Objectives: . The prognostic value of the pretreatment albumin-to-alkaline phosphatase ratio (AAPR) in head and neck cancer (HNC) remains uncertain. This meta-analysis aimed to evaluate the predictive role of AAPR for survival outcomes in patients with HNC. Methods: . A comprehensive search of the Cochrane Library, PubMed, and Embase databases was conducted to identify relevant studies published up to July 30, 2024. We included studies on AAPR and survival outcomes in HNC patients. Results: . Eight studies comprising 1,737 HNC patients were analyzed using random-effects models. Lower AAPR values were significantly correlated with worse overall survival (hazard ratio [HR], 2.08), progression-free survival (HR, 2.00), and disease-free survival (HR, 2.18). Sensitivity analyses confirmed the robustness of these results, with no significant publication bias detected. Conclusion . Our findings suggest that pretreatment AAPR could serve as a valuable and cost-effective prognostic indicator in HNC, potentially aiding clinicians in risk stratification and treatment decision-making. However, additional validation studies are warranted to confirm its clinical applicability.

[Objective]By integrating multi-parameter magnetic resonance imaging(MRI)radiomics features,an interpretable machine learning model was constructed to accurately predict pre-operative traditional Chinese medicine(TCM)syndromes in breast cancer patients,providing objective and quantitative basis for clinical TCM syndrome types differentiation.[Methods]A total of 315 patients pathologically diagnosed breast cancer from the First Affiliated Hospital of Zhejiang Chinese Medical University between June 2019 to October 2023 were retrospectively included.The preoperative TCM syndromes of the patients were classified into liver depression with phlegm coagulation,chongren dysregulation and positive deficiency blazing toxin type.These patients were randomly allocated to the training(221 cases)and validation sets(94 cases)in a ratio of 7:3.Radiomics features in dynamic contrast-enhanced imaging(DCE),diffusion-weighted imaging(DWI)and apparent diffusion coefficient(ADC)images of each patient were extracted by using Kruskal-Wallis rank sum test,Spearman correlation analysis and least absolute shrinkage and selection operator(LASSO)polynomial Logistic regression screening for radiomics features.Then,light gradient boosting machine(LightGBM)was used to construct the syndrome prediction model.[Results]A total of 2 832 radiomics features were extracted from the DCE,DWI and ADC images of each patient.After a series of feature selection methods,the total numbers of features for DCE,DWI,ADC and three-sequence combination were 3,3,3 and 7 respectively.The machine learning model integrating multi-sequence MRI image information had the best performance,with micro-averaged area under the curve(AUC)of 0.852 and 0.838 for the training and validation sets,and macro-averaged AUC of 0.833 and 0.810 for the training and validation sets respectively.Shapley additive explanation(SHAP)analysis showed that inverse difference moment normalization(Idmn)of the ADC sequence,average gray level(Mean)of the ADC sequence and inverse variance of the DCE sequence were the three features that contributed most to the prediction results.[Conclusion]The interpretable machine learning model constructed in this study can accurately predict pre-operative TCM syndromes in breast cancer patients.SHAP interpretability analysis provides quantitative contribution value information,which can help clinicians understand the model prediction process and provide a new perspective for the application of traditional Chinese medicine in the treatment of breast cancer.

Objective:In recent years,the economic operating risks of public hospitals are gradually increased.Conducting a scientific assessment of hospital debt risk is crucial to preventing hospitals from falling into financial distress.It aims to evaluate the debt risk of public hospitals in Shenzhen,with a particular focus on the impact of special bonds on debt risk.Methods:Based on the 2022-2023 financial reports and data on special bondsof public hospitals in Shenzhen,factor analysis was employed to assess the debt risk of public hospitals with and without special bonds.Results:Tertiary hospitals,municipal hospitals,specialized hospitals,and general hospitals generally exhibited lower debt risk.Factors related to debt risk primarily included cash flow variables such as medical service income,net assets,and debt repayment capacity indicators,including current ratio and cash ratio.When considering the impact of special bonds,the weight of cash flow indicators and long-term debt repayment capacity indicators increased.Debt risk for municipal hospitals shifted towards higher-risk levels,while district-level hospitals saw a shift towards lower-risk levels,particularly for district-level traditional Chinese medicine hospitals.Conclusion:The debt risk of public hospitals in Shenzhen is significantly influenced by special bonds.It is recommended to strengthen the management of special bond funds,optimize hospital cash flow,and improve debt repayment capacity to reduce debt risk.

