This study innovatively incorporates on-site teaching with the International Museum of Stomatology into the curriculum to establish an immersive and intuitive teaching mode,promoting education from both theoretical and practical dimensions.The teaching effect is comprehensively evaluated to explore the pathway to optimization.Multi-dimensional questionnaires are designed to collect feedback data from students on teaching satisfaction,knowledge mastery,professional identity,and humanistic literacy perception,fol-lowed by in-depth quantitative and qualitative analyses.The results demonstrate that this teaching mode significantly enhances literacy,playing a critical role in helping stomatological students fully understand professional knowledge and humanistic connotations while sub-stantially improving their professional identity.This teaching mode gives a direction for innovative stomatological education,holds sig-nificant importance for cultivating stomatological professionals with both clinical skills and humanistic literacy,possessing substantial potential for promotion,application,and further refinement.

Objective:To develop a deep learning-based automated analysis system for precise segmentation of the optic cup and disc in fundus images and automatic measurement of the vertical cup-to-disc ratio (CDR) for early risk assessment and screening of chronic glaucoma.Methods:The proposed automated system comprised three modules: a dual coding-attention U-net (DCoAtUNet) segmentation network for optic cup and disc segmentation, a conditional random field (CRF) post-processing module, and a CDR measurement and glaucoma screening module based on the segmentation results.The system was designed to enhance boundary detection accuracy and measurement stability and its performance was evaluated on the publicly available Drishti-GS dataset.The dataset was divided into a training set and a test set in a 1∶1 ratio.Dice coefficient and intersection over union (IoU) were used to quantify segmentation accuracy and regional consistency, while accaracy, precision, recall, and F1-score were employed to assess glaucoma screening performance.Results:The DCoAtUNet combined with CRF post-processing achieved Dice coefficients of 0.976 0 for the optic disc and 0.908 1 for the optic cup, with corresponding IoU values of 0.953 4 and 0.837 9, demonstrating high segmentation precision and stability in boundary identification and region overlap.In glaucoma screening, the system achieved an accuracy of 0.843 1, precision of 0.840 9, recall of 0.973 7, and F1-score of 0.902 4, indicating good diagnostic sensitivity and accuracy.Conclusions:By integrating high-precision segmentation and automated measurement strategies, the DCoAtUNet+ CRF model significantly improves the accuracy and stability of CDR evaluation.It effectively assists in identifying high-risk individuals during early glaucoma screening and shows strong potential for clinical application in computer-aided diagnosis workflows.

Objective:To develop a deep learning-based automated analysis system for precise segmentation of the optic cup and disc in fundus images and automatic measurement of the vertical cup-to-disc ratio (CDR) for early risk assessment and screening of chronic glaucoma.Methods:The proposed automated system comprised three modules: a dual coding-attention U-net (DCoAtUNet) segmentation network for optic cup and disc segmentation, a conditional random field (CRF) post-processing module, and a CDR measurement and glaucoma screening module based on the segmentation results.The system was designed to enhance boundary detection accuracy and measurement stability and its performance was evaluated on the publicly available Drishti-GS dataset.The dataset was divided into a training set and a test set in a 1∶1 ratio.Dice coefficient and intersection over union (IoU) were used to quantify segmentation accuracy and regional consistency, while accaracy, precision, recall, and F1-score were employed to assess glaucoma screening performance.Results:The DCoAtUNet combined with CRF post-processing achieved Dice coefficients of 0.976 0 for the optic disc and 0.908 1 for the optic cup, with corresponding IoU values of 0.953 4 and 0.837 9, demonstrating high segmentation precision and stability in boundary identification and region overlap.In glaucoma screening, the system achieved an accuracy of 0.843 1, precision of 0.840 9, recall of 0.973 7, and F1-score of 0.902 4, indicating good diagnostic sensitivity and accuracy.Conclusions:By integrating high-precision segmentation and automated measurement strategies, the DCoAtUNet+ CRF model significantly improves the accuracy and stability of CDR evaluation.It effectively assists in identifying high-risk individuals during early glaucoma screening and shows strong potential for clinical application in computer-aided diagnosis workflows.

This study innovatively incorporates on-site teaching with the International Museum of Stomatology into the curriculum to establish an immersive and intuitive teaching mode,promoting education from both theoretical and practical dimensions.The teaching effect is comprehensively evaluated to explore the pathway to optimization.Multi-dimensional questionnaires are designed to collect feedback data from students on teaching satisfaction,knowledge mastery,professional identity,and humanistic literacy perception,fol-lowed by in-depth quantitative and qualitative analyses.The results demonstrate that this teaching mode significantly enhances literacy,playing a critical role in helping stomatological students fully understand professional knowledge and humanistic connotations while sub-stantially improving their professional identity.This teaching mode gives a direction for innovative stomatological education,holds sig-nificant importance for cultivating stomatological professionals with both clinical skills and humanistic literacy,possessing substantial potential for promotion,application,and further refinement.

