BackgroundAs a high prevalence disorder， schizophrenia has caused significant burden to family and society due to the impairment of occupational and social function. Currently， the dominant vocational training model in China follows a paternalistic， clinician-led decision-making approach. Although it improves patients' social function to some extent， it undermines their autonomy and treatment adherence. Therefore， it is urgently necessary to explore a new intervention method to enhance treatment compliance and social function in patients. ObjectiveTo explore the impact of shared decision-making oriented vocational training on social function in hospitalized schizophrenia patients， so as to provide references for rehabilitation interventions. MethodsA total of 68 patients diagnosed with schizophrenia according to the International Classification of Diseases， tenth edition （ICD-10） criteria were consecutively enrolled from January to June 2024 at The Third People's Hospital of Wenjiang Distric， Chengdu. Participants were randomly allocated into the research group （n=34） and the control group （n=34） using a random number table method. Both groups received routine rehabilitation training， while the research group received shared decision-making oriented vocational training for 12 weeks， 2 times a week for 2 hours each time. Before and at the 4^th and 12^th week of intervention， two groups were evaluated by General Self-Efficacy Scale （GSES）， Stigma Scale for Mental Illness （SSMI）， Scale of Social function of Psychosis Inpatients （SSFPI） and Inpatient Psychiatric Rehabilitation Outcome Scale （IPROS）. ResultsA total of 63 participants completed the study， with 30 cases in the research group and 33 cases in the control group. Repeated measures ANOVA revealed statistically significant time effects and interaction effects in both groups for GSES， SSMI， SSFPI and IPROS scores （F=20.451， 16.022； 26.193， 12.944； 23.957， 5.023； 11.776， 3.985， P<0.05 or 0.01）， while no significant group effects were observed （F=0.188， 0.742， 1.878， 0.474， P>0.05）. At the 12^th week of intervention， there were statistically significant differences in GSES， SSMI， SSFPI and IPROS scores between the two groups. ConclusionShared decision-making oriented vocational training may help to improve social function in patients with schizophrenia. ［Funded by 2023 Chengdu Medical Research Project （number， 2023468）］

OBJECTIVE To standardize the main processes and related technical links of the clinical comprehensive evaluation of drugs， and provide guidance and reference for improving the quality of comprehensive evaluation evidence and its transformation and application value. METHODS The construction of Guideline for the Workflow of Clinical Comprehensive Evaluation of Drugs was based on the standard guideline formulation method of the World Health Organization （WHO）， strictly followed the latest definition of guidelines by the Institute of Medicine of the National Academy of Sciences of the United States， and conformed to the six major areas of the Guideline Research and Evaluation Tool Ⅱ. Delphi method was adopted to construct the research questions； research evidence was established by applying the research methods of evidence-based medicine. The evidence quality classification system of the Chinese Evidence-Based Medicine Center was adopted for evidence classification and evaluation. The recommendation strength was determined by the recommendation strength classification standard formulated by the Oxford University Evidence-Based Medicine Center， and the recommendation opinions were formed through the expert consensus method. RESULTS & CONCLUSIONS The Guideline for the Workflow of Clinical Comprehensive Evaluation of Drugs covers 4 major categories of research questions， including topic selection， evaluation implementation， evidence evaluation， and application and transformation of results. The formulation of this guideline has standardized the technical links of the entire process of clinical comprehensive evaluation of drugs， which can effectively guide the high-quality and high-efficient development of this work， enhance the standardized output and transformation application value of evaluation evidence， and provide high-quality evidence support for the scientific decision-making of health and the rationalization of clinical medication.

