Background Acute kidney injury (AKI) is a major complication following cardiac surgery that substantially increases morbidity and mortality. Current diagnostic guidelines based on elevated serum creatinine and/or the presence of oliguria potentially delay its diagnosis. We presented a series of models for predicting AKI after cardiac surgery based on electronic health record data. Methods We enrolled 1457 adult patients who underwent cardiac surgery at Nanjing First Hospital from January 2017 to June 2019. 193 clinical features, including demographic characteristics, comorbidities and hospital evaluation, laboratory test, medication, and surgical information, were available for each patient. The number of important variables was determined using the sliding windows sequential forward feature selection technique (SWSFS). The following model development methods were introduced: extreme gradient boosting (XGBoost), random forest (RF), deep forest (DF), and logistic regression. Model performance was accessed using the area under the receiver operating characteristic curve (AUROC). We additionally applied SHapley Additive exPlanation (SHAP) values to explain the RF model. AKI was defined according to Kidney Disease Improving Global Outcomes guidelines. Results In the discovery set, SWSFS identified 16 important variables. The top 5 variables in the RF importance matrix plot were central venous pressure, intraoperative urine output, hemoglobin, serum potassium, and lactic dehydrogenase. In the validation set, the DF model exhibited the highest AUROC (0.881, 95% confidence interval [CI] 0.831–0.930), followed by RF (0.872, 95% CI 0.820–0.923) and XGBoost (0.857, 95% CI 0.802–0.912). A nomogram model was constructed based on intraoperative longitudinal features, achieving an AUROC of 0.824 (95% CI 0.763–0.885) in the validation set. The SHAP values successfully illustrated the positive or negative contribution of the 16 variables attributed to the output of the RF model and the individual variable’s effect on model prediction. Conclusions Our study identified 16 important predictors and provided a series of prediction models to enhance risk stratification of AKI after cardiac surgery. These novel predictors might aid in choosing proper preventive and therapeutic strategies in the perioperative management of AKI patients.

(1) Background: The association between metabolic obesity phenotypes and incident lung cancer (LC) remains unclear. (2) Methods: Based on the combination of baseline BMI categories and metabolic health status, participants were categorized into eight groups: metabolically healthy underweight (MHUW), metabolically unhealthy underweight (MUUW), metabolically healthy normal (MHN), metabolically unhealthy normal (MUN), metabolically healthy overweight (MHOW), metabolically unhealthy overweight (MUOW), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). The Cox proportional hazards model and Mendelian randomization (MR) were applied to assess the association between metabolic obesity phenotypes with LC risk. (3) Results: During a median follow-up of 9.1 years, 3654 incident LC patients were confirmed among 450,482 individuals. Compared with participants with MHN, those with MUUW had higher rates of incident LC (hazard ratio (HR) = 3.24, 95% confidence interval (CI) = 1.33–7.87, p = 0.009). MHO and MHOW individuals had a 24% and 18% lower risk of developing LC, respectively (MHO: HR = 0.76, 95% CI = 0.61–0.95, p = 0.02; MHO: HR = 0.82, 95% CI = 0.70–0.96, p = 0.02). No genetic association of metabolic obesity phenotypes and LC risk was observed in MR analysis. (4) Conclusions: In this prospective cohort study, individuals with MHOW and MHO phenotypes were at a lower risk and MUUW were at a higher risk of LC. However, MR failed to reveal any evidence that metabolic obesity phenotypes would be associated with a higher risk of LC.

