In the study of oral orthodontics, the dental tissue models play an important role in finite element analysis results. Currently, the commonly used alveolar bone models mainly have two kinds: the uniform and the non-uniform models. The material of the uniform model was defined with the whole alveolar bone, and each mesh element has a uniform mechanical property. While the material of the elements in non-uniform model was differently determined by the Hounsfield unit (HU) value of computed tomography (CT) images where the element was located. To investigate the effects of different alveolar bone models on the biomechanical responses of periodontal ligament (PDL), a clinical patient was chosen as the research object, his mandibular canine, PDL and two kinds of alveolar bone models were constructed, and intrusive force of 1 N and moment of 2 Nmm were exerted on the canine along its root direction, respectively, which were used to analyze the hydrostatic stress and the maximal logarithmic principal strain of PDL under different loads. Research results indicated that the mechanical responses of PDL had been affected by alveolar bone models, no matter the canine translation or rotation. Compared to the uniform model, if the alveolar bone was defined as the non-uniform model, the maximal stress and strain of PDL were decreased by 13.13% and 35.57%, respectively, when the canine translation along its root direction; while the maximal stress and strain of PDL were decreased by 19.55% and 35.64%, respectively, when the canine rotation along its root direction. The uniform alveolar bone model will induce orthodontists to choose a smaller orthodontic force. The non-uniform alveolar bone model can better reflect the differences of bone characteristics in the real alveolar bone, and more conducive to obtain accurate analysis results.
ObjectiveTo develop and validate a prediction model for postoperative hepatic dysfunction after Stanford type A aortic dissection (TAAD), providing a reference for early identification and intervention. MethodsWe retrospectively enrolled the patients with TAAD who underwent surgical treatment at Renmin Hospital of Wuhan University from August 2022 to August 2025 and at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from December 2022 to June 2025. The dataset was randomly divided into a training set and a validation set at a ratio of 7.5 : 2.5. Independent predictors were identified using univariate analysis and multivariate logistic regression. Predictive models were developed using logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost). Model performance was assessed using receiver operating characteristic (ROC) curves, DeLong tests, calibration curves, and decision curve analysis (DCA). A nomogram was constructed based on the optimal model. ResultsA total of 482 patients were included, comprising 368 males and 114 females, with a median age of 54 (45, 62) years. Among them, 214 (44.4%) patients developed postoperative hepatic dysfunction. Multivariable analysis identified five independent predictors: preoperative serum creatinine level, pericardial effusion, postoperative mechanical ventilation duration, procalcitonin, and total bilirubin (all P<0.05). In the validation cohort, the area under the curve (AUC) values of the LR, RF, and XGBoost models were 0.741, 0.725, and 0.712, respectively, with the LR model demonstrating the best overall performance. The DeLong test showed no significant difference in AUC among the three models (P>0.05). Calibration curves demonstrated better agreement for the LR model. The DCA indicated that the LR model provided greater net benefit across a wider range of threshold probabilities. ConclusionMachine learning models do not outperform traditional LR in predicting postoperative hepatic dysfunction after TAAD. The LR model demonstrats more stable predictive performance and greater clinical utility. The nomogram developed based on this model may provide a reference for individualized clinical risk assessment.