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r is causal in one out of five. The established RoPE score cutoff of ⩾7 for having a probable PFO-associated stroke may overestimate the probability in patients deriving from populations with high PFO prevalence.

The endoscopic evaluation is crucial for the management and treatment of ulcerative colitis (UC). Currently, the Mayo Endoscopic Score (MES) and the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) are two major endoscopic score systems to evaluate the status of mucosal inflammation and disease activity. However, in both MES and UCEIS systems, the disease extent is not included. The Degree of Ulcerative Colitis Burden of Luminal Inflammation (DUBLIN) score is a simple clinical score which is calculated as a product of the MES (0-3) and the extent of disease (E1-E3). The objective of this study was to compare the correlation among DUBLIN, UCEIS and MES, and also investigate the clinical characteristics for predicting treatment failure in patients with active UC.

Between March 2015 and April 2019, 172 patients who were previously diagnosed with UC and had undergone colonoscopy were recruited in this study. We retrospectively reviewed the endoscopic scores and clinical characteristics at the time of tium- to long-term treatment failure in active UC patients.

The DUBLIN score shows superior diagnostic performances in terms of sensitivity value compared with the UCEIS. Moreover, multivariate analysis indicates that DUBLIN ⩾4 is an independent factor for predicting medium- to long-term treatment failure in active UC patients.

The unprecedented situation caused by the coronavirus disease 2019 (COVID-19) pandemic has profoundly affected endoscopic practice in regard to access, volume, and workflow. Anisomycin We aimed to assess the potential changes in the technical outcomes of endoscopic retrograde cholangiopancreatography (ERCP) procedures carried out in patients with confirmed SARS-CoV-2 infection.

We conducted an international, multicenter, retrospective, matched case-control study of ERCP procedures carried out in patients with confirmed COVID-19. The main outcome was technical success of the procedure as assessed by the endoscopist, and the secondary outcome was the development of procedure-related adverse events. Each case was matched in a 14 ratio with controls extracted from each center's database in order to identify relevant changes in outcome measures compared with the pre-pandemic era.

Eighteen procedures performed in 16 COVID-19 patients [14 men, 65 years (9-82)] and 67 controls were included in the final analysis. Technica minimal risk in dedicated centers.

Recent evidence has shown that the complete blood count (CBC) is abnormal in patients with Crohn's disease (CD). We aimed to investigate an effective CBC parameter and explore its impact on disease activity in a large CD cohort.

We performed a retrospective analysis of patients with established CD who underwent clinically indicated endoscopy at four tertiary centres in China between 2016 and 2020. Individual variables of the Simple Endoscopic Score for CD, CBC parameters, C-reactive protein (CRP) levels, erythrocyte sedimentation rate, and faecal calprotectin (FC) were independently reviewed by different investigators. The hold-out method was used to verify the predictive power of the established model.

Data from a total of 1388 endoscopic procedures performed for 882 eligible CD patients were available with routine blood parameters and related indicators. The model using platelet-to-lymphocyte percentage ratio (PLpR) had high accuracy for identifying patients in endoscopic remission (ER), with an area under the curve (AUC) of 0.785 [95% confidence interval (CI) 0.784-0.787], which was comparable with that for CRP (AUC 0.775, 95% CI 0.774-0.777). Notably, the AUC of PLpR was significantly higher than that of CRP in patients with colonic disease and with a history of surgery. Moreover, after combining the FC with PLpR, the AUC value of FC + PLpR increased up to 0.892 (95% CI 0.890-0.894) for identifying ER.

We explored an index (PLpR) to identify CD patients in ER based on platelet and lymphocyte percentage from the CBC. PLpR helped evaluate the degree of disease activity and monitor the therapeutic response.

We explored an index (PLpR) to identify CD patients in ER based on platelet and lymphocyte percentage from the CBC. PLpR helped evaluate the degree of disease activity and monitor the therapeutic response.

Mucosal healing (MH) is the key aim of the treat-to-target strategy for patients with Crohn's disease (CD). The efficacy of infliximab (IFX) on MH in different ileocolonic segments is unclear. The aim of this study was to investigate endoscopic MH in different ileocolonic segments in patients with CD who received IFX treatment.

A retrospective, single-center study was performed in patients with active ileocolonic CD between January 2012 and December 2018. All patients underwent IFX treatment for at least 30 weeks. The MH of five ileocolonic segments was assessed by the Simple Endoscopic Score for CD (SES-CD) at baseline, 14/22 weeks and 30/38 weeks. The SES-CD values were analyzed by a mixed-effects model after the correction for confounding factors.

A total of 101 eligible patients were included. The baseline endoscopic severity was similar across segments. At 30/38 weeks, the greatest changes in the SES-CD ulcer size and ulcerated surface subscores were -94.29% and -94.32% both in the transverse colonnt. The transverse colon showed the highest CMH rate, whereas the right colon with stenosis showed the poorest improvement. The differing propensities of ileocolonic segments may provide an individualized IFX treatment strategy.Identifying patients with high risk of hip fracture is a great challenge in osteoporosis clinical assessment. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold standard in osteoporosis clinical assessment. However, its classification accuracy is only around 65%. In order to improve this accuracy, this paper proposes the use of Machine Learning (ML) models trained with data from a biomechanical model that simulates a sideways-fall. Machine Learning (ML) models are models able to learn and to make predictions from data. During a training process, ML models learn a function that maps inputs and outputs without previous knowledge of the problem. The main advantage of ML models is that once the mapping function is constructed, they can make predictions for complex biomechanical behaviours in real time. However, despite the increasing popularity of Machine Learning (ML) models and their wide application to many fields of medicine, their use as hip fracture predictors is still limited.