Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery datasets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5×10−8) and used pathway analysis to identify JAK-STAT/IL12/IL27 signaling and cytokine-cytokine pathways, for which relevant therapies exist.

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Background Colorectal cancer often presents with obstruction needing urgent, potentially life-saving decompression. The comparative efficacy and safety of endoluminal stenting versus emergency surgery as initial treatment for such patients is uncertain. Methods Patients with left-sided colonic obstruction and radiological features of carcinoma were randomized to endoluminal stenting using a combined endoscopic/fluoroscopic technique followed by elective surgery 1–4 weeks later, or surgical decompression with or without tumour resection. Treatment allocation was via a central randomization service using a minimization procedure stratified by curative intent, primary tumour site, and severity score (Acute Physiology And Chronic Health Evaluation). Co-primary outcome measures were duration of hospital stay and 30-day mortality. Secondary outcomes were stoma formation, stenting completion and complication rates, perioperative morbidity, 6-month survival, 3-year recurrence, resource use, adherence to chemotherapy, and quality of life. Analyses were undertaken by intention to treat. Results Between 23 April 2009 and 22 December 2014, 245 patients from 39 hospitals were randomized. Stenting was attempted in 119 of 123 allocated patients (96.7 per cent), achieving relief of obstruction in 98 of 119 (82.4 per cent). For the 89 per cent treated with curative intent, there were no significant differences in 30-day postoperative mortality (3.6 per cent (4 of 110) versus 5.6 per cent (6 of 107); P = 0.48), or duration of hospital stay (median 19 (i.q.r. 11–34) versus 18 (10–28) days; P = 0.94) between stenting followed by delayed elective surgery and emergency surgery. Among patients undergoing potentially curative treatment, stoma formation occurred less frequently in those allocated to stenting than those allocated to immediate surgery (47 of 99 (47.5 per cent) versus 72 of 106 (67.9 per cent); P = 0.003). There were no significant differences in perioperative morbidity, critical care use, quality of life, 3-year recurrence or mortality between treatment groups. Conclusion Stenting as a bridge to surgery reduces stoma formation without detrimental effects. Registration number: ISRCTN13846816 (http://www.controlled-trials.com).

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A recommender system plays a vital role in information filtering and retrieval, and its application is omnipresent in many domains. There are some drawbacks such as the cold-start and the data sparsity problems which affect the performance of the recommender model. Various studies help with drastically improving the performance of recommender systems via unique methods, such as the traditional way of performing matrix factorization (MF) and also applying deep learning (DL) techniques in recent years. By using DL in the recommender system, we can overcome the difficulties of collaborative filtering. DL now focuses mainly on modeling content descriptions, but those models ignore the main factor of user-item interaction. In the proposed hybrid Bayesian stacked auto-denoising encoder (HBSADE) model, it recognizes the latent interests of the user and analyzes contextual reviews that are performed through the MF method. The objective of the model is to identify the user's point of interest, recommending products/services based on the user's latent interests. The proposed two-stage novel hybrid deep learning-based collaborative filtering method explores the user's point of interest, captures the communications between items and users and provides better recommendations in a personalized way. We used a multilayer neural network to manipulate the nonlinearities between the user and item communication from data. Experiments were to prove that our HBSADE outperforms existing methodologies over Amazon-b and Book-Crossing datasets.