[Objective] To analyze the influencing factors of repeat blood donor lapsing using a zero-inflated poisson regression model (ZIP). [Methods] The blood donation behavior of 12 498 whole blood donors from 2020 was tracked until December 31, 2023. The factors influencing the frequency of blood donations in a given year was analyzed using ZIP, and donors with 0 blood donation in that year were considered to have lapsed. The changes in relevant influencing factors associated with each blood donation were measured and modeled for analysis. [Results] The zero-inflated part of ZIP showed that the risk of lapsing of male blood donors was 2.24 times that of female blood donors (OR 95% CI:1.864-2.696, P<0.001); the risk of lapsing of the 35-44 age group and over 45 age group was respectively 40% (OR 95% CI:0.455-0.790, P<0.001) and 61%(OR 95% CI:0.268-0.578, P<0.001) lower than that of the under 25 age group; the risk of lapsing for those who have donated blood twice and ≥3 times was respectively 50% (OR 95% CI:0.405-0.609, P<0.001) and 81% (OR 95% CI:0.154-0.225, P<0.001) lower than that of first-time donors; the risk of lapsing of those with junior high or high school education was 1.2 times that of those with a college degree or higher (OR 95% CI:1.033-1.384, P<0.05); the risk of lapsing for the divorced group was 2.02 times that of the married group (OR 95% CI:1.445-2.820, P<0.001); the risk of lapsing for those with an income (Yuan) of 10 000 to 50 000, 50 000 to 100 000 and more than 100 000 was respectively 0.67 (OR 95% CI:0.552-0.818, P<0.001), 0.72 (OR 95% CI:0.591-0.884, P=0.002) and 0.67 (OR 95% CI:0.535-0.834, P<0.001) times that of those with an income (Yuan) of less than 10 000. The results of the Poisson part are consistent with the results of the zero-inflated part in terms of age and education level. [Conclusion] Blood donor lapsing is overall related to factors such as gender, age, donation frequency, education, marital status and family income. It's essential to care for those blood donors prone to lapse to retain more regular blood donors.

Objective To develop the Expert Consensus on Sensitive Indicators for Assessment of the Quality of Nursing in Operating Theatre and provide a scientific and practical guidance for improving the quality of nursing in operating theatre.Methods The writing team established by the Operating Room Nursing Professional Committee of Guangdong Nursing Association conducted systematic literature retrieval and screening,and used the updated clinical Guidelines for Research and Evaluation Ⅱ in UK 2017.AGREE Ⅱ and the evidence evaluation system of the Australian JBI(Joanna Briggs Institute,JBI)Evidence-Based Health Care Center evidence level system(2016 Edition)comprehensively analyzed the evidence related to the sensitive indicators for evaluating the quality of operating room nursing and the suggestions of the writing group members.The first draft was formed based on the three-dimensional quality evaluation theoretical framework of"structure-process-result".Through the Delphi method,after two rounds of expert consultations and members'votes,the first draft was deeply revised and improved.Results Based on the three-dimensional quality evaluation theoretical framework of"structure-process-outcome"proposed by American scholar Donabedian,the expert consensus finally included five primary indicators:basic nursing quality,quality indicators of patient safety,quality indicators of hospital infection control,quality indicators of medication and safety management,and quality indicators of specialised nursing in operating theatre.The secondary indicators consisted of one structural indicator(management of commonly used instrument and equipment in operating theatre)and 17 process indicators(e.g.,infusion and blood transfusion management,body temperature management,etc.).The tertiary indicators included 26 process indicators and 11 outcome indicators(e.g.,incidence of adverse reactions of infusion during surgery,incidence of intra-operative hypothermia,etc.).Conclusion The evidence-and guideline-based Expert Consensus on Sensitive Indicators for Assessment of the Quality of Nursing in Operating Theatre based on eviclence and guidelines was established through rigorous evidence-based methods.It is operational and practical,and offers theoretical support and practical guidance for the managers of operating theatre to improve the quality of nursing.