Objective:To observe the intervention effect of personalized nutrition program combined with rehabilitation training in stroke rehabilitation patients.Methods:86 stroke rehabilitation patients who were admitted to our hospital from January 2023 to June 2024 were prospectively selected,they were divided into control group and study group according to the random number table method,with 43 cases in each group,the control group received rehabilitation training,while the study group received personalized nutrition program combine with rehabilitation training.Simple Fugl Meyer motor function(FMA)score,immune function indicators[immunoglobulin(Ig)A,IgG,complement C3,IgM,complement C4],National Institutes of Health Stroke Scale(NIHSS),nutritional status indicators[albumin(ALB),prealbumin(PA),total protein(TP),hemoglobin(HB)],Stroke Specific Quality of Life Scale(SS-QOL),Barthel Index(BI)score were compared between the two groups.Results:NIHSS score in the study group at 8 weeks after intervention was lower than that in the control group,and SS-QOL score,BI score,FMA score,IgM,IgA,IgG,complement C3,complement C4,ALB,HB,TP and PA were higher than those in the control group(P＜0.05).Conclusion:Personalized nutrition program combined with rehabilitation training in stroke rehabilitation patients,can reduce neurological damage,improve limb motor function,enhance nutritional status,immunity,and quality of life.

Objective To construct a quality evaluation index system for non-medical technical services in public hospitals,aiming to provide a more scientific and feasible basis for evaluating patient service quality.Methods Based on literature research,the SERVQUAL theory was used to preliminarily formulate a comprehensive and full-cycle evaluation index for patient service quality.The final evaluation index system was determined through expert consultation,and the Analytic Hierarchy Process was used to determine the weights of the evaluation indices.Results The questionnaire response rates for the two rounds of expert consultation were both 100％.The authority coefficients of the experts were 0.82 and 0.80,respectively,indicating a high level of expert authority.The Kendall's W coefficient increased from 0.088 to 0.107 between the two rounds,and the difference was statistically significant (P<0.001),indicating good consistency of expert opinions.The consistency results of each judgment matrix were less than 0.1,passing the consistency test.The final evaluation index system consisted of six primary indicators (tangibility,reliability,responsiveness,assurance,empathy,and continuity) and 33 secondary indicators.Conclusion The selection of evaluation indicators revolves around the core service processes of outpatient,inpatient,and other hospital services,focusing on key elements at various critical junctures that influence patient experience.The evaluation indicators aim to guide hospitals to improve not only essential needs but also desired improvements in service quality,thus promoting the continuous enhancement of hospital services.

