Journal of investigative medicine : the official publication of the American Federation for Clinical Research
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Electronic cigarette use by and perceptions of middle and high school students in the United States.
Electronic cigarettes (e-cigarettes) provide a novel source of nicotine and may lead to experimentation by adolescents and eventually to their use of conventional cigarettes. The National Youth Tobacco Survey collected information from a nationally representative sample of students in 2016 to determine their experiences with conventional cigarettes and e-cigarettes, their intentions to use these products in the future, and their perceptions of harm and addiction associated with these products. We analyzed these data with ordered probit regression models to determine possible associations with the intention to try e-cigarettes and conventional cigarettes. ⋯ Less than 1% of the students responded "Definitely yes" to the question, "Do you think you will try an e-cig?" The odds ratios for an intention to try e-cigarettes increased as the perception of harm decreased; these ratios increased from 1.0 for "A lot of harm" to 5.85 (95% CI: 3.51, 9.75) for "No harm." In 2016, the majority of students thought that e-cigarettes could cause some harm. This survey indicates that most students have not tried e-cigarettes or conventional cigarettes. The minority of students who think that e-cigarettes pose no harm and students in the ages 14 and 16 are more likely to try them.
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Creatine kinase (CK), the key enzyme in regulating energy metabolism, is demonstrated to be correlated with insulin resistance. Type 2 diabetes mellitus (T2DM) is considered as a risk factor for developing low muscle mass. The purpose of this investigation was to evaluate whether serum CK is associated with low muscle mass in T2DM patients. ⋯ Linear regression analysis showed that SMI was correlated with age, BMI, DBP, and CK in female subjects. In addition, CK was correlated with BMI and fasting plasma glucose in male and female T2DM groups. CK is inversely correlated with low muscle mass in T2DM patients.
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Glycated hemoglobin A1c (HbA1c) has been recognized as a predictor of cardiovascular events. However, the relationship between HbA1c and coronary artery disease (CAD) in the Chinese population has yet to be systematically explored. In addition, factors associated with HbA1c were generally analyzed linearly, thereby failing to appreciate more complex nonlinear associations. ⋯ Both HbA1c > 7.2% and HbA1c < 5.7% were associated with the presence of MI. In conclusion, HbA1c value was highly associated with the severity of coronary artery stenosis in the whole study population, and in CAD patients without diagnosed diabetes. Compared with patients with HbA1c levels between 6.0% and 7.0%, HbA1c < 5.7% and HbA1c > 7.2% were associated with higher presence of MI.
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The hyperinflammatory immune response in severe COVID-19 infection shares features with secondary hemophagocytic lymphohistiocytosis (sHLH) in the form of fever, cytopenia, elevated inflammatory markers, and high mortality. There are contrasting opinions regarding utility of HLH 2004 or HScore in the diagnosis of severe COVID-19-related hyperinflammatory syndrome (COVID-HIS). This was a retrospective study of 47 patients of severe COVID-19 infection, suspected to have COVID-HIS and 22 patients of sHLH to other illnesses, to evaluate the diagnostic utility and limitations of HLH 2004 and/or HScore in context to COVID-HIS and to also evaluate the utility of Temple criteria for predicting severity and outcome in COVID-HIS. ⋯ Serum ferritin (p = 0.02), lactate dehydrogenase (p = 0.02), direct bilirubin (p = 0.02), and C-reactive protein (p = 0.03) were associated with mortality in COVID-HIS. Both HScore and HLH-2004 criteria perform poorly for identifying COVID-HIS. Presence of bone marrow hemophagocytosis may help to identify about one-third of COVID-HIS missed by the Temple Criteria.
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To analyze the heterogeneity between different cell types in pediatric Wilms tumor (WT) tissue, and identify the differentially expressed genes (DEGs) of malignant tumor cells, thereby establishing a prognostic model. The single-cell sequencing data of pediatric WT tissues were downloaded from the public database. Data filtration and normalization, principal component analysis, and T-distributed stochastic neighbor embedding cluster analysis were performed using the Seurat package of R language. ⋯ In the external validation dataset, the AUC value of this nomogram model was 0.826. Based on the single-cell RNA-seq, we recognized cell clusters in the WT tissue of children, identified prognostic biomarkers in malignant tumor cells, and established a comprehensive prognostic model. Our findings might provide new ideas and methods for the diagnosis and treatment of WT.