Herein we report the synthesis and characterization of electro-conductive chitosan–gelatin–agar (Cs-Gel-Agar) based PEDOT: PSS hydrogels for tissue engineering. Cs-Gel-Agar porous hydrogels with 0–2.0% (v/v) PEDOT: PSS were fabricated using a thermal reverse casting method where low melting agarose served as the pore template. Sample characterizations were performed by means of scanning electron microscopy (SEM), attenuated total reflectance–Fourier transform infrared spectroscopy (ATR–FTIR), X-ray diffraction analysis (XRD) and electrochemical impedance spectroscopy (EIS). Our results showed enhanced electrical conductivity of the cs-gel-agar hydrogels when mixed with DMSO-doped PEDOT: PSS wherein the optimum mixing ratio was observed at 1% (v/v) with a conductivity value of 3.35 × 10⁻⁴ S cm⁻¹. However, increasing the PEDOT: PSS content up to 1.5 % (v/v) resulted in reduced conductivity to 3.28 × 10⁻⁴ S cm⁻¹. We conducted in vitro stability tests on the porous hydrogels using phosphate-buffered saline (PBS) solution and investigated the hydrogels’ performances through physical observations and ATR–FTIR characterization. The present study provides promising preliminary data on the potential use of Cs-Gel-Agar-based PEDOT: PSS hydrogel for tissue engineering, and these, hence, warrant further investigation to assess their capability as biocompatible scaffolds.

We propose 2D NbSe ₂ H/g-ZnO vdW type-II heterostructure as a promising photocatalytic material for water splitting.

The use of anthropometric measurements in machine learning algorithms for hypertension prediction enables the development of simple, non-invasive prediction models. However, different machine learning algorithms were utilized in conjunction with various anthropometric data, either alone or in combination with other biophysical and lifestyle variables. It is essential to assess the impacts of the chosen machine learning models using simple anthropometric measurements. We developed and tested 13 machine learning methods of neural network, ensemble, and classical categories to predict hypertension in adolescents using only simple anthropometric measurements. The imbalanced dataset of 2461 samples with 30.1% hypertension subjects was first partitioned into 90% for training and 10% for validation. The training dataset was reduced to eight simple anthropometric measurements: age, C index, ethnicity, gender, height, location, parental hypertension, and waist circumference using correlation coefficient. The Synthetic Minority Oversampling Technique (SMOTE) combined with random under-sampling was used to balance the dataset. The models with optimal hyperparameters were assessed using accuracy, precision, sensitivity, specificity, F1-score, misclassification rate, and AUC on the testing dataset. Across all seven performance measures, no model consistently outperformed the others. LightGBM was the best model for all six performance metrics, except sensitivity, whereas Decision Tree was the worst. We proposed using Bayes’ Theorem to assess the models’ applicability in the Sarawak adolescent population, resulting in the top four models being LightGBM, Random Forest, XGBoost, and CatBoost, and the bottom four models being Logistic Regression, LogitBoost, SVM, and Decision Tree. This study demonstrates that the choice of machine learning models has an effect on the prediction outcomes.

Trifunctional MoTe₂/As has been designed to address the limitation of a single layer material.