MXene hydrogels: fundamentals and applications.

Hydrogels have recently garnered tremendous interest due to their potential application in soft electronics, human-machine interfaces, sensors, actuators, and flexible energy storage. Benefiting from their impressive combination of hydrophilicity, metallic conductivity, high aspect ratio morphology, and widely tuneable properties, when two-dimensional (2D) transition metal carbides/nitrides (MXenes) are incorporated into hydrogel systems, they offer exciting and versatile platforms for the design of MXene-based soft materials with tunable application-specific properties. The intriguing and, in some cases, unique properties of MXene hydrogels are governed by complex gel structures and gelation mechanisms, which require in-depth investigation and engineering at the nanoscale. On the other hand, the formulation of MXenes into hydrogels can significantly increase the stability of MXenes, which is often the limiting factor for many MXene-based applications. Moreover, through simple treatments, derivatives of MXene hydrogels, such as aerogels, can be obtained, further expanding their versatility. This tutorial review intends to show the enormous potential of MXene hydrogels in expanding the application range of both hydrogels and MXenes, as well as increasing the performance of MXene-based devices. We elucidate the existing structures of various MXene-containing hydrogel systems along with their gelation mechanisms and the interconnecting driving forces. We then discuss their distinctive properties stemming from the integration of MXenes into hydrogels, which have revealed an enhanced performance, compared to either MXenes or hydrogels alone, in many applications (energy storage/harvesting, biomedicine, catalysis, electromagnetic interference shielding, and sensing).

Prediction of Cardiovascular Complication in Patients with Newly Diagnosed Type 2 Diabetes Using an XGBoost/GRU-ODE-Bayes-Based Machine-Learning Algorithm.

BACKGRUOUND: Cardiovascular disease is life-threatening yet preventable for patients with type 2 diabetes mellitus (T2DM). Because each patient with T2DM has a different risk of developing cardiovascular complications, the accurate stratification of cardiovascular risk is critical. In this study, we proposed cardiovascular risk engines based on machine-learning algorithms for newly diagnosed T2DM patients in Korea. METHODS: To develop the machine-learning-based cardiovascular disease engines, we retrospectively analyzed 26,166 newly diagnosed T2DM patients who visited Seoul St. Mary's Hospital between July 2009 and April 2019. To accurately measure diabetes-related cardiovascular events, we designed a buffer (1 year), an observation (1 year), and an outcome period (5 years). The entire dataset was split into training and testing sets in an 8:2 ratio, and this procedure was repeated 100 times. The area under the receiver operating characteristic curve (AUROC) was calculated by 10-fold cross-validation on the training dataset. RESULTS: The machine-learning-based risk engines (AUROC XGBoost=0.781±0.014 and AUROC gated recurrent unit [GRU]-ordinary differential equation [ODE]-Bayes=0.812±0.016) outperformed the conventional regression-based model (AUROC=0.723± 0.036). CONCLUSION: GRU-ODE-Bayes-based cardiovascular risk engine is highly accurate, easily applicable, and can provide valuable information for the individualized treatment of Korean patients with newly diagnosed T2DM.

Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness.

The early prediction of diabetes can facilitate interventions to prevent or delay it. This study proposes a diabetes prediction model based on machine learning (ML) to encourage individuals at risk of diabetes to employ healthy interventions. A total of 38,379 subjects were included. We trained the model on 80% of the subjects and verified its predictive performance on the remaining 20%. Furthermore, the performances of several algorithms were compared, including logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), Cox regression, and XGBoost Survival Embedding (XGBSE). The area under the receiver operating characteristic curve (AUROC) of the XGBoost model was the largest, followed by those of the decision tree, logistic regression, and random forest models. For the survival analysis, XGBSE yielded an AUROC exceeding 0.9 for the 2- to 9-year predictions and a C-index of 0.934, while the Cox regression achieved a C-index of 0.921. After lowering the threshold from 0.5 to 0.25, the sensitivity increased from 0.011 to 0.236 for the 2-year prediction model and from 0.607 to 0.994 for the 9-year prediction model, while the specificity showed negligible changes. We developed a high-performance diabetes prediction model that applied the XGBSE algorithm with threshold adjustment. We plan to use this prediction model in real clinical practice for diabetes prevention after simplifying and validating it externally.