Rigidity of the outer shell predicted by a protein intrinsic disorder model sheds light on the COVID-19 (Wuhan-2019-nCoV) infectivity.

COVID-19 has a significant impact on public health and poses a challenge to medical staffs, especially to front-line medical staffs who are exposed to direct contact with patients. To understand the psychological stress status of medical staffs during the outbreak of COVID-19. Random sample questionnaire survey was conducted among 2110 medical staffs and 2158 college students in all provinces of china through a questionnaire which was compiled and completed through the Questionnaire Star platform relying on Wechat, QQ and other social software. The differences in psychological stress status of different groups were compared through This article is protected by copyright. All rights reserved. Accepted Articlethe analysis of the questionnaire. Results revealed that in all provinces of china, medical staffs scored significantly higher on all items of psychological stress than college students(P<0.001). In Wuhan, medical staff scored significantly higher than college students in all items of psychological stress(P<0.001). While for medical staff, the group in Wuhan area scored significantly higher than the group outside Wuhan on "Thought of being in danger", "The possibility of self-illness", "Worrying about family infection"(P<0.05), "Poor sleep quality", "Needing psychological guidance" and "Worrying about being infected"(P<0.01) items in the psychological stress questionnaire, and in the item of "Confidence in the victory of the epidemic", the group in Wuhan area scored significantly lower than the group in the area outside Wuhan(P<0.05). The emotion, cognition, physical and mental response of front-line medical staff showed obvious "exposure effect", and psychological crisis intervention strategy can be helpful.

Objective T cell inflammation plays pivotal roles in obesity-associated type 2 diabetes (T2DM). The identification of dominant sources of T cell inflammation in humans remains a significant gap in understanding disease pathogenesis. We hypothesized that cytokine profiles from circulating T cells identify T cell subsets and T cell cytokines that define T2DM-associated inflammation. Methods We used multiplex analyses to quantify T cell-associated cytokines in αCD3/αCD28-stimulated PBMCs, or B cell-depleted PBMCs, from subjects with T2DM or BMI-matched controls. We subjected cytokine measurements to multivariate (principal component and partial least squares) analyses. Flow cytometry detected intracellular TNFα in multiple immune cells subsets in the presence/absence of antibodies that neutralize T cell cytokines. Results T cell cytokines were generally higher in T2DM samples, but Th17 cytokines are specifically important for classifying individuals correctly as T2DM. Multivariate analyses indicated that B cells support Th17 inflammation in T2DM but not control samples, while monocytes supported Th17 inflammation regardless of T2DM status. Partial least squares regression analysis indicated that both Th17 and Th1 cytokines impact %HbA1c. Conclusions Among various T cell subsets, Th17 cells are major contributors to inflammation and hyperglycemia, and are uniquely supported by B cells in obesity-associated T2DM.

Aims/hypothesis: The aim of this study was to further elucidate the relationship between resistin and insulin sensitivity, body fat distribution and the metabolic syndrome in humans. Methods: We measured plasma resistin levels in 177 non-diabetic subjects (75 male, 102 female; age 32-75 years). BMI, waist circumference, blood pressure, lipids, glucose, plasminogen-activator inhibitor 1 (PAI-1), adiponectin and leptin levels were also measured. The insulin sensitivity index (S I ) was quantified using Bergman's minimal model. Intra-abdominal fat (IAF) and subcutaneous fat (SQF) areas were quantified by CT scan. Presence of metabolic syndrome criteria was determined using the National Cholesterol Education Program Adult Treatment Panel III guidelines. Results: When subjects were divided into categories based on BMI (< or ≥27.5 kg/m 2 ) and S I (< or ≥ 7×10 −5 min −1 [pmol/l] −1 ), resistin levels did not differ between the lean, insulinsensitive (n=53, 5.36±0.3 ng/ml), lean, insulin-resistant (n=67, 5.70±0.4 ng/ml) and obese, insulin-resistant groups (n=48, 5.94±0.4 ng/ml; ANOVA p=0.65). Resistin correlated with age (r=−0.22, p<0.01), BMI (r=0.16, p=0.03) and SQF (r=0.19, p=0.01) but not with S I

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