Objective: To design and establish a new method for flow cytometry-based detection of commonly observed highly expressed antigens on red blood cells, and to further evaluate the differences and distribution characteristics of antigen expression levels between ABO blood type homozygotes and heterozygotes in healthy individuals. Methods: Residual blood samples after donor blood type identification by Shanghai Blood Center in April 2024 were collected. Among them, samples of 19 homozygous and 19 heterozygous individuals of type A and type B were selected. Then the expression level of ABO antigen on red blood cells were detected using the new method established in this study and the traditional aldehyde fixed red blood cell method. Both methods were tested independently three times and the results were compared. Results: The mean values of the three detection results of the new method was (×10 /RBC): AA homozygous 3.3±0.5, AO heterozygous 2.8±0.3, BB homozygous 3.6±0.3, BO heterozygous 3.1±2.8. The mean values of the three detection results of the aldehyde fixation method were AA homozygous 5.9±0.9, AO heterozygous 5.0±1.4, BB homozygous 3.8±0.6, and BO heterozygous 3.3±0.4. The average antigen distribution of each genotype followed a normal distribution. Comparing the average antigen expression levels of homozygotes and heterozygotes, both methods showed that A/B homozygotes had higher antigen levels than heterozygotes, with AA being 1.17 to 1.18 times that of AO and BB being 1.15 to 1.16 times that of BO. Comparing the inter batch differences in the three test results of two methods, the new method showed no significant difference in the three test results for four genotypes (P>0.05). The aldehyde fixation method showed significant differences in the test results for all three genotypes (P<0.01) except for BB homozygotes (P>0.05). The reliability and reproducibility of the new method were better than those of the traditional aldehyde fixation method. Conclusion: The antigen expression level of ABO homozygotes is higher than that of heterozygotes, and the difference in antigen level between type A homozygotes and heterozygotes is slightly higher than that of type B. The new method is superior to traditional aldolization fixation methods.

Objective:To analyze the association between daily physical activity patterns and dyslipidemia among people receiving physical examination aged 40-65 years.Methods:This cross-sectional study consecutively enrolled 864 participants aged 40-65 years and met the inclusion and exclusion criteria who underwent health check-ups at the Physical Examination Center of Fuquan First People′s Hospital from March to November in 2022. The data of general characteristics, physical activity, physical examination findings, and lipid profiles were collected. The daily physical activity patterns were identified using K-means clustering analysis. The unconditional binary logistic regression was employed to explore the associations between these activity patterns and dyslipidemia, followed by subgroup analyses.Results:The physical activity of the 864 study participants (517 males and 347 females) included in the analysis was divided into 4 patterns (G1: low physical activity; G2: active commuting; G3: housework; G4: leisure exercise). Using G1 as a reference, after adjusting for confounders, G4 was negatively associated with low high density lipoprotein cholesterol (HDL-C) ( OR=0.37, 95% CI: 0.14-1.00) ( P=0.05). In the male, G3 was negatively associated with dyslipidemia ( OR=0.44, 95% CI: 0.21-0.93) and low HDL-C ( OR=0.25, 95% CI: 0.10-0.68) (both P<0.05). In the subjects aged 50 years and above, G2 was negatively associated with dyslipidemia ( OR=0.52, 95% CI: 0.30-0.90), hypertriglyceridemia ( OR=0.50, 95% CI: 0.28-0.90) and low HDL-C ( OR=0.47, 95% CI: 0.24-0.91) (all P<0.05). In those who never or occasionally stayed up late, G2 was negatively associated with hypertriglyceridemia ( OR=0.31, 95% CI: 0.13-0.75) ( P<0.05); in those who stayed up late often, G4 was negatively associated with dyslipidemia ( OR=0.33, 95% CI: 0.13-0.85) and low HDL-C ( OR=0.19, 95% CI: 0.04-0.84) (both P<0.05). In the centrally obese population, G2 was negatively associated with dyslipidemia ( OR=0.55, 95% CI: 0.35-0.88) and hypertriglyceridemia ( OR=0.54, 95% CI: 0.33-0.86) (both P<0.05). Conclusions:Association between different physical activity patterns and dyslipidemia varied among adults aged 40-65 years undergoing health check-ups. Leisure-time exercise is associated with a reduced risk of dyslipidemia, while household activities also emerges as a beneficial factor linked to lower dyslipidemia risk particularly in the male population.

