No abstract

The results suggested that Nrf2 suppressed intestinal fibrosis by inhibiting ROS/TGF-β1/SMADs pathway in vivo and in vitro.

No abstract

Background Most of the mortality resulting from COVID-19 has been associated with severe disease. Effective treatment of severe cases remains a challenge due to the lack of early detection of the infection. Objective This study aimed to develop an effective prediction model for COVID-19 severity by combining radiological outcome with clinical biochemical indexes. Methods A total of 46 patients with COVID-19 (10 severe, 36 nonsevere) were examined. To build the prediction model, a set of 27 severe and 151 nonsevere clinical laboratory records and computerized tomography (CT) records were collected from these patients. We managed to extract specific features from the patients’ CT images by using a recently published convolutional neural network. We also trained a machine learning model combining these features with clinical laboratory results. Results We present a prediction model combining patients’ radiological outcomes with their clinical biochemical indexes to identify severe COVID-19 cases. The prediction model yielded a cross-validated area under the receiver operating characteristic (AUROC) score of 0.93 and an F1 score of 0.89, which showed a 6% and 15% improvement, respectively, compared to the models based on laboratory test features only. In addition, we developed a statistical model for forecasting COVID-19 severity based on the results of patients’ laboratory tests performed before they were classified as severe cases; this model yielded an AUROC score of 0.81. Conclusions To our knowledge, this is the first report predicting the clinical progression of COVID-19, as well as forecasting severity, based on a combined analysis using laboratory tests and CT images.

Background Although numerous risk loci for ulcerative colitis (UC) have been identified in the human genome, the pathogenesis of UC remains unclear. Recently, multiple transcriptomic analyses have shown that aberrant gene expression in the colon tissues of UC patients is associated with disease progression. A pioneering study also demonstrated that altered post-transcriptional regulation is involved in the progression of UC. Here, we provide a genome-wide analysis of alternative splicing (AS) signatures in UC patients. We analyzed three datasets containing 74 tissue samples from UC patients and identified over 2000 significant AS events. Results Skipped exon and alternative first exon were the two most significantly altered AS events in UC patients. The immune response-related pathways were remarkably enriched in the UC-related AS events. Genes with significant AS events were more likely to be dysregulated at the expression level. Conclusions We present a genomic landscape of AS events in UC patients based on a combined analysis of two cohorts. Our results indicate that dysregulation of AS may have a pivotal role in determining the pathogenesis of UC. In addition, our study uncovers genes with potential therapeutic implications for UC treatment.