OBJECTIVE To study the quality standard of the ethnic medicine Diplazium lanceum， and to provide reference for its quality control.METHODS The medicinal material of D. lanceum was identified by properties， microscopy， and thin-layer chromatography（TLC）， and its moisture， ash， and extract contents were determined. The contents of chlorogenic acid and rutin in 10 batches of D. lanceum were determined by high-performance liquid chromatography . RESULTS The rhizome of D. lanceum is slender and creeping， the leaf is leathery and shriveled， and yellow sporangia are borne on the abaxial surface of the leaf. The transverse section of the leaf shows inclusions， sclerenchyma， phloem cells， etc.； the transverse section of the petiole shows ground tissue， sclerenchyma， endodermis， etc.； the transverse section of the rhizome shows ground tissue， tracheids， vascular bundles， etc.； and the powder shows spiral vessels and brown inclusions. The TLC identification results showed that the chromatogram of the test sample exhibited spots of the same color at the corresponding positions as the chromatogram of the chlorogenic acid reference substance. The content ranges of moisture， total ash， acid-insoluble ash， extract， chlorogenic acid， and rutin in the 10 batches of samples were 10.18%-12.43%， 7.08%-19.12%， 0.95%-10.62%， 14.13%-26.05%， 0.113 1-1.015 9 mg/g， and 0.009 9-0.208 9 mg/g， respectively. CONCLUSIONS In the quality standard of D. lanceum， the moisture content should be ≤12.5%， total ash content should be ≤20.0%， acid-insoluble ash content should be ≤11.0%， extract content should be ≥14.0%， and chlorogenic acid content should be ≥0.11 mg/g. The established method is simple， specific， and reproducible， and can provide a scientific basis for the quality control of D. lanceum.

Objective To investigate the effects of combined exposure to benzene series, dust and noise on occupational high-frequency hearing loss (HFHL) in the electronics manufacturing industry. Methods A total of 740 employees from an electronic manufacturing enterprise were selected as the study subjects. According to the detection of occupational hazard factors, they were divided into noise group, noise benzene group, and noise dust group, with 325, 167, and 248 people, respectively. Occupational health examinations were conducted on the study participants to analyze their HFHL detection status and influencing factors. Results Among all participants (n=740), 456 (61.62%) were male, 284 (38.38%) were female, and 123 (15.62%) were detected with HFHL. The detection rate of HFHL increased with age and length of service (P<0.01). According to the exposure factor grouping, the detection rates of HFHL in the noise group, noise benzene group, and noise dust group were 12.00%, 19.16%, and 20.97%, respectively (P<0.05). Multivariate logistic regression analysis showed that workers aged ≥ 40 years, with 10-20 years of work experience, ≥ 20 years of work experience, exposure to noise and benzene series, and exposure to noise and dust had an increased risk of developing HFHL to 1.734, 1.923, 3.071, 1.706, and 1.527, respectively (P<0.05). Conclusion Combined exposure to noise and benzene series, combined exposure to noise and dust, as well as age and extended length of service may increase the risk of HFHL among workers.

Objective To investigate the drivers,stakeholders,core tasks,and differentiated development models of digital transformation in Chinese hospitals,develop a hospital digital transformation model,and propose advancement pathways.Methods Leveraging ROCCIPI theory,socio-technical systems theory,and social network theory,a multiple case study approach was employed to analyze four representative Chinese hospitals,examining the driving factors,social network relationship,and core tasks of digital transformation.Results Hospital digital transformation is a complex process driven by regulations,opportunities,and capabilities,requiring efficient collaboration among stakeholders focused on patient services,clinical operations,hospital management,and security.It identified three development models-ecology-oriented,regional integration,and grassroots enhancement—based on the distinct characteristics of the hospitals.A theoretical model for digital transformation in four Chinese hospitals was developed,along with proposed pathways and strategies.Conclusion It presents a digital transformation model and advancement pathways for hospitals through multiple case analyses,addressing the limited perspectives of existing research and providing a reference for practice.

