Objective:To explore the characteristics of T lymphocyte subsets and cytokines in hyperlipidemia-induced acute pancreatitis (HLAP) and its prognostic value.Methods:This study included 184 patients with acute pancreatitis (AP) admitted to the First Affiliated Hospital of Xiamen University from January 2018 to May 2021. Based on disease etiology, there were 92 HLAP cases and 92 non-hyperlipidemia-induced AP (NHLAP) cases. Stratified by disease severity according to 2012 Atlanta classification criteria, the patients were divided into the severe subgroup (SAP) and non-severe subgroup (NSAP). Peripheral venous blood samples were taken from all patients on day 1, 3, and 5 after admission. T lymphocyte subsets were determined by flow cytometry, and cytokines were detected by flow fluorometry. The number of CD4 +% and CD8 +% and the expression of cytokines were compared by Student’s t test or Mann-Whitney U analysis. Logistic regression analyses were performed to identify risk factors for severe AP, and a receiver operating characteristic (ROC) curve was constructed to predict severe AP. Statistical significance was taken as P<0.05. Results:Compared with the NHLAP group, patients in the HLAP group had lower CD4 +%, while higher levels of IL-2 on day 1 ( P<0.05), and had also lower CD4 +%, while higher levels of IL-4, IL-6, and IL-10 on day 3 ( P<0.05). Furthermore, IL-6 and IL-10 levels of the HLAP group were significantly increased compared to the NHLAP group on day 5 ( P<0.05). IL-10 levels in the SAP subgroup were significantly higher than those in the NSAP subgroup on day 1 ( P<0.05). Compared with the NSAP subgroup, the SAP subgroup had elevated levels of IL-2, IL-4, IL-6, IL-10 and IFN-γ on day 3 (all P<0.05), and had lower CD4 +%, while increased levels of IL-6 and IL-10 on day 5 (all P<0.05). Multivariate Logistic regression analysis showed that IL-10 was an immune indicator of independent risk factor for severe AP in the HLAP group on day 1 ( OR=1.139, 95% CI: 1.038-1.251, P<0.05). Finally, ROC analysis showed that the area under the curve of IL-10 to assess HLAP with severe AP was 0.772, and the best cut-off value for predicting severe AP was 5.6 pg/mL, with a sensitivity of 83.3% and a specificity of 68.8%. Conclusions:Changes of CD4 +% and cytokines are different between the HLAP and NHLAP groups. IL-10 can be used as a predictor of early disease severity in patients with HLAP.

OBJECTIVE：To provide assistance for the smooth implementation of classification management policies of retail pharmacies in Guangdong province and the scientific supervision of retail pharmacies by relevant departments. METHODS ：In this study，key interviews were conducted among 68 interviewees，involving experts from Guangdong drug regulatory department ，head of retail pharmacies and research experts on relevant policies of universities. The current situation ，problems and suggestions of the classification management policy of retail pharmacies in Guangdong province were summarized. RESULTS & CONCLUSIONS ： The implementation of the classification management policies for retail pharmacies in Guangdong province operated well. Drug regulatory authorities at all levels could conduct daily supervision in accordance with policy requirements. Retail pharmacies operated in strict accordance with the classification management policy and established a relatively complete quality management system. However ，there were still problems such as difficulty in policy implementation ，insufficient policy clarity ，increased regulatory pressure and regulatory risks from government regulatory agencies ，rising operating costs and increasing pressure in retail pharmacies，the large gap of licensed pharmacists ，difficult to investigate and deal with “affiliation of certificate ”behavior，and difficult survival for remote pharmacies ，and the impact on the convenience of drug use of the public. It is suggested to improve the content of the policy ，introduce related supporting measures ，strengthen the construction of the supervisory team ，increase policy publicity，improve the ways and methods to investigate and deal with the “affiliation of certificate ”behavior of licensed pharmacists， and help retail pharmacies to diversify their operations and chain operations. It is recommended that relevant government departments should further improve the content of the policy and actively adjust the supervision methods to make the policy better implemented.

OBJECTIVE：To analyze th e risk factors of marketing authorization holder （MAH）pharmacovigilance outsourcing and propose risk management and control strategies ， and to provide reference for MAH to successfully implement pharmacovigilance outsourcing and regulatory authorities to formulate corresponding regulatory strategies. METHODS ：Based on the principal-agent theory and brainstorming method ，the risk factors were preliminarily determined ，and a questionnaire was designed. The questionnaire survey was carried out among pharmacovigilance staff of 200 MAH in Guangdong province by random sampling. The factor analysis method was used to statistically analyze the questionnaire data ，and the comprehensive risk factors and their respective factor scores were summarized. RESULTS & CONCLUSIONS ：A total of 200 questionnaires were distributed ， and 154 valid questionnaires were returned ，with effective recovery rate of 77.00％. The results of factor analysis method showed that there were 4 comprehensive risk factors involved in MAH pharmacovigilance outsourcing activities ，which were service providers and outsourcing management factor （3.792 score），MAH factor （3.766 score），regulations factor （3.626 score）and market factor （3.610 score）from high to low. In view of the above factors ，it is suggested that MAH should focus on improving outsourcing contract ，establishing information communication mechanism with service providers ，strengthening auditing and management，strengthening pharmacovigilance ability and personnel building ，defining appropriate pharmacovigilance outsourcing business contents ， and fully doing pre-service investigation of service providers ， when MAH conducts outsourcing of pharmacovigilance. It is suggested that relevant departments should formulate pharmacovigilance entrusted quality management specifications or guidelines as soon as possible and strengthen the supervision of service providers. It is suggested to try to establish a pharmacovigilance outsourcing industry associa tion， andstandardize the industry behavior by giving full play to the influence of the association ，so as to avoid the outsourcing risk.

BACKGROUND: Artificial intelligence (AI) technology represented by deep learning has made remarkable achievements in digital pathology, enhancing the accuracy and reliability of diagnosis and prognosis evaluation. The spatial distribution of CD3 + and CD8 + T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer (CRC). This study aimed to investigate CD3 CT (CD3 + T cells density in the core of the tumor [CT]) prognostic ability in patients with CRC by using AI technology. METHODS: The study involved the enrollment of 492 patients from two distinct medical centers, with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation cohort. To facilitate tissue segmentation and T-cells quantification in whole-slide images (WSIs), a fully automated workflow based on deep learning was devised. Upon the completion of tissue segmentation and subsequent cell segmentation, a comprehensive analysis was conducted. RESULTS: The evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3 CT and CD3-CD8 (the combination of CD3 + and CD8 + T cells density within the CT and invasive margin) in predicting mortality (C-index in training cohort: 0.65 vs. 0.64; validation cohort: 0.69 vs. 0.69). The CD3 CT was confirmed as an independent prognostic factor, with high CD3 CT density associated with increased overall survival (OS) in the training cohort (hazard ratio [HR] = 0.22, 95% confidence interval [CI]: 0.12-0.38, P <0.001) and validation cohort (HR = 0.21, 95% CI: 0.05-0.92, P = 0.037). CONCLUSIONS We quantify the spatial distribution of CD3 + and CD8 + T cells within tissue regions in WSIs using AI technology. The CD3 CT confirmed as a stage-independent predictor for OS in CRC patients. Moreover, CD3 CT shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.
Humans ; Lymphocytes, Tumor-Infiltrating ; Colorectal Neoplasms ; Artificial Intelligence ; Reproducibility of Results ; Prognosis ; CD8-Positive T-Lymphocytes ; Tumor Microenvironment