A novel real-time model for predicting acute kidney injury in critically ill patients within 12 hours.

BACKGROUND: A major challenge in prevention and early treatment of acute kidney injury (AKI) is the lack of high-performance predictors in critically ill patients. Therefore, we innovatively constructed U-AKIpredTM for predicting AKI in critically ill patients within 12 h of panel measurement. METHODS: The prospective cohort study included 680 patients in the training set and 249 patients in the validation set. After performing inclusion and exclusion criteria, 417 patients were enrolled in the training set and 164 patients were enrolled in the validation set finally. AKI was diagnosed by Kidney Disease Improving Global Outcomes (KDIGO) criteria. RESULTS: Twelve urinary kidney injury biomarkers (mALB, IgG, TRF, α1MG, NAG, NGAL, KIM-1, L-FABP, TIMP2, IGFBP7, CAF22 and IL-18) exhibited good predictive performance for AKI within 12 h in critically ill patients. U-AKIpredTM, combined with three crucial biomarkers (α1MG, L-FABP and IGFBP7) by multivariate logistic regression analysis, exhibited better predictive performance for AKI in critically ill patients within 12 h than the other twelve kidney injury biomarkers. The area under the curve (AUC) of the U-AKIpredTM, as a predictor of AKI within 12 h, was 0.802 (95% CI: 0.771-0.833, P < 0.001) in the training set and 0.844 (95% CI: 0.792-0.896, P < 0.001) in validation cohort. A nomogram based on the results of the training and validation sets of U-AKIpredTM was developed which showed optimal predictive performance for AKI. The fitting effect and prediction accuracy of U-AKIpredTM was evaluated by multiple statistical indicators. To provide a more flexible predictive tool, the dynamic nomogram (https://www.xsmartanalysis.com/model/U-AKIpredTM) was constructed using a web-calculator. Decision curve analysis (DCA) and a clinical impact curve were used to reveal that U-AKIpredTM with the three crucial biomarkers had a higher net benefit than these twelve kidney injury biomarkers respectively. The net reclassification index (NRI) and integrated discrimination index (IDI) were used to improve the significant risk reclassification of AKI compared with the 12 kidney injury biomarkers. The predictive efficiency of U-AKIpredTM was better than the NephroCheck® when testing for AKI and severe AKI. CONCLUSION: U-AKIpredTM is an excellent predictive model of AKI in critically ill patients within 12 h and would assist clinicians in identifying those at high risk of AKI.

[Research progress on interactions between luteolin (glucosides) and drug-metabolizing enzyme].

The paper summarizes the interactions between luteolin (glucosides) and drug-metabolizing enzyme from the literature of recent years and our research work. The metabolism of luteolin is chiefly mediated by phase II metabolic enzyme. Its glucosides are firstly hydrolyzed into aglycone in intestinal tract, and then absorbed and metabolized. Luteolin has the effect on the induction of CYP3A, and on the inhibition of CYPIA, 1B and 2E. Also, luteolin is an effective inhibitor of CYP2B6, CYP2C9 and CYP2D6. Luteolin can induce and inhibit UGTs and SULTs. It can also inhibit multi ABC transport proteins. Understanding the interactions between luteolin (glucosides) and drug-metabolizing enzyme has an important significance in guiding clinical use of the drug.

[Metabolism-based interaction of diphenytriazol and flavone compounds].

OBJECTIVE: To observe the metabolism-based interaction of diphenytriazol and flavone compounds. METHODS: Flavone compounds kaempferol, isoharmnten and Elsholtzia blanda benth extract were chosen as the substrate of glucuronidation in the phase II metabolism. The metabolism was investigated in different rat liver microsome incubates pretreated with beta-naphthoflavone (BNF), diphenytriazol and tea oil (control). The concentrations of residual substrate were determined by HPLC. Quercetin and kaempferol were coincubated with diphenytriazol in control microsome to evaluate the inhibition for phase I metabolism. The concentration of diphenytriazol was determined by HPLC. RESULT: The phase II metabolic activity of kaempferol, isoharmnten and Elsholtzia blanda benth extract in diphenytriazol-treated microsome was more potent than that in BNF-treated microsome (P<0.01). The phase I metabolism of diphenytriazol was markedly inhibited by quercetin and kaempferol, with the inhibition constants (Ki) (12.41 +/-0.26)microg . ml(-1) and (7.97 +/-0.08)microg . ml(-1), respectively. CONCLUSION: Diphenytriazol demonstrates metabolism-based interaction with flavone compounds in vitro.