Objective:To explore the safety and efficacy of intraosseous regional administration (IORA) of vancomycin for preventing infection in primary total knee arthroplasty (TKA).Methods:A total of 124 patients with knee osteoarthritis undergoing TKA between February 2024 and May 2024 at nine hospitals were enrolled. Preoperative infection prophylaxis involved either IORA (0.5 g vancomycin administered via intraosseous regional infusion before incision) or intravenous infusion (1 g vancomycin via peripheral vein). The IORA group included 15 males and 47 females with a median age of 66.5 years (range, 60.0-70.0 years), while the intravenous group included 14 males and 48 females with a median age of 66.0 years (range, 61.8-70.3 years) years. Intraoperative samples were collected including fat and synovium tissues after incision, before prosthesis placement, and after tourniquet release; distal femoral cancellous bone during femoral osteotomy; proximal tibial cancellous bone during tibial osteotomy; proximal intercondylar cancellous bone before prosthesis placement; and peripheral blood from non-infused arms at surgery initiation and after tourniquet release. Vancomycin concentrations were measured using liquid chromatography-tandem mass spectrometry. Vital sign changes were recorded from admission to 5~10 minutes post-IORA (IORA group) or post-incision (intravenous group). Follow-ups were conducted on postoperative day 1 and 3, and at 1 and 3 months, to document complications including IORA-related adverse events, periprosthetic joint infections, surgical site infections, red man syndrome, acute kidney injury, deep vein thrombosis and so on.Results:Vancomycin concentrations in bone, fat, and synovial tissue samples were significantly higher in the IORA group than in the intravenous group ( P<0.05), while vancomycin concentrations in blood samples were significantly lower in the IORA group than in the intravenous group ( P<0.05). Only 7.3%(41/558) of tissue samples in the IORA group had vancomycin concentrations below 2.0 μg/g (the minimum inhibitory concentration of vancomycin against coagulase-negative staphylococcus), compared to 59.3%(331/558) in the intravenous group (χ 2=11.285, P<0.001). In the intravenous group, 16.9%(21/124) of blood samples had vancomycin concentrations exceeding 15.0 mg/L (the threshold associated with a significantly increased risk of nephrotoxicity), while all concentrations in the IORA group were below this threshold, the difference was statistically significant (χ 2=22.943, P<0.001). There were no statistically significant difference ( P>0.05) in vital signs changes before and after vancomycin administration between the two groups. Two patients in the intravenous group experienced incision exudate, while no other related complications occurred in either group. Conclusions:Compared to the traditional intravenous infusion of 1 g vancomycin, intraosseous injection of a low dose (0.5 g) of vancomycin achieves higher local tissue concentrations in the knee joint with a lower incidence of adverse reactions and is safe for infection prophylaxis. Despite guidelines not recommending the routine use of vancomycin for preventing infection after primary TKA, intraosseous injection of 0.5 g vancomycin may be considered intraoperatively for primary TKA in the following scenarios: patients in medical institutions with a high prevalence of methicillin-resistant staphylococcus aureus (MRSA) infections, patients with potential preoperative MRSA colonization, or patients with cephalosporin allergy.

