Comparing LASSO and random forest models for predicting neurological dysfunction among fluoroquinolone users

Post-Transcriptional Regulator RBM47 Stabilizes FBXO2 mRNA to Advance Osteoarthritis Development: WGCNA Analysis and Experimental Validation

Osteoarthritis (OA) is a common chronic joint degenerative disease and a major cause of disability in the elderly. However, the current intervention strategies cannot effectively improve OA, and the pathogenesis of OA remains elusive. The present study identified RNA binding motif protein 47 (RBM47) as an upstream modulator of key dysregulation gene co-expression module based on weighted gene co-expression network analysis (WGCNA) analysis and least absolute shrinkage and selection operator (Lasso) modeling. Subsequently, data from real-time quantitative PCR and western blot analysis revealed that RBM47 was upregulated in OA models in vivo and in vitro compared with normal controls. Functional analysis results from the MTT assay, flow cytometry, evaluation of LDH activities and inflammatory mediators, and western blot analysis of extracellular matrix (ECM) proteins, showed that RBM47 knockdown significantly alleviated inflammation, apoptosis, and ECM degradation in interleukin 1β (IL-1β)-treated chondrocytes. Mechanistically, RBM47 bound to F box only protein 2 (FBXO2) and stabilized FBXO2 messenger RNA (mRNA) to promote the phosphorylation of signal transducer and activator of transcription 3 (STAT3) in chondrocytes. Results from the recovery assay showed that the re-activation of STAT3 signaling by overexpressing FBXO2 or STAT3 counteracted the alleviating effect of RBM47 downregulation on IL-1β-induced inflammation, apoptosis, and ECM degradation. Altogether, our findings illustrate that RBM47 stabilizes FBXO2 mRNA to advance OA development by activating STAT3 signaling, which enhances our understanding of the molecular regulatory mechanisms underlying the development of OA.

Identification of risk factors for infection after mitral valve surgery through machine learning approaches

Background

Selecting features related to postoperative infection following cardiac surgery was highly valuable for effective intervention. We used machine learning methods to identify critical perioperative infection-related variables after mitral valve surgery and construct a prediction model.

Methods

Participants comprised 1223 patients who underwent cardiac valvular surgery at eight large centers in China. The ninety-one demographic and perioperative parameters were collected. Random forest (RF) and least absolute shrinkage and selection operator (LASSO) techniques were used to identify postoperative infection-related variables; the Venn diagram determined overlapping variables. The following ML methods: random forest (RF), extreme gradient boosting (XGBoost), Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), AdaBoost, Naive Bayesian (NB), Logistic Regression (LogicR), Neural Networks (nnet) and artificial neural network (ANN) were developed to construct the models. We constructed receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) was calculated to evaluate model performance.

Results

We identified 47 and 35 variables with RF and LASSO, respectively. Twenty-one overlapping variables were finally selected for model construction: age, weight, hospital stay, total red blood cell (RBC) and total fresh frozen plasma (FFP) transfusions, New York Heart Association (NYHA) class, preoperative creatinine, left ventricular ejection fraction (LVEF), RBC count, platelet (PLT) count, prothrombin time, intraoperative autologous blood, total output, total input, aortic cross-clamp (ACC) time, postoperative white blood cell (WBC) count, aspartate aminotransferase (AST), alanine aminotransferase (ALT), PLT count, hemoglobin (Hb), and LVEF. The prediction models for infection after mitral valve surgery were established based on these variables, and they all showed excellent discrimination performance in the test set (AUC > 0.79).

Conclusions

Key features selected by machine learning methods can accurately predict infection after mitral valve surgery, guiding physicians in taking appropriate preventive measures and diminishing the infection risk.