Objective:To systematically review the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures.Methods:PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang Database and VIP Database were systematically searched to collect literature on the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures from inception to June 30, 2024. The languages were limited to Chinese and English. Two researchers screened the literature according to the inclusion and exclusion criteria. Data extraction was performed using the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS), encompassing basic study characteristics, model development features, and model performance metrics. The predictors, validation methods, presentation formats, and predictive performance of the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures were evaluated. The prediction model risk-of-bias assessment tool (PROBAST) was employed to assess risk of bias and applicability of the included studies.Results:A total of 11 studies, comprising of 16 prediction models, were included, with a total sample size of 283-1 508 patients and a pulmonary infection incidence rate of 5.4%-16.25%. The independent predictive factors repeatedly included in the models were age, American Society of Anesthesiologists (ASA) scale, preoperative comorbidities, chronic obstructive pulmonary disease (COPD), preoperative albumin level, white blood cell count (WBC), and C-reactive protein (CRP) level. The models were internally validated in 7 studies and externally validated in 3. The models were visualized in the form of a nomogram in 7 studies and a web-based risk calculator in 1. Model prediction performance was analyzed: (1) In terms of the discrimination, 9 studies reported the area under the receiver operating characteristic curve (AUC), with the overall AUC range of 0.664-0.905. (2) In terms of the calibration, 5 studies had Hosmer-Lemeshow test, with the P-values all above 0.05; 2 studies reported the calibration plots, with the slopes close to 1 and the Brier scores of 0.016 and 0.112; 4 studies reported the sensitivity of the models of 73.91%-92.40% and specificity of 57.10%-92.41%. According to PROBAST, all 11 studies exhibited certain risk of bias while maintaining favorable applicability. Conclusions:Age, ASA scale, preoperative comorbidities, COPD, preoperative albumin level, WBC, and CRP level are found to be independent predictive factors repeatedly reported in the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures. The existing models demonstrate a robust overall prediction performance despite certain risks of bias.

Objective:To investigate the application of time series models in forecasting pre-hospital emergency ambulance trips in Handan City and develop the LPro ensemble model for improved prediction accuracy to support emergency resource allocation.Methods:Pre-hospital emergency data from Handan Emergency Medical Command Center (2019-2023) were retrospectively analyzed. From 324 799 original records, 289 949 valid records were included after cleaning. The training set (2019-2022: 215 918 records) included 35 527 records in 2019, 52 015 in 2020, 61 836 in 2021, and 66 540 in 2022. The validation set (2023) contained 74 031 records. ARIMA, linear trend seasonal, exponential smoothing, and Prophet models were fitted to the training set. The LPro ensemble model was constructed using MAPE-based weighting (linear trend seasonal model: 0.38, Prophet: 0.62). Performance metrics included MAPE, RMSE, MAE, and R 2. Results:Data showed annual growth (compound annual growth rate 23.27%) and seasonal patterns (October peaks, February troughs). Ambulance dispatches increased annually with monthly cyclical patterns. For 2023 validation predictions: ARIMA (MAPE 8.76%, RMSE 619, MAE 491, R 2 0.4563), linear trend seasonal (MAPE 9.83%, RMSE 671, MAE 545, R 2 0.3608), Prophet (MAPE 8.43%, RMSE 562, MAE 503, R 2 0.5513), exponential smoothing (MAPE 8.08%, RMSE 643, MAE 410, R 2 0.4124). LPro model showed superior performance (MAPE 7.05%, RMSE 491, MAE 393, R 2 0.6570), with 16.37% lower MAPE, 12.63% lower RMSE, 21.87% lower MAE, and 19.17% higher R 2 versus Prophet. Conclusion:The LPro ensemble model substantially enhances prediction accuracy and reliability, offering scientific support for emergency resource optimization and dispatch scheduling in Handan City.

Objective:To explore the application effects of a four-in-one blended teaching model integrating artificial intelligence, virtual reality, 3D printing, and case-based learning (CBL) in the clinical teaching of spinal tumors.Methods:We divided 89 students on training in the Department of Orthopedics of Peking University Third Hospital from September 2022 to August 2024 into control group ( n=47) and experimental group ( n=42). The control group adopted traditional teaching, and the experimental group adopted the four-in-one teaching model. At the end of clinical teaching, an artificial intelligence test and a questionnaire survey were administered to the students to evaluate the teaching effects. The two groups were compared using the independent samples t-test with the use of SPSS 27.0. Results:The experimental group was superior to the control group with significant improvements in the answer accuracy rate (66.67%, χ2=9.44, P=0.002), learning interest [(4.50±0.63), t=2.75, P=0.007], theoretical knowledge mastery [(4.64±0.69), t=7.74, P<0.001], clinical thinking [(4.48±0.71), t=9.08, P<0.001], practical skills [(4.13±0.89), t=2.69, P=0.009], scientific research innovation [(4.71±0.59), t=9.28, P<0.001], teacher-student interaction [(4.74±0.54), t=12.76, P<0.001], and classroom attention [(4.69±0.52), t=12.64, P<0.001]. At the same time, the students in the experimental group put forward numerous constructive feedback. Conclusions:The four-in-one blended teaching model combining artificial intelligence, virtual reality, 3D printing, and CBL can help undergraduate medical students better recognize and diagnose spinal tumors with a correct clinical thinking path, achieving good teaching effects.

