Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

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 analyze the epidemiological characteristics and genetic evolution of human coronavirus OC43（HCoV-OC43）in Hangzhou from 2018 to 2023，and provide molecular epidemiological basis for the prevention and control of HCoV-OC43 infection.Methods:A total of 11 904 samples of cases with acute respiratory tract infection were collected from two sentinel hospitals in Hangzhou. The collection period was from January 2018 to December 2023. HCoV-OC43 was detected by real-time fluorescent quantitative RT-PCR. The positive samples were amplified by PCR using specific primers，and the amplified products were sequenced using third-generation nanopore technology for the whole genome. The genetic evolution features were analyzed.Results:A total of 265 HCoV-OC43 positive samples were detected，with a positive detection rate of 2.23%（265/11 904）. There was no significant difference in detection rate between males and females（χ 2=0.042， P>0.05）；the detection rate varied significantly among different age groups（χ 2=60.104， P<0.01），with higher detection rates observed in children under five years old and individuals over 65 years old. The infection peaks in 2018-2019 and 2023 occurred in spring and summer，while the infection peak in 2020-2022 was in summer and autumn. A total of 85 complete HCoV-OC43 whole genome sequences were obtained through high-throughput sequencing. The evolutionary analysis indicated that the K genotype was predominant in Hangzhou from 2018 to 2023. In 2019，both J and K genotypes co-existed. In 2023，a new genotype，temporarily named genotype L，was discovered，which originated from genotypes J and K. The K genotype had 22 specific amino acid variation sites，most of which were located in the important domain of S protein. The J genotype had seven specific amino acid variation sites，most of which were located in non-structural proteins. The new genotype L had 10 specific amino acid mutation sites，which were distributed across six proteins，and these mutation sites had the most extensive coverage. Conclusions:HCoV-OC43 is an important pathogen causing respiratory diseases in children and the elderly in the Hangzhou area，and there are multiple genotypes in the Hangzhou area，with a new recombinant genotype L discovered in 2023.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: To explore the relative importance of different food attributes and levels in food decision making of school aged children, and to understand their meal preferences, so as to provide the evidence for formulating precise intervention strategies for dietary behaviours of school aged children. Methods: From May to June 2024, a total of 854 children aged 11 to 15 years old were selected from 2 middle schools (each school in urban and rural areas) in both Hubei Province and Anhui Province by stratified cluster random sampling method to conduct a D-optimal discrete choice experiment. The mixed Logit model was used to analyze children s preference for meal attributes and different levels, and to calculate the relative importance (RI) of attributes and willingness to pay (WTP) in meal choices. Results: The included five food attributes had statistical significance on meal choice of school aged children ( P <0.05). The relative importance of food attributes affecting the meal choices of school aged children in descending order were dining mode ( RI =31.26%), food varieties ( RI =30.56%), cooking method( RI =23.84%), taste( RI =8.06%) and price ( RI =6.27%). Among them, school aged children preferred home cooked meals ( β =0.74) (WTP=86.3 yuan),varied foods(grain/tubers+vegetables+fish, meat, eggs and beans) ( β =0.61) (WTP=71.9 yuan), fried/roasted cooking ( β =0.51) and spicy taste ( β =0.33).Price was negatively correlated with meal choices( β =-0.01) ( P <0.05). Based on residential area and body mass index (BMI), the stratified analysis showed that dining mode was highest in the relative importance for rural children with overweight and obese children ( RI =31.28%,34.17%), both of whom preferred home cooked meals ( β =0.76, 0.91), and meals containing fish, meat, eggs and beans with grain/tubers or grain/tubers and vegetables in terms of food choice (area: β =0.53, 0.53 ; BMI: β =0.55, 0.56) ( P <0.05). Conclusions School aged children have different preferences for different attributes of meals. The quality of school meals should be improved,the cost of buying healthy meals should be reduced,targeted family health education should be carried out,and healthy cooking methods should be advocated.

