900-0.912), respectively. In the initial evaluation of 25,536 individuals without RI patients whose DLM was defined as having a higher risk had a significantly higher chance of developing RI than those in the low-risk group (17.2% vs. 2.4%, p < 0.001). The sensitivity map indicated that the DLM focused on the QRS complex and T-wave for detecting RI. CONCLUSION: The DLM demonstrated high performance for RI detection and prediction using 12-, 6-, single-lead ECGs.