Objective: To explore joint effects between body fat mass and insulin resistance with dyslipidemia in children, in order to provide scientific evidence for the prevention and treatment of dyslipidemia in children. Methods: Data was derived from the second follow up survey (2021) of the Huantai Childhood Cardiovascular Health Cohort. The complete information of a total of 1 322 children was included in the study. Multivariate Logistic regression model was used to analyze the association among fat mass percentage (FMP), fat mass index (FMI), subcutaneous fat mass (SFM) and visceral fat mass (VFM) and dyslipidemia. Restrictive cubic spline model was used to analyze dose response relationship between levels of each of the four body fat mass indicators and dyslipidemia. Multivariate Logistic regression model was used to analyze the association of interaction of body fat mass indicators and insulin resistance (IR) with dyslipidemia. Results: Boys had higher VFM and fasting plasma glucose (FPG) levels, while FMP, FMI, SFM, total cholesterol (TC), triglycerides (TG), and high density lipoprotein cholesterol (HDL-C) levels were all lower than those of girls ( t/Z =3.22, 2.58, -15.85, -7.35, -6.49, -2.40, -4.05 , -2.40, P <0.05). After adjusting for all covariates, compared with children with normal FMP, those with higher FMP had an increased likelihood of elevated triglyceride levels ( OR =4.26, 95% CI =2.58-7.09) and low HDL-C levels ( OR =4.10, 95% CI =2.51-6.76). FMI, SFM, and VFM observed similar results to FMP ( P <0.05). Additionally, the additive interaction analyses showed that all four indicators of elevated body fat mass interacted with IR, increasing the likelihood of dyslipidemia in children ( P <0.05). There were linear or nonlinear dose response association between each of four body fat mass indicators and dyslipidemia. Conclusions Elevated body fat mass increases the likelihood of dyslipidemia in children, and the likelihood of dyslipidemia further would increase if children have concomitant IR. Therefore, it is necessary to pay more attention to children with elevated body fat mass and IR to prevent the occurrence of dyslipidemia.

Using big data and artificial intelligence to establish a multi-point monitoring, early warning, and disposal system to achieve early warning and intervention of infectious disease outbreaks is an important means of controlling the spread of the epidemic. Taking Xiaoshan district as an example, this study analyzes the monitoring contents, warning methods, and application effectiveness of the infectious disease monitoring, early warning and disposal system. Based on Xiaoshan′s health big data resources, the system starts with syndrome, disease diagnosis and etiology. Through advanced technologies such as artificial intelligence and block chain, it realizes early identification of infectious disease outbreaks, data fusion, multi-cross collaboration, and closed-loop management. It has improved the sensitivity of clustered outbreaks monitoring and the effectiveness of epidemic disposal and provided a reference for grassroots disease prevention and control departments to establish an infectious disease monitoring and early warning system.

Using big data and artificial intelligence to establish a multi-point monitoring, early warning, and disposal system to achieve early warning and intervention of infectious disease outbreaks is an important means of controlling the spread of the epidemic. Taking Xiaoshan district as an example, this study analyzes the monitoring contents, warning methods, and application effectiveness of the infectious disease monitoring, early warning and disposal system. Based on Xiaoshan′s health big data resources, the system starts with syndrome, disease diagnosis and etiology. Through advanced technologies such as artificial intelligence and block chain, it realizes early identification of infectious disease outbreaks, data fusion, multi-cross collaboration, and closed-loop management. It has improved the sensitivity of clustered outbreaks monitoring and the effectiveness of epidemic disposal and provided a reference for grassroots disease prevention and control departments to establish an infectious disease monitoring and early warning system.

Objective To explore the application value of adjusting contrast medium dosage according to total body weight(TBW),lean body weight(LBW)and body surface area(BSA)in coronary computed tomography angiography(CCTA)of overweight patients.Methods A total of 150 patients with body mass index(BMI)≥24 kg/m2 undergoing CCTA examination were prospectively selected and randomly divided into TBW group,LBW group,and BSA group,with 50 patients in each group.All three groups used contrast medium iodixanol(320 mg I/mL)and the fixed injection time was 12 s.The dosage of contrast medium was 0.8 mL/kg(TBW);1.04 mL/kg(LBW)for male and 1.11 mL/kg(LBW)for female;BSA 30 mL/m2.The dosage and flow rate of contrast medium were compared among the three groups,the image quality was evaluated by subjective and objective ways,and the correlation between arterial enhancement and TBW,LBW and BSA were analyzed.Results According to a 5-point scale,the image quality of three groups met the clinical diagnostic requirements(both≥3 points).Compared with TBW group,the dosage of contrast medium in LBW group and BSA group decreased by 13.38％and 10.62％respectively,and the flow rate decreased by 13.41％and 10.61％respectively,and the differences were statistically significant(P＜0.05).The coronary CT values,signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)in LBW and BSA groups were lower than those in TBW group(P＜0.05),and the variation range of CT values in LBW group were the smallest.There were no statistical differences in subjective scores of image quality among the three groups(P＞0.05).The coronary CT values were slightly correlated with TBW(r=0.342,P=0.015),and were not correlated with LBW or BSA(r=-0.207,P=0.150;r=-0.204,P=0.156).Conclusion LBW is the best body index to calculate the dosage of contrast medium for coronary artery enhancement in overweight patients.

