OBJECTIVES: This study aimed to use machine learning algorithms to build a prediction model of the first permanent molar caries of 9-year-old children in Suzhou and screen out risk factors. METHODS: Random stratified whole group sampling was applied to randomly select 9-year-old students from 38 primary schools in 14 townships and streets in Wuzhong District for oral examination and questionnaire survey. Multifactor Logistics regression was used to analyze the risk factors of tooth decay. The data set was randomly divided into training sets and verification sets according to 8∶2, and R 4.3.1 was used to build five machine learning algorithms: random forest, decision tree, extreme gradient boosting (XGBoost), Logistics regression, and lightweight gradient enhancement (LightGBM). The predictive effect of these five models was evaluated using the area under the characteristic curve (AUC). The marginal contribution of quantitative characteristics to the caries prediction model was determined through Shapley additive explanations (SHAP). RESULTS: This study included 7 225 samples that met the standard. The caries rate of the first permanent molar was 54.96%. Multifactor Logistic regression analysis showed that sweet drinks, dessert and candy, snack frequency, and snacks before going to bed after brushing teeth were correlated with the occurrence of first permanent molar caries (P<0.05). The AUC values of decision tree, Logistic regression, LightGBM, random forest, and XGBoost were 75.5%, 83.9%, 88.6%, 88.9%, and 90.1%, respectively. Compared with the variables after single heat coding, the SHAP value of high-frequency sweets (such as dessert candy ≥2 times a day, mother's sugary diet ≥2 times a day) and bad oral hygiene habits (such as frequent snacks before going to bed after brushing teeth and irregular brushing teeth) exhibited the highest positive. CONCLUSIONS XGBoost algorithm has a good prediction effect for first permanent molar caries in 9-year-old children. High-frequency sweet factors and bad oral hygiene habits have a strong positive impact on the risk of first permanent molar caries and are key drivers that can be used in the formulation of targeted interventions.
Humans ; Dental Caries/epidemiology* ; Child ; Machine Learning ; China/epidemiology* ; Molar ; Risk Factors ; Female ; Logistic Models ; Male ; Decision Trees ; Algorithms

Objective To explore the effect of sorafenib on HeLa cell proliferation by inducing cell apoptosis and autophagy and its impact on drug resistance.Methods The drug-resistant cell strains were constructed through in-termittent induction method,with concentrations of 0,2.5,5.0,7.5,10.0,15.0,20.0 μmol/L.HeLa cells were incubated with increasing concentrations of sorafenib with each concentration for 1 week.The drug-resistant cell strains with stable passages were collected.MTT assay was used to detect the effect of sorafenib on cell prolifer-ation.Cell cycle distribution was analyzed by flow cytometry.The change in the expression of drug-resistant and ap-optotic genes in the parents and drug-resistant cell strains under different drug concentrations was examined by semi-quantitative PCR.The changes of apoptotic related marker proteins LC3-Ⅰ and LC3-Ⅱ were detected by Westernblot.Results Stable drug-resistant strains were successfully obtained;Drug-treated cells were more blocked in the G1 phase.In drug-resistant cells,the expression of apoptosis suppressor gene Bcl-2 was significantly decreased and the apoptotic gene Bax as well as the drug-resistant genes were all significantly increased(P＜0.05).The LC3-Ⅱ/LC3-Ⅰ ratio of drug-resistant cells was significantly higher than that of parent cells(P＜0.05).Conclusions Sorafenib may block the cell cycle,suppress malignant cell proliferation and promote autophage.On one hand,autophagy participates in the development of cell drug resistance and promotes cell survival.On the other hand,drug-induced autophagy may activate some of apoptotic signaling pathway in drug-resistant cells and promote the reversal of cell drug resistance.