Objective:To study the relationship between serum adiponectin level and early vascular aging (EVA).Methods:The cross-sectional study method was used. Six hundred and seventy-two subjects who completed health checkup from June to December 2023 in the Affiliated Hospital of Qingdao University were selected. The subjects were divided into the EVA group (237 cases) and the control group (435 cases) based on brachial-ankle pulse wave velocity (baPWV). According to the adiponectin tertiles method, the subjects were divided into low adiponectin subgroup (2.4 to 6.6 mg/L, 225 cases), medium adiponectin subgroup (6.7 to 9.1 mg/L, 227 cases) and high adiponectin subgroup (9.2 to 19.8 mg/L, 220 cases). The basic demographic information, past history and serological indexes were recorded. Univariate and multivariate binary Logistic regression analyses were used to analyze the risk factors for EVA, and multivariate Logistic regression was used to analyze the effect of adiponectin on EVA.Results:The male proportion, age, body mass index (BMI), systolic blood pressure, diastolic blood pressure, triglycerides (TG), fasting blood glucose (FBG), uric acid, glycated hemoglobin (HbA 1c), homocysteine, baPWV and alcohol history proportion in EVA group were significantly higher than those in control group: 64.98% (154/237) vs. 53.33% (232/435), 53 (47, 57) years old vs. 46 (39, 52) years old, (26.34 ± 3.37) kg/m 2 vs. (25.16 ± 3.91) kg/m 2, (132.27 ± 15.48) mmHg (1 mmHg = 0.133 kPa) vs. (117.30 ± 13.04) mmHg, (81.79 ± 11.04) mmHg vs. (71.93 ± 10.10) mmHg, 1.45 (1.03, 2.03) mmol/L vs. 1.08 (0.76, 1.65) mmol/L, 5.52 (5.03, 6.21) mmol/L vs. 5.14 (4.77, 5.56) mmol/L, (380.04 ± 96.43) μmol/L vs. (362.18 ± 94.94) μmol/L, 5.80 (5.50, 5.90)% vs. 5.70 (5.40, 5.82)%, 10.70 (9.01, 12.90) μmol/L vs. 9.96 (8.30, 12.20) μmol/L, 1 586 (1 511, 1 719) cm/s vs. 1 299 (1 215, 1 367) cm/s and 19.41% (46/237) vs. 13.56% (59/435), the adiponectin was significantly lower than that in control group: 7.00 (5.70, 8.75) mg/L vs. 8.40 (6.40, 10.60) mg/L, and there were statistical differences ( P<0.01 or <0.05). There were no statistical differences in total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), creatinine and smoking history proportion between two groups ( P>0.05). The male proportion, BMI, systolic blood pressure, diastolic blood pressure, TG, FBG, uric acid, creatinine, HbA 1c, homocysteine, EVA incidence, baPWV, smoking history proportion and alcohol history proportion in low adiponectin subgroup and medium adiponectin subgroup were significantly higher than those in high adiponectin subgroup, furthermore, the indexes except HbA 1c in low adiponectin subgroup were significantly higher than those in medium adiponectin subgroup, and there were statistical differences ( P<0.05); the HDL-C in low adiponectin subgroup and medium adiponectin subgroup was significantly lower than that in high adiponectin subgroup, furthermore, that in low adiponectin subgroup was significantly lower than that in medium adiponectin subgroup, and there were statistical differences ( P<0.05); there were no statistical differences in age, TC and LDL-C among the three subgroups ( P>0.05). Univariate binary Logistic regression analysis result showed that age, male, BMI, alcohol history, systolic blood pressure, diastolic blood pressure, TG, FBG, uric acid and HbA 1c were the risk factors for EVA ( P<0.01 or <0.05), while the adiponectin was a protective factor for EVA ( P<0.01). Multivariate binary Logistic regression analysis result showed that age, systolic blood pressure, TG and FBG were risk factors for EVA ( OR = 1.098, 1.066, 1.209 and 1.268; 95% CI 1.069 to 1.127, 1.050 to 1.082, 1.007 to 1.451 and 1.069 to 1.502; P<0.01 or <0.05), while adiponectin was a protective factor ( OR = 0.892, 95% CI 0.828 to 0.962, P<0.01). Multivariable Logistic regression analysis result showed that adiponectin consistently remained a protective factor for EVA across unadjusted, preliminary adjusted and fully adjusted covariate models ( OR = 0.553, 0.580 and 0.576; 95% CI 0.451 to 0.678, 0.440 to 0.764 and 0.435 to 0.763; P<0.01). Conclusions:The serum APN level is negatively correlated with the risk of EVA, which may be an independent protective factor for the EVA.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective To explore the role of sevoflurane(SEV)in sepsis-induced acute lung injury(ALI)and observe its impact on the Wnt/β-catenin signaling pathway.Methods Forty C57 mice were randomly divided into 4 groups(n=10 each):Sham,CLP,SEV,and SEV+XAV(β-catenin inhibitor).A sepsis model was established via cecal ligation and puncture.Lung injury was evaluated using HE staining,lung wet/dry weight ratio,and TUNEL staining.Levels of inflammatory factors(TNF-α,IL-1β,IL-6)were detected by ELISA.Oxidative stress indices(SOD,MDA,ROS)were measured by colorimetry and flow cytometry.Hindlimb blood perfusion and oxygenation were assessed with laser speckle flowmetry.Expressions of key Wnt pathway molecules and down-stream target genes(c-Myc,Cyclin D1)were detected by RT-qPCR and Western blot.Co-localization of β-catenin and SP-C(a marker of type Ⅱ alveolar epithelial cells)in lung tissues was determined by