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Despite the meteoric rise in genome-wide association studies for metabolic diseases (MetD) over the last few years, our understanding of the pathogenesis of these diseases is still far from complete. Recent developments have established that MetD arises from complex interactions between host genetics, the gut microbiome, and the environment. However, our knowledge of the genetic and microbiome components involved, and the underlying molecular mechanisms remains limited. Here, we review and summarize recent studies investigating the genetic and microbiome basis of MetD. Then, given the critical importance of study-individual's ancestry in these studies, we leverage 4932 whole genome sequence (WGS) samples from 18 world-wide ethnic groups to examine genetic diversity in currently reported variants associated with metabolic diseases. The analyses show marked differences in gene-specific proportion of pathogenic SNPs, and gene-specific SNPs minor allele frequencies across ethnic groups, highlighting the importance of population- and ethnic-specific investigations in pinpointing the causative factors for MetD. We conclude with a discussion of research areas where further investigation on interactions between host genetics, microbiome, and the environment is needed.We examined the prevalence of measles antibody among 12,349 newly hired HCP between 2009-2019 and found that younger HCP were significantly more likely to have no immunity. Compared to a 92.2% seropositive rate among 1,057 persons hired at age > 50, only 84.4% of ~10,000 HCP less then 40 years of age at hire had protective antibody (p less then .00001). These findings have important implications for measles prevention in the healthcare setting.M. abscessus persisted for over two weeks in Dead Sea brine and staphylococcal organisms were able to survive from three to 11 days. The uniqueness of the Dead Sea's chemical composition, particularly its extremely high salinity, eventually leads to the destruction of M. abscessus and S. aureus, including MRSA organisms.This cohort study examines the extent to which hypertensive disorders mediate the association between migraines and maternal stroke.The pluripotency factor, OCT4 gene is a stemness marker that is involved in the tumorigenicity of different cancer types and knowing about molecular mechanisms of its regulation is crucially important. To date, a few microRNAs (miRNAs) are known to be regulators of OCT4 gene expression. Selleckchem NS 105 Looking for the novel miRNAs which are capable of regulating OCT4 gene expression, our bioinformatics analysis introduced hsa-miR-3658 (miR-3658) as a bona fide candidate. Then, RT-qPCR results indicated that miR-3658 expression is decreased in colorectal cancer (CRC) tumor tissues, compared with normal pairs. Furthermore, RT-qPCR and western blot analysis showed that the OCT4 gene has been down-regulated following the miR-3658 overexpression. Consistently, dual-luciferase assay supported the direct interaction of miR-3658 with the 3'-UTR sequence of OCT4 gene. Unlike in HCT116 cells, overexpression of miR-3658 in SW480 cells brought about growth inhibition, cell cycle arrest and reduced cell migration, detected by flow cytometry, and scratch test assay. Overall, these findings demonstrated that miR-3658 as a tumor suppressor miRNA exerts its effect against OCT4 gene expression, and it has the potential of being used as a prognostic marker and therapeutic target against colorectal cancer.This cohort study reports a decade of increases and decreases in insulin prices and cost burden for insured patients with diabetes.Importance Changes in evidence-based practice and guideline recommendations depend on high-quality randomized clinical trials (RCTs). Commercial device and pharmaceutical manufacturers are frequently involved in the funding, design, conduct, and reporting of trials, the implications of which have not been recently analyzed. Objective To evaluate the design, outcomes, and reporting of contemporary randomized clinical trials of invasive cardiovascular interventions and their association with the funding source. Design, setting, and participants This cross-sectional study analyzed published RCTs between January 1, 2008, to May 31, 2019. The trials included those involving coronary, vascular and structural interventional cardiology, and vascular and cardiac surgical procedures. Main outcomes and measures We assessed (1) trial characteristics, (2) finding of a statistically significant difference in the primary end point favoring the experimental intervention, (3) reporting of implied treatment advantage in trials a statistically significantly greater likelihood of favorable outcomes reporting (exponent of regression coefficient β, 2.80; 95% CI, 1.09-7.18; P = .03) and with the reporting of findings that are inconsistent with the trial results. Discrepancies between the registered and published primary outcomes were found in 82 trials (38.0%), without differences in trial sponsorship. A median (IQR) number of 5 (2.8-12.5) patients experiencing a different outcome would have change statistically significant results to nonsignificant. Commercial sponsorship was associated with a greater number of patients (exponent of regression coefficient β, 1.29; 95% CI, 1.00-1.66; P = .04). Conclusions and relevance These results suggest that contemporary RCTs of invasive cardiovascular interventions are relatively small and fragile, have short follow-up, and have limited power to detect large treatment effects. Commercial support appeared to be associated with differences in trial design, results, and reporting.Study objectives Develop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings. Methods Using a clinical dataset of polysomnograms from 6,431 patients (MGH-PSG dataset), we trained a deep neural network to classify sleep stages based on scalp EEG data. The algorithm consists of a convolutional neural network (CNN) for feature extraction, followed by a recurrent neural network (RNN) that extracts temporal dependencies of sleep stages. The algorithm's inputs are 4 scalp EEG bipolar channels (F3-C3, C3-O1, F4-C4, C4-O2), which can be derived from any standard PSG or scalp EEG recording. We initially trained the algorithm on the MGH-PSG dataset and used transfer learning to fine-tune it on a dataset of long-term (24-72 hour) scalp EEG recordings from 112 patients (scalpEEG dataset). Results The algorithm achieved a Cohen's kappa of 0.74 on the MGH-PSG holdout testing set and cross-validated Cohen's kappa of 0.78 after optimization on the scalpEEG dataset.
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