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 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.

ObjectiveTo evaluate the clinical efficacy and safety of the traditional Chinese medicine （TCM） compound Chaiqin Xiaoyong Decoction （柴芩消痈饮， CXD） combined with Jinhuang Ointment （金黄膏， JO） in treating the nodular stage of acne mastitis of liver meridian heat accumulation type. MethodsA randomized， double-blind， placebo-controlled clinical trial was conducted. A total of 108 patients with liver meridian heat accumulation type acne mastitis in the nodular stage were randomly assigned to a treatment group and a control group， with 54 patients in each group. Both groups received topical application of JO once daily at a thickness of 3~5 mm for 8 hours， along with standard nursing care. On this basis， the treatment group received oral CXD granules， while the control group received placebo granules， administered twice daily， 3 sachets per dose， for 14 consecutive days. Clinical efficacy， TCM symptom scores， nodule size， visual analogue scale （VAS） pain scores， white blood cell （WBC） count， C-reactive protein （CRP） level， and systemic immune-inflammation index （SII） were compared. At the end of treatment， efficacy and safety indicators were evaluated. A 6-month follow-up was conducted to compare the proportion of patients undergoing surgical treatment. ResultsThe total clinical efficacy rate in the treatment group was 90.38% （47/52）， significantly higher than 32.00% （16/50） in the control group （P＜0.01）. The treatment group also showed significantly lower TCM symptom scores， VAS scores， nodule size， WBC count， CRP level， and SII （P＜0.05 or P＜0.01）. During follow-up， the surgical intervention rate in the treatment group was 5.77% （3/52）， lower than 14.00% （7/50） in the control group， with a statistically significant difference （P＜0.01）. No significant abnormalities were observed in safety indicators before and after treatment in either group. ConclusionCXD effectively reduces nodule size and alleviates symptoms such as redness and pain in patients with acne mastitis of liver meridian heat accumulation type， improves TCM symptom scores， enhances overall clinical efficacy， and demonstrates good safety.

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.

Thyroid dysfunction(TD)is a prevalent disorder of the endocrine system.Owing to its non-specific clinical symptoms and the limitations inherent in current diagnostic approaches,there is a pressing need to further investigate its underlying pathogenesis and identify novel biomarkers.As a pivotal component of systems biology,omics technologies offer a promising avenue for uncovering the molecular mechanisms of TD by integrating high-throughput data.This thesis presents a comprehensive review of recent advancements in genomics,transcriptomics,proteomics,metabolomics,lipidomics,and multi-omics research related to TD.It examines genetic variations and epigenetic regulatory mechanisms from multiple molecular levels,including DNA,RNA,proteins,and metabolites.Furthermore,it elucidates the metabolic disturbances associated with TD and the modes of action of relevant therapeutic agents,contributing to the development of potential biomarkers and drug targets for early detection and intervention.By synthesizing findings across various omics platforms,this thesis aims to delineate the complex network interactions underlying TD and to provide valuable insights and strategies for future clinical management,as well as personalized diagnosis and treatment.

Objective Biomarkers associated with liver cancer CD8+T cells identified and prognostic models established by single-cell RNA sequencing and Bulk RNA sequencing.Methods Single-cell datasets of liver cancer were downloaded from the GEO database and differentially expressed genes in CD8+T cells between patients and controls were extracted by single-cell RNA sequencing.Gene expression profiling data and clinical data of liver cancer were downloaded from the TCGA database,and modular genes with relevance to CD8+T cells were screened using the CIBERSORT algorithm and WGCNA technology.Differential genes and modular genes were taken as intersecting genes and subjected to GO and KEGG analyses,and a prognostic model was established by applying univariate COX regression analysis and LASSO algorithm.The predictive effectiveness of the model in both internal and external datasets is validated by K-M and ROC curves.The effectiveness of the model's prediction in both internal and external datasets was validated by K-M and ROC curves.High-and low-risk groups were divided according to the median value of the risk score,and the distribution of infiltrating immune cells and tumor mutations between high-and low-risk groups were analyzed.Results A prognostic model with nine genes was constructed,and the K-M and ROC curves showed that the model had good predictive ability in both the internal and external datasets,and there were significant differences in the distribution of infiltrating immune cells and gene mutations in the high-and low-risk groups.Conclusion In this study,a novel prognostic model based on CD8+T cells was developed using bioinformatic method in combination with single-cell RNA sequencing and Bulk RNA sequencing technologies,which provides a reliable theoretical basis for prognostic improvement and survival prediction of liver cancer patients.

