Acute kidney injury (AKI) is a common and serious complication after the repair of Type A acute aortic dissection (TA-AAD). However, previous models have failed to account for the impact of blood pressure fluctuations on predictive performance. This study aims to develop machine learning (ML) models combined with intraoperative medicine and blood pressure time-series data to improve the accuracy of early prediction for postoperative AKI risk. Indicators reflecting the duration and depth of hypotension were obtained by analyzing continuous mean arterial pressure (MAP) monitored intraoperatively with multiple thresholds (<65, 60, 55, 50) set in the study. The predictive features were selected by logistic regression and the least absolute shrinkage and selection operator (LASSO), and 4 ML models were built based on the above features. The performance of the models was evaluated by area under receiver operating characteristic curve (AUROC), calibration curve and decision curve analysis (DCA). Shapley additive interpretation (SHAP) was used to explain the prediction models. Among the indicators reflecting intraoperative hypotension, 65 mmHg showed a statistically superior difference to other thresholds in patients with or without AKI (p < .001). Among 4 models, the extreme gradient boosting (XGBoost) model demonstrated the highest AUROC: 0.800 (95% 0.683–0.917) and sensitivity: 0.717 in the testing set and was verified the best-performing model. The SHAP summary plot indicated that intraoperative urine output, cumulative time of mean arterial pressure lower than 65 mmHg outside cardiopulmonary bypass (OUT_CPB_MAP_65 time), autologous blood transfusion, and smoking were the top 4 features that contributed to the prediction model. With the introduction of intraoperative blood pressure time-series variables, we have developed an interpretable XGBoost model that successfully achieve high accuracy in predicting the risk of AKI after TA-AAD repair, which might aid in the perioperative management of high-risk patients, particularly for intraoperative hemodynamic regulation.

Drug-induced hyperglycemia/diabetes is a global issue. Some drugs induce hyperglycemia by activating the pregnane X receptor (PXR), but the mechanism is unclear. Here, we report that PXR activation induces hyperglycemia by impairing hepatic glucose metabolism due to inhibition of the hepatocyte nuclear factor 4-alpha (HNF4α)‒glucose transporter 2 (GLUT2) pathway. The PXR agonists atorvastatin and rifampicin significantly downregulated GLUT2 and HNF4α expression, and impaired glucose uptake and utilization in HepG2 cells. Overexpression of PXR downregulated GLUT2 and HNF4α expression, while silencing PXR upregulated HNF4α and GLUT2 expression. Silencing HNF4α decreased GLUT2 expression, while overexpressing HNF4α increased GLUT2 expression and glucose uptake. Silencing PXR or overexpressing HNF4α reversed the atorvastatin-induced decrease in GLUT2 expression and glucose uptake. In human primary hepatocytes, atorvastatin downregulated GLUT2 and HNF4α mRNA expression, which could be attenuated by silencing PXR. Silencing HNF4α downregulated GLUT2 mRNA expression. These findings were reproduced with mouse primary hepatocytes. Hnf4α plasmid increased Slc2a2 promoter activity. Hnf4α silencing or pregnenolone-16α-carbonitrile (PCN) suppressed the Slc2a2 promoter activity by decreasing HNF4α recruitment to the Slc2a2 promoter. Liver-specific Hnf4α deletion and PCN impaired glucose tolerance and hepatic glucose uptake, and decreased the expression of hepatic HNF4α and GLUT2. In conclusion, PXR activation impaired hepatic glucose metabolism partly by inhibiting the HNF4α‒GLUT2 pathway. These results highlight the molecular mechanisms by which PXR activators induce hyperglycemia/diabetes.

Background and Purpose: The clinical use of tirofiban for patients with acute ischemic stroke (AIS) who underwent mechanical thrombectomy (MT) remains controversial. We aimed to evaluate the safety and efficacy of tirofiban combined with MT in AIS patients. Methods: Patients with AIS who underwent MT from January 2014 to December 2018 were enrolled in three stroke units in China. Subgroup analyses were performed based on stroke etiology which was classified according to the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) criteria. Safety outcomes were in-hospital intracerebral hemorrhage (ICH), symptomatic intracerebral hemorrhage (sICH) and mortality at 3-month. Efficacy outcomes were favorable functional outcome and functional independence at 3-month and neurological improvement at 24 h, 3 d and discharge. Results: In patients with large artery atherosclerosis (LAA) stroke, multivariate analyses revealed that tirofiban significantly decreased the odds of in-hospital ICH (adjusted OR = 0.382, 95% CI 0.180–0.809) and tended to increase the odds of favorable functional outcome at 3-month (adjusted OR = 3.050, 95% CI 0.969–9.598). By contrast, in patients with cardioembolism (CE) stroke, tirofiban was not associated with higher odds of favorable functional outcome at 3-month (adjusted OR = 0.719, 95% CI 0.107–4.807), but significantly decreased the odds of neurological improvement at 24 h and 3d (adjusted OR = 0.185, 95% CI 0.047–0.726; adjusted OR = 0.268, 95% CI 0.087–0.825). Conclusions: Tirofiban combined with MT appears to be safe and effective in LAA patients, but has no beneficial effect on CE patients.