Objective:To evaluate the detection rate, anatomical distribution, and influencing factors of bone marrow edema-like signal (BMELS) in the knees of amateur marathon runners.Methods:This was a cross-sectional study. This study publicly recruited amateur marathon runners through the Hangzhou Long-distance Running Association from January 2019 to December 2024. Based on knee magnetic resonance imaging (MRI) results, participants were divided into a BMELS-positive group (BMELS present in at least one knee) and a BMELS-negative group. General clinical information was collected from participants using a questionnaire. All participants underwent a knee MRI scan to screen for the presence of BMELS and to assess its severity. Logistic regression models were used to analyze the associated factors of BMELS in amateur marathon runners, while Spearman′s correlation analysis assessed the correlation between BMELS grade and these factors.Results:A total of 60 subjects (120 knee joints) were enrolled, including 39 males (65%), aged (40.0±8.4) years. Of these, 39 were in the BMELS-positive group, and 21 were in the BMELS-negative group. The BMELS detection rate for the 120 knee joints of these 60 subjects was 53.3% (64/120). BMELS were detected in 43.3%(52/120) of the femur, 34.2% (41/120) of tibia and 17.5% (21/120) of patella. Multivariate logistic regression analysis showed an independent positive correlation between monthly running volume and knee BMELS in amateur marathon runners ( OR=1.007, 95% CI: 1.000-1.013, P=0.035). Spearman′s correlation analysis showed a weak positive correlation between the BMELS grade of the knees and the monthly running volume of amateur marathon runners ( r s=0.360, P=0.005). Conclusions:The detection rate of knee BMELS is high in amateur marathon runners, and they are distributed in a way that is characteristic of the region, with a higher incidence in the medial femoral condyle. In this population, monthly running volume is independently associated with knee BMELS, with a higher grade associated with greater monthly running volume.

Objective:To explore the effects of workload factors and social psychological factors on work-related musculoskeletal disorders (WMSDs) , construct a preventive decision-assisted ensemble learning model, and propose screening methods with clinical significance.Methods:In October 2022, 1071 workers from optoelectronic enterprises were selected as the research subjects by cluster sampling method. The general situation of workers, workload factors, social psychological factors and the occurrence of WMSDs were collected by using questionnaires. logistic regression, Extreme Gradient Boosting (XGBoost) , ensemble learning and classification chain model were adopted to explore the key factors influencing WMSDs, and the area under curve (AUC) was used to evaluate the model performance.Results:The incidence of WMSDs among workers in optoelectronic enterprises in the past year was 47.7% (511/1071) , among which the incidence of multi-site WMSDs was 54.4% (278/511) . logistic regression analysis showed that prolonged sitting, personnel shortage and forward neck tilt were risk factors for the occurrence of WMSDs in workers ( P<0.05) . XGBoost identified the key social psychological factors influencing WMSDs as low mood, mental tension, perceived happiness level, psychological calmness and tranquility. The integrated classification chain model based on the ordered label order had certain efficacy (AUC>0.7) when analyzing WMSDs at the neck, waist, shoulder, back, elbow and hip positions. Conclusion:Workload factors are the main risk factors for the occurrence of WMSDs among workers in optoelectronic enterprises, and social psychological factors also have potential influences. The construction of a classification chain model can accurately identify the occurrence of WMSDs in multiple parts. The alternating prevention strategy of workload factors and social psychological factors has important public health significance.

Objective:To investigate the interaction of workplace noise, body mass index (BMI) and systemic inflammatory response on hypertension.Methods:In January 2019, 1124 male workers from an automobile factory in Wuhan were selected by cluster random sampling method. The study population was divided into normal body weight group (BMI<24 kg/m 2) and overweight group (BMI≥24 kg/m 2) according to BMI, and were followed up for 3 years. The occupational health examination of the workers was carried out every year, blood routine and blood biochemical indexes were collected, and the information of the workers' age, BMI, type of work and age of exposure to injury were collected through field questionnaires. The noise intensity of the workplace of the enterprise was measured for 3 consecutive years, and the equivalent sound level was calculated according to the working time. Normal and skewness measurements were described by Mean±SD and [ M ( Q1, Q3) ], respectively. t-test or Wilcoxon rank sum test was used for inter-group comparisons. Categorical variables were expressed by frequency and percentage (%), and Chi-square test was used for inter-group comparisons. Mixed effects models and generalized estimation equations were fitted to analyze the relationship between occupational noise, systemic inflammation, and their interaction terms with blood pressure and hypertension risk. Results:Univariate analysis revealed that the white blood cell counts, neutrophil counts, lymphocyte counts and eosinophil counts were positively associated with systolic blood pressure and diastolic blood pressure in overweight group ( n=467), respectively ( P<0.05). The participants with white blood cell counts≥5.94×10 9/L, neutrophil counts≥3.31×10 9/L, lymphocyte counts ≥2.03×10 9/L, and eosinophil counts ≥0.12×10 9/L had increased risk of hypertension by 52% ( RR=1.52, 95% CI: 1.11-2.05), 37% ( RR=1.37, 95% CI: 1.01-1.83), 58% ( RR=1.58, 95% CI: 1.18-2.09), and 52% ( RR=1.52, 95% CI: 1.12-2.06), respectively (all P<0.05). Multivariate analysis found a positive association between BMI and the risk of hypertension in the overweight group ( P<0.05), the participants with lymphocyte counts≥2.03×10 9/L and eosinophil counts≥0.12×10 9/L had increased risk of hypertension by 39% ( RR=1.39, 95% CI: 1.01-1.89) and 46% ( RR=1.46, 95% CI: 1.06-1.98), respectively (all P<0.05). There was no correlation between BMI and the risk of hypertension in normal weight group ( P>0.05). The multiplicative interaction of occupational noise (≥80 dB) with white blood cell counts (≥5.94×10 9/L) and lymphocyte counts (≥2.03×10 9/L) increased the risk of hypertension by 100% ( RR=2.00, 95% CI: 1.06-3.55) and 89% ( RR=1.89, 95% CI: 1.01-3.32) (all P<0.05) in overweight group, respectively. Additionally, a significant additive interaction was observed between noise exposure (≥80 dB) and basophil counts (≥0.03×10 9/L) on hypertension risk in the overweight group (RERI=0.49, 95% CI: 0.02-0.96, P<0.05) . Conclusion:The combined exposure of occupational noise and systemic inflammation may increase the risk of hypertension in overweight workers in automobile manufacturing plants, and the two have additive and multiplicative interactions.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

