Browse Articles
Discover research articles across all indexed journals
Exosomes derived from hypoxic mesenchymal stem cell ameliorate premature ovarian insufficiency by reducing mitochondrial oxidative stress
Environmental constraints and diffusion shaped the global transition to food production
Population genetic structure and historical demography of Saccostrea echinata in the Northern South China sea and Beibu Gulf
Utilization of indocyanine green for intraoperative sentinel lymph node mapping in canine mammary tumors
The effects of the generative adversarial network and personalized virtual reality platform in improving frailty among the elderly
Maternal dietary DHA and EPA supplementation ameliorates adverse cardiac outcomes in THC-exposed rat offspring
Abstract Cannabis use in pregnancy is associated with low birthweight outcomes. Recent preclinical data suggests that maternal Δ9-tetrahydrocannabinol (THC) exposure leads to decreases in birthweight followed by early cardiac deficits in offspring. Currently, no studies have explored an intervention for these maternal THC-induced deficits. Omega-3 fatty acids have been shown to exhibit cardioprotective effects. In this present study, we demonstrated that maternal dietary supplementation of omega-3 fatty acids ameliorates both THC-induced fetal growth and postnatal cardiac deficits in offspring. Our data indicates this may be underpinned by alterations in cardiac and hepatic fatty acids and reduction in markers of cardiac collagen deposition. Interestingly, the cardioprotective effects of omega-3s may be further underscored by decreased signaling of the cardiac endocannabinoid system. With increasing rates of cannabis use in pregnancy and recent evidence of subsequent cardiometabolic aberrations in offspring, our data suggests a potential intervention for THC-induced fetal growth and cardiac disturbances in offspring.
Comparison of diagnostic tests for chronic endometritis and endometrial dysbiosis in recurrent implantation failure: Impact on pregnancy outcomes
Does swallow rehabilitation improve recovery of swallow function after treatment for advanced head and neck cancer
Elevated IAP in critically ill patients associated with increased AKI incidence: a cohort study from the MIMIC-IV database
Protective effects of Rosa roxburghii Tratt. extract against UVB-induced inflammaging through inhibiting the IL-17 pathway
Establishment of a novel experimental animal model for the treatment of tibial segmental bone defects in juvenile sheep
Higher dominant muscle strength is mediated by motor unit discharge rates and proportion of common synaptic inputs
Exploring a patient-specific in vitro pipeline for stratification and drug response prediction of microglia-based therapeutics
Risk factors and an optimized prediction model for urosepsis in diabetic patients with upper urinary tract stones
Abstract To identify independent risk factors for urosepsis in diabetic patients with upper urinary tract stones (UUTS) and develop a prediction model to facilitate early detection and diagnosis, we retrospectively reviewed medical records of patients admitted between January 2020 and June 2023. Patients were divided based on the quick Sequential Organ Failure Assessment (qSOFA) score. The least absolute shrinkage and selection operator (LASSO) regression analysis was used for variable selection to form a preliminary model. The model was optimized and validated using the receiver operating characteristic (ROC) curve, the Hosmer-Lemeshow test and calibration curve, and decision curve analysis (DCA). A nomogram was constructed for visualization. A total of 434 patients were enrolled, with 66 cases and 368 controls. Six optimal predictors were identified: underweight, sarcopenia, poor performance status, midstream urine culture, urinary leukocyte count, and albumin-globulin ratio (AGR). The midstream urine culture was excluded due to its inability to provide rapid results. The final model demonstrated good prediction accuracy and clinical utility, with no significant difference in performance compared to the initial model. The study developed a prediction model for urosepsis risk in diabetic patients with UUTS, presenting a convenient tool for timely diagnosis, particularly in non-operated patients.