Objective:To explore the safety and efficacy of intraosseous regional administration (IORA) of vancomycin for preventing infection in primary total knee arthroplasty (TKA).Methods:A total of 124 patients with knee osteoarthritis undergoing TKA between February 2024 and May 2024 at nine hospitals were enrolled. Preoperative infection prophylaxis involved either IORA (0.5 g vancomycin administered via intraosseous regional infusion before incision) or intravenous infusion (1 g vancomycin via peripheral vein). The IORA group included 15 males and 47 females with a median age of 66.5 years (range, 60.0-70.0 years), while the intravenous group included 14 males and 48 females with a median age of 66.0 years (range, 61.8-70.3 years) years. Intraoperative samples were collected including fat and synovium tissues after incision, before prosthesis placement, and after tourniquet release; distal femoral cancellous bone during femoral osteotomy; proximal tibial cancellous bone during tibial osteotomy; proximal intercondylar cancellous bone before prosthesis placement; and peripheral blood from non-infused arms at surgery initiation and after tourniquet release. Vancomycin concentrations were measured using liquid chromatography-tandem mass spectrometry. Vital sign changes were recorded from admission to 5~10 minutes post-IORA (IORA group) or post-incision (intravenous group). Follow-ups were conducted on postoperative day 1 and 3, and at 1 and 3 months, to document complications including IORA-related adverse events, periprosthetic joint infections, surgical site infections, red man syndrome, acute kidney injury, deep vein thrombosis and so on.Results:Vancomycin concentrations in bone, fat, and synovial tissue samples were significantly higher in the IORA group than in the intravenous group ( P<0.05), while vancomycin concentrations in blood samples were significantly lower in the IORA group than in the intravenous group ( P<0.05). Only 7.3%(41/558) of tissue samples in the IORA group had vancomycin concentrations below 2.0 μg/g (the minimum inhibitory concentration of vancomycin against coagulase-negative staphylococcus), compared to 59.3%(331/558) in the intravenous group (χ 2=11.285, P<0.001). In the intravenous group, 16.9%(21/124) of blood samples had vancomycin concentrations exceeding 15.0 mg/L (the threshold associated with a significantly increased risk of nephrotoxicity), while all concentrations in the IORA group were below this threshold, the difference was statistically significant (χ 2=22.943, P<0.001). There were no statistically significant difference ( P>0.05) in vital signs changes before and after vancomycin administration between the two groups. Two patients in the intravenous group experienced incision exudate, while no other related complications occurred in either group. Conclusions:Compared to the traditional intravenous infusion of 1 g vancomycin, intraosseous injection of a low dose (0.5 g) of vancomycin achieves higher local tissue concentrations in the knee joint with a lower incidence of adverse reactions and is safe for infection prophylaxis. Despite guidelines not recommending the routine use of vancomycin for preventing infection after primary TKA, intraosseous injection of 0.5 g vancomycin may be considered intraoperatively for primary TKA in the following scenarios: patients in medical institutions with a high prevalence of methicillin-resistant staphylococcus aureus (MRSA) infections, patients with potential preoperative MRSA colonization, or patients with cephalosporin allergy.

Objective:To investigate the relationship between body mass index(BMI) and osteoporosis using Mendelian randomization analysis.Methods:The genetic variation strongly related to BMI was selected as the instrumental variables in the collection data set of the genome-wide association study(GWAS). The MR-Egger regression, weighted median estimator(WME), inverse variance weighted(IVW), simple mode and weighted mode were used for Mendelian randomization(MR) analysis. The causal association between BMI and osteoporosis was evaluated by odds ratio and 95% confidence interval. The MR-APSS method was applied to make the causal inference results based on MR more reliable. The Linkage disequilibrium score regression was applied to evaluate the genetic correlation, and the horizontal pleiotropy test, heterogeneity test, and leave-one-out method were used to evaluate whether the results were reliable, The influence of heterogeneity and horizontal pleiotropy were reduced by the MR-PRESSO outlier test.Results:A total of 421 SNPs were included, with inverse variance-weighted method as the main analysis approach. The calculated OR value and 95% CI were 0.994(95% CI 0.992-0.997), indicating a protective effect of BMI on osteoporosis. The MR-APSS method showed that the effect of BMI on osteoporosis was statistically significant. Linkage disequilibrium score regression demonstrated a genetic correlation between BMI and osteoporosis. MR-Egger regression intercept showed no horizontal pleiotropy of instrumental variables, and the funnel plot showed no bias in instrumental variables. Leave-one-out analysis confirmed robust results. Conclusion:There may be a negative causal relationship between BMI and osteoporosis and BMI is a protective factor for osteoporosis.

Objective To explore whether liver cirrhosis markers aspartate aminotransferase-to-platelet ratio index(APRI)and fibrosis-4 index(FIB-4)based on blood biochemical indicators can predict disease free survival(DFS)and overall survival(OS)in patients with hepatocellular carcinoma(HCC)after resection.Methods 300 patients with HCC who underwent radical resection in Zhongshan Hospital,Fudan University from February 2005 to July 2017 were enrolled and the clinicopathological characteristics,recurrence and survival of these patients were retrospectively collected.The relationships between APRI,FIB-4 and postoperative recurrence and survival were evaluated.The ROC curve was used to evaluate the predictive values of APRI,FIB-4.Results The median follow-up of 300 patients was 61 months.Univariate Cox regression analysis showed that APRI,FIB-4,vascular invasion were risk factors affecting postoperative DFS and OS.The multivariate Cox regression analysis showed that vascular invasion was the independent risk factor for postoperative DFS(HR=1.518,95%CI 1.024-2.252,P=0.038)and OS(HR=2.301,95%CI 1.270-4.167,P=0.006).The time dependent ROC(time-ROC)curve showed that AUCs of APRI and FIB-4 predicting 1-year,3-year,and 5-year DFS were 0.555-0.596,which were 0.600-0.679 when predicting 1-year,3-year,and 5-year OS.Conclusions The predictive value of APRI and FIB-4 based on blood biochemical indicators alone for postoperative DFS and OS in HCC patients is limited.