[Objective]By integrating multi-parameter magnetic resonance imaging(MRI)radiomics features,an interpretable machine learning model was constructed to accurately predict pre-operative traditional Chinese medicine(TCM)syndromes in breast cancer patients,providing objective and quantitative basis for clinical TCM syndrome types differentiation.[Methods]A total of 315 patients pathologically diagnosed breast cancer from the First Affiliated Hospital of Zhejiang Chinese Medical University between June 2019 to October 2023 were retrospectively included.The preoperative TCM syndromes of the patients were classified into liver depression with phlegm coagulation,chongren dysregulation and positive deficiency blazing toxin type.These patients were randomly allocated to the training(221 cases)and validation sets(94 cases)in a ratio of 7:3.Radiomics features in dynamic contrast-enhanced imaging(DCE),diffusion-weighted imaging(DWI)and apparent diffusion coefficient(ADC)images of each patient were extracted by using Kruskal-Wallis rank sum test,Spearman correlation analysis and least absolute shrinkage and selection operator(LASSO)polynomial Logistic regression screening for radiomics features.Then,light gradient boosting machine(LightGBM)was used to construct the syndrome prediction model.[Results]A total of 2 832 radiomics features were extracted from the DCE,DWI and ADC images of each patient.After a series of feature selection methods,the total numbers of features for DCE,DWI,ADC and three-sequence combination were 3,3,3 and 7 respectively.The machine learning model integrating multi-sequence MRI image information had the best performance,with micro-averaged area under the curve(AUC)of 0.852 and 0.838 for the training and validation sets,and macro-averaged AUC of 0.833 and 0.810 for the training and validation sets respectively.Shapley additive explanation(SHAP)analysis showed that inverse difference moment normalization(Idmn)of the ADC sequence,average gray level(Mean)of the ADC sequence and inverse variance of the DCE sequence were the three features that contributed most to the prediction results.[Conclusion]The interpretable machine learning model constructed in this study can accurately predict pre-operative TCM syndromes in breast cancer patients.SHAP interpretability analysis provides quantitative contribution value information,which can help clinicians understand the model prediction process and provide a new perspective for the application of traditional Chinese medicine in the treatment of breast cancer.

Objective:In recent years,the economic operating risks of public hospitals are gradually increased.Conducting a scientific assessment of hospital debt risk is crucial to preventing hospitals from falling into financial distress.It aims to evaluate the debt risk of public hospitals in Shenzhen,with a particular focus on the impact of special bonds on debt risk.Methods:Based on the 2022-2023 financial reports and data on special bondsof public hospitals in Shenzhen,factor analysis was employed to assess the debt risk of public hospitals with and without special bonds.Results:Tertiary hospitals,municipal hospitals,specialized hospitals,and general hospitals generally exhibited lower debt risk.Factors related to debt risk primarily included cash flow variables such as medical service income,net assets,and debt repayment capacity indicators,including current ratio and cash ratio.When considering the impact of special bonds,the weight of cash flow indicators and long-term debt repayment capacity indicators increased.Debt risk for municipal hospitals shifted towards higher-risk levels,while district-level hospitals saw a shift towards lower-risk levels,particularly for district-level traditional Chinese medicine hospitals.Conclusion:The debt risk of public hospitals in Shenzhen is significantly influenced by special bonds.It is recommended to strengthen the management of special bond funds,optimize hospital cash flow,and improve debt repayment capacity to reduce debt risk.

Objective To explore the factors influencing blood drug concentrations of first-line anti-tuberculosis drugs in fixed-dose combinations by analyzing therapeutic drug monitoring data from tuberculosis patients receiving these regimens.Methods This retrospective study enrolled 224 patients who received treatment at Guangzhou Chest Hospital between January 2020 and December 2024.All participants underwent standardized therapy during the intensive phase,with therapeutic drug monitoring of first-line anti-tuberculosis drugs(ANTDs),including isoniazid(INH)and rifampicin(RFP).Data collection was completed in January 2025,at which time clinical records and measured INH and RFP plasma concentrations were updated.Data analysis was conducted from January to February 2025.Eight baseline variables—gender,age,hypoproteinemia(serum albumin<35 g/L),glomerular filtration rate(GFR),and others—were collected.Univariate chi-square tests and multivariate logistic regression analyses were performed to identify independent risk factors associated with subtherapeutic INH and RFP plasma concentrations.Results Among the study participants,71.43%(160/224)exhibited blood drug concentrations below the reference range for INH,compared to 41.07%(92/224)for RFP.The mean blood concentrations(mg/L,±SD)were 2.532±1.371 for INH and 9.428±4.317 for RFP,respectively.One-way analysis indicated significant associations between male gender,positive etiological test results,and subtherapeutic RFP concentrations(P<0.05),suggesting statistically significant differences.Multivariate regression analysis further revealed that male gender(OR=1.992,95%CI:1.094～3.628)and positive etiological tests(OR=1.929,95%CI:1.058～3.517)were independent risk factors for low RFP levels.Conclusions This study demonstrates that therapeutic drug monitoring(TDM)frequently identifies subtherapeutic RFP concentrations in tuberculosis patients undergoing treatment.Multivariate analysis reveals that male sex and positive pathogen test results are independent risk factors associated with low RFP plasma levels.Consequently,clinicians should exercise heightened vigilance in patients exhibiting these characteristics,promptly implementing TDM to guide individualized dose adjustments.Such an approach is crucial for optimizing treatment efficacy and minimizing the risk of drug resistance development.

