Summary: To determine the differences in sweat composition between sweat induced by thermal stress alone and that induced by physical exercise, seven young healthy men first sat in a hot room and then performed running exercise. A 20-minute stay in a climate chamber at 40°C resulted in a 5 % reduction in body weight. The same body weight loss was induced by running exercise. Both sodium and chloride concentrations were much lower in the sweat induced by thermal exposure than that induced by the running exercise (p <0.01), while urea nitrogen and creatinine concentrations were significantly higher after thermal exposure than after the running exercise (p <0.01). Potassium concentrations did not differ significantly with either procedure. These findings suggest that sweat composition varies with the kind of induction and that more salt seems to be lost through exerciseinduced sweating than by just sitting in a hot environment.
The 3D structures of an antitumor glycosylsterol OSW-1 and its closely related congener were investigated by NMR studies and an X-ray crystallographic analysis. The disaccharide moiety was found as a structural scaffold for the formation of a hydrophobic cluster by the biologically required functionalities.
Primary aldosteronism (PA) is associated with an increased risk of cardiometabolic diseases, especially in unilateral subtype. Despite its high prevalence, the case detection rate of PA is limited, partly because of no clinical models available in general practice to identify patients highly suspicious of unilateral subtype of PA, who should be referred to specialized centers. The aim of this retrospective cross-sectional study was to develop a predictive model for subtype diagnosis of PA based on machine learning methods using clinical data available in general practice. Overall, 91 patients with unilateral and 138 patients with bilateral PA were randomly assigned to the training and test cohorts. Four supervised machine learning classifiers; logistic regression, support vector machines, random forests (RF), and gradient boosting decision trees, were used to develop predictive models from 21 clinical variables. The accuracy and the area under the receiver operating characteristic curve (AUC) for predicting of subtype diagnosis of PA in the test cohort were compared among the optimized classifiers. Of the four classifiers, the accuracy and AUC were highest in RF, with 95.7% and 0.990, respectively. Serum potassium, plasma aldosterone, and serum sodium levels were highlighted as important variables in this model. For feature-selected RF with the three variables, the accuracy and AUC were 89.1% and 0.950, respectively. With an independent external PA cohort, we confirmed a similar accuracy for feature-selected RF (accuracy: 85.1%). Machine learning models developed using blood test can help predict subtype diagnosis of PA in general practice.
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