BACKGROUND: The finite element method has been wildly used in stomatology,but,the modeling methods and models were different and lack of standard.There is not biomechanical analysis software which specialized indental implantology.OBJECTIVE: To compile software that can generate alveoli-implant complex finite element models.METHODS: Based on PC,CAD,finite element analysis,database and electronic table software,software that includes the functions of modeling,database and assembles was programmed using Visual Basic for Application programming language.RESULTS AND CONCLUSION: Various kinds of alveoli-implant models could obtain though the software.A software package that can generate alveoli-implant finite element models rapidly and automatically could be compiled with the complex of AutoDesk Mechanical Desktop 2009DX,ANSYS Workbench 10.1 and Visual Basic for Application.

Objective:To investigate the diagnostic value of endoscopic ultrasound elastography (EUS-E) in endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) for malignant occupying lesions in gastrointestinal adjacent tissue.Methods:Clinical data of 54 patients (57 lesions) undergoing EUS-FNA from January 2020 to April 2021 in the General Hospital of Ningxia Medical University were collected. Thirty patients (31 lesions) who received FNA assisted by EUS-E from May 2020 to February 2021 were enrolled in the EUS-E group, and 24 patients (26 lesions) who underwent routine EUS-FNA without EUS-E in the non-EUS-E group. The diagnostic efficacy of EUS-FNA was evaluated.The diagnostic efficacy of EUS-E group and non EUS-E group was compared. EUS-E score of EUS-E group was analyzed.Results:The overall sensitivity, specificity, positive predictive value, negative predictive value and accuracy of EUS-FNA in the diagnosis of malignant occupying lesions in gastrointestinal adjacent tissue were 80.5% (33/41), 100.0% (16/16), 100.0% (33/33), 66.7% (16/24) and 86.0% (49/57), respectively. There were no significant differences in sensitivity [78.6% (22/28) VS 84.6% (11/13), P=0.232] or accuracy [83.8% (31/37) VS 90.0% (18/20), P=0.156] of EUS-FNA for pancreatic lesions and other lesions (mediastinal and celiac lesions). Postoperative complications occurred in 1 patient (1.85%, 1/54). Also there were no significant differences in sensitivity [84.0% (21/25) VS 81.3% (13/16), P=0.186] or accuracy [87.1% (27/31) VS 88.5% (23/260, P=0.186] of diagnosis of malignant occupying lesions between EUS-E group and non-EUS-E group. In the EUS-E group, EUS-E score≥3 was highly consistent with the definite diagnosis ( Kappa=0.63). Conclusion:EUS-FNA is a safe and effective cytological and pathological method for diagnosis in gastrointestinal adjacent tissue. EUS-E score can well predict benign and malignant lesions, but EUS-FNA assisted by EUS-E does not show superiority in diagnostic sensitivity or accuracy.

Objective To observe the value of artificial neural network(ANN)model based on recursive feature elimination-support vector machine(RFE-SVM)for differentiating ductal carcinoma in situ(DCIS)and DCIS complicated with microinvasion(DCISM).Methods Totally 296 female patients with single breast cancer(244 cases of DCIS and 52 cases of DCISM)were retrospectively collected as training set.Then 120 female patients with single breast cancer(87 cases of DCIS and 33 cases of DCISM)were prospectively enrolled as validation set.The general data,mammography and MRI findings were compared between sets.The optimal feature subsets for establishing ANN model were screened.Receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the efficacy of ANN model for differentiating DCIS and DCISM.Results Ki-67 index,the minimum apparent diffusion coefficient(ADCmin),nuclear grade,ADCheterogeneity,maximum diameter of lesion,patient's age,P63,lesion enhancement type,calcification status and necrosis were the selected top 10 optimal feature subsets.The accuracy,sensitivity,specificity,positive predictive,negative predictive and AUC of ANN model for differentiating DCIS and DCISM was 91.55％,63.46％,97.54％,84.62％,92.61％and 0.950 in training set,respectively,while was 80.00％,69.70％,83.91％,62.16％,87.95％and 0.896 in validation set,respectively.The calibration curves of ANN model were consistent with the ideal curves in both training and validation set(P=0.355,0.480),which also expressed high clinical net benefit.Conclusion ANN model based on SVM-RFE could be used to differentiate DCIS and DCISM effectively.

Objective:To investigate the morbidity of cerebrovascular disease among residents ≥30 years in Pengzhou, Sichuan Province, and analyze the effect of physical activity level on the risk of morbidity of cerebrovascular disease.Methods:From 2004 to 2008, people from Pengzhou, Sichuan Province were randomly selected. All the local people aged 30-79 were asked to receive a questionnaire survey, physical examination, and long-term follow-up to determine the morbidity of cerebrovascular disease. The physical activity level and the morbidity of cerebrovascular disease were described, and Cox proportional hazard regression models were used to evaluate the association of domain-specific physical activity with the risk of morbidity of cerebrovascular disease.Results:In 55 126 participants, there were 5 290 new cases of cerebrovascular disease, with a cumulative incidence of 9.60%. After the adjustment for multiple confounding factors, multivariate Cox proportional hazard regression analysis showed that increased levels of occupational, transportation, and total physical activity reduced the risk of cerebrovascular disease and its subtypes (cerebral hemorrhage, cerebral infarction). The highest group of occupational physical activity level had the lowest risk of cerebrovascular disease, with a hazard ratio ( HR) value of 0.81 (95% CI: 0.75-0.88), the highest group of transportation physical activity level had the lowest risk of cerebrovascular disease, with an HR value of 0.84 (95% CI: 0.78-0.91), the highest group of total physical activity level had the lowest risk of cerebrovascular disease, with an HR value of 0.87 (95% CI: 0.80-0.94), compared with the lowest group of corresponding physical activity. No association was found between the household/leisure-time physical activity level and the risk of cerebrovascular disease and its subtypes (cerebral hemorrhage, cerebral infarction). Conclusions:In project areas of Pengzhou, Sichuan Province, increased physical activity has been associated with reduced morbidity of cerebrovascular disease and its subtypes (cerebral hemorrhage, cerebral infarction). Increased levels of physical activity in adults are encouraged for health benefits.