Objective:To explore the construction and validation of a nomogram model for predicting poor survival prognosis in patients with clear cell renal cell carcinoma（ccRCC）based on deep learning of pathological images.Methods:This study was an observational cohort study. The original pathological images and clinicopathological data（TCGA cohort）of 378 patients with ccRCC were obtained from the Cancer Genome Atlas Database（TCGA）for model training. A total of 301 patients with ccRCC who underwent surgical treatment at Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2010 to December 2020（Renji cohort）and 214 patients with ccRCC who underwent surgical treatment at the First People’s Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2012 to December 2018（General cohort）were included for model validation. Their original pathological images and clinical pathological data were collected. A clustering-constrained attention and multi-instance learning method was used to accurately identify sub-regions of the images to classify and extract features of the pathological images. A deep learning-based disease-free survival prognosis prediction model（DL-DFS）was constructed through a weakly supervised learning strategy. The clinical pathological features and DL-DFS were further combined to construct a nomogram model for the clinical prognosis of ccRCC patients. Univariate and multivariate Cox regression analyses were employed to evaluate the independent risk factors for disease-free survival（DFS）. The efficacy of the predictive model were evaluated by the receiver operating characteristic curve（ROC）with area under the curve（AUC），respectively. Survival analysis was conducted using the Kaplan-Meier curve.Results:DL-DFS could accurately predict the DFS status of ccRCC patients in 5 years after surgery. Through ROC analysis in the training cohort，the AUC value reached 0.75（ P < 0.001）. In the Renji cohort and the General cohort，the AUC values were 0.65（ P < 0.001）and 0.81（ P < 0.001），respectively. Through Kaplan-Meier survival analysis，we found that DL-DFS could identify ccRCC patients with high survival risks. The hazard ratio in the training cohort was 3.86（95% CI 2.36-6.30， P < 0.001）. The hazard ratio in the Renji cohort and General cohort were 1.97（95% CI 1.03-3.80， P = 0.009）and 4.66（95% CI 1.80-12.06， P = 0.008），respectively. Univariate and multivariate Cox regression analyses indicated that DL-DFS risk score，tumor grade，and tumor stage could act as prognostic risk factors for patients with ccRCC（ P < 0.05）. Considering that age was a common prognostic risk factor for patients with renal cancer，a nomogram model was constructed by combining the DL-DFS risk score with patient age，tumor grade，and tumor stage. The AUC of this model for predicting the 5-year DFS of ccRCC patients after surgery was 0.87，which was significantly higher than that of DL-DFS（AUC = 0.74），tumor stage（AUC = 0.84），tumor grade（AUC = 0.72），and patient age（AUC = 0.56）in the TCGA cohort（all P<0.05）. In the Renji cohort and the General cohort，the AUC of the nomogram model were 0.78 and 0.86 respectively，which was significantly higher than that of DL-DFS（0.65 and 0.81），tumor stage（0.72 and 0.69），tumor grade（0.64 and 0.77），and patient age（0.56 and 0.63）. Conclusions:In this study a DL-DFS for ccRCC patients was constructed. Then a nomogram model was constructed by combining the DL-DFS risk value with patient age，tumor grade，and tumor stage. This nomogram model demonstrated superior predictive performance compared to DL-DFS alone in evaluating the DFS prognosis of ccRCC patients，which still needs to be further verified in prospective clinical studies.

