Objective To understand mosquito density, species composition, and seasonal variations around the Beijing Capital International Airport(BCIA)and provide scientific evidence for mosquito-borne disease prevention and control. Methods Meteorological data were collected from 2016 to 2022, and mosquito density was monitored at seven surveillance sites around the BCIA from May to October each year using CO2-baited mosquito traps to analyze the relationship between mosquito density and meteorological factors. Results In total,68 518 female mosquitoes were captured, with a mosquito density of 10.20 per light · hour. The dominant species was Culex pipiens pallens(73.90%), followed by Aedes albopictus(9.71%)and Ae. vexans(9.29%);the mosquito density was highest in 2016(14.8 per light·hour)and lowest in 2022(4.46 per light·hour). Significant statistical differences were observed in mosquito density among different species(F=18.118, P<0.05). The density of Cx. pipiens pallens and other mosquito species showed a mutually exclusive trend; the peak of mosquito density varied based on years and habitats, with significant statistical differences in mosquito density among different habitats(F=8.504, P<0.05). The highest mosquito density was observed near the Wenyu River(21.50 per light·hour), whereas the lowest was recorded at the BCIA construction site(2.44 per light· hour). The monthly average temperature(r=0.595), monthly average highest temperature(r=0.575), and monthly average lowest temperature(r=0.624)showed moderate positive correlations, whereas the monthly average air pressure(r=-0.484)showed a moderate negative correlation. The monthly average minimum temperature was included in the regression model(F=25.575, P<0.000), and the equation Y=1.029X4-8.181 was used. The monthly average air pressure 2 months prior(b=-2.418, β=-1.619, P<0.05)and the monthly average relative humidity(b=-0.739, β=-1.201, P<0.05)significantly negatively predicted the mosquito density. Notably, the regression equation used was y=2526.170+(-2.418)X9+(-0.739)X13. The exposure-response analysis revealed that the density of mosquitoes was not linearly related to the average monthly air pressure, rainfall, and duration of sunshine. Conclusions The dominant mosquito species around the BCIA was Cx. pipiens pallens, Ae. albopictus, and Ae. vexans. During the control of Cx. pipiens pallens, the impact on other mosquito species must also be considered. Environmental management, breeding sites, and scientific use of pesticides should be prioritized based on the activity periods of mosquitoes. Further studies on mosquitoes and meteorological factors should be conducted to provide references and novel avenues for mosquito control.

Objective:To investigate the mechanism of the synergistic anti-leukemia effect of the combination of Bcl-2 inhibitor venetoclax(VEN)and histone deacetylase(HDAC)inhibitor chidamide(CDM)in T-cell acute lymphoblastic leukemia(T-ALL).Methods:The effect of VEN combined with CDM on the proliferation of T-ALL CEM and MOLT-4 cell lines was detected by CCK-8 assay.And the effects on the cell cycle and apoptosis were detected by flow cytometry.Cell cycle protein and apoptosis-related protein expression were detected by Western blot.The key pathways of VEN combined with CDM in T-ALL were screened through network pharmacology analysis,and verifying them in T-ALL cell lines,T-ALL patient cells and public databases.Results:VEN combined with CDM displayed a synergistic effect on cell proliferation of CEM and MOLT-4 cells.In cell cycle experiment,VEN combined with CDM induced G0/G1 phase arrest in CEM and MOLT-4 cells.Western blot experiment showed that VEN combined with CDM could significantly downregulate the expression of cyclin E2 and CDK2 and upregulate the expression of p21Waf1/Cip1.In the apoptosis experiment,VEN combined with CDM could significantly induce the apoptosis of CEM and MOLT-4 cells.Western blot experiment demonstrated that VEN combined with CDM promoted endogenous apoptosis by downregulating Mcl-1 and upregulating Bax and cleaved caspase-3 protein levels.Network pharmacology analysis identified 10 hub genes.KEGG enrichment analysis revealed the cell cycle,PI3K-AKT signaling pathway,and its downstream FoxO signaling pathway were significantly enriched.GO enrichment analysis revealed the G1/S transition of mitotic cell cycle,cyclin-dependent protein kinase holoenzyme complex,and kinase activity were significantly enriched.Western blot experiment showed that VEN combined with CDM could significantly downregulate the protein level of PI3K,AKT,and p-AKT,and upregulate FoxO1 in CEM and MOLT-4 cells.In T-ALL patients,FoxO1 showed significantly lower expression compared to the normal donors,and the same result was verified in the GSE13159 and GSE26713 datasets.Conclusion:The combination of VEN and CDM exerts synergistic anti-leukemia effects by inhibiting cellular proliferation,inducing G0/G1,phase arrest and promoting apoptosis through PI3K/AKT/FoxO1 axis in T-ALL.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective To analyze the implementation of MPOWER tobacco control policies in different regions and populations in China,as well as the impact of perceptions of tobacco control policies on individual smoking behavior and quit intentions,to promote the fairness of policy implementation and protection for vulnerable groups.Methods A multivariable regression model was constructed utilizing raw data from the China Adult Tobacco Survey to analyze disparities in perceived MPOWER policy implementation among various social demographics and its impact on smoking behavior and quitting intentions.Results Regarding protection from tobacco smoke(P),local economic level,urban-rural divide were significantly correlated with awareness of comprehensive smoking bans.For offering help to quit smoking(O),local tobacco industry revenue and individual age were associated with the doctor's advice for quitting.As to the warning about the harm of tobacco(W),economic level,geography and urban-rural disparity were correlated with the visibility of health warnings.About the tobacco advertising,promotion and sponsorship(E),geography was related to the exposure to tobacco advertisements,local tobacco industry revenue was associated with the tobacco promotion.For tobacco taxes(R),education level and age were significantly correlated with tobacco affordability.People who perceived comprehensive smoking bans(OR=0.69,95%CI:0.59-0.81)was associated with less smoking behavior,while people perceiving tobacco promotional activities(OR=2.51,95%CI:2.00-3.17)were more likely to smoke.Additionally,people who perceived comprehensive smoking bans(OR=1.70,95%CI:1.25-2.31)and health warning(OR=2.09,95%CI:1.48-3.01)had higher intention to quit smoking.Conclusion In economically disadvantaged regions and among specific socially vulnerable groups(such as low-income individuals,rural residents,and the elderly)in China,the perception of tobacco control policy implementation is relatively low,the perception of tobacco control policies can influence smoking behavior and quitting intentions.Legislative and enforcement efforts should be increased targeting these groups with lower perceptions of the policies to enhance the fairness of tobacco control measures.

