Objective To address the issue that current methods for classifying teeth in cone beam computed tomography(CBCT)images overly rely on precise segmentation and lack utilization of tooth morphology and positional information,a tooth classification method based on multi-view projection and Transformer architecture is proposed for accurately classifying teeth from CBCT images across all age groups,including pediatric cases,into 52 categories.Methods The coarse-to-fine tooth classification task was accomplished after enhancing the utilization of spatial positional information of the teeth by incorporating multi-view projection,integrating Transformer architecture,and combining semantic segmentation with instance segmentation.The two-digit notation system of the Federation Dentaire Internationale was adopted for classifying the teeth in CBCT images,and evaluating the effectiveness of multi-view fusion.Results The improved method effectively classified and numbered both permanent and deciduous teeth,achieving a tooth-level classification accuracy of 0.982.Conclusion The tooth classification method based on multi-view projection and Transformer architecture successfully integrates tooth category and positional information,and improves the accuracies of tooth classification and numbering,providing a more precise foundation for the formulation of personalized treatment schemes.

Objective To address the issue that current methods for classifying teeth in cone beam computed tomography(CBCT)images overly rely on precise segmentation and lack utilization of tooth morphology and positional information,a tooth classification method based on multi-view projection and Transformer architecture is proposed for accurately classifying teeth from CBCT images across all age groups,including pediatric cases,into 52 categories.Methods The coarse-to-fine tooth classification task was accomplished after enhancing the utilization of spatial positional information of the teeth by incorporating multi-view projection,integrating Transformer architecture,and combining semantic segmentation with instance segmentation.The two-digit notation system of the Federation Dentaire Internationale was adopted for classifying the teeth in CBCT images,and evaluating the effectiveness of multi-view fusion.Results The improved method effectively classified and numbered both permanent and deciduous teeth,achieving a tooth-level classification accuracy of 0.982.Conclusion The tooth classification method based on multi-view projection and Transformer architecture successfully integrates tooth category and positional information,and improves the accuracies of tooth classification and numbering,providing a more precise foundation for the formulation of personalized treatment schemes.

Objective Starting from the actual numbers of health personnel of tertiary public hospitals of Traditional Chinese Medicine(TCM),to quantitatively analyze the influencing factors on the allocation of human resources and obtain a prediction model.Methods The balanced panel data from 517 Tertiary Public TCM Hospitals in the period of 2011-2020 were collected,and the two-way fixed effects model was used to empirically analyze the impact of scale,demand and other factors on the actual number of health personnel in these hospitals.Result The number of beds is a key factor affecting the human resource allocation of Public TCM Hospitals,and various factors such as de-mand,policy,price,efficiency,and administrative management also have significant impacts on the allocation.The demand for outpatient services,government financial support,and efficiency of resource utilization are all promoting factors,while the increase in human resource prices,income generation efficiency,and administrative manage-ment levels have negative effects.A prediction model is proposed.Conclusion The planning principle of matching bed numbers with human resources allocation is in line with the actual environment.When predicting the total personnel allocation or authorized strength,various factors should also be fully considered,which can provide reference for the formulation of human resource policies in Public TCM Hospitals.

Objective To investigate the correlation between helicobacter pylori (H.pylori) infection with gastric muco?sa pathologic changes in chronic gastritis. Methods A total of 250 patients with chronic gastritis who came to Tianjin Union Medicine Center from November 2011 to March 2013 were collected in this study. Electronic gastroscope examina?tions, pathology and Urea-14C breath tests were performed in patients. There were 153 cases with chronic atrophic gastritis (CAG group), and 97 cases without chronic atrophic gastritis (non-CAG group). The positive rate of H.pylori was compared between two groups. At the same time the positive rate of H.pylori was compared between different parts of CAG patients. The positive rates of H.pylori were compared between different pathologic features of chronic gastritis (active degree, the de?gree of inflammation, atrophy and intestinal classification). Results The positive rate of H.pylori was higher in CAG pa?tients than that of non-CAG patients (70.6%vs 35%,χ2=30.552). The positive rate of H.pylori was higher in antral and cor?pus atrophy of CAG group than that of antral atrophy (82.6%vs 65.4%,χ2=4.578). With the aggravating activity of gastritis, the inflammation, chronic gastritis, atrophy and intestinal classification, the positive rate of H.pylori was increased (χ2=200.643, 206.715, 73.286, 218.432). Conclusion H.pylori infection is related with chronic gastritis, chronic atrophic gas?tritis. And antral and corpus atrophy CAG is closely related with H.pylori infection.