This study was aimed at preliminarily assessing the prevalence of latent tuberculosis infection(LTBI)among cattle farming and slaughterhouse workers across Wuxue,Xianning,and Yichang Cities in Hubei Province,and exploring associated risk factors.Data on cattle farming and slaughterhouse workers were gathered via a questionnaire.LTBI detection was performed with a tu-berculin skin test and interferon-gamma release assay,and influencing factors were subsequently analyzed.The LTBI prevalence among cattle farming and slaughterhouse personnel in the three cities was 30.50%,and a higher rate was observed in slaughterhouse workers(39.01%)than cattle farmers(21.63%)(P<0.01).Multifactor analysis indicated that working in slaughterhouses(95%CI:1.582-3.878),having a history of tuberculosis(95%CI:1.377-25.057)or BCG vaccination(95%CI:1.229-3.285),and having a college education or above(95%CI:0.303-0.859)were significant factors influencing LTBI positivity in these personnel.Having more than 30 years of work experience(95%CI:1.303-18.782)was a risk factor for personnel at cattle breeding farms.Among slaugh-terhouse personnel,having a college education or above(95%CI:0.164-0.894),11-20 years of work experience(95%CI:0.122-0.994),or a history of tuberculosis(95%CI:1.661-64.397);performing logistics work(95%CI:3.234-126.424);and working in slaughter-related positions(95%CI:1.209-19.639)were associated with LTBI positivity.Therefore,the slaughterhouse workers in the three cities had higher LTBI rates than the cattle farming workers,thus underscoring the need for increased attention to personnel in logistics and slaughter-related positions.

Thyroid dysfunction(TD)is a prevalent disorder of the endocrine system.Owing to its non-specific clinical symptoms and the limitations inherent in current diagnostic approaches,there is a pressing need to further investigate its underlying pathogenesis and identify novel biomarkers.As a pivotal component of systems biology,omics technologies offer a promising avenue for uncovering the molecular mechanisms of TD by integrating high-throughput data.This thesis presents a comprehensive review of recent advancements in genomics,transcriptomics,proteomics,metabolomics,lipidomics,and multi-omics research related to TD.It examines genetic variations and epigenetic regulatory mechanisms from multiple molecular levels,including DNA,RNA,proteins,and metabolites.Furthermore,it elucidates the metabolic disturbances associated with TD and the modes of action of relevant therapeutic agents,contributing to the development of potential biomarkers and drug targets for early detection and intervention.By synthesizing findings across various omics platforms,this thesis aims to delineate the complex network interactions underlying TD and to provide valuable insights and strategies for future clinical management,as well as personalized diagnosis and treatment.

Objective Biomarkers associated with liver cancer CD8+T cells identified and prognostic models established by single-cell RNA sequencing and Bulk RNA sequencing.Methods Single-cell datasets of liver cancer were downloaded from the GEO database and differentially expressed genes in CD8+T cells between patients and controls were extracted by single-cell RNA sequencing.Gene expression profiling data and clinical data of liver cancer were downloaded from the TCGA database,and modular genes with relevance to CD8+T cells were screened using the CIBERSORT algorithm and WGCNA technology.Differential genes and modular genes were taken as intersecting genes and subjected to GO and KEGG analyses,and a prognostic model was established by applying univariate COX regression analysis and LASSO algorithm.The predictive effectiveness of the model in both internal and external datasets is validated by K-M and ROC curves.The effectiveness of the model's prediction in both internal and external datasets was validated by K-M and ROC curves.High-and low-risk groups were divided according to the median value of the risk score,and the distribution of infiltrating immune cells and tumor mutations between high-and low-risk groups were analyzed.Results A prognostic model with nine genes was constructed,and the K-M and ROC curves showed that the model had good predictive ability in both the internal and external datasets,and there were significant differences in the distribution of infiltrating immune cells and gene mutations in the high-and low-risk groups.Conclusion In this study,a novel prognostic model based on CD8+T cells was developed using bioinformatic method in combination with single-cell RNA sequencing and Bulk RNA sequencing technologies,which provides a reliable theoretical basis for prognostic improvement and survival prediction of liver cancer patients.

This study was aimed at preliminarily assessing the prevalence of latent tuberculosis infection(LTBI)among cattle farming and slaughterhouse workers across Wuxue,Xianning,and Yichang Cities in Hubei Province,and exploring associated risk factors.Data on cattle farming and slaughterhouse workers were gathered via a questionnaire.LTBI detection was performed with a tu-berculin skin test and interferon-gamma release assay,and influencing factors were subsequently analyzed.The LTBI prevalence among cattle farming and slaughterhouse personnel in the three cities was 30.50%,and a higher rate was observed in slaughterhouse workers(39.01%)than cattle farmers(21.63%)(P<0.01).Multifactor analysis indicated that working in slaughterhouses(95%CI:1.582-3.878),having a history of tuberculosis(95%CI:1.377-25.057)or BCG vaccination(95%CI:1.229-3.285),and having a college education or above(95%CI:0.303-0.859)were significant factors influencing LTBI positivity in these personnel.Having more than 30 years of work experience(95%CI:1.303-18.782)was a risk factor for personnel at cattle breeding farms.Among slaugh-terhouse personnel,having a college education or above(95%CI:0.164-0.894),11-20 years of work experience(95%CI:0.122-0.994),or a history of tuberculosis(95%CI:1.661-64.397);performing logistics work(95%CI:3.234-126.424);and working in slaughter-related positions(95%CI:1.209-19.639)were associated with LTBI positivity.Therefore,the slaughterhouse workers in the three cities had higher LTBI rates than the cattle farming workers,thus underscoring the need for increased attention to personnel in logistics and slaughter-related positions.