This study was aimed at establishing a sensitive and specific sandwich ELISA detection method for Brucella.We screened monoclonal capture antibodies and detection antibodies for Brucella detection,and optimized and determined the opti-mal antibody coating time and concentration,as well as the optimal blocking solution,blocking time,and yin-yang critical val-ue.The specificity of this method was verified by examination of other bacteria prone to cross-reacting with Brucella.The sen-sitivity of the method was verified by detection of a gradient dilution of inactivated Brucella.Moreover,the sandwich ELISA detection results were compared with test tube agglutination and qPCR results.The selected capture antibody was 4A12,and the selected detection antibody was 6C12.Experimental analysis indicated that the optimal coating concentration for the 4A12 capture antibody was 5 μg/mL,and the optimal dilution ratio for the 6C12 detection antibody was 1∶2000.The optimal coating conditions were overnight at 4℃,and blocking with 5％skim milk powder for 2 hours.The established double antibody sand-wich ELISA method reacted with only Brucella but not other bacteria,thus demonstrating the method's good specificity.Inac-tivated Brucella solution was still detectable after dilution to 1 × 105 CFU/mL,thus demonstrating the method's good sensitiv-ity.The intra-and inter batch coefficients of variation were both below 10％,thus indicating the method's good repeatability.Thus,this study successfully established a dual antibody sandwich ELISA method for Brucella detection,which has good spe-cificity and sensitivity,and might provide an effective approach for the precise diagnosis and effective prevention and control of brucellosis.

Objective:To explore the effects of workload factors and social psychological factors on work-related musculoskeletal disorders (WMSDs) , construct a preventive decision-assisted ensemble learning model, and propose screening methods with clinical significance.Methods:In October 2022, 1071 workers from optoelectronic enterprises were selected as the research subjects by cluster sampling method. The general situation of workers, workload factors, social psychological factors and the occurrence of WMSDs were collected by using questionnaires. logistic regression, Extreme Gradient Boosting (XGBoost) , ensemble learning and classification chain model were adopted to explore the key factors influencing WMSDs, and the area under curve (AUC) was used to evaluate the model performance.Results:The incidence of WMSDs among workers in optoelectronic enterprises in the past year was 47.7% (511/1071) , among which the incidence of multi-site WMSDs was 54.4% (278/511) . logistic regression analysis showed that prolonged sitting, personnel shortage and forward neck tilt were risk factors for the occurrence of WMSDs in workers ( P<0.05) . XGBoost identified the key social psychological factors influencing WMSDs as low mood, mental tension, perceived happiness level, psychological calmness and tranquility. The integrated classification chain model based on the ordered label order had certain efficacy (AUC>0.7) when analyzing WMSDs at the neck, waist, shoulder, back, elbow and hip positions. Conclusion:Workload factors are the main risk factors for the occurrence of WMSDs among workers in optoelectronic enterprises, and social psychological factors also have potential influences. The construction of a classification chain model can accurately identify the occurrence of WMSDs in multiple parts. The alternating prevention strategy of workload factors and social psychological factors has important public health significance.