ChatGPT Combining Machine Learning for the Prediction of Nanozyme Catalytic Types and Activities.

The design of nanozymes with superior catalytic activities is a prerequisite for broadening their biomedical applications. Previous studies have exerted significant effort in theoretical calculation and experimental trials for enhancing the catalytic activity of nanozyme. Machine learning (ML) provides a forward-looking aid in predicting nanozyme catalytic activity. However, this requires a significant amount of human effort for data collection. In addition, the prediction accuracy urgently needs to be improved. Herein, we demonstrate that ChatGPT can collaborate with humans to efficiently collect data. We establish four qualitative models (random forest (RF), decision tree (DT), adaboost random forest (adaboost-RF), and adaboost decision tree (adaboost-DT)) for predicting nanozyme catalytic types, such as peroxidase, oxidase, catalase, superoxide dismutase, and glutathione peroxidase. Furthermore, we use five quantitative models (random forest (RF), decision tree (DT), Support Vector Regression (SVR), gradient boosting regression (GBR), and fully connected deep neuron network (DNN)) to predict nanozyme catalytic activities. We find that GBR model demonstrates superior prediction performance for nanozyme catalytic activities (R2 = 0.6476 for Km and R2 = 0.95 for Kcat). Moreover, an open-access web resource, AI-ZYMES, with a ChatGPT-based nanozyme copilot is developed for predicting nanozyme catalytic types and activities and guiding the synthesis of nanozyme. The accuracy of the nanozyme copilot's responses reaches more than 90% through the retrieval augmented generation. This study provides a new potential application for ChatGPT in the field of nanozymes.

Association between systemic immune-inflammation index and diabetes: a population-based study from the NHANES.

Background: Systemic Immune-Inflammation Index (SII) has been reported to be associated with diabetes. We aimed to assess possible links between SII and diabetes. Methods: Data were obtained from the 2017-2020 National Health and Nutrition Examination Survey (NHANES) database. After removing missing data for SII and diabetes, we examined patients older than 20 years. Simultaneously, the relationship between SII and diabetes was examined using weighted multivariate regression analysis, subgroup analysis, and smooth curve fitting. Results: There were 7877 subjects in this study, the average SII was 524.91 ± 358.90, and the prevalence of diabetes was 16.07%. Weighted multivariate regression analysis found that SII was positively associated with diabetes, and in model 3, this positive association remained stable (OR = 1.04; 95% CI: 1.02-1.06; p = 0.0006), indicating that each additional unit of SII, the possibility of having diabetes increased by 4%. Gender, age, BMI, regular exercise, high blood pressure, and smoking did not significantly affect this positive link, according to the interaction test (p for trend>0.05). Discussion: Additional prospective studies are required to examine the precise connection between higher SII levels and diabetes, which may be associated with higher SII levels.