Objective:To analyze the results of national external quality assessment (EQA) for transfusion compatibility test in 2023, and provide reference for quality management of clinical transfusion compatibility testing.Methods:The EQA of clinical transfusion compatibility testing by NCCL was performed 3 times in 2023 among included laboratories. The panel consisting of 22 samples was distributed to 4 186 laboratories across 31 provinces (Including 2 961 tertiary hospital laboratories, 1 085 secondary hospital laboratories, 23 primary hospital laboratories, 106 blood station laboratories and 11 independent clinical laboratories). Each panel contains 11 red blood cell and 11 plasma samples per 1.5 ml/tube. Each participant laboratory of the EQA program was required to carry out the detection and return results in expected time. Statistical analysis and evaluation on the reported results were conducted by NCCL from the aspects of regional distribution, laboratory grading, testing methodology, reagent and testing system usage.Results:The qualification rates of EQA for five items including ABO positive typing, ABO reverse typing, RhD blood type, antibody screening, and cross matching were 96.68%, 95.10%, 96.46%, 95.32%, and 91.04%, respectively. The EQA qualification rate of tertiary hospital laboratories was 87.77% (2 599/2 961), which was significantly higher than the 77.79% (844/1 085) of secondary hospital laboratories. There were significant differences in the qualification rate of participating laboratories among different regions. The utilization rates of micro column agglutination method in ABO positive typing, ABO reverse typing, RhD blood type, antibody screening, and cross matching were 80.81% (10 080/12 474), 75.06% (9 337/12 440), 81.38% (10 118/12 433), 89.59% (11 104/12 394) and 76.25% (9 495/12 453), respectively. The qualification rate of micro column agglutination method was significantly higher than that of saline slide method in ABO positive typing detection ( P<0.05). The qualification rate of micro column agglutination method was significantly higher than that of the polyamine method and anti-human globulin test tube method in antibody screening ( P<0.05). There were statistically significant differences in qualification rate of 7 reagents in ABO reverse typing, antibody screening and cross matching ( P<0.05). There was no statistically significant difference in the qualification rate between the two detection systems for other reagents, except for the ABO reverse typing where the qualification rate of reagent 1 in a single system was higher than that in a mixed system ( P<0.05). Conclusion:The testing capabilities of clinical laboratories in different regions and different type varied significantly in China. Micro column agglutination method was the most popular selection in transfusion compatibility testing. The regents used in these laboratories showed good performance. However, the detection efficiency of some reagents still need to be improved. EQA could be used to evaluate, monitor, and improve the quality of testing.

Objective: To analyze the results of national external quality assessment (EQA) for transfusion compatibility test from 2018 to 2023, with the aim of providing references for improving laboratory testing quality and ensuring the safety of clinical blood transfusion. Methods: Three EQA programs were conducted annually, each distributing 22 quality assessment samples. Participating transfusion laboratories were required to complete testing within specified deadlines and to submit results along with documentation of testing methodologies, reagents, and equipment used. National Center for Clinical Laboratories (NCCL) conducted statistical analysis of laboratory results, evaluated testing outcomes and related circumstances, and provided feedback to participating laboratories. EQA data from transfusion laboratories across China from 2018 to 2023 were collected and systematically analyzed. Results: From 2018 to 2023, the qualification rates for all five items (ABO forward typing, ABO reverse typing, Rh blood group typing, antibody screening, and cross-matching) were 67.59%, 77.11%, 77.38%, 72.78%, 79.96%, and 85.16%, respectively. The mean qualification rates for ABO forward typing, ABO reverse typing, RhD blood group typing, antibody screening, and cross-matching over the past six years were 96.25%±0.59%, 90.45%±4.52%, 96.05%±0.71%, 90.88%±2.86%, and 88.34%±3.48%, respectively. The qualification rates in 2019, 2020, 2022, and 2023 all showed a stable trend of "blood stations>tertiary hospitals>secondary hospitals". The mean qualification rate of laboratories in secondary hospitals from 2018 to 2023 was significantly lower than those of laboratories in tertiary hospitals and blood stations (P<0.05), while no significant difference was observed between laboratories in tertiary hospitals and blood stations (P>0.05). The micro column agglutination method was the most widely used in all five tests. In the four test items, namely ABO forward typing, ABO reverse typing, antibody screening, and cross-matching, there was a statistically significant difference in the qualification rate of micro column agglutination method compared to other methods (P<0.05). There was a statistical difference in the qualification rate between manual and automated detection using micro column agglutination method in the cross-matching tests (P<0.05), whereas no significant difference was noted for the other test items (P>0.05). Conclusion: From 2018 to 2023, the number of laboratories participating in EQA activities has been increasing year by year, and the qualification rate has shown an overall upward trend. The type of laboratory is a key factor affecting the qualification rate, and the testing capabilities of some laboratories still need to be improved. The micro column agglutination method is widely used in transfusion compatibility tests. The established EQA program effectively monitors quality issues in laboratories, drives continuous improvement, and ensures sustained enhancement of testing standards to safeguard clinical blood safety.

