Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients.

Background. Acute kidney injury (AKI) has long been recognized as a common and important complication of acute pancreatitis (AP). In the study, machine learning (ML) techniques were used to establish predictive models for AKI in AP patients during hospitalization. This is a retrospective review of prospectively collected data of AP patients admitted within one week after the onset of abdominal pain to our department from January 2014 to January 2019. Eighty patients developed AKI after admission (AKI group) and 254 patients did not (non-AKI group) in the hospital. With the provision of additional information such as demographic characteristics or laboratory data, support vector machine (SVM), random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost) were used to build models of AKI prediction and compared to the predictive performance of the classic model using logistic regression (LR). XGBoost performed best in predicting AKI with an AUC of 91.93% among the machine learning models. The AUC of logistic regression analysis was 87.28%. Present findings suggest that compared to the classical logistic regression model, machine learning models using features that can be easily obtained at admission had a better performance in predicting AKI in the AP patients.

Clinical, cellular, and bioinformatic analyses reveal involvement of WRAP53 overexpression in carcinogenesis of lung adenocarcinoma.

Lung cancer, of which non-small cell lung cancer accounts for 80%, remains a leading cause of cancer-related mortality and morbidity worldwide. Our study revealed that the expression of WD repeat containing antisense to P53 (WRAP53) is higher in lung-adenocarcinoma specimens than in specimens from adjacent non-tumor tissues. The prevalence of WRAP53 overexpression was significantly higher in patients with tumor larger than 3.0 cm than in patients with tumor smaller than 3.0 cm. The depletion of WRAP53 inhibits the proliferation of lung-adenocarcinoma A549 and SPC-A-1 cells via G1/S cell-cycle arrest. Several proteins interacting with WRAP53 were identified through co-immunoprecipitation and liquid chromatography/mass spectrometry. These key proteins indicated previously undiscovered functions of WRAP53. These observations strongly suggested that WRAP53 should be considered a promising target in the prevention or treatment of lung adenocarcinoma.