Machine Learning-Based Prediction Method for Tremors Induced by Tacrolimus in the Treatment of Nephrotic Syndrome.

Tremors have been reported even with a low dose of tacrolimus in patients with nephrotic syndrome and are responsible for hampering the day-to-day work of young active patients with nephrotic syndrome. This study proposes a neural network model based on seven variables to predict the development of tremors following tacrolimus. The sensitivity and specificity of this algorithm are high. A total of 252 patients were included in this study, out of which 39 (15.5%) experienced tremors, 181 patients (including 32 patients who experienced tremors) were randomly assigned to a training dataset, and the remaining were assigned to an external validation set. We used a recursive feature elimination algorithm to train the training dataset, in turn, through 10-fold cross-validation. The classification performance of the classifer was then used as the evaluation criterion for these subsets to find the subset of optimal features. A neural network was used as a classification algorithm to accurately predict tremors using the subset of optimal features. This model was subsequently tested in the validation dataset. The subset of optimal features contained seven variables (creatinine, D-dimer, total protein, calcium ion, platelet distribution width, serum kalium, and fibrinogen), and the highest accuracy obtained was 0.8288. The neural network model based on these seven variables obtained an area under the curve (AUC) value of 0.9726, an accuracy of 0.9345, a sensitivity of 0.9712, and a specificity of 0.7586 in the training set. Meanwhile, the external validation achieved an accuracy of 0.8214, a sensitivity of 0.8378, and a specificity of 0.7000 in the validation dataset. This model was capable of predicting tremors caused by tacrolimus with an excellent degree of accuracy, which can be beneficial in the treatment of nephrotic syndrome patients.

A chitosan-vitamin C based injectable hydrogel improves cell survival under oxidative stress.

Stem cell transplantation technology provides the cell reconstruction of damaged heart a completely new therapy approach. Due to the inappropriate microenvironment such as reactive oxygen radicals caused by ischemic infarct, the survival and retention rates of cell transplantation are not desirable. A thermo sensitive chitosan-vitamin C (CSVC) hydrogel scaffold was developed to reduce oxidative stress injury after myocardial infarction, thereby increasing the cell survival rate of cell transplantation. Vitamin C was conjugated on the chitosan chain by electrostatic adsorption. Compared to chitosan, CSVC complex had a higher solubility and stronger antioxidant property. CSVC hydrogel has suitable gelation time and injectable properties. Scanning electron microscopy showed that chitosan hydrogels had three-dimensional porous structure with irregular pores interconnected throughout the construct. Live/dead and H&E staining results showed that CSVC hydrogel can support the survival and adhesion of cardiomyocytes. Compared with chitosan hydrogel, CSVC hydrogel can clearly improve the survival of cardiomyocytes and reduce the ROS level under H2O2-induced oxidative stress conditions. These results suggest that CSVC hydrogel has the potential to support the survival of cardiomyocytes in tissue engineering.