Objective To explore the promoting effect of long-term administration of nicotiflorin on the recovery of neurological function in rats with cerebral ischemia-reperfusion injury （CIRI）. Methods The CIRI model was established and nicotiflorin was injected intraperitoneally after 1 hour of obstruction for 8 weeks. Tail suspension deflection experiment, balance beam experiment and water maze test were performed in the 2nd, 4th and 8th weeks. After 8 weeks, TTC staining was used to observe the volume of infarct atrophy, transcriptome sequencing was employed to screen differential expressed genes （DEGs） and highly enriched pathways were analyzed, Western-bloting and Elisa were used to assess proteins expression related to the pyroptosis pathway and inflammatory cytokines IL-1β and IL-18. Results By long-term administration of nicotiflorin, the contralateral deflection rate was significantly reduced and beam experiment score of CIRI rats was balanced, the number of crossing the platform in water maze test was increased （P<0.05）, the volume of cerebral infarction atrophy was decreased （P<0.01）, which significantly promoted the recovery of neurological function in rats. Transcriptome sequencing found that the expression of genes in the pyroptosis-related signaling pathways in the brain tissue of rats in the nicotiflorin group was significantly down-regulated （P<0.05）. Western-blot and Elisa experiments showed that nicotiflorin reduced the expression levels of Caspase-1 and GSDMD-N and other pyroptosis-related proteins, and at the same time, the release of inflammatory factors IL-1β and IL-18 was significantly reduced （P<0.05）, indicating that nicotiflorin could inhibit the inflammatory process of pyroptosis. Conclusion Nicotiflorin exhibited a significant long-term promotion effect on the recovery of neurological function in CIRI rats, which potentially attributed from its ability to inhibit pyroptosis.

Objective:To analyze the latent prevalence of hepatitis B and type 2 diabetes and their correlation through an observational study.Methods:This study used a case-control design. The cases with diabetes were recruited through the diabetes management system and village doctors, while the controls without diabetes were screened from volunteers recruited by village health clinics. Capillary blood samples were collected from the study participants for the measurement of real-time blood glucose level, and venous blood samples were taken from them for the detections of HBV serological markers. Firth logistic regression model was used to fit the relationship between HBsAg positive status and diabetes status.Results:The study included 1 218 diabetes patients, 62 patients with impaired fasting glucose and 491 cases without diabetes. In the cases without diagnosis of diabetes, 11.15% had impaired fasting blood glucose and 4.43% had diabetes. Among those who reported no or unknown diagnosis of hepatitis B, 1.73% were positive for HBsAg, while 18.80% were positive for both HBV core antibody and surface antibody, indicating latent infection of hepatitis B virus. In the non-diabetes group, 0.81% reported hepatitis B history, and in the diabetes group, 2.76% reported hepatitis B history. After adjustment, the HBsAg positive rate was higher in the diabetes group ( OR=2.90, 95% CI: 1.21-6.91). Conclusions:Both diabetes and hepatitis B exhibited a high degree of latent prevalence. The HBsAg positive rate was significantly higher in those with diabetes than in those without diabetes, indicating a potential correlation. These findings highlighted the importance of strengthened screening and management of comorbidities.

Objective:To analyze the latent prevalence of hepatitis B and type 2 diabetes and their correlation through an observational study.Methods:This study used a case-control design. The cases with diabetes were recruited through the diabetes management system and village doctors, while the controls without diabetes were screened from volunteers recruited by village health clinics. Capillary blood samples were collected from the study participants for the measurement of real-time blood glucose level, and venous blood samples were taken from them for the detections of HBV serological markers. Firth logistic regression model was used to fit the relationship between HBsAg positive status and diabetes status.Results:The study included 1 218 diabetes patients, 62 patients with impaired fasting glucose and 491 cases without diabetes. In the cases without diagnosis of diabetes, 11.15% had impaired fasting blood glucose and 4.43% had diabetes. Among those who reported no or unknown diagnosis of hepatitis B, 1.73% were positive for HBsAg, while 18.80% were positive for both HBV core antibody and surface antibody, indicating latent infection of hepatitis B virus. In the non-diabetes group, 0.81% reported hepatitis B history, and in the diabetes group, 2.76% reported hepatitis B history. After adjustment, the HBsAg positive rate was higher in the diabetes group ( OR=2.90, 95% CI: 1.21-6.91). Conclusions:Both diabetes and hepatitis B exhibited a high degree of latent prevalence. The HBsAg positive rate was significantly higher in those with diabetes than in those without diabetes, indicating a potential correlation. These findings highlighted the importance of strengthened screening and management of comorbidities.