Objective To explore the effect of exenatide on oxidative stress in the hypothalamus of diabetes mice and its potential mechanism.Methods After one week of adaptive feeding,C57BL/6J mice were randomly divided into the CON group(normal chaw diet),the T2DM group(high-fat diet,HFD),and the T2DM+Exe group(HFD+exenatide).After 8 weeks of HFD,mice in the T2DM+Exe group were intraperitoneally injected with exenatide[24 nmol/(kg·d)]for 8 weeks.The weight and glucose and lipid metabolism levels of the mice were measured,and the levels of inflammatory and adipokine factors in mice were detected using the ELISA method.Western Blot was used to detect the expression of melanocortin receptor-4(MC4R)and proopiomelanocor-tin(POMC)in the hypothalamus.Hypothalamic mitochondria were extracted,and the content of mitochondrial reactive oxygen species(ROS)was measured using a flow cytometer.The content of malondialdehyde(MDA)and the activities of superoxide dismutase(SOD)in the mitochondria were detected using assay kits.Changes in the ultrastructure of mitochondria were observed using a transmission electron microscope.In vitro experiments,pal-mitic acid(PA)and exenatide were used to treat hypothalamic GT1-7 cells,and short hairpin RNA(shRNA)was used to silence the melanocortin 4 receptor(MC4R),and observe the cellular oxidative stress and lipid deposition.Results Compared with the CON group,the T2DM group mice showed a significant increase in glucose and lipid metabolism indicators,pro-inflammatory factors,and adipose factor levels(P<0.05),the expression of MC4R and POMC proteins in the hypothalamus were decreased(P<0.05),and the mitochondrial ROS and MDA content in the hypothalamus significantly were increased(P<0.05),while SOD and CAT activities were decreased(P<0.05).Mitochondrial morphology was abnormal.After intervention with exenatide,the above indicators were signifi-cantly improved.After inhibiting MC4R expression in vitro experiments,compared with the intervention group with exenatide,the ROS and MDA content was significantly increased(P<0.05),SOD activity was decreased(P<0.05),and lipid deposition occurred in the cells.Conclusions Exenatide exhibits a protective effect on hypotha-lamic oxidative stress injury in diabetic mice,and this mechanism may be associated with the upregulation of MC4R expression.

Purpose To investigate the value of intratumoral and different ranges of peritumoral radiomics features of the primary lesion of breast cancer based on ultrasound images in predicting axillary lymph node metastasis(ALNM),and to explore the best peritumoral range.Materials and Methods A total of 312 cases confirmed by pathology in breast cancer patients with preoperative ultrasound images from June 2022 to February 2024 in Binzhou Medical University Hospital were retrospectively enrolled,and were randomly divided into training set and testing set according to the 7∶3 proportion.The tumor border of the ultrasound images was manually delineated as the intratumoral region of interest,and the peritumoral region of interest was obtained by conformal automatically extended different range(1,2,3,4 and 5 mm).The radiomics features were screened.Based on the selected optimal radiomics features,random forest classifier was used to construct three types of radiomics models(intratumoral model,5 peritumoral models,and 5 intratumoral+peritumoral models).The performance and clinical practicability of the models was assessed the area under the curve(AUC)and decision curve analysis.Results The AUCs of the intratumoral+peritumoral radiomics models for predicting ALNM in the training set and test set were 0.807-0.873,0.728-0.780,respectively,which were superior to those of the single intratumoral radiomics models(0.822,0.758)and peritumoral radiomics models(0.722-0.768,0.650-0.710).The intratumoral+peritumoral 3 mm radiomics model showed the best predictive performance,with AUC of 0.873 in the training set and 0.780 in the test set,respectively,and the decision curve showed that the model had a good clinical net benefit.Conclusion The combined intratumoral and peritumoral radiomics features of the primary lesion of breast cancer based on ultrasound images can effectively predict ALNM,and 3 mm peritumoral may be the best peritumoral range for predicting ALNM.