Objective To observe the effect of abdominal acupuncture in treating allergic rhinitis(AR).Methods Twenty-seven AR patients who attended Fangshan Hospital,Beijing University of Chinese Medicine from August to October 2022 were selected.They were divided into treatment group(15 cases)and control group(12 cases)according to randomized numerical table method.The treatment group received abdominal acupuncture.The control group at the same point was used one-time sterile cannula acupuncture to simulate acupuncture,but no needle was inserted into the acupoint.Two groups were treated,3 times a week for 4 weeks.The visual analogue scale(VAS),rhinoconjunctivitis quality of life questionnaire(RQLQ),Pittsburgh sleep quality index(PSQI)scores,and the histamine(HIS),leukotriene D4(LTD4),immunoglobulin E(IgE)levels were compared between two groups before and after treatment.Results At each time point after treatment,the VAS,RQLQ,PSQI scores and HIS,LTD4,IgE levels of patients in treatment group were significantly lower than those before treatment(P<0.05).There were no statistically significant differences in VAS,RQLQ,and PSQI scores,the HIS,LTD4,and IgE levels after 2-week treatment in both groups(P>0.05).After 4-week treatment and follow-up 4-week,the VAS,RQLQ,and PSQI scores,the HIS,and LTD4 levels in treatment group were significantly lower than those in control group(P<0.05).Conclusion Abdominal acupuncture has good therapeutic effect on AR and significantly improves life quality of the patients,which can reduce the levels of HIS,LTD4 and IgE.The therapy is worthy of clinical application.

Objective:To construct classification prediction models for gamma passing rate using radiomics-based machine learning approaches and data from multiple radiotherapy institutions and evaluate the models′ performance.Methods:The data from 572 volumetric-modulated arc therapy (VMAT) patients across three radiotherapy institutions (514 for training and 58 for testing)were retrospectively collected. Additionally, 45 VMAT plans were collected from a single institution as an independent external validation set. For all the data, a three-dimensional dose validation approach based on actual measurements of phantoms was utilized, and gamma analysis was performed at the 3%/2 mm criterion using a dose threshold of 10%, absolute doses, and global normalization. After radiomic features were extracted from dose files, feature selection was performed using the random forest (RF) method and RF combined with Shapley Additive exPlanation (SHAP). Then, feature subsets of varying sizes (10, 20, 30, 40, and 50) were selected based on feature rankings. Using these subsets as inputs, data training was conducted using the Extreme Gradient Boosting (XGBoost) algorithm. Finally, the models′ classification performance was assessed using the area under the curve (AUC) values and F1-score.Results:Under the 3%/2 mm criterion, all models performed the best in the case of 20 feature subsets. The optimal prediction model established based on the feature selection using RF exhibited AUC and F1-score of 0.88 and 0.89, respectively on the testing set and 0.82 and 0.90, respectively, on the validation set. The optimal prediction model built based on the feature selection using RF combined with SHAP yielded AUC and F1-score of 0.86 and 0.92 on the testing set and 0.87 and 0.89, respectively, on the validation set, along with superior robustness. Therefore, the second model possessed certain advantages over the first model.Conclusions:For multicenter dose verification result, it is feasible to construct a machine learning prediction model with high classification performance using radiomic features derived from dose files, combined with feature selection based on SHAP. This approach can assist in advancing the clinical applications and implementation of gamma passing rate prediction models.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective The aim of this study is to analyze the current situation and influencing factors of specialist nurses'work in 72 tertiary hospitals in Anhui Province which can provide suggestions for the training and use of specialist nurses.Methods Using stratified sampling,a survey of 2,400 specialist nurses in 72 tertiary hospitals across the province were recruited and investigated by a self-administered questionnaire on the current work status and competencies of specialist nurses,from December 2022 to January 2023.The one-way ANOVA and multiple linear regression methods to analyze the influencing factors.Results A total of 2,248 valid questionnaires were returned from specialist nurses,with a validity rate of 93.67%.Specialist nurses scored(19.07±5.11)for their current work status and(17.07±2.84)for their ability to work.The results of multiple linear regression analyses showed that the level and nature of the hospital,title,position,method of obtaining training qualifications,working time in the speciality after training,and the competence of the specialist nurses were the factors influencing the current status of the specialist nurses'work(P<0.05).Conclusion The current status of specialist nurses'work in tertiary hospitals in Anhui Province is relatively satisfactory,and it is influenced by the level and nature of the hospital,the way of training and selection,and factors of the specialist nurses themselves.It is recommended to pay attention to the subsequent use and training of specialist nurses to improve their ability to work and provide high-quality professional care to patients.