Scientific research laboratory is the base of scientific research and talent training in the university.During the construction and development of"world-class universities and first-class disciplines",laboratory safety management has been facing urgent challenges.Based on the laboratory management methods of Zhongshan School of Medicine of Sun Yat-sen University,we analyzed the classification and characteristics of discipline laboratories,laboratory safety access training and assessment,and summarized the actual situation and construction of safety management system for basic medi-cal laboratories.This paper expounded on laboratory management,such as management system construction,safety man-agement,safety inspection and supervision,safety awareness of all staff and system execution,so as to provide a feasible and creative scientific guidance and basis for university laboratory biosafety management,and establish a healthy and safe experimental environment to ensure the safety of laboratory work.

Background::Breast cancer is ranked among the most prevalent malignancies in the Chinese female population. However, comprehensive reports detailing the latest epidemiological data and attributable disease burden have not been extensively documented.Methods::In 2018, high-quality cancer surveillance data were recorded in 700 population-based cancer registries in China. We extracted data on female breast cancers (International Classification of Diseases, Tenth Revision [ICD-10]: C50) and estimated the incidence and mortality in 2022 according to the baseline data and corresponding trends from 2010 to 2018. Pathological types were classified according to the ICD for Oncology, 3rd Edition codes. Disability-adjusted life years (DALYs) were calculated as the sum of the years of life lost (YLLs) and years lived with disability (YLDs).Results::In 2022, approximately 357,200 new female breast cancer cases and 75,000 deaths occurred in China, accounting for 15.59% and 7.94% of total new cancer cases and deaths, respectively. The age-standardized incidence rate (ASIR) was 33.04 per 100,000. When analyzed by pathological type, the ASIRs for papillary neoplasms, invasive breast carcinoma, rare and salivary gland-type tumors, and other types were 1.13, 29.79, 0.24, and 1.88 per 100,000, respectively. The age-standardized mortality rate (ASMR) was 6.10 per 100,000. A total of 2,628,000 DALYs were found to be attributable to female breast cancer in China, comprising 2,278,300 YLLs and 349,700 YLDs. The ASIR, ASMR, and age-standardized rate (ASR) for DALYs in urban areas were consistently higher than those in rural areas. We observed a four-fold increase in the ASIR and ASR for DALYs and an eight-fold increase in the ASMR among females over 55 years compared with those aged under 55 years.Conclusion::These data provide invaluable insights into the latest epidemiology of female breast cancer in China and highlight the urgency for disease prevention and control strategy formulation.

Objective To investigate the correlation of neutrophil to high-density lipoprotein cho-lesterol ratio(NHR)and lipoprotein(a)[Lp(a)]with occurrence of acute ischemic stroke(AIS)in elderly patients with non-valvular atrial fibrillation(NVAF).Methods A total of 301 elderly NVAF patients admitted to Hefei First People's Hospital from May 2020 to May 2023 were en-rolled,and according to whether they were combined with AIS or not,they were divided into stroke group(143 cases)and non-stroke group(158 cases).Binary logistic regression analysis was used to screen risk factors,and ROC curve was plotted to assess their predictive values for AIS.Results Lp(a)and NHR in stroke group were significantly higher than those in non-stroke group,the difference was statistically significant(P＜0.01).Multivariate binary logistic regression analysis showed that NHR and Lp(a)were independent risk factors for death with NVAF com-bined AIS(OR=1.728,95％CI:1.367-2.186,P=0.000;OR=1.005,95％CI:1.002-1.007,P=0.000);ROC curve analysis indicated that the AUC value of NHR and Lp(a)in diagnosing AIS in NVAF patients was 0.668 and 0.735,respectively,and that of their combination was 0.789,which was significantly higher than that of NHR and Lp(a)alone(P＜0.01).Conclusion NHR and Lp(a)are independently related to AIS in NVAF patients,and their combination has certain diagnos-tic efficacy.

Background/Aims: The accuracy of endosonographers in diagnosing gastric subepithelial lesions (SELs) using endoscopic ultrasonography (EUS) is influenced by experience and subjectivity. Artificial intelligence (AI) has achieved remarkable development in this field. This study aimed to develop an AI-based EUS diagnostic model for the diagnosis of SELs, and evaluated its efficacy with external validation. Methods: We developed the EUS-AI model with ResNeSt50 using EUS images from two hospitals to predict the histopathology of the gastric SELs originating from muscularis propria. The diagnostic performance of the model was also validated using EUS images obtained from four other hospitals. Results: A total of 2,057 images from 367 patients (375 SELs) were chosen to build the models, and 914 images from 106 patients (108 SELs) were chosen for external validation. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the model for differentiating gastrointestinal stromal tumors (GISTs) and non-GISTs in the external validation sets by images were 82.01%, 68.22%, 86.77%, 59.86%, and 78.12%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in the external validation set by tumors were 83.75%, 71.43%, 89.33%, 60.61%, and 80.56%, respectively. The EUS-AI model showed better performance (especially specificity) than some endosonographers.The model helped improve the sensitivity, specificity, and accuracy of certain endosonographers. Conclusions We developed an EUS-AI model to classify gastric SELs originating from muscularis propria into GISTs and non-GISTs with good accuracy. The model may help improve the diagnostic performance of endosonographers. Further work is required to develop a multi-modal EUS-AI system.