Purpose To investigate the value of intratumoral and different ranges of peritumoral radiomics features of the primary lesion of breast cancer based on ultrasound images in predicting axillary lymph node metastasis(ALNM),and to explore the best peritumoral range.Materials and Methods A total of 312 cases confirmed by pathology in breast cancer patients with preoperative ultrasound images from June 2022 to February 2024 in Binzhou Medical University Hospital were retrospectively enrolled,and were randomly divided into training set and testing set according to the 7∶3 proportion.The tumor border of the ultrasound images was manually delineated as the intratumoral region of interest,and the peritumoral region of interest was obtained by conformal automatically extended different range(1,2,3,4 and 5 mm).The radiomics features were screened.Based on the selected optimal radiomics features,random forest classifier was used to construct three types of radiomics models(intratumoral model,5 peritumoral models,and 5 intratumoral+peritumoral models).The performance and clinical practicability of the models was assessed the area under the curve(AUC)and decision curve analysis.Results The AUCs of the intratumoral+peritumoral radiomics models for predicting ALNM in the training set and test set were 0.807-0.873,0.728-0.780,respectively,which were superior to those of the single intratumoral radiomics models(0.822,0.758)and peritumoral radiomics models(0.722-0.768,0.650-0.710).The intratumoral+peritumoral 3 mm radiomics model showed the best predictive performance,with AUC of 0.873 in the training set and 0.780 in the test set,respectively,and the decision curve showed that the model had a good clinical net benefit.Conclusion The combined intratumoral and peritumoral radiomics features of the primary lesion of breast cancer based on ultrasound images can effectively predict ALNM,and 3 mm peritumoral may be the best peritumoral range for predicting ALNM.

Objective:To explore the application effects of a four-in-one blended teaching model integrating artificial intelligence, virtual reality, 3D printing, and case-based learning (CBL) in the clinical teaching of spinal tumors.Methods:We divided 89 students on training in the Department of Orthopedics of Peking University Third Hospital from September 2022 to August 2024 into control group ( n=47) and experimental group ( n=42). The control group adopted traditional teaching, and the experimental group adopted the four-in-one teaching model. At the end of clinical teaching, an artificial intelligence test and a questionnaire survey were administered to the students to evaluate the teaching effects. The two groups were compared using the independent samples t-test with the use of SPSS 27.0. Results:The experimental group was superior to the control group with significant improvements in the answer accuracy rate (66.67%, χ2=9.44, P=0.002), learning interest [(4.50±0.63), t=2.75, P=0.007], theoretical knowledge mastery [(4.64±0.69), t=7.74, P<0.001], clinical thinking [(4.48±0.71), t=9.08, P<0.001], practical skills [(4.13±0.89), t=2.69, P=0.009], scientific research innovation [(4.71±0.59), t=9.28, P<0.001], teacher-student interaction [(4.74±0.54), t=12.76, P<0.001], and classroom attention [(4.69±0.52), t=12.64, P<0.001]. At the same time, the students in the experimental group put forward numerous constructive feedback. Conclusions:The four-in-one blended teaching model combining artificial intelligence, virtual reality, 3D printing, and CBL can help undergraduate medical students better recognize and diagnose spinal tumors with a correct clinical thinking path, achieving good teaching effects.

Objective To explore the effect of exenatide on oxidative stress in the hypothalamus of diabetes mice and its potential mechanism.Methods After one week of adaptive feeding,C57BL/6J mice were randomly divided into the CON group(normal chaw diet),the T2DM group(high-fat diet,HFD),and the T2DM+Exe group(HFD+exenatide).After 8 weeks of HFD,mice in the T2DM+Exe group were intraperitoneally injected with exenatide[24 nmol/(kg·d)]for 8 weeks.The weight and glucose and lipid metabolism levels of the mice were measured,and the levels of inflammatory and adipokine factors in mice were detected using the ELISA method.Western Blot was used to detect the expression of melanocortin receptor-4(MC4R)and proopiomelanocor-tin(POMC)in the hypothalamus.Hypothalamic mitochondria were extracted,and the content of mitochondrial reactive oxygen species(ROS)was measured using a flow cytometer.The content of malondialdehyde(MDA)and the activities of superoxide dismutase(SOD)in the mitochondria were detected using assay kits.Changes in the ultrastructure of mitochondria were observed using a transmission electron microscope.In vitro experiments,pal-mitic acid(PA)and exenatide were used to treat hypothalamic GT1-7 cells,and short hairpin RNA(shRNA)was used to silence the melanocortin 4 receptor(MC4R),and observe the cellular oxidative stress and lipid deposition.Results Compared with the CON group,the T2DM group mice showed a significant increase in glucose and lipid metabolism indicators,pro-inflammatory factors,and adipose factor levels(P<0.05),the expression of MC4R and POMC proteins in the hypothalamus were decreased(P<0.05),and the mitochondrial ROS and MDA content in the hypothalamus significantly were increased(P<0.05),while SOD and CAT activities were decreased(P<0.05).Mitochondrial morphology was abnormal.After intervention with exenatide,the above indicators were signifi-cantly improved.After inhibiting MC4R expression in vitro experiments,compared with the intervention group with exenatide,the ROS and MDA content was significantly increased(P<0.05),SOD activity was decreased(P<0.05),and lipid deposition occurred in the cells.Conclusions Exenatide exhibits a protective effect on hypotha-lamic oxidative stress injury in diabetic mice,and this mechanism may be associated with the upregulation of MC4R expression.