Objective: To investigate the epidemiological and spatio-temporal characteristics of influenza in Jiaxing City, Zhejiang Province, so as to provide insights into perfecting the prevention and control strategies of influenza. Methods: Data of influenza in Jiaxing City from 2019 to 2023 were collected from the Chinese Disease Prevention and Control Information System. Population data of the same period were collected from the Zhejiang Health Information Network Reporting System. The epidemiological characteristics of influenza were analyzed using descriptive analysis. Vector map information was collected from the Open Street Map, and the spatio-temporal clustering characteristics of influenza were analyzed using spatial autocorrelation and spatio-temporal scanning. Results: A total of 181 501 cases of influenza were reported in Jiaxing City from 2019 to 2023, with an average annual reported incidence of 653.93/105. The majority of cases were aged 5 to <15 years (59 785 cases, 32.94%). The majority of the occupations were students (78 239 cases, 43.11%) and pre-school children (33 715 cases, 18.58%). The county (city, district) with the highest reported incidence was Haining City (1 451.70/105), and the town (street) with the highest reported incidence was Chang'an Town (1 932.78/105). Spatial autocorrelation analysis showed that the incidence of influenza in Jiaxing City from 2019 to 2023 had positive spatial correlations (all Moran's I>0, all P<0.05), with a high-high clustering in the southern region. Spatio-temporal scanning analysis showed that there was a spatio-temporal clustering of influenza in Jiaxing City from 2019 to 2023, with the southern region being the primary-type clustering area and the period between November and January of the following year being the clustering time. Conclusion There was a significant spatio-temporal clustering of influenza in Jiaxing City from 2019 to 2023, with winter being the peak season and the southern region being the primary area.

ObjectiveTo investigate the intervention effects and mechanisms of the flavonoid hyperoside （Hyp） on lipopolysaccharide （LPS）-induced inflammation in the zebrafish model. MethodsZebrafish larvae were either microinjected with 0.5 g·L^-1 LPS or immersed in 1 g·L^-1 LPS for the modeling of inflammation. The larvae were then treated with Hyp at 25， 50， and 100 mg·L^-1 through immersion for four consecutive days. The inflammatory phenotypes were assessed by analyzing the mortality rate， malformation rate， body length， and yolk sac area ratio. Behavioral tests were conducted to evaluate the inflammatory stress responses， and macrophage migration was observed by fluorescence microscopy. Additionally， the mRNA levels of inflammation-related genes， including interleukin-1β （IL-1β）， interleukin-6 （IL-6）， chemokine C-C motif ligand 2 （CCL2）， chemokine C-X3-C motif receptor 1 （CX3CR1）， chemokine C-C motif receptor 2 （CCR2）， and genes associated with the Toll-like receptor 4 （TLR4）/myeloid differentiation factor 88 （MyD88）/nuclear factor-kappa B （NF-κB） signaling pathway， were measured by Real-time quantitative polymerase chain reaction（Real-time PCR）. ResultsCompared with the pure water injection group， the model group exhibited increased mortality， malformation rates and yolk sac area ratio （P0.01）， reduced body length （P0.01）， increased total swimming distance and high-speed swimming duration （P0.01）， and up-regulated mRNA levels of TLR4， MyD88， NF-κB， IL-1β， IL-6， CCL2， CX3CR1， and CCR2 （P0.01）. Hyp at low， medium and high doses， as well as aspirin， reduced the mortality and malformation rates （P0.05，P0.01）， increased the body length （P0.05，P0.01）， decreased the yolk sac area ratio （P0.01）， reduced the high-speed swimming duration （P0.01）， and down-regulated the mRNA levels of TLR4， MyD88， NF-κB， IL-1β， IL-6， CCL2， CX3CR1， and CCR2 （P0.05，P0.01） compared with the model group. ConclusionHyp may modulate the TLR4/MyD88/NF-κB pathway to ameliorate inflammatory phenotypes and alleviate stress conditions in zebrafish， thereby exerting the anti-inflammatory effect.

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.