Objective To compare TyG index between the patients with CHF and ADHF to eluci-date the metabolic difference between these two stages.Methods A total of 1156 HF patients ad-mitted in Taizhou People's Hospital between January 2020 and December 2022 were enrolled,and according to 2021 ESC Guidelines for Diagnosis and Treatment of Acute and Chronic Heart Fail-ure,they were divided into CHF group(365 cases)and ADHF group(791 cases).The clinical da-ta,results of laboratory tests,and cardiovascular history were collected,and TyG index was calcu-lated.All-cause death outcome was observed in ADHF patients during a follow-up of 1 year.Results The TyG index was significantly lower in the ADHF group than the CHF group[8.27(7.99,8.62)vs 8.35(8.04,8.75),P=0.001].In the ADHF group,the TyG index was positively correlated with SBP,DBP,TC,TG,LDL-C,FPG,HbA1c,BMI,and LVEF,and negatively with age(P＜0.01).In the CHF group,the index was positively correlated with DBP,TC,TG,LDL-C,FPG,BMI,and HbA1c,and negatively with age(P＜0.05,P＜0.01).Both univariate and multiva-riate logistic regression analyses indicated that the TyG index was a protective factor for ADHF(OR=0.647,95％CI:0.503～0.832,P=0.001;OR=0.694,95％CI:0.536～0.898,P=0.005).Multivariate logistic regression analysis showed that the index in ADHF patients was a protective factor for one-year all-cause mortality(OR=0.483,95％CI:0.254-0.916;P=0.026).Conclusion TyG index might be regarded as an important marker for assessing the metabolic status in HF patients and predicting the prognosis in ADHF patients.

Deutetrabenazine(DTBZ)is a selective oral small molecule inhibitor of vesicular monoamine transporter 2(VMAT2).Its pharmacological action works by inhibiting VMAT2,thereby reducing the release of presynaptic dopamine and alleviating tardive dyskinesia symptoms caused by long-term use of dopamine receptor antagonists.Compared with tetrabenazine,DTBZ has longer half-life,lower peak plasma concentration,and smaller plasma concentration fluctuations.Clinical studies demonstrate that DTBZ significantly improves abnormal involuntary movement in patients with tardive dyskinesia and has a favourable safety profile.Based on available clinical evidence and practical experience,this paper discuss the common questions about DTBZ including the suitable population,dose,duration of treatment,combination administration with antipsychotics,efficacy assessment and application in special populations.This article aimed to provide guidance and recommendations on clinical application of DTBZ for clinicians.

