Objective: To investigate the factors influencing intraoperative blood transfusion in patients with hip fractures and to develop a machine learning (ML) model for predicting this risk. Methods: A total of 424 patients with hip fractures who underwent surgical treatment between November 2022 and March 2025 in our hospital were selected. Key feature variables of intraoperative blood transfusion risk were identified using the Boruta algorithm. Four different ML algorithms—support vector machine (SVM), linear discriminant analysis (LDA), mixed discriminant analysis (MDA), and extreme gradient boosting (XGBoost)—were used to develop predictive models for intraoperative blood transfusion risk. The predictive performance of the four ML models were evaluated using accuracy, precision, receiver operating characteristic (ROC) curves, precision-recall curves (PRC), precision-recall gain curves (PRGC), and F1 scores. Shapley additive interpretation (SHAP) was used to interpret the final model. Results: Among the 424 patients, 77(18.2%) received intraoperative blood transfusion. The Boruta algorithm identified albumin (ALB), activated partial thromboplastin time (APTT), types of anesthesia, types of fracture, and hemoglobin (Hb) as key feature variables for predicting intraoperative blood transfusion risk. In model evaluation, the SVM model outperforms the other three models across multiple metrics, including the area under the receiver operating characteristic curve (AUC), recall, recall gain, accuracy, precision, F1 score, and the area under the precision-recall curve (PRC-AUC). The SVM model, interpreted and visualized based on SHAP values, effectively predicted intraoperative blood transfusion risk in patients with hip fracture. A visual online application was developed based on the SVM model (https://pbo-nomogram.shinyapps.io/blood/). Conclusion: Preoperative low ALB and Hb levels, prolonged APTT, general anesthesia, and intertrochanteric fractures are risk factors for intraoperative blood transfusion in hip fracture patients. The risk prediction model for intraoperative blood transfusion constructed based on the SVM algorithm has optimal performance, which provides new ideas and methods for the clinical early identification of hip fracture patients with high transfusion risk and the implementation of targeted interventions.

ObjectiveTo address the limitations of the current quality standard for Polygalae Radix（PR）， which relies on a single component for quality assessment and struggles to holistically control its intrinsic quality， by constructing a comprehensive quality evaluation system integrating "macro-characterization of chemical profile， synchronous quantification of multiple index components， and quantitative analysis of multi-components by single marker（QAMS） for key component groups". This study aims to facilitate the scientific revision of the quality standard for PR. MethodsHigh performance liquid chromatography（HPLC） characteristic chromatograms were established for 11 batches of PR medicinal materials（YZ）， 10 batches of PR decoction pieces（YP）， and 10 batches of licorice-processed PR decoction pieces（ZYZ）， followed by similarity evaluation and identification of common peaks. HPLC-QAMS was developed for xanthones（sibiricaxanthone B， polygalaxanthone Ⅺ， polygalaxanthone Ⅲ） in the characteristic chromatograms. Simultaneously， the external standard method（ESM） was used to determine the contents of the corresponding xanthones and 3，6'-disinapoyl sucrose in YZ， YP， and ZYZ， followed by multivariate statistical analysis and Spearman correlation analysis. ResultsThe similarity between the characteristic chromatograms of 31 batches of PR samples and the reference chromatogram was>0.9. A total of 13 common peaks were identified， and 10 of these peaks were characterized through reference standard comparison. The successfully constructed QAMS method showed that the relative correction factors（RCFs） of sibiricaxanthone B and polygalaxanthone Ⅺ to polygalaxanthone Ⅲ were 0.76 and 0.88， and their relative retention times（RRTs） were 0.85 and 0.97， respectively. The results calculated by the QAMS method showed no significant difference from those obtained by the ESM. According to the limit standard for polygalaxanthone Ⅲ in the 2020 edition of the Pharmacopoeia of the People's Republic of China（hereinafter referred to as the Chinese Pharmacopoeia）， the pass rate of 31 batches of samples was only 19.35%. Multivariate statistical analysis indicated certain compositional differences between different batches of YZ and YP， as well as between YP and ZYZ， with 3，6'-disinapoyl sucrose identified as the main differentiating component. Furthermore， correlation analysis revealed that the content of polygalaxanthone Ⅲ was positively correlated with the contents of sibiricaxanthone B and polygalaxanthone Ⅺ， but showed no association with the content of 3，6'-disinapoyl sucrose. ConclusionIt is recommended that the content limit for polygalaxanthone Ⅲ in YZ，YP and ZYZ be revised to not less than 0.07%， or the total content of polygalaxanthone Ⅲ， sibiricaxanthone B and polygalaxanthone Ⅺ be not less than 0.18%. The newly established triple quality control model of "holistic control via characteristic chromatograms， precise quantification of oligosaccharide esters， and efficient detection of xanthones by QAMS" provides a systematic and precise solution for quality evaluation of PR and similar Chinese herbal medicines.