Objective:To analyze the association between daily physical activity patterns and dyslipidemia among people receiving physical examination aged 40-65 years.Methods:This cross-sectional study consecutively enrolled 864 participants aged 40-65 years and met the inclusion and exclusion criteria who underwent health check-ups at the Physical Examination Center of Fuquan First People′s Hospital from March to November in 2022. The data of general characteristics, physical activity, physical examination findings, and lipid profiles were collected. The daily physical activity patterns were identified using K-means clustering analysis. The unconditional binary logistic regression was employed to explore the associations between these activity patterns and dyslipidemia, followed by subgroup analyses.Results:The physical activity of the 864 study participants (517 males and 347 females) included in the analysis was divided into 4 patterns (G1: low physical activity; G2: active commuting; G3: housework; G4: leisure exercise). Using G1 as a reference, after adjusting for confounders, G4 was negatively associated with low high density lipoprotein cholesterol (HDL-C) ( OR=0.37, 95% CI: 0.14-1.00) ( P=0.05). In the male, G3 was negatively associated with dyslipidemia ( OR=0.44, 95% CI: 0.21-0.93) and low HDL-C ( OR=0.25, 95% CI: 0.10-0.68) (both P<0.05). In the subjects aged 50 years and above, G2 was negatively associated with dyslipidemia ( OR=0.52, 95% CI: 0.30-0.90), hypertriglyceridemia ( OR=0.50, 95% CI: 0.28-0.90) and low HDL-C ( OR=0.47, 95% CI: 0.24-0.91) (all P<0.05). In those who never or occasionally stayed up late, G2 was negatively associated with hypertriglyceridemia ( OR=0.31, 95% CI: 0.13-0.75) ( P<0.05); in those who stayed up late often, G4 was negatively associated with dyslipidemia ( OR=0.33, 95% CI: 0.13-0.85) and low HDL-C ( OR=0.19, 95% CI: 0.04-0.84) (both P<0.05). In the centrally obese population, G2 was negatively associated with dyslipidemia ( OR=0.55, 95% CI: 0.35-0.88) and hypertriglyceridemia ( OR=0.54, 95% CI: 0.33-0.86) (both P<0.05). Conclusions:Association between different physical activity patterns and dyslipidemia varied among adults aged 40-65 years undergoing health check-ups. Leisure-time exercise is associated with a reduced risk of dyslipidemia, while household activities also emerges as a beneficial factor linked to lower dyslipidemia risk particularly in the male population.

Objective:To explore the construction and validation of a nomogram model for predicting poor survival prognosis in patients with clear cell renal cell carcinoma（ccRCC）based on deep learning of pathological images.Methods:This study was an observational cohort study. The original pathological images and clinicopathological data（TCGA cohort）of 378 patients with ccRCC were obtained from the Cancer Genome Atlas Database（TCGA）for model training. A total of 301 patients with ccRCC who underwent surgical treatment at Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2010 to December 2020（Renji cohort）and 214 patients with ccRCC who underwent surgical treatment at the First People’s Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2012 to December 2018（General cohort）were included for model validation. Their original pathological images and clinical pathological data were collected. A clustering-constrained attention and multi-instance learning method was used to accurately identify sub-regions of the images to classify and extract features of the pathological images. A deep learning-based disease-free survival prognosis prediction model（DL-DFS）was constructed through a weakly supervised learning strategy. The clinical pathological features and DL-DFS were further combined to construct a nomogram model for the clinical prognosis of ccRCC patients. Univariate and multivariate Cox regression analyses were employed to evaluate the independent risk factors for disease-free survival（DFS）. The efficacy of the predictive model were evaluated by the receiver operating characteristic curve（ROC）with area under the curve（AUC），respectively. Survival analysis was conducted using the Kaplan-Meier curve.Results:DL-DFS could accurately predict the DFS status of ccRCC patients in 5 years after surgery. Through ROC analysis in the training cohort，the AUC value reached 0.75（ P < 0.001）. In the Renji cohort and the General cohort，the AUC values were 0.65（ P < 0.001）and 0.81（ P < 0.001），respectively. Through Kaplan-Meier survival analysis，we found that DL-DFS could identify ccRCC patients with high survival risks. The hazard ratio in the