Objective:To explore the application effect of LEARNS(L: listen; E: establish; A: adopt; R: reinforce; N: name; S: strengthen) model in health education for patients undergoing coronary computed tomography angiography(CCTA) examination, so as to provide basis for improving image quality and optimizing examination process.Methods:One hundred and sixteen patients who underwent CCTA examination at the Second Hospital of Shanxi Medical University from April to September 2023 were selected as the research subjects by convenience sampling method. A non-concurrent controlled study was conducted, with 58 patients examined from April to June 2023 as the control group to implement conventional health education, and 58 patients examined from July to September 2023 as the observation group to implement LEARNS model health education. The differences in examination knowledge scores, anxiety level, coronary artery image quality, examination duration and health education satisfaction between the two groups were compared.Results:There were 31 males and 27 females in the control group, aged (51.97 ± 9.39) years old. There were 30 males and 28 females in the observation group, aged (53.48 ± 8.95) years old. There was no significant difference in the examination knowledge scores and anxiety level before the intervention between the two groups ( P>0.05). After the intervention, the examination knowledge score of the patients in the observation group was (12.95 ± 1.15) points, which was higher than (9.02 ± 1.55) points in the control group, the difference was statistically significant ( t=-15.53, P<0.05). The acceptance rate of image quality in the observation group was 96.6% (56/58), which was higher than 87.9% (51/58), but the difference was not statistically significant ( χ2=1.93, P>0.05). The proportion of patients with level 1 image quality in the observation group was 81.0%(47/58), which was higher than 55.2%(32/58) in the control group, the difference was statistically significant ( χ2=8.93, P<0.05). The total score of health education satisfaction in the observation group was (39.81 ± 1.81) points, which was higher than (31.19 ± 3.10) points in the control group, the difference was statistically significant ( t=-18.27, P<0.05). The score of anxiety scale and duration of examination in the observation group were (36.81 ± 2.12) points and (6.72 ± 1.02) min respectively, which were significantly lower than (41.12 ± 2.46) points and (9.40 ± 1.49) min in the control group, the differences were statistically significant ( t=10.11, 11.29, both P<0.05). Conclusions:LEARNS model is used for health education of patients undergoing CCTA examination, which helps to improve patients′ knowledge level, health education satisfaction and coronary image quality. It can also alleviate examination anxiety and shorten examination time. It is worthy of promotion.

Objective:To investigate the risk factors of iodinated contrast agent extravasation in tumor patients with intravenous high pressure injection, and to develop a nomogram model of contrast agent extravasation, so as to provide reference for the detection of extravasation risk factors before examination.Methods:A retrospective case-control study was used in this study. Two hundred and two tumor patients with iodinatedcontrast agent extravasation who underwent enhanced CT examination in the Second Hospital of Shanxi Medical University, Shanxi People′s Hospital and Shanxi tumor Hospital from September 2021 to September 2023 were selected by random sampling method as the case group, and 202 tumor patients without iodinated contrast agent extravasation who underwent enhanced CT examination in the above 3 hospitals during the same period were randomly selected as the control group. Multivariate Logistic regression was used to analyze the independent risk factors of iodinated contrast agent extravasation and a nomogram prediction model was established by R4.3.1 statistical software. The receiver operating characteristic (ROC) curve, calibration curve and Hosmer-Lemeshow goodness of fit test were used to evaluate the predictive efficiency of the model.Results:A total of 404 tumor patients were included in this study. There were 97 males and 105 females in the case group, aged 30-88 (62.97 ± 0.91) years old; and 123 males and 79 females in the control group, aged 31-85 (61.38 ± 0.73) years old. Multivariate Logistic regression analysis showed that age, sex, injection rate, contrast agent concentration, contrast osmotic concentration, long-term chemotherapy and nurses′ working years were independent risk factors for iodinated contrast agent extravasation ( OR values were 0.306-6.365, all P<0.05). The area under the ROC curve predicted by the nomogram model was 0.814 (95% CI 0.714-0.875), the specificity was 78.1%, the sensitivity was 70.2%, the slope of calibration curve was close to 1, and the Hosmer-Lemeshow goodness of fit test χ2 = 11.47, P>0.05. Conclusions:The nomogram model established in this study can accurately predict the risk of iodinated contrast agent extravasation in tumor patients, and provide a reference for the detection of high-risk factors of extravasation before examination.