Automated machine learning models for nonalcoholic fatty liver disease assessed by controlled attenuation parameter from the NHANES 2017-2020.

Background: Nonalcoholic fatty liver disease (NAFLD) is recognized as one of the most common chronic liver diseases worldwide. This study aims to assess the efficacy of automated machine learning (AutoML) in the identification of NAFLD using a population-based cross-sectional database. Methods: All data, including laboratory examinations, anthropometric measurements, and demographic variables, were obtained from the National Health and Nutrition Examination Survey (NHANES). NAFLD was defined by controlled attenuation parameter (CAP) in liver transient ultrasound elastography. The least absolute shrinkage and selection operator (LASSO) regression analysis was employed for feature selection. Six algorithms were utilized on the H2O-automated machine learning platform: Gradient Boosting Machine (GBM), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost), and Deep Learning (DL). These algorithms were selected for their diverse strengths, including their ability to handle complex, non-linear relationships, provide high predictive accuracy, and ensure interpretability. The models were evaluated by area under receiver operating characteristic curves (AUC) and interpreted by the calibration curve, the decision curve analysis, variable importance plot, SHapley Additive exPlanation plot, partial dependence plots, and local interpretable model agnostic explanation plot. Results: A total of 4177 participants (non-NAFLD 3167 vs NAFLD 1010) were included to develop and validate the AutoML models. The model developed by XGBoost performed better than other models in AutoML, achieving an AUC of 0.859, an accuracy of 0.795, a sensitivity of 0.773, and a specificity of 0.802 on the validation set. Conclusions: We developed an XGBoost model to better evaluate the presence of NAFLD. Based on the XGBoost model, we created an R Shiny web-based application named Shiny NAFLD (http://39.101.122.171:3838/App2/). This application demonstrates the potential of AutoML in clinical research and practice, offering a promising tool for the real-world identification of NAFLD.

Electronic-Structure-Modulated Cu,Co-Coanchored N-Doped Nanocarbon as a Difunctional Electrocatalyst for Hydrogen Evolution and Oxygen Reduction Reactions.

To alleviate the problems of environmental pollution and energy crisis, aggressive development of clean and alternative energy technologies, in particular, water splitting, metal-air batteries, and fuel cells involving two key half reactions comprising hydrogen evolution reaction (HER) and oxygen reduction (ORR), is crucial. In this work, an innovative hybrid comprising heterogeneous Cu/Co bimetallic nanoparticles homogeneously dispersed on a nitrogen-doped carbon layer (Cu/Co/NC) was constructed as a bifunctional electrocatalyst toward HER and ORR via a hydrothermal reaction along with post-solid-phase sintering technique. Thanks to the interfacial coupling and electronic synergism between the Cu and Co bimetallic nanoparticles, the Cu/Co/NC catalyst showed improved catalytic ORR activity with a half-wave potential of 0.865 V and an excellent stability of more than 30 h, even compared to 20 wt% Pt/C. The Cu/Co/NC catalyst also exhibited excellent HER catalytic performance with an overpotential of below 149 mV at 10 mA/cm2 and long-term operation for over 30 h.

Exploration of the correlation between facet joints cross-sectional area asymmetry and cervical disc herniation.

PURPOSE: To evaluate the association between facet joints cross-sectional area asymmetry (FCAA) and cervical intervertebral disc herniation (CDH). METHODS: Overall, we retrospectively recruited 390 consecutive patients with CDH who underwent surgical treatment at our institution and 50 normal participants. Clinical variables and radiological findings related to CDH were collected. RESULTS: Patients with CDH were more likely to have a higher absolute value of the facet asymmetry factor (FAF) (p < .001), in which the FAF value of the left group was significantly higher than the other groups (p < .001) and the right group was lower than the central group (p < .001). 9.62% (C3/4), 12.19% (C4/5), 8.70% (C5/6), and 8.14% (C6/7) were determined as cutoff values for each variable that maximized sensitivity and specificity. Furthermore, multivariate analysis showed that cross-sectional area asymmetry of the facet joint (FCAA) was an independent risk factor for the occurrence of CDH. Also, the Chi-square test showed a significant difference in the distribution of the degeneration classification of the disc between the facet-degenerated group and the nondegenerated group at C5/6 (p = 0.026) and C6/7 (p = 0.005) in the facet asymmetry (FA) group. CONCLUSIONS: FCAA is evaluated as an independent risk factor for CDH and associated with the orientation of disc herniation. And facet joint orientation may also play a role in cervical spine degeneration rather than facet joint tropism.

The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction.

Background: Lymph node metastasis (LNM) is considered an essential prognosis factor for adenocarcinoma of the esophagogastric junction (AEG), which also affects the treatment strategies of AEG. We aimed to evaluate automated machine learning (AutoML) algorithms for predicting LNM in Siewert type II T1 AEG. Methods: A total of 878 patients with Siewert type II T1 AEG were selected from the Surveillance, Epidemiology, and End Results (SEER) database to develop the LNM predictive models. The patients from two hospitals in Suzhou were collected as the test set. We applied five machine learning algorithms to develop the LNM prediction models. The performance of predictive models was assessed using various metrics including accuracy, sensitivity, specificity, the area under the curve (AUC), and receiver operating characteristic (ROC) curve. Results: Patients with LNM exhibited a higher proportion of male individuals, a poor degree of differentiation, and submucosal infiltration, with statistical differences. The deep learning (DL) model demonstrated relatively good accuracy (0.713) and sensitivity (0.868) among the five models. Moreover, the DL model achieved the highest AUC (0.781) and sensitivity (1.000) in the test set. Conclusion: The DL model showed good predictive performance among five AutoML models, indicating the advantage of AutoML in modeling LNM prediction in patients with Siewert type II T1 AEG.