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https://www.selleckchem.com/products/plx8394.html In 2017, New Caledonia experienced an outbreak of severe dengue causing high hospital burden (4379 cases, 416 hospital admissions, 15 deaths). We decided to build a local operational model predictive of dengue severity, which was needed to ease the healthcare circuit. We retrospectively analyzed clinical and biological parameters associated with severe dengue in the cohort of patients hospitalized at the Territorial Hospital between January and July 2017 with confirmed dengue, in order to elaborate a comprehensive patient's score. Patients were compared in univariate and multivariate analyses. Predictive models for severity were built using a descending step-wise method. Out of 383 included patients, 130 (34%) developed severe dengue and 13 (3.4%) died. Major risk factors identified in univariate analysis were age, comorbidities, presence of at least one alert sign, platelets count < 30 × 10 /L, prothrombin time < 60%, AST and/or ALT > 10 N, and previous dengue infection. Severity was not inflreaks of enhanced severity in order to improve patients' medical management and hospitalization flow. The association between triglyceride glucose (TyG) index and depression is unclear. We conducted this analysis to explore whether higher TyG index is associated with a higher odd of depression. This was an observational study using data from the National Health and Nutrition Examination Survey (2005-2018), a cross-sectional and nationally representative database. Depression was assessed using the Patient Health Questionnaire-9 (PHQ-9). TyG index was calculated based on the equation as follows ln [triglyceride (mg/dL) × fasting blood glucose (mg/dL)/2], and participants were divided into quartiles based on TyG index. Weighted multivariable logistic regression models were used to explore the relationship between the TyG index and depression. A total of 13,350 patients were included, involving 1001 (7.50%) individuals with depressi

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