Background: Polygenic risk score (PRS) is widely regarded as a predictor of genetic susceptibility to disease, applied to individuals to predict the risk of disease occurrence. When the gene-environment (G×E) interaction is considered, the traditional PRS prediction model directly uses PRS to interact with the environment without considering the interactions between each variant and environment, which may lead to prediction performance and risk stratification of complex diseases are not promising.Methods: We developed a method called interaction PRS (iPRS), reconstructing PRS by leveraging G×E interactions. Two extensive simulations evaluated prediction performance, risk stratification, and calibration performance of the iPRS prediction model, and compared it with the traditional PRS prediction model. Real data analysis was performed using existing data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial study to predict genetic susceptibility, pack-years of smoking history, and G×E interactions in patients with lung cancer.Results: Two extensive simulations indicated iPRS prediction model could improve the prediction performance of disease risk, the accuracy of risk stratification, and clinical calibration performance compared with the traditional PRS prediction model, especially when antagonism accounted for the majority of the interaction. PLCO real data analysis also suggested that the iPRS prediction model was superior to the PRS prediction model in predictive effect (p = 0.0205).Conclusion: IPRS prediction model could have a good application prospect in predicting disease risk, optimizing the screening of high-risk populations, and improving the clinical benefits of preventive interventions among populations.

Autophagy and endoplasmic reticulum stress (ER stress) are important in numerous pathological processes in traumatic brain injury (TBI). Growing evidence has indicated that pyroptosis-associated inflammasome is involved in the pathogenesis of TBI. Platelet derived growth factor (PDGF) has been reported to be as a potential therapeutic drug for neurological diseases. However, the roles of PDGF, autophagy and ER stress in pyroptosis have not been elucidated in the TBI. This study investigated the roles of ER stress and autophagy after TBI at different time points. We found that the ER stress and autophagy after TBI were inhibited, and the expressions of pyroptosis-related proteins induced by TBI, including NLRP3, Pro-Caspase1, Caspase1, GSDMD, GSDMD P30, and IL-18, were decreased upon PDGF treatment. Moreover, the rapamycin (RAPA, an autophagy activator) and tunicamycin (TM, an ER stress activator) eliminated the PDGF effect on the pyroptosis after TBI. Interestingly, the sodium 4-phenylbutyrate (4-PBA, an ER stress inhibitor) suppressed autophagy but 3-methyladenine (3-MA, an autophagy inhibitor) not for ER stress. The results revealed that PDGF improved the functional recovery after TBI, and the effects were markedly reversed by TM and RAPA. Taken together, this study provides a new insight that PDGF is a potential therapeutic strategy for enhancing the recovery of TBI.

Background It is controversial whether hemodialysis affects the efficacy of the antiplatelet agents. We aimed to investigate the impact of hemodialysis on efficacies of the antiplatelet agents in coronary artery disease (CAD) patients complicated with end‑stage renal disease (ESRD). Methods 86 CAD patients complicated with ESRD requiring hemodialysis were consecutively enrolled. After 5-day treatment with aspirin and clopidogrel or ticagrelor, the platelet aggregations induced by arachidonic acid (PLAA) or adenosine diphosphate (PLADP), and the P2Y12 reaction unit (PRU) were measured before and after hemodialysis. The propensity matching score method was adopted to generate a control group with normal renal function from 2439 CAD patients. Results In patients taking aspirin, the PLAA remained unchanged after hemodialysis. In patients taking clopidogrel, the PLADP (37.26 ± 17.04 vs. 31.77 ± 16.09, p = 0.029) and corresponding clopidogrel resistance (CR) rate (23 [48.9%] vs.14 [29.8%], p = 0.022) significantly decreased after hemodialysis, though PRU remained unchanged. Subgroup analysis indicated that PLADP significantly decreased while using polysulfone membrane (36.8 ± 17.9 vs. 31.1 ± 14.5, p = 0.024). In patients taking ticagrelor, PLADP, and PRU remained unchanged after hemodialysis. ESRD patients had higher incidences of aspirin resistance (AR) and CR compared to those with normal renal function (AR: 16.1% vs. 0%, p = 0.001; CR: 48.4% vs. 24.8%, p = 0.024). Conclusion Hemodialysis does not have negative effect on the efficacies of aspirin, clopidogrel and ticagrelor in ESRD patients with CAD. On the contrary, clopidogrel response may be improved while using the polysulfone membrane during hemodialysis. ESRD patients have higher incidences of AR and CR compared with those with normal renal function. Trial registration ClinicalTrials.gov Identifier: NCT03330223.