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Objective: Understand the cognition of mobile internet active population on the risk factors of diabetes. Methods: The internet survey through anonymous questionnaires with independent IP was carried out, in which the mobile internet active population were evaluated on their cognition degree to the risk factors of diabetes. Results: The awareness of the risk factors of diabetes in mobile internet active population is not high, and it is recommended to increase the popularization of risk factors of diabetes in mobile terminals to control and prevent diabetes.

To explore the risk factors for thyroid nodules and their correlation with diabetes and stroke, the authors conducted a study on 1000 patients with metabolic syndrome (MS). The analysis included variables such as gender, age, familial thyroid disease, salt intake, iodine intake, smoking, alcohol consumption, sleep quality, mental stress, exercise, BMI, blood pressure, diabetes, and baseline nodules. The Apriori algorithm of machine learning was used to derive 12 association rules ($\mathrm{\text{confidence}}\ge 0.5$), and the decision tree algorithm was used to derive 20 effective knowledge rules. The results showed that iodine intake, salt intake, BMI, and advanced age were high-risk factors for thyroid nodules. Exercise, BMI, and age were strongly correlated, while exercise, mental stress, iodine intake, and salt intake showed a strong correlation. Exercise, sleep, smoking, and alcohol consumption influenced mental stress, while age, diseases (diabetes, hypertension, obesity), and lifestyle habits influenced sleep quality. The risk of diabetes and stroke increased in patients with thyroid nodules, and there was a strong correlation among diabetes, stroke, and thyroid nodules.

Objective: The objective of this study was to determine the characteristics that increase the likelihood of acute kidney injury (AKI) in patients with severe community-acquired pneumonia (SCAP) and to create a predictive nomogram for AKI. Methods: This study comprised individuals who received a diagnosis of SCAP over the period from January 01, 2019, to December 31, 2023. The patients were categorized into two groups: AKI and non-AKI. The clinical and demographic characteristics of the patients were extracted from their medical records. An analysis was conducted to compare the rates of survival at 30 and 90 days among various groups. A multivariate analysis was performed to discover the autonomous risk factors linked to SCAP. The nomogram was built based on these parameters. A receiver operating characteristics (ROC) curve study was performed to assess the predictive accuracy of the nomogram, namely by measuring the area under the curve (AUC). Results: Initial screening was conducted on a total of 1218 patients. After excluding 744 individuals who did not meet the exclusion criteria, a total of 474 patients, with an average age of $74.22\pm 15.16$ years and a female representation of 33.3%, were selected for inclusion in this study. The prevalence of AKI in the subjects with SCAP was 47.7%. Out of these instances, 39.8% were categorized as AKI stage 1, 31.0% as AKI stage 2, and 29.2% as AKI stage 3. Those diagnosed with AKI exhibited a significantly higher mortality rate at both the 30-day and 90-day marks in comparison to those who did not have AKI. The independent risk factors for AKI were determined to include age, male gender, chronic renal disease, diabetes, and the utilization of nonsteroidal anti-inflammatory medicines (NSAIDs). In addition, higher levels of baseline serum creatinine and uric acid were identified as risk factors for AKI. The final predictive nomogram achieved an AUC of 0.811, with a 95% confidence interval (CI) ranging from 0.773 to 0.849. Conclusion: Our nomogram can serve as a valuable tool for evaluating the progression of AKI in patients with SCAP.

This study leveraged a large-scale dataset from NHANES 2013–2014 to gain insights into periodontitis pathogenesis and develop predictive tools. After cleaning and preprocessing the data, 15 crucial factors were identified from over 100 potential risk factors and utilized as input features for four machine learning algorithms: support vector machines (SVM), random forest (RF), neural network and XGBoost. The models were evaluated for periodontitis prediction performance through internal validation metrics such as specificity, accuracy, precision, recall and accuracy (area under the curve (AUC)). Notably, education level, household income and smoking status emerged as key risk factors, aligning with medical literature. While SVM and RFs excelled in specificity and accuracy, neural networks surpassed in precision and recall for periodontitis patients. XGBoost offered a balanced performance, making it a versatile choice. The feature importance analysis underscored the profound influence of socioeconomic factors and unhealthy lifestyle habits on periodontal health. This study contributes novel approaches and insights for periodontitis prevention and treatment, demonstrating clinical and societal significance. Future research should focus on optimizing and externally validating the model to enhance its generalizability and accuracy.