Objective:To construct classification prediction models for gamma passing rate using radiomics-based machine learning approaches and data from multiple radiotherapy institutions and evaluate the models′ performance.Methods:The data from 572 volumetric-modulated arc therapy (VMAT) patients across three radiotherapy institutions (514 for training and 58 for testing)were retrospectively collected. Additionally, 45 VMAT plans were collected from a single institution as an independent external validation set. For all the data, a three-dimensional dose validation approach based on actual measurements of phantoms was utilized, and gamma analysis was performed at the 3%/2 mm criterion using a dose threshold of 10%, absolute doses, and global normalization. After radiomic features were extracted from dose files, feature selection was performed using the random forest (RF) method and RF combined with Shapley Additive exPlanation (SHAP). Then, feature subsets of varying sizes (10, 20, 30, 40, and 50) were selected based on feature rankings. Using these subsets as inputs, data training was conducted using the Extreme Gradient Boosting (XGBoost) algorithm. Finally, the models′ classification performance was assessed using the area under the curve (AUC) values and F1-score.Results:Under the 3%/2 mm criterion, all models performed the best in the case of 20 feature subsets. The optimal prediction model established based on the feature selection using RF exhibited AUC and F1-score of 0.88 and 0.89, respectively on the testing set and 0.82 and 0.90, respectively, on the validation set. The optimal prediction model built based on the feature selection using RF combined with SHAP yielded AUC and F1-score of 0.86 and 0.92 on the testing set and 0.87 and 0.89, respectively, on the validation set, along with superior robustness. Therefore, the second model possessed certain advantages over the first model.Conclusions:For multicenter dose verification result, it is feasible to construct a machine learning prediction model with high classification performance using radiomic features derived from dose files, combined with feature selection based on SHAP. This approach can assist in advancing the clinical applications and implementation of gamma passing rate prediction models.

Objective:To construct a risk identification and early warning management model,and to explore its value in the risk control and management of active medical devices maintenance and repair. Methods:The risk identification and early warning knowledge base of active medical equipment maintenance and repair was constructed from three aspects:basic data,core data and auxiliary data. The risk evaluation index system was designed in combination with the equipment operating status,and the weight was assigned by coefficient of variation and the extension cloud algorithm was used to evaluate the risk level,so as to form a hierarchical early warning trigger path and a three-dimensional early warning intervention scheme of personnel,system and process. A total of 287 active medical devices in clinical use in the Second Hospital of Shanxi Medical University from 2022 to 2023 were selected,and 261 devices used in the period from January to December 2022 were managed by conventional management methods,270 active medical devices (including 244 in use under conventional management method) used from January to December 2023 were managed by active medical equipment maintenance and repair risk identification and early warning model (referred to as risk identification model management). The equipment maintenance and repair management effects of the two management methods were compared from the aspects of safety level assessment and risk hazard statistics,and business capability of personnel involved in equipment management were assessed and evaluated. Results:The risk rate of active medical equipment managed by risk identification model was 7.8％ (21/270),which was lower than that of conventional management method,and the difference was statistically significant (x2=8.773,P<0.05). Among the 2839 maintenance and repair activities carried out by the risk identification model management method,safety risk hazards of large medical equipment,ECG monitoring equipment,life support emergency equipment and medical testing equipment occurred 75,19,82 and 11 times,respectively,with the hidden danger rates of 2.6％,0.7％,2.9％ and 0.4％,which were all lower than those of the conventional management method,and the difference was statistically significant (x2=27.989,24.580,46.654,12.604,P<0.05). The pass rates of 92 medical equipment managers participating in the risk identification model management method in maintenance management,quality monitoring,fault handling and risk response were 95.7％ (88/92),98.9％ (91/92),92.4％ (85/92) and 97.8％ (90/92),respectively,which were higher than those of the conventional management method,the difference was statistically significant (x2=4.901,4.016,6.368,5.176,P<0.05). Conclusion:The risk identification and early warning model based on coefficient of variation weighting and extension cloud algorithm can reduce the risk level of active medical devices maintenance and repair,control the occurrence probability of potential safety hazards,and improve the support level of maintenance and repair management.