Aim To study the effect of p-benzoyl sali-droside(pOBz)on angiogenesis after ischemic stroke and to explore the underlying mechanism.Methods The MCAO model was prepared by suture method.Rats were divided into four groups:sham,MCAO,pOBz administration,and edaravone positive control,treated for seven days.The mNSS was used to assess the neurological impairment.Western blotting was em-ployed to detect CD31,NICD,and Hes-1 protein ex-pression,while immunofluorescence staining was ap-plies to quantify CD31-positive cells in ischemic brain tissue.In vitro an OGD/R model was established in HUVECs.Following treatment with varying pOBz con-centrations(0.01,0.1,1 μmol·L-1),the CCK-8 as-say was uses to measure cell viability,and in vitro tube formation assay was utilized to evaluate angiogenesis.Western blotting was employed again to assess CD31,NICD and Hes-1 protein levels.To further elucidate the mechanism,HUVEC were treated with the Notch inhibitor DAPT prior to grouping and pOBz administra-tion,and the same parameters were evaluated.Results pOBz significantly reduced the mNSS score of MCAO rats,increased CD31-positive cell counts,and upregu-lated CD31,NICD,and Hes-1 protein expression(P＜0.01).In vitro results further showed that pOBz could dose-dependently increase the survival rate and angio-genesis ability of HUVEC induced by OGD/R,and promote CD31,NICD and Hes-1 proteins(P＜0.01),and Notch inhibitor DAPT could reverse the above effects of pOBz.Conclusion pOBz promotes angio-genesis in HUVEC,and its mechanism involves activa-tion of the Notch signaling pathway.

Objective:To explore the mediating effect of illness acceptance between self-compassion and rehabilitation motivation in stroke patients with partial disability.Methods:By convenience sampling method, stroke patients who attended the neurology outpatient clinic of Xinxiang Central Hospital from February to December 2024 were selected as the research subjects. The General Information Questionnaire, Self-Compassion Scale (SCS), Chinese version of Acceptance of Illness Scale (AIS-CHI), and Stroke Rehabilitation Motivation Scale (SRMS) were used for the survey. Pearson correlation analysis was applied to analyze the relationships among self-compassion, illness acceptance, and rehabilitation motivation. AMOS 21.0 software was used to establish a structural equation model and verify the mediating effect.Results:A total of 300 questionnaires were distributed, and 289 valid ones were recovered, with an effective recovery rate of 96.33%. Among the 289 stroke patients with partial disability, the total score of SCS was (65.73±5.50), the total score of AIS-CHI was (17.46±5.62), and the total score of SRMS was (77.18±10.97). Pairwise positive correlations were found between self-compassion, illness acceptance, and rehabilitation motivation (all P<0.05). Self-compassion had a direct positive effect on rehabilitation motivation ( β=0.328, P<0.01) and a direct positive effect on illness acceptance ( β=0.439, P<0.01). Illness acceptance played a partial mediating role between self-compassion and rehabilitation motivation, and the mediating effect accounted for 31.38% of the total effect (0.150/0.478) . Conclusions:The levels of self-compassion, illness acceptance, and rehabilitation motivation in stroke patients with partial disability need to be further improved. Illness acceptance exerts a partial mediating effect between self-compassion and rehabilitation motivation. Clinically, the rehabilitation motivation of patients can be enhanced by improving their levels of self-compassion and illness acceptance.