2 resultados para Linear regression analysis
em Bioline International
Resumo:
Objective: The study was performed to investigate the association of interleukin 17 (IL 17) or angiotensin II (Ang II) with refractory hypertension risk in hemodialysis patients. Methods: Ninety hemodialysis patients were enrolled into this study, and those with hypertension were divided into two groups. The Easy-to-Control Hypertension group (ECHG) had fifty patients, while the refractory hypertension group (RHG) had forty patients. Twenty healthy individuals were recruited as the control group. IL17 and Ang II were determined using a human IL 17 / Ang II enzyme-linked immunosorbent assay kit. Serum IL 17 and Ang II concentrations in RHG patients were higher than those in ECHG patients. Results: Serum IL 17 and Ang II concentrations in both patient groups were higher than those in the control group. Linear regression analysis showed a positive correlation between IL 17 and Ang II. In multivariate regression analysis, we found that IL17 and Ang II were associated with refractory hypertension risk in hemodialysis patients. Conclusion: IL17 and Ang II were associated with refractory hypertension risk in hemodialysis patients. There was also a positive correlation between IL 17and Ang II.
Resumo:
Purpose: To investigate the spectrum-effect relationships between high performance liquid chromatography (HPLC) fingerprints and duodenum contractility of charred areca nut (CAN) on rats. Methods: An HPLC method was used to establish the fingerprint of charred areca nut (CAN). The promoting effect on contractility of intestinal smooth was carried out to evaluate the duodenum contractility of CAN in vitro. In addition, the spectrum-effect relationships between HPLC fingerprints and bioactivities of CAN were investigated using multiple linear regression analysis (backward method). Results: Fourteen common peaks were detected and peak 3 (5-Hydroxymethyl-2-furfural, 5-HMF) was selected as the reference peak to calculate the relative retention time of 13 other common peaks. In addition, the equation of spectrum-effect relationships {Y = 3.818 - 1.126X1 + 0.817X2 - 0.045X4 - 0.504X5 + 0.728X6 - 0.056X8 + 1.122X9 - 0.247X13 - 0.978X14 (p < 0.05, R2 = 1)} was established in the present study by the multiple linear regression analysis (backward method). According to the equation, the absolute value of the coefficient before X1, X2, X4, X5, X6, X8, X9, X13, X14 was the coefficient between the component and the parameter. Conclusion: The model presented in this study successfully unraveled the spectrum-effect relationship of CAN, which provides a promising strategy for screening effective constituents of areca nut.