Objective To investigate the effects of ischemia time and storage periods on RNA quality in fresh-frozen breast cancer(BC)and esophageal cancer(EC)tissue samples in order to establish evidence-based protocols for biobank sample management.Methods The tumor(T)and paired normal(N)tissue samples from 6 cases of BC and 6 cases of EC were collected and cryopreserved in Biobank,Shantou Central Hospital.Mirror paraffin-embedded tissues were simultaneously prepared into sections for morphological analysis.The samples were divided into two groups of<15 min and 15-30 min according to ischemia time,and RNA quality was analyzed at 4 storage periods of 8-10 months(T1),14-16 months(T2),26-28 months(T3)and 38-40 months(T4).Results In 96 analyzed samples,93.8%(90/96)exhibited high quality(RIN≥6),with 89.6%(43/48)in BC and 97.9%(47/48)in EC.Significant differences in RIN were observed between BC group and EC group(8.050 vs.8.600,P=0.009).In EC group,RIN value was significantly negatively correlated with RNA yield(P<0.001).Moreover,RIN values of tumor-normal pairs exhibited markedly significant differences(7.550 vs.9.000,P<0.001).In contrast,no significant difference was detected in BC group(8.200 vs.7.700,P=0.348).Statistical analysis showed that RIN value was positively correlated with 28S/18S(P<0.001),but had no correlation with tumor content(P=0.676)and necrotic content(P=0.055).Neither ischemia time(<15 min vs.15-30 min:8.200 vs.8.300,P=0.932)nor storage periods(T1-T4:8.400,7.700,8.450,8.600,P=0.163)compromised RNA quality.Conclusion Organ origin and tissue type could influence RNA quality of fresh-frozen tissue samples.However,limited ischemia time(≤30 min)and long-term storage period(38-40 months)do not adversely affect RNA quality in fresh-frozen breast cancer and esophageal cancer tissue samples.

Deutetrabenazine(DTBZ)is a selective oral small molecule inhibitor of vesicular monoamine transporter 2(VMAT2).Its pharmacological action works by inhibiting VMAT2,thereby reducing the release of presynaptic dopamine and alleviating tardive dyskinesia symptoms caused by long-term use of dopamine receptor antagonists.Compared with tetrabenazine,DTBZ has longer half-life,lower peak plasma concentration,and smaller plasma concentration fluctuations.Clinical studies demonstrate that DTBZ significantly improves abnormal involuntary movement in patients with tardive dyskinesia and has a favourable safety profile.Based on available clinical evidence and practical experience,this paper discuss the common questions about DTBZ including the suitable population,dose,duration of treatment,combination administration with antipsychotics,efficacy assessment and application in special populations.This article aimed to provide guidance and recommendations on clinical application of DTBZ for clinicians.

Objective To investigate the effects of ischemia time and storage periods on RNA quality in fresh-frozen breast cancer(BC)and esophageal cancer(EC)tissue samples in order to establish evidence-based protocols for biobank sample management.Methods The tumor(T)and paired normal(N)tissue samples from 6 cases of BC and 6 cases of EC were collected and cryopreserved in Biobank,Shantou Central Hospital.Mirror paraffin-embedded tissues were simultaneously prepared into sections for morphological analysis.The samples were divided into two groups of<15 min and 15-30 min according to ischemia time,and RNA quality was analyzed at 4 storage periods of 8-10 months(T1),14-16 months(T2),26-28 months(T3)and 38-40 months(T4).Results In 96 analyzed samples,93.8%(90/96)exhibited high quality(RIN≥6),with 89.6%(43/48)in BC and 97.9%(47/48)in EC.Significant differences in RIN were observed between BC group and EC group(8.050 vs.8.600,P=0.009).In EC group,RIN value was significantly negatively correlated with RNA yield(P<0.001).Moreover,RIN values of tumor-normal pairs exhibited markedly significant differences(7.550 vs.9.000,P<0.001).In contrast,no significant difference was detected in BC group(8.200 vs.7.700,P=0.348).Statistical analysis showed that RIN value was positively correlated with 28S/18S(P<0.001),but had no correlation with tumor content(P=0.676)and necrotic content(P=0.055).Neither ischemia time(<15 min vs.15-30 min:8.200 vs.8.300,P=0.932)nor storage periods(T1-T4:8.400,7.700,8.450,8.600,P=0.163)compromised RNA quality.Conclusion Organ origin and tissue type could influence RNA quality of fresh-frozen tissue samples.However,limited ischemia time(≤30 min)and long-term storage period(38-40 months)do not adversely affect RNA quality in fresh-frozen breast cancer and esophageal cancer tissue samples.

Pancreatic cancer is a malignant tumor with a very poor prognosis,characterized by early me-tastasis and high invasiveness,and is unresponsive to traditional treatments like chemotherapy and radio-therapy.In recent years,the study of metabolic products has become a new hotspot in pancreatic cancer research,showing that the metabolic reprogramming of tumor cells is a key factor for their growth and pro-liferation,and that regulatory factors of metabolic pathways may serve as new therapeutic targets.Meta-bolic reprogramming primarily manifests as alterations in three major nutrient metabolic pathway and oxi-dative phosphorylation processes.Additionally,the tumor microenvironment of pancreatic cancer exhibits unique metabolic features.Mechanistic studies are actively underway,and future research may focus on integrating omics,artificial intelligence,and other novel research techniques to further explore how meta-bolic changes drive the development of pancreatic cancer and to improve treatment strategies,including the development of targeted drugs and metabolomics-based diagnostic tools.