Objective:To systematically review the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures.Methods:PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang Database and VIP Database were systematically searched to collect literature on the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures from inception to June 30, 2024. The languages were limited to Chinese and English. Two researchers screened the literature according to the inclusion and exclusion criteria. Data extraction was performed using the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS), encompassing basic study characteristics, model development features, and model performance metrics. The predictors, validation methods, presentation formats, and predictive performance of the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures were evaluated. The prediction model risk-of-bias assessment tool (PROBAST) was employed to assess risk of bias and applicability of the included studies.Results:A total of 11 studies, comprising of 16 prediction models, were included, with a total sample size of 283-1 508 patients and a pulmonary infection incidence rate of 5.4%-16.25%. The independent predictive factors repeatedly included in the models were age, American Society of Anesthesiologists (ASA) scale, preoperative comorbidities, chronic obstructive pulmonary disease (COPD), preoperative albumin level, white blood cell count (WBC), and C-reactive protein (CRP) level. The models were internally validated in 7 studies and externally validated in 3. The models were visualized in the form of a nomogram in 7 studies and a web-based risk calculator in 1. Model prediction performance was analyzed: (1) In terms of the discrimination, 9 studies reported the area under the receiver operating characteristic curve (AUC), with the overall AUC range of 0.664-0.905. (2) In terms of the calibration, 5 studies had Hosmer-Lemeshow test, with the P-values all above 0.05; 2 studies reported the calibration plots, with the slopes close to 1 and the Brier scores of 0.016 and 0.112; 4 studies reported the sensitivity of the models of 73.91%-92.40% and specificity of 57.10%-92.41%. According to PROBAST, all 11 studies exhibited certain risk of bias while maintaining favorable applicability. Conclusions:Age, ASA scale, preoperative comorbidities, COPD, preoperative albumin level, WBC, and CRP level are found to be independent predictive factors repeatedly reported in the risk prediction models for postoperative pulmonary infection in elderly patients with hip fractures. The existing models demonstrate a robust overall prediction performance despite certain risks of bias.

Objective:Dynamic navigation approaches are widely employed in the context of implant placement surgery.Implant surgery can be divided into immediate and delayed surgery according to the time of implantation.This retrospective study was developed to compare the accuracy of dynamic naviga-tion system for immediate and delayed implantations.Methods:In the study,medical records from all patients that had undergone implant surgery between August 2019 and June 2021 in the First Clinical Di-vision of the Peking University School and Hospital of Stomatology were retrospectively reviewed.There were 97 patients[53 males and 44 females,average age(47.14±11.99)years]and 97 implants(de-layed group:51;immediate group:46)that met with study inclusion criteria and were included.Implant placement accuracy was measured by the superposition of the planned implant position in the preoperative cone beam computed tomography(CBCT)image and the actual implant position in the postoperative CBCT image.The 3-dimensional(3D)entry deviation(3D deviation in the coronal aspect of the alveolar ridge),3D apex deviation(3D deviation in the apical area of the implant)and angular deviation were analyzed as the main observation index when comparing these two groups.The 2-dimensional(2D)horizontal deviation of the entry point and apex point,and the deviation of entry point depth and apex point depth were the secondary observation index.Results:The overall implant restoration survival rate was 100％,and no mechanical or biological complications were reported.The implantation success rate was 100％.The 3D entry deviation,3D apex deviation and angular deviation of all analyzed implants were(1.146±0.458)mm,(1.276±0.526)mm,3.022°±1.566°,respectively;while in the de-layed group these respective values were(1.157±0.478)mm,(1.285±0.481)mm and 2.936°±1.470° as compared with(1.134±0.440)mm,(1.265±0.780)mm,3.117°±1.677° in the immediate group.No significant differences(P=0.809,P=0.850,P=0.575)in accuracy were observed when comparing these two groups.Conclusion:Dynamic computer-assisted implant surgery system promotes accurate implantation,and both the immediate and delayed implantations exhibit similar levels of accura-cy under dynamic navigation system that meets the clinical demands.Dynamic navigation system is feasi-ble for immediate implantation.