To explore exposure to 22 persistent organic pollutants (POPs) and incident type 2 diabetes in a population-based, prospective cohort.
 
  This case-cohort study on 753 participants without type 2 diabetes at baseline, was followed-up over nine years, as part of the French D.E.S.I.R. cohort. We measured 22 POPs in fasting serum at baseline. The associations between baseline POP concentrations, pre-adjusted for lipids, BMI, age and sex, with incident type 2 diabetes, were assessed using Prentice-weighted Cox regression models (time scale age), adjusted for traditional confounding factors. POPs were also modelled summed in functional groups polychlorinated biphenyls (∑PCB) and organochlorines (∑OC) and also individually, after log-transformation, in adjusted Cox models.
 
  There were 200 incident diabetes cases over nine years. Pre-adjusted POP concentrations were not related to diabetes risk for any of the 22 POPs examined. The fully-adjusted hazard ratios (HRs) per interquartile range of the pre-adjusted POPs, ranged from 0.87 (95% CI 0.64,1.19) to 1.22 (0.93,1.59,). For dichlorodiphenyldichloroethylene(p, p'-DDE) and dichlorodiphenyltrichloroethane (p, p'-DDT), the HRs were 1.09 (0.83,1.43) and 0.89 (0.70,1.13), respectively. The HRs for PeCB, HCB, β-HCH, γ-HCH, oxychlordane, trans-nonachlor were 0.98 (0.85,1.13), 1.06 (0.84,1.33), 1.22 (0.93,1.59), 1.13 (0.89,1.42), 1.00 (0.76,1.31), 0.86 (0.66,1.13), respectively. HRs for ∑PCB, ∑OC and for individual log-transformed POPs did not differ significantly from one.
 
  We did not observe any relations between exposure to POPs and diabetes in this population-based cohort. These results do not support causal inferences reported in previous studies linking serum POP concentrations and diabetes risk.
 We did not observe any relations between exposure to POPs and diabetes in this population-based cohort. These results do not support causal inferences reported in previous studies linking serum POP concentrations and diabetes risk.
  Only a minority of patients who receive an implantable cardioverter-defibrillator (ICD) on the basis of left ventricular ejection fraction receive appropriate ICD therapy. Peri-infarct scar zone assessed by late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) is a possible substrate for ventricular tachyarrhytmias (VTAs).
 
  The aim of our prospective study was to determine whether LGE-CMR parameters can predict the occurrence of VTA in patients with ischemic cardiomyopathy (ICM).
 
  Two hundred sixteen patients with ICM underwent CMR imaging before primary or secondary ICD implantation and were prospectively followed. We assessed CMR indices and CMR scar characteristics (infarct core and peri-infarct zone) to predict outcome and VTAs.
 
  Patients were followed up for 1497 days (interquartile range 697-2237 days). Forty-seven patients (21%) received appropriate therapy during follow-up. Patients with appropriate ICD therapy had smaller core scar (31.5% ± 8.5% vs 36.8% ± 8.9%; P = .0004) but larger peri-infarct scar (12.4% ± 2.6% vs 10.5% ± 2.9%; P=.0001) than did patients without appropriate therapy. In multivariate Cox regression analysis, peri-infarct scar (hazard ratio 1.15; 95% confidence interval 1.07-1.24; P = .0001) was independently and significantly associated with VTAs whereas left ventricular ejection fraction, right ventricular ejection fraction, core scar, and left atrial ejection fraction were not.
 
  Scar extent of peri-infarct border zone was significantly associated with appropriate ICD therapy. Thus, LGE-CMR parameters can identify a subgroup of patients with ICM and an increased risk of life-threatening VTAs.
 Scar extent of peri-infarct border zone was significantly associated with appropriate ICD therapy. Thus, LGE-CMR parameters can identify a subgroup of patients with ICM and an increased risk of life-threatening VTAs.
  COVID-19 followed a mortal course in some young patients without any underlying factors, however, it followed a very benign course in some very older individuals with multiple comorbidities. These observations question if some genetic factors may be related to the vulnerability and poor prognosis of the disease. In this study, we aimed to investigate whether MBL2 gene B variant at codon 54 (rs1800450) were related to the variabilities in clinical course of this infection.
 
  284 PCR-confirmed COVID-19 patients and 100 healthy controls were included in the study.  https://www.selleckchem.com/products/SB-202190.html  COVID-19 patients were subdivided according to the clinical features and clinical characteristics were analyzed. DNAs of all patients and controls were examined for the codon 54 A/B (gly54asp rs1800450) variation in exon 1 of the MBL2 gene.
 
  In univariate analysis, BB genotype of MBL2 gene was more common among COVID-19 cases compared with controls (10.9% vs 1.0%, respectively; OR=12.1, 95%CI=1.6-90.1, p=0.001). Multivariate analyses, adjusted for are related to a higher risk for a more severe clinical course of COVID-19 infection in some respects. Our findings may have potential future implications, e.g. for use of MBL protein as potential therapeutics or prioritize the individuals with B variants during vaccination strategies.To develop a modified predictive model for severe COVID-19 in people infected with Sars-Cov-2. We developed the predictive model for severe patients of COVID-19 based on the clinical date from the Tumor Center of Union Hospital affiliated with Tongji Medical College, China. A total of 151 cases from Jan. 26 to Mar. 20, 2020, were included. Then we followed 5 steps to predict and evaluate the model data preprocessing, data splitting, feature selection, model building, prevention of overfitting, and Evaluation, and combined with artificial neural network algorithms. We processed the results in the 5 steps. In feature selection, ALB showed a strong negative correlation (r = 0.771, P less then 0.001) whereas GLB (r = 0.661, P less then 0.001) and BUN (r = 0.714, P less then 0.001) showed a strong positive correlation with severity of COVID-19. TensorFlow was subsequently applied to develop a neural network model. The model achieved good prediction performance, with an area under the curve value of 0.953(0.889-0.