immunofluorescence staining.Results Compared with the Sham group,the CLP group exhibited significant increases in sepsis severity,lung pathological damage including alveolar structure destruction,inflammatory infiltration,and apoptosis,elevation in pro-inflammatory cytokine levels,and significant decrease in SOD and increase in MDA and ROS.Additionally,lower limb blood flow and oxygenation levels were significantly reduced,while the expression of β-catenin and its downstream target genes,as well as the co-localization signal and fluorescence intensity of β-catenin with SP-C,were significantly downregulated(all P<0.05).Compared with the CLP group,the SEV group showed significant improvements in all these indicators.However,compared with the SEV group,the SEV+XAV group demon-strated a reversed protective effect,with all indicators approaching the levels observed in the CLP group(all P<0.05).Conclusion Sevoflurane alleviates sepsis-induced ALI by activating Wnt/β-catenin signaling pathway,exerting anti-inflammatory and antioxidant effects,and enhancing the expression and localization of β-catenin in type Ⅱ alveolar epithelial cells.

Objective To explore the evidence for Traditional Chinese Medicine(TCM)in the treatment of premature ovarian insufficiency(POI)based on evidence map and re-evaluation of systematic reviews.Methods CNKI,WanFang Data,VIP,SinoMed,PubMed,Embase,Cochrane Library and Web of Science database were electronically searched to collect systematic reviews(SR)/Meta-analysis on the treatment of POI with TCM from the inception to March 31,2025.The reporting quality,methodological quality,and evidence quality of the included studies were evaluated using the PRISMA 2020 Statement,AMSTAR 2 Checklist,and GRADE system,respectively.The interventions,number of studies,and evidence grades were comprehensively displayed using evidence map.Results A total of 15 SR/Meta-analysis were included,comprising 9 Chinese articles and 6 English articles.The PRISMA 2020 checklist evaluation revealed that 8 articles had certain deficiencies in reporting,while 7 articles demonstrated relatively complete reporting.Based on the AMSTAR 2 checklist,5 articles were rated as high-level and 10 as very low-level.A total of 10 primary outcome indicators were involved,reported 133 times.When classified using the GRADE system,there were 20 pieces of moderate-quality evidence,58 pieces of low-quality evidence,and 55 pieces of very low-quality evidence.The evidence map showed that TCM alone or in combination with hormone therapy could effectively treat POI,reduce follicle-stimulating hormone and luteinizing hormone levels,increase estradiol levels,and improve clinical manifestations and TCM syndrome manifestations.Conclusion TCM has certain advantages in the treatment of POI,enhancing the overall treatment effect,alleviating clinical symptoms of low estrogen,and regulating sex hormone levels to some extent.However,there are deficiencies in methodological quality and reporting quality,and the level of evidence is not high.Therefore,the findings should be used with caution in clinical practice.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective To explore the role of sevoflurane(SEV)in sepsis-induced acute lung injury(ALI)and observe its impact on the Wnt/β-catenin signaling pathway.Methods Forty C57 mice were randomly divided into 4 groups(n=10 each):Sham,CLP,SEV,and SEV+XAV(β-catenin inhibitor).A sepsis model was established via cecal ligation and puncture.Lung injury was evaluated using HE staining,lung wet/dry weight ratio,and TUNEL staining.Levels of inflammatory factors(TNF-α,IL-1β,IL-6)were detected by ELISA.Oxidative stress indices(SOD,MDA,ROS)were measured by colorimetry and flow cytometry.Hindlimb blood perfusion and oxygenation were assessed with laser speckle flowmetry.Expressions of key Wnt pathway molecules and down-stream target genes(c-Myc,Cyclin D1)were detected by RT-qPCR and Western blot.Co-localization of β-catenin and SP-C(a marker of type Ⅱ alveolar epithelial cells)in lung tissues was determined by immunofluorescence staining.Results Compared with the Sham group,the CLP group exhibited significant increases in sepsis severity,lung pathological damage including alveolar structure destruction,inflammatory infiltration,and apoptosis,elevation in pro-inflammatory cytokine levels,and significant decrease in SOD and increase in MDA and ROS.Additionally,lower limb blood flow and oxygenation levels were significantly reduced,while the expression of β-catenin and its downstream target genes,as well as the co-localization signal and fluorescence intensity of β-catenin with SP-C,were significantly downregulated(all P<0.05).Compared with the CLP group,the SEV group showed significant improvements in all these indicators.However,compared with the SEV group,the SEV+XAV group demon-strated a reversed protective effect,with all indicators approaching the levels observed in the CLP group(all P<0.05).Conclusion Sevoflurane alleviates sepsis-induced ALI by activating Wnt/β-catenin signaling pathway,exerting anti-inflammatory and antioxidant effects,and enhancing the expression and localization of β-catenin in type Ⅱ alveolar epithelial cells.