OBJECTIVE To investigate the components and metabolites of leaves of Cyclocarya paliurus in rats plasma. METHODS UHPLC-Q/TOF-MS method was applied to identify the components and metabolites of rat plasma. The prototype components and metabolites were analyzed in rats plasma of leaves of Cyclocarya paliurus water extract after intragastric administration by comparing the chromatograms of Cyclocarya paliurus, blank serum and drug-containing serum. RESULTS A total of 15 compounds including 6 prototype components and 9 metabolites were identified. The main prototype components in blood were caffeylquinic acid, flavonoids and saponins, and the metabolic pathways were methylation and hydroxylation. CONCLUSION This experiment showes that the prototype components absorbed into blood may be the potential bioactive components in leaves of Cyclocarya paliurus, providing a scientific basis for clarifying its material basis in pharmacodynamics.

Objective:To establish a long-term mortality rate prediction model for patients aged 60 years and over with atrial fibrillation and coronary heart disease using the machine learning method, and identify the corresponding risk factors of mortality.Methods:In this retrospective cohort study, a total of 329(11 cases lost of follow-up)patients with 183 males(55.6%)and 146 females(44.4%), aged(77.8±7.3)years, and 142 patients aged 80 years or older(43.2%)were selected in our hospitals from January 2013 to March 2015.And their clinical data on atrial fibrillation and coronary heart disease were analyzed.They were divided into the death group(151 cases)and the survival group(167 cases)according to the survival outcome.In addition, 60 patients aged 60 years and over admitted to our hospitals from April to July 2015 with atrial fibrillation and coronary heart disease were selected as external data validation set.The clinical data included age, gender, body mass index, diagnosis, co-morbidity, laboratory indicators, electrocardiogram, echocardiogram, treatment data.These patients were followed up for at least 6 years, and the main adverse cardiovascular and cerebrovascular events(MACCE), including death, were recorded.Finally, the data of the enrolled patients were randomly divided into the training set and the test set according to the ratio of 9∶1, Different models were established to predict the long-term mortality of patients with atrial fibrillation and coronary heart disease by machine learning algorithm.The optimal model was established by substituting external data(60 cases)into the model for verification and comparison.The top 20 risk factors for mortality were determined by Shapley additive explanation(SHAP)algorithm.Results:A total of 329 hospitalized patients were included in this study, the overall median follow-up time was 77.0 months(95% CI: 54.0~84.0), 11 cases lost during follow-up(3.3%), and 151 cases died(45.9%). The analysis found that the areas under the ROC curve for a support vector machine(SVM)model, k-Nearest Neighbor(KNN)model, decision tree model, random forest model, ADABoost model, XGBoost model and logistic regression model were 0.76, 0.75, 0.75, 0.91, 0.86, 0.85 and 0.81, respectively.The random forest model had the highest prediction efficiency, with the accuracy of 0.789 and F1 value of 0.806, which was better than the logistic regression model[the Area Under Receiver Operating Characteristic Curve(AUC): 0.91 vs.0.81, P<0.05]. D-dimer, age, number of MACCE, left ventricular ejection fraction, serum albumin level, anemia, New York Heart Association(NYHA)grade, history of old myocardial infarction, estimated glomerular filtration rate(eGFR)and resting heart rate were important risk factors for predicting long-term mortality. Conclusions:The random forest model based on machine learning method can predict the long-term mortality of patients with atrial fibrillation and coronary heart disease aged 60 years and over, have a good identification ability.Its accuracy is higher than that of the traditional Logistic regression model.Reducing the long-term mortality and improving the long-term outcomes can be achieved by intervening on D-dimer levels, correcting hypoproteinemia and anemia, improving cardiac function and controlling resting ventricular rates.

Human challenge trial (HCT) is a test in which human volunteers are intentionally infected with pathogens in order to evaluate the efficacy of candidate preventive or therapeutic drugs. During the COVID-19 pandemic, the HCT of vaccines has aroused people's attention due to its significant advantages over clinical trial. This paper introduces the concept, development and application of HCT, the advantages and limitations of HCT for vaccine evaluation, and the consideration of future HCT of COVID-19 vaccine in China.