Objective: To investigate the mechanism of shed syndecan-4 (sSDC4) in temporomandibular joint osteoarthritis (TMJOA) in rats, aiming to provide experimental evidence for its prevention and treatment. Methods: This study was approved by the Institutional Animal Ethics Committee. Twelve 6-week-old female Sprague Dawley (SD) rats were randomly divided into two groups. They received a single intra-articular injection into the bilateral superior cavity of temporomandibular joint, which consisted of either 50 μL of 4 mg/mL monosodium iodoacetate (TMJOA model group) or 50 μL of phosphate-buffered saline (PBS, control group). After 4 weeks, the mandibular condylar cartilage was harvested for hematoxylin & eosin (H&E) staining, Safranin O-fast green (SO) staining, and type II collagen (Col-Ⅱ) immunohistochemical staining to assess the degree of cartilage degeneration. The synovium of the temporomandibular joint was collected for immunohistochemical staining to detect the expression levels of interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) to evaluate the degree of synovial inflammation. Synovial fluid from the temporomandibular joint cavity was collected to measure sSDC4 levels by enzyme-linked immunosorbent assay (ELISA). In addition, 12 6-week-old female SD rats were randomly divided into a His-SDC4 group and a control group, receiving injections into the bilateral superior cavity of temporomandibular joint of either 100 ng/mL (50 μL) of His-SDC4 protein or 50 μL of PBS once every 3 days for a total of 28 days. The same experimental procedures were performed for H&E staining, SO staining, and immunohistochemical staining (Col-Ⅱ IL-6, TNF-α) to observe condylar cartilage degeneration and detect synovial inflammation. Rat synovial fibroblasts and condylar chondrocytes were cultured in vitro and randomly divided into a His-SDC4-stimulated (10 ng/mL) group and control group. Perform CCK-8 cytotoxicity assays and observe cellular morphology under optical microscopy, the mRNA expression levels of IL-6 and TNF-α were detected by real-time quantitative polymerase chain reaction (RT-qPCR), and the levels of IL-6 and TNF-α in cell culture supernatants were measured by ELISA. Results: Compared with the control group, the TMJOA group showed decreased condylar cartilage thickness, percentage of SO-positive area, and percentage of Col-Ⅱ-positive area (all P<0.001); an increased synovitis score (P<0.001) and increased percentages of IL-6- and TNF-α-positive cells in the synovium (all P<0.001); and a significant increase in sSDC4 levels in the synovial fluid (P=0.011). Following intra-articular injection of His-SDC4, condylar cartilage thickness, percentage of SO-positive area, and percentage of Col-Ⅱ-positive area all decreased (all P<0.001); the synovitis score increased (P=0.006), and the percentages of IL-6- and TNF-α-positive cells in the synovium increased (all P<0.001). In vitro experiments showed that His-SDC4 stimulation significantly upregulated the expression levels of IL-6 and TNF-α in both synovial fibroblasts and condylar chondrocytes (all P<0.01), and the levels of these two cytokines in the culture supernatants also significantly increased (all P<0.01). Conclusion During TMJOA progression, the level of sSDC4 in the synovial fluid is significantly elevated, which can directly stimulate synovial fibroblasts and condylar chondrocytes to secrete more pro-inflammatory cytokines, forming a vicious cycle that accelerates TMJOA progression.

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.

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 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 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.

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.