ObjectiveTo address the limitations of the current quality standard for Polygalae Radix（PR）， which relies on a single component for quality assessment and struggles to holistically control its intrinsic quality， by constructing a comprehensive quality evaluation system integrating "macro-characterization of chemical profile， synchronous quantification of multiple index components， and quantitative analysis of multi-components by single marker（QAMS） for key component groups". This study aims to facilitate the scientific revision of the quality standard for PR. MethodsHigh performance liquid chromatography（HPLC） characteristic chromatograms were established for 11 batches of PR medicinal materials（YZ）， 10 batches of PR decoction pieces（YP）， and 10 batches of licorice-processed PR decoction pieces（ZYZ）， followed by similarity evaluation and identification of common peaks. HPLC-QAMS was developed for xanthones（sibiricaxanthone B， polygalaxanthone Ⅺ， polygalaxanthone Ⅲ） in the characteristic chromatograms. Simultaneously， the external standard method（ESM） was used to determine the contents of the corresponding xanthones and 3，6'-disinapoyl sucrose in YZ， YP， and ZYZ， followed by multivariate statistical analysis and Spearman correlation analysis. ResultsThe similarity between the characteristic chromatograms of 31 batches of PR samples and the reference chromatogram was>0.9. A total of 13 common peaks were identified， and 10 of these peaks were characterized through reference standard comparison. The successfully constructed QAMS method showed that the relative correction factors（RCFs） of sibiricaxanthone B and polygalaxanthone Ⅺ to polygalaxanthone Ⅲ were 0.76 and 0.88， and their relative retention times（RRTs） were 0.85 and 0.97， respectively. The results calculated by the QAMS method showed no significant difference from those obtained by the ESM. According to the limit standard for polygalaxanthone Ⅲ in the 2020 edition of the Pharmacopoeia of the People's Republic of China（hereinafter referred to as the Chinese Pharmacopoeia）， the pass rate of 31 batches of samples was only 19.35%. Multivariate statistical analysis indicated certain compositional differences between different batches of YZ and YP， as well as between YP and ZYZ， with 3，6'-disinapoyl sucrose identified as the main differentiating component. Furthermore， correlation analysis revealed that the content of polygalaxanthone Ⅲ was positively correlated with the contents of sibiricaxanthone B and polygalaxanthone Ⅺ， but showed no association with the content of 3，6'-disinapoyl sucrose. ConclusionIt is recommended that the content limit for polygalaxanthone Ⅲ in YZ，YP and ZYZ be revised to not less than 0.07%， or the total content of polygalaxanthone Ⅲ， sibiricaxanthone B and polygalaxanthone Ⅺ be not less than 0.18%. The newly established triple quality control model of "holistic control via characteristic chromatograms， precise quantification of oligosaccharide esters， and efficient detection of xanthones by QAMS" provides a systematic and precise solution for quality evaluation of PR and similar Chinese herbal medicines.