Objective: To compare the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) model, the Prophet model, and the Bayesian structural time series (BSTS) model in forecasting the incidence of hand, foot, and mouth disease (HFMD) , so as to provide a basis for optimizing the early warning system of this disease. Methods: Weekly incidence data of HFMD in Longgang District, Shenzhen City from 2014 to 2024 were collected. The HFMD incidence data from 2014-2019 and 2023 were used as the training set to construct SARIMA, Prophet, and BSTS models, while the data from 2024 were used as the test set to compare and evaluate the predictive performance of the three models. The technique for order preference by similarity to ideal solution (TOPSIS) method was employed to calculate the C-value. This approach integrates multiple evaluation metrics, such as the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and symmetric mean absolute percentage error (SMAPE), to comprehensively assess model performance. Results: A total of 150 111 cases of HFMD were reported in Longgang District from 2014 to 2024, with an average annual incidence of 400.72/105. The weekly incidence fluctuated between 0 and 63.78/105, exhibiting a bimodal seasonal pattern characterized by a primary peak from May to July and a secondary peak from September to October. In the training set, all three models demonstrated a good fit to the bimodal epidemic trend of HFMD, with the BSTS model achieving the best fit. The BSTS model yielded performance metrics as follows: MAE=0.124, MSE=0.050, RMSE=0.223, SMAPE=0.021, and a C-value of 1.000. In the test set, all three models, including SARIMA, Prophet, and BSTS, performed well for short-term predictions (≤16 weeks), with the Prophet model showing relatively superior predictive performance. However, the prediction accuracy of all models declined as the forecast horizon extended. During the primary peak period (May-July), the Prophet model exhibited better predictive performance, whereas the BSTS model performed relatively better during the secondary peak period (September-October). Conclusions For the short-term forecasting of weekly HFMD incidence, the Prophet model outperformed both the SARIMA and BSTS models. During the primary peak period, the Prophet model demonstrated superior predictive performance, whereas the BSTS model exhibited better accuracy in forecasting the secondary peak period.

Objective:To understand the current status and influencing factors for science and technology ethics awareness among medical postgraduates, explore scientific cultivation path, and provide empirical evidence for higher medical colleges to carry out science and technology ethics education.Methods:The study adopted a cluster sampling method and designed a questionnaire. A total of 2 095 medical postgraduates from Hebei Medical University participated in the survey from June to December 2023. SPSS 24.0 was used for non-parametric tests, and the ordered logistic regression analysis was used to explore the influencing factors.Results:A total of 1 992 valid questionnaires were collected, with a valid response rate of 95.08% (1 922/2 095). The qualified rate of science and technology ethics awareness among medical postgraduates was 90.76% (1 808/1 992), with 48.24% (961/1 992) of students falling into the good interval and 16.21% (323/1 992) falling into the excellent interval. The regression analysis showed that sex, academic category, students' attention to science and technology ethical issues, and understanding of ethical principles were significant influencing factors for medical postgraduates' science and technology ethics awareness ( P<0.05). Conclusions:The science and technology ethics awareness of medical postgraduates is at a medium level. Higher medical colleges should start from the needs and adhere to the direction of lifelong education to build a multi-subject, all-round, and all-process cultivation path. They can help students cultivate ethical awareness by improving the educational system, building a scientific teaching system, and creating a social education atmosphere. They can also help students internalize ethical norms and externalize them in their behaviors, and ensure that they can maintain ethical sensitivity in scientific and technological activities, thereby cultivating high-quality new medical and health professionals who can anticipate scientific and technological risks and assume ethical responsibilities.