training cohort was 3.86（95% CI 2.36-6.30， P < 0.001）. The hazard ratio in the Renji cohort and General cohort were 1.97（95% CI 1.03-3.80， P = 0.009）and 4.66（95% CI 1.80-12.06， P = 0.008），respectively. Univariate and multivariate Cox regression analyses indicated that DL-DFS risk score，tumor grade，and tumor stage could act as prognostic risk factors for patients with ccRCC（ P < 0.05）. Considering that age was a common prognostic risk factor for patients with renal cancer，a nomogram model was constructed by combining the DL-DFS risk score with patient age，tumor grade，and tumor stage. The AUC of this model for predicting the 5-year DFS of ccRCC patients after surgery was 0.87，which was significantly higher than that of DL-DFS（AUC = 0.74），tumor stage（AUC = 0.84），tumor grade（AUC = 0.72），and patient age（AUC = 0.56）in the TCGA cohort（all P<0.05）. In the Renji cohort and the General cohort，the AUC of the nomogram model were 0.78 and 0.86 respectively，which was significantly higher than that of DL-DFS（0.65 and 0.81），tumor stage（0.72 and 0.69），tumor grade（0.64 and 0.77），and patient age（0.56 and 0.63）. Conclusions:In this study a DL-DFS for ccRCC patients was constructed. Then a nomogram model was constructed by combining the DL-DFS risk value with patient age，tumor grade，and tumor stage. This nomogram model demonstrated superior predictive performance compared to DL-DFS alone in evaluating the DFS prognosis of ccRCC patients，which still needs to be further verified in prospective clinical studies.

Objective:To analyze the results of national external quality assessment (EQA) for transfusion compatibility test in 2023, and provide reference for quality management of clinical transfusion compatibility testing.Methods:The EQA of clinical transfusion compatibility testing by NCCL was performed 3 times in 2023 among included laboratories. The panel consisting of 22 samples was distributed to 4 186 laboratories across 31 provinces (Including 2 961 tertiary hospital laboratories, 1 085 secondary hospital laboratories, 23 primary hospital laboratories, 106 blood station laboratories and 11 independent clinical laboratories). Each panel contains 11 red blood cell and 11 plasma samples per 1.5 ml/tube. Each participant laboratory of the EQA program was required to carry out the detection and return results in expected time. Statistical analysis and evaluation on the reported results were conducted by NCCL from the aspects of regional distribution, laboratory grading, testing methodology, reagent and testing system usage.Results:The qualification rates of EQA for five items including ABO positive typing, ABO reverse typing, RhD blood type, antibody screening, and cross matching were 96.68%, 95.10%, 96.46%, 95.32%, and 91.04%, respectively. The EQA qualification rate of tertiary hospital laboratories was 87.77% (2 599/2 961), which was significantly higher than the 77.79% (844/1 085) of secondary hospital laboratories. There were significant differences in the qualification rate of participating laboratories among different regions. The utilization rates of micro column agglutination method in ABO positive typing, ABO reverse typing, RhD blood type, antibody screening, and cross matching were 80.81% (10 080/12 474), 75.06% (9 337/12 440), 81.38% (10 118/12 433), 89.59% (11 104/12 394) and 76.25% (9 495/12 453), respectively. The qualification rate of micro column agglutination method was significantly higher than that of saline slide method in ABO positive typing detection ( P<0.05). The qualification rate of micro column agglutination method was significantly higher than that of the polyamine method and anti-human globulin test tube method in antibody screening ( P<0.05). There were statistically significant differences in qualification rate of 7 reagents in ABO reverse typing, antibody screening and cross matching ( P<0.05). There was no statistically significant difference in the qualification rate between the two detection systems for other reagents, except for the ABO reverse typing where the qualification rate of reagent 1 in a single system was higher than that in a mixed system ( P<0.05). Conclusion:The testing capabilities of clinical laboratories in different regions and different type varied significantly in China. Micro column agglutination method was the most popular selection in transfusion compatibility testing. The regents used in these laboratories showed good performance. However, the detection efficiency of some reagents still need to be improved. EQA could be used to evaluate, monitor, and improve the quality of testing.