OBJECTIVE To investigate the neuroprotective effect and mechanism of eleutheroside B （ELB） on Parkinson’s disease （PD） model mice by regulating the IκB kinase β （IKKβ）/nuclear factor-κB （NF-κB） signaling pathway. METHODS Fifty mice were randomly divided into normal control group， model group， positive control group （selegiline hydrochloride， 10 mg/kg）， and ELB low-dose and high-dose groups （80， 160 mg/kg）， with 10 mice in each group. Each group was given relevant medicine or normal saline intragastrically for 14 consecutive days. Starting from the 10th day of administration， the model group and all administration groups were intraperitoneally injected with 1-methyl-4-phenyl-1，2，3，6-tetrahydropyridine （MPTP） 30 mg/kg， for five consecutive days to establish the chronic PD model. After the last administration for 24 h， six mice were randomly selected from each group to test their behavioral abilities； detect the levels of interleukin-1β （IL-1β）， IL-10， tumor necrosis factor-α （TNF-α） in brain tissue and their mRNA expressions were measured， and positive expression of tyrosine hydroxylase （TH）， protein expressions of TH， α -synuclein （ α -syn）， ionized calcium-binding adaptor molecule 1 （Iba-1）， as well as phosphorylation levels of IKKβ and NF-κB p65 proteins in the brain tissue were detected. The ultrastructure of neurons in substantia nigra was observed. RESULTS Compared with the model group， rotarod endurance time and climbing score of each administration group （except for the ELB low-dose group） were increased significantly （ P ＜0.05）， while the levels and mRNA expressions of IL-1β， TNF-α， α -syn， and Iba-1， as well as phosphorylation levels of IKKβ and NF-κB p65 proteins in brain tissue were decreased significantly （except for TNF-α in the ELB low-dose group）. Conversely， the level and mRNA expression of IL-10 （except for the ELB low-dose group）， TH positive expression and protein expressions were significantly increased （ P ＜0.05）. Typical neurodegenerative pathological changes， such as neuronal karyopyknosis， mitochondrial swelling and vacuolization， and endoplasmic reticulum dilation， all showed varying degrees of improvement. CONCLUSIONS ELB may exert neuroprotective effects by inhibiting the activation of the IKKβ/NF-κB signaling pathway， alleviating inflammatory responses， reducing abnormal α -syn aggregation and neuronal loss， and further improving motor dysfunction in PD mice.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective: To investigate the self-monitoring blood pressure behavior and its influencing factors among residents in Jiangsu Province, so as to provide the basis for strengthening proactive blood pressure monitoring among residents. Methods: Permanent residents aged 35-75 years in six counties (cities, districts), Jiangsu Province, were selected using the stratified cluster sampling method in 2023. Data on basic information, disease history, and self-monitoring blood pressure behavior were collected, height and weight were measured to calculate the body mass index (BMI); and blood glucose and lipid levels were measured. Self-monitoring blood pressure behavior was defined as having measured blood pressure at least once in the past three months. Factors affecting self-monitoring blood pressure behavior were identified using a multivariable logistic regression model. Results: A total of 12 475 residents were surveyed, including 5 748 males and 6 727 females, with a male-to-female ratio of 1∶1.17. There were 3 855 residents aged 45-<55 years (30.90%) and 5 511 residents who had self-monitoring blood pressure behaviors (44.18%). Multivariable logistic regression analysis showed that the residents who were males (OR=1.167, 95%CI: 1.081-1.261), lived in rural areas (OR=1.430, 95%CI: 1.321-1.547), aged 45-75 years (45-<55 years, OR=1.384, 95%CI: 1.241-1.543; 55-<65 years, OR=1.397, 95%CI: 1.243-1.570; 65-75 years, OR=1.196, 95%CI: 1.049-1.363), had an annual household income ≥30 000 yuan (30 000-<60 000 yuan, OR=1.190, 95%CI: 1.072-1.321; 60 000-<110 000 yuan, OR=1.330, 95%CI: 1.191-1.485; ≥110 000 yuan, OR=1.746, 95%CI: 1.536-1.984), were overweight (OR=1.170, 95%CI: 1.070-1.280) or obese (OR=1.248, 95%CI: 1.120-1.391), were unaware (OR=1.221, 95%CI: 1.103-1.353) or aware (OR=3.937, 95%CI: 3.575-4.335) of having hypertension, were aware of having diabetes (OR=1.538, 95%CI: 1.354-1.749), and aware of having dyslipidemia (OR=1.265, 95%CI: 1.106-1.447) were more likely to have self-monitoring blood pressure behaviors. Conclusions Among the residents aged 35-75 years in Jiangsu Province, 44.18% had self-monitoring blood pressure behavior. Gender, place of residence, age, annual household income, BMI, hypertension, diabetes, and dyslipidemia were identified as influencing factors for self-monitoring blood pressure behavior.

ObjectiveTo explore the establishment of performance assessment indicators for the classification and grading of public health staff in district-level Centers for Disease Control and Prevention (CDCs), and to provide a basis for such evaluations. MethodsThrough literature review and group interviews, performance evaluation indicators were developed based on competency evaluation. Experts were invited to evaluate the weight of performance evaluation indicators for public health staff from different categories, with the average value used to represent the weight of each indicator. ResultsTwenty-nine experts from universities in Shanghai, municipal CDCs, and district CDCs participated, yielding an expert authority coefficient of 0.86. The performance evaluation indicators for department managers were categorized into three levels, with 4 indicators at the primary level, 16 indicators at the secondary level, and 42 indicators at the tertiary level, while those for general staff included 4 primary indicators, 15 secondary indicators, and 36 tertiary indicators. Significant differences were observed in the weight coefficients of the primary indicators (internal operations, professional work, and learning and growth) between department managers and general staff. The top three secondary indicators for department managers were department management, monitoring and prevention, and level of expertise. For mid-level and senior staff, the top three secondary indicators were monitoring and prevention, level of expertise, and research work. The top three secondary indicators for junior staff were monitoring and prevention, professional expertise, and professional attitude. No significant statistical differences were found among tertiary indicators. ConclusionThe developed performance evaluation indicators are reliable. Staff at different levels and classifications should be evaluated using different performance evaluation standards to accurately reflect individual performance and contributions.