Objective To establish the therapeutic effect prediction model of platelet transfusion in hematological patients,and receiver operating characteristic(ROC)curve and clinical cases are used to evaluate the clinical application value of the predic-tion model.Methods A total of 485 patients with hematological diseases who received platelet transfusion therapy in Kailuan General Hospital from January 2020 to December 2023 were selected,corrected count increment(CCI)was used to divide the patients into platelet transfusion effective group(n=340)and transfusion ineffective group(n=145).Multivariate Logistic regres-sion analysis was used to establish the prediction model of platelet infusion efficacy,and ROC curve was used to evaluate the application effect of the forcasting model.109 clinical cases were used to verify the practical application effect of the model,and the sensitivity,specificity and accuracy were calculated.Results Among 485 patients with hematological diseases,the incidence of ineffective platelet transfusion was 29.90%(145/485).Compated with the effective group,the ineffective group had more previous platelet transfusions was higher,and the difference was statistically significant(t=-4.435,P<0.05).In the ineffective group,there were more cases of hyperplenism,aplastic anemia and lymphoma,higher infection rate and higher positive rate of platelet antibody,and the differences were statistically significant(χ2=6.301～37.522,all P<0.05).Multivariate Logistic regres-sion analysis found that previous platelet infusion times,infection,leukemia,aplastic anemia and platelet antibodies were risk factors for ineffective platelet transfusion in patients with hematological diseases(Wald χ2=5.224～21.548,all P<0.05).Based on these risk factors,platelet infusion effect prediction models 1 and 2 were constructed.ROC curve was used to evaluate the application effect of the prediction model.The area under the curve(AUC),cut-offpoint,sensitivity and specificity of model 1 were 0.884,0.042,82.35%,88.89%.The AUC,cut-offpoint,corresponding sensitivity and specificity of prediction model 2 were 0.910,59.784,81.18%,94.44%,respectively.The Z values of model 1 and model 2 were 12.159 and 13.151,respectively.The prediction effect of model 2 was better than that of model 1.The actual application results showed that the sensitivity,specificity and accuracy of prediction model 1,2 were 85.71%,92.05%,90.89%and 90.48%,93.18%,92.66%,respectively.Conclusion The ineffective rate of platelet transfusion in hematological patients is relatively high.The prediction models 1 and 2 for platelet transfusion effectiveness have good results in predicting ineffective platelet transfusion,and prediction model 2 is better than pre-diction model 1,which can provide reliable basis for hematological patients on accurate platelet transfusion.

Background and Aims:CNLC stage IIIb hepatocellular carcinoma(HCC)is often accompanied by extrahepatic metastases and carries a poor prognosis.The optimal treatment strategy for these patients remains controversial,and the role of local therapy lacks robust evidence.This study aimed to compare overall survival(OS)between patients receiving combined local and systemic therapy versus systemic therapy alone,and to assess the prognostic impact of oligometastatic status and the cumulative duration of no evidence of disease(NED).Methods:A retrospective analysis was conducted on 76 CNLC stage IIIb HCC patients treated at Xiangya Hospital from January 2017 to December 2023.Forty patients received systemic therapy plus local therapy(local therapy group),and 36 received systemic therapy alone(no local therapy group).OS was compared between the two groups.Subgroup analyses were performed for oligometastatic and non-oligometastatic patients to evaluate the benefit of local therapy.In the local therapy group,the correlation between cumulative NED duration and OS was also examined.Results:The 1-,2-,3-,and 5-year OS rates were 89.0%vs.66.7%,64.3%vs.25.6%,35.3%vs.8.7%,and 8.3%vs.0.0%for the local therapy and no local therapy groups,respectively,with a statistically significant difference(P=0.003).Among oligometastatic patients,the local therapy group had significantly better OS than the no local therapy group(P=0.008),whereas no significant difference was observed in non-oligometastatic patients(P>0.05).Multivariate analysis identified oligometastases as an independent prognostic factor(HR=2.213,P=0.045).In the local therapy group,cumulative NED duration was strongly correlated with OS(r=0.851,P<0.001).Local therapy was well tolerated,with no treatment-related deaths observed.Conclusion:For CNLC stage IIIb HCC patients with well-controlled intrahepatic disease,local therapy can significantly prolong survival,particularly in those with oligometastases.Achieving and maintaining NED may represent an important therapeutic goal in this patient population.

Guidelines for Digital Ancient Books of TCM Indexing(T/CIATCM 119-2024)is based on the theoretical knowledge,disciplinary methods,and practical applications of TCM classical cataloging.Taking digital ancient books of TCM as the object,it systematically reveals the content of TCM knowledge,which is an essential indexing processing standard for building an intelligent retrieval system for TCM ancient books,and can provide support for the deep development and innovative utilization of TCM knowledge.It can not only promote the co-construction and sharing of ancient book resources in the TCM industry,but also promote the standardization construction and application of TCM information.This standard specifies the principles,methods,and examples of free indexing of digital ancient books of TCM based on their original content.It is applicable to the indexing and processing of digital ancient books of TCM for TCM professional libraries and related institutions,and to the data processing and construction of various types of TCM ancient book databases.