Objective To investigate the in vitro antiviral effects of Blumea balsamifera(L.)DC.extract against Coxsackievirus B5(CVB5).Methods A series of dilutions of Coxsackievirus were prepared and co-cultured with RD cells to determine the TCID50 value.Subsequently,different concentrations of the extract were added to a 96-well plate containing RD cells to evaluate their impact on cell viability.The ability of Blumea balsamifera extract to inhibit Coxsackievirus was further observed in the 96-well plate containing RD cells and the extract.Results The TCID50 value of Coxsackie virus solution was 10-7.67.The inhibition rate of Blumea balsamifera extract against Coxsackievirus increased with concentration,with an IC50 value of 7.26 mg/L.At a concen-tration of 50 mg/L,the extract caused a decrease in RD cell viability(P＜0.05),but within the concentration range of 6.25 to 50 mg/L,it increased the viability of virus-infected RD cells(P＜0.05),with a selectivity index(SI)exceeding 6.89.Conclusion Blumea balsamifera(L.)DC.extract exhibits in vitro activity against Coxsackievirus.

Objective: To preliminarily develop a multidimensional blood donor role scale based on role theory and systematically compare the psychosocial characteristic differences between award-winning donors and first-time donors in Guangzhou, and to provide an empirical reference for formulating differentiated donor retention strategies. Methods: A cross-sectional survey design was adopted. A random sample of award-winning donors and concurrently sampled first-time donors yielding 1 361 valid responses collected (721 from the award group, 640 from the first-time group). Exploratory factor analysis was used to assess the scale structure. Data were post-stratified and weighted according to the gender and age distributions of the general donor population. Independent samples t-tests, multivariate analysis of covariance (MANCOVA), and generalized linear models were employed to compare dimensional scores between the two groups. A paired t-test was conducted to analyze the annual donation frequency of award-winning donors before and after receiving the award. Results: Exploratory factor analysis yielded a 5-factor structure, including Role Identity and Expectations, Role Adaptation and Maintenance, Role Environment and Experience, Role Relationships and Conflict, and Role Incentives and Rewards, with a cumulative variance contribution rate of 56.43%. The scale demonstrated good internal consistency reliability (Cronbach's α=0.906). Known-group validity test showed that award-winning donors scored significantly higher than first-time donors on Role Identity and Expectations (t=4.366, P<0.001, d=0.240), Role Adaptation and Maintenance (t=5.436, P<0.001, d=0.500), and Role Relationships and Conflict (t=4.844, P<0.001, d=0.220). These differences remained significant after controlling for selected demographic variables (MANCOVA, Wilks' λ=0.943, P<0.001). Generalized linear models suggested that donation frequency was an independent predictor for these dimensions. Additionally, the annual donation frequency of award-winning donors was slightly higher after receiving the award than before (t=2.007, P=0.045). Conclusion: The preliminary blood donor role scale demonstrates acceptable reliability and validity and can effectively distinguish groups with different donation behavior characteristics. The study reveals that award-winning donors exhibit more positive psychological characteristics across multiple role identity dimensions and maintain their donation behavior after receiving an award. External incentives and internal role identity may jointly contribute to behavioral persistence. The findings provide a preliminary reference for further exploring the formation pathways of donor role identity and developing differentiated donor retention strategies.