ObjectiveTo investigate the effect of Yiqi Yangyin Jiedu Huayu prescription on minimal hepatic encephalopathy in liver cirrhosis based on intestinal metabolomics. MethodsA total of 11 patients with liver cirrhosis who were hospitalized in Beijing Ditan Hospital， Capital Medical University， from March to May 2024， and were diagnosed with MHE based on psychometric hepatic encephalopathy score were enrolled as subjects， and 11 healthy family members of the patients were enrolled as control group. Fecal samples were collected for metabolomics analysis from the control group and the patients with MHE before and after treatment with Yiqi Yangyin Jiedu Huayu prescription， and a population cohort study was conducted to investigate the effect of Yiqi Yangyin Jiedu Huayu prescription on intestinal metabolism of patients with MHE. The Fisher’s exact test was used for categorical data between two groups； the independent samples t-test was used for comparison of normally distributed continuous data between two groups， the paired t-test was used for comparision before and after treatment within the same group， and the Wilcoxon rank-sum test was used for comparison of non-normally distributed continuous data between two groups. ResultsA total of 29 differentially expressed metabolites were detected between the MHE group and the control group， mainly amino acids， organic acids， organic amines， carbohydrates， fatty acids， and vitamins， and there were 12 upregulated metabolites and 17 downregulated metabolites in the MHE group， which were mainly enriched in the metabolic pathways of ornithine， branched-chain amino acid， and aromatic amino acid. After the treatment with Yiqi Yangyin Jiedu Huayu prescription， 80 differentially expressed metabolites were detected in the patients with MHE， mainly carbohydrates， organic acids， and amino acids， and there were 56 upregulated metabolites and 24 downregulated metabolites， which were mainly enriched in the metabolic pathways of ornithine， branched-chain amino acid， and aromatic amino acid. ConclusionYiqi Yangyin Jiedu Huayu prescription can exert a therapeutic effect on patients with MHE by regulating intestinal metabolism.

Tissue-resident memory T cells （T_RM cells） are a subset of memory T cells that reside in tissues， exhibit tissue specificity， and do not recirculate. When potential hazards threaten the liver， such as pathogen invasion （bacteria， viruses， etc.） and excessive autoimmune responses， T_RM cells are essential as the first line of immune defense， playing an important role in viral hepatitis， autoimmune liver disease， metabolic dysfunction-associated fatty liver disease， liver cirrhosis， and liver transplantation. Here， we present the immunophenotypes of T_RM cells in the liver and their surface markers and transcriptional profiles， aiming to clarify the role of T_RM cells in chronic liver diseases and explore their potential function as therapeutic targets in immunotherapy.

Porcine arthritis,one of the common chronic diseases in large-scale pig farms,can signifi-cantly reduce the production performance of meat pigs.In this study,a strain of Haemophilus pa-rasuis(HPS)was isolated from the joint fluid of a lame pig.The HPS was analyzed in terms of se-rotypes,virulence genes,and resistance genes.Additionally,it was treated with sensitive antibiotics to provide a theoretical basis for the comprehensive prevention and treatment of arthritis in meat pigs in future production settings.A strain of HPS type 14 was isolated from the joint fluid of dis-eased pigs.The HPS isolate demonstrated sensitivity to β-lactams and tetracyclines,while florfeni-col and polymyxin effectively inhibited its growth at low concentrations.However,the bacteria ex-hibited resistance to sulfonamides and ciprofloxacin.The treatment of affected pigs with clinical ar-thritis using doxycycline and enrofloxacin injections proved effective.Compared to the infected group,in which the sick pigs experienced difficulty flexing their carpal and tarsal joints and exhibi-ted significant lameness,the pigs in the treatment group showed marked improvement.Their joints were only slightly swollen,and the clinical symptoms of arthropathy were alleviated.