Objective To explore the relationship between hemorheology and Carotid Atherosclerosis.Methods The clinical data of 153 patients who underwent both hemorheological testing and carotid artery ultrasound were divided into a CAS group(n=96)and a non-CAS group(n=57)based on ultrasound findings.Clinical data and laboratory indicators were compared between two groups.Multivariate logistic regression analysis was used to explore the influencing factors of CAS.The ROC curves graph were drawn to observe the role of hemorheological indicators in predicting CAS and select the optimal cutoff value based on the maximum Youden index.Results The CAS group demonstrated higher levels in age,BMI,RBC aggregation index,low&high shear reduced viscosity of whole blood,plasma viscosity and fibrinogen compared to the non-CAS group(P<0.05).The multivariate logistic regression analysis showed that plasma viscosity(OR=38.270,95%CI:1.206～1214.508),age(OR=1.119,95%CI:1.065～1.176)were risk factors for the occurrence of CAS(P<0.05).The ROC curves showed that the area under the curve(AUC)of plasma viscosity and age were 0.623、0.728.Conclusion CAS patients have high levels of plasma viscosity and advanged age compared to the patient without CAS.Elevated plasma viscosity and age is a risk factor for CAS,with plasma viscosity≥1.46 mPa·s,over the age of 56.5 as a significant value for predicting CAS.

Objective:To systematically evaluate the risk prediction model for readmission in patients after percutaneous coronary intervention, and to provide reference for medical staff to apply or optimize relevant risk assessment tools.Methods:China National Knowledge Infrastructure, VIP, Wanfang Data, China Biomedical Literature Database, and Cochrane Library, PubMed, Embase, CINAHL, Scopus and Web of Science were searched for the related studies on readmission risk prediction models in patients with percutaneous coronary intervention from the establishment of the databases to April 25, 2024. Two researchers independently screened the literature, extracted data, and evaluated the risk of bias in the included studies.Results:A total of eleven articles were included, involving sixteen readmission risk prediction models, with readmission rates ranging from 0.70% to 31.44% and the areas under subjects′working characteristic curves ranging from 0.604 to 0.899. Calibration methods were reported in ten models, five studies reported processing methods of missing data, and external validation was used in three studies. The overall risk of bias was higher. The top six predictors of repeated reports in the readmitted model were age, renal insufficiency, sex, congestive heart failure, diabetes and health insurance.Conclusions:The readmission risk prediction models had good predictive performance. However, the quality of the model methodology was limited. It is necessary to improve the research quality in data sources, measurement and definition of predictive factors, processing of missing data and model evaluation. In the future, data mining can be used to apply the readmission prediction model in the early stage of admission, so as to identify high-risk patients as early as possible and effectively prevent the occurrence of readmission.

Objective:To analyze the relevant studies on fertility concerns intervention programs for breast cancer survivors of childbearing age, and identify the specific content, outcome indicators and intervention effects of intervention, in order to provide reference for clinical staff to implement relevant intervention.Methods:According to the research method of scope review, the studies on the fertility concerns with breast cancer patients of childbearing age in China National Knowledge Infrastructure, WanFang, VIP, China Biomedical Literature Database, CINAHL, PubMed, Cochrane Library, Web of Science etc. were searched. The search period was from database establishment to May 9, 2024. The included literature was classified and analyzed.Results:A total of 12 articles were included, 9 were randomized controlled trials, 2 were self-controlled before and after studies, and 1 was a prospective cohort study. Fertility concerns intervention programs included group fertility counseling centered on couples, fertility support based on a multidisciplinary team collaboration model, and fertility management based on "Internet + reproductive health". The duration of intervention was usually 4 or 24 weeks. The intervention had different effects on the level of fertility concerns, psychological state and marital status.Conclusions:At present, the intervention programs for fertility concerns of breast cancer survivors of childbearing age reflect the importance, professionalism, immediacy and interactivity, but China is in the initial stage, and there are still shortcomings in multidisciplinary teams, fertility referral system, and Internet platform construction. Future research should build a breast cancer fertility intervention program suitable for our country, and promote its application through the Internet.