RESUMEN
Background: Hypertension is a major public health problem, and its resulting other cardiovascular diseases are the leading cause of death worldwide. In this study, we constructed a convenient and high-performance hypertension risk prediction model to assist in clinical diagnosis and explore other important influencing factors. Methods: We included 8,073 people from NHANES (2017-March 2020), using their 120 features to form the original dataset. After data pre-processing, we removed several redundant features through LASSO regression and correlation analysis. Thirteen commonly used machine learning methods were used to construct prediction models, and then, the methods with better performance were coupled with recursive feature elimination to determine the optimal feature subset. After data balancing through SMOTE, we integrated these better-performing learners to construct a fusion model based for predicting hypertension risk on stacking strategy. In addition, to explore the relationship between serum ferritin and the risk of hypertension, we performed a univariate analysis and divided it into four level groups (Q1 to Q4) by quartiles, with the lowest level group (Q1) as the reference, and performed multiple logistic regression analysis and trend analysis. Results: The optimal feature subsets were: age, BMI, waist, SBP, DBP, Cre, UACR, serum ferritin, HbA1C, and doctors recommend reducing salt intake. Compared to other machine learning models, the constructed fusion model showed better predictive performance with precision, accuracy, recall, F1 value and AUC of 0.871, 0.873, 0.871, 0.869 and 0.966, respectively. For the analysis of the relationship between serum ferritin and hypertension, after controlling for all co-variates, OR and 95% CI from Q2 to Q4, compared to Q1, were 1.396 (1.176-1.658), 1.499 (1.254-1.791), and 1.645 (1.360-1.989), respectively, with P < 0.01 and P for trend <0.001. Conclusion: The hypertension risk prediction model developed in this study is efficient in predicting hypertension with only 10 low-cost and easily accessible features, which is cost-effective in assisting clinical diagnosis. We also found a trend correlation between serum ferritin levels and the risk of hypertension.
RESUMEN
Two Gram-negative, aerobic, motile, rod-shaped bacteria, designated strains 908033(T) and 908087(T), were isolated from a seawater sample collected from the East China Sea. Chemotaxonomic characteristics of the two isolates included the presence of iso-C(15 : 0), iso-C(17 : 0) and iso-C(17 : 1)omega9c as the major cellular fatty acids and Q-8 as the predominant ubiquinone. The genomic DNA G+C contents of strains 908033(T) and 908087(T) were 45.5 and 45.2 mol%, respectively. Phylogenetic analyses based on 16S rRNA gene sequences revealed that the new isolates were related to members of the genus Pseudidiomarina, showing levels of similarity of 95.8-96.6 % with the type strains of recognized species of the genus. The results of DNA-DNA hybridization experiments among these two isolates and Pseudidiomarina sediminum CICC 10319(T), in combination with chemotaxonomic and phenotypic data, demonstrated that the new isolates represent two novel species of the genus Pseudidiomarina, for which the names Pseudidiomarina donghaiensis sp. nov. (type strain 908033(T)=CGMCC 1.7284(T)=JCM 15533(T)) and Pseudidiomarina maritima sp. nov. (type strain 908087(T)=CGMCC 1.7285(T)=JCM 15534(T)) are proposed.
