Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy.

The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in the Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a least absolute shrinkage and selection operator (LASSO) regression model and a 10-fold cross-validation. We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley additive explanation (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others, and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model and can possibly be biomarkers for DN.

Current perspectives on mesenchymal stem cells as a potential therapeutic strategy for non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) has emerged as a significant health challenge, characterized by its widespread prevalence, intricate natural progression and multifaceted pathogenesis. Although NAFLD initially presents as benign fat accumulation, it may progress to steatosis, non-alcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma. Mesenchymal stem cells (MSCs) are recognized for their intrinsic self-renewal, superior biocompatibility, and minimal immunogenicity, positioning them as a therapeutic innovation for liver diseases. Therefore, this review aims to elucidate the potential roles of MSCs in alleviating the progression of NAFLD by alteration of underlying molecular pathways, including glycolipid metabolism, inflammation, oxidative stress, endoplasmic reticulum stress, and fibrosis. The insights are expected to provide further understanding of the potential of MSCs in NAFLD therapeutics, and support the development of MSC-based therapy in the treatment of NAFLD.

Interpretable machine learning identifies metabolites associated with glomerular filtration rate in type 2 diabetes patients.

Objective: The co-occurrence of kidney disease in patients with type 2 diabetes (T2D) is a major public health challenge. Although early detection and intervention can prevent or slow down the progression, the commonly used estimated glomerular filtration rate (eGFR) based on serum creatinine may be influenced by factors unrelated to kidney function. Therefore, there is a need to identify novel biomarkers that can more accurately assess renal function in T2D patients. In this study, we employed an interpretable machine-learning framework to identify plasma metabolomic features associated with GFR in T2D patients. Methods: We retrieved 1626 patients with type 2 diabetes (T2D) in Liaoning Medical University First Affiliated Hospital (LMUFAH) as a development cohort and 716 T2D patients in Second Affiliated Hospital of Dalian Medical University (SAHDMU) as an external validation cohort. The metabolite features were screened by the orthogonal partial least squares discriminant analysis (OPLS-DA). We compared machine learning prediction methods, including logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost). The Shapley Additive exPlanations (SHAP) were used to explain the optimal model. Results: For T2D patients, compared with the normal or elevated eGFR group, glutarylcarnitine (C5DC) and decanoylcarnitine (C10) were significantly elevated in GFR mild reduction group, and citrulline and 9 acylcarnitines were also elevated significantly (FDR<0.05, FC > 1.2 and VIP > 1) in moderate or severe reduction group. The XGBoost model with metabolites had the best performance: in the internal validate dataset (AUROC=0.90, AUPRC=0.65, BS=0.064) and external validate cohort (AUROC=0.970, AUPRC=0.857, BS=0.046). Through the SHAP method, we found that C5DC higher than 0.1µmol/L, Cit higher than 26 µmol/L, triglyceride higher than 2 mmol/L, age greater than 65 years old, and duration of T2D more than 10 years were associated with reduced GFR. Conclusion: Elevated plasma levels of citrulline and a panel of acylcarnitines were associated with reduced GFR in T2D patients, independent of other conventional risk factors.

A captivating approach to elevate the detection of Al3+ ions incorporates the utilization of a tripodal receptor intricately embellishing the surface of zinc oxide.

The FDA (Food and Drug Administration, (USA)) lists ZnO as a material that is widely acknowledged to be safe. ZnO NPs with a range of tiny particle sizes were made using the precipitation process. ZnO nanoparticles' surface is embellished with a tripodal sensor containing naphthol units. The assembly with the same receptor decorated on ZnO NPs is contrasted with the cation detection capabilities of the purified tripodal receptor. The UV-visible spectrophotometric analysis was conducted to study the state transitions of the receptor and the decorated ZnO receptor. A positive selectivity to Al3+ cations is determined by the fluorescence study under ideal circumstances. The particle size and surface morphologies are determined by DLS and SEM analysis for the same receptor - TP1 and embellished with a tripodal receptor TP2. Using a fluorescence switch-on Photoinduced Electron Transfer (PET) mechanism, the receptor coated on ZnO detects the presence of Al3+ ions with specificity. The binding constant value was determined using the B-H plot equation. Binding stoichiometry for [TP1-Al3+, TP2-Al3+] showed a 1:1 ratio. The fluorescence switches ON-OFF process of the ZnO surface adorned - TP2 with Tripodal receptor- TP1 was used to create molecular logic gates, which can function as a module for sensors and molecular switches. The addition of Na2EDTA in the solution of the [TP1; TP2 - Al3+] complex resulted in a noticeable reduction in the emission of fluorescence. This finding offers compelling support for the reversibility of the chemosensor. To enable the practical application of this sensor, we have developed a cassette containing receptors TP1 and TP2. Successfully, it can detect Al3+ metal ions. We performed a comprehensive assessment of the dependability and appropriateness of our approach in measuring the concentration of Al3+ ions in wastewater produced by important industrial procedures.