Environmental pollution is a significant public health challenge worldwide, and investigating the causal relationship between environmental exposure and population health outcomes is a key objective of environmental epidemiology research. In recent years, the complexity of environmental exposures has increasingly come to the forefront, making it challenging for observational studies that dominate environmental epidemiology to accurately estimate causal effects. Causal inference methods are particularly advantageous in controlling for confounding factors, thus holding great potential in environmental epidemiology research. Researchers can use appropriate causal inference methods to simulate the process of randomization, providing strong support for revealing the causal relationship between environmental exposure and health outcomes. However, there is a lack of reviews on the application of causal inference methods in environmental epidemiology studies in China. Therefore, this study introduced the basic principles of common causal inference statistical methods in environmental epidemiology, summarized the applicable conditions, advantages and disadvantages of various methods, and provided R software implementation codes for these methods, aiming to offer guidance for optimizing research design and practicing causal inference statistical methods.

On June 27， 2026， the Asian Pacific Association for the Study of the Liver （APASL） released updated management guidelines for chronic HBV infection， which systematically elaborates on screening， vaccination， antiviral treatment， and hepatocellular carcinoma surveillance and other aspects in 13 key areas， and provides comprehensive recommendations. This article summarizes and makes an excerpt of the key recommendations from the updated guidelines.

[Purpose]To analyze the incidence and mortality of malignant tumors in cancer regis-tration areas of Heilongjiang Province in 2019 and the trend from 2013 to 2019.[Methods]The incidence and mortality data of malignant tumors reported by the Heilongjiang provincial cancer registries from 2013 to 2019 were collected,and the quality of data was assessed.The crude in-cidence/mortality rate,age-standardized incidence/mortality rate by Chinese standard population(ASIRC/ASMRC)and world standard population(ASIRW/ASMRW),0～74 years old cumulative rate were calculated.Joinpoint 4.6.0 software was used to calculate the average annual percentage change(AAPC)of ASIRC/ASMRC for the trend analysis from 2013 to 2019.[Results]In 2019,there were 16 732 new cases of malignant tumors in the cancer registration areas of Heilongjiang Province,including 8 639 males and 8 093 females.The crude incidence rate was 295.37/105,with an ASIRC and ASIRW of 167.10/105 and 164.18/105,respectively.There were 10 988 malig-nant tumor deaths,including 6 540 males and 4 448 females.The crude mortality rate was 193.97/105,with an ASMRC and ASMRW of 101.22/105 and 101.66/105,respectively.The inci-dence and mortality of malignant tumors increased rapidly after the age of 55,and the incidence and mortality of males were slightly higher than those of females.The top five malignant tumors of high incidence were lung cancer,female breast cancer,colorectal cancer,liver cancer and thy-roid cancer,and the top five malignant tumors of high mortality were lung cancer,liver cancer,colorectal cancer,stomach cancer and female breast cancer.From 2013 to 2019,the ASIRC of malignant tumors in cancer registration areas increased from 153.08/105 in 2013 to 167.10/105 in 2019,and the ASMRC increased from 92.22/105 in 2013 to 101.22/105 in 2019,but there was no statistical difference in the change trend.[Conclusion]The incidence and mortality of malignant tumors in Heilongjiang Province remain high.Lung cancer,female breast cancer,colorectal can-cer,liver cancer and stomach cancer should be the focus of cancer prevention and control.

Various therapeuti modailities have been engineered for lung cancer treatment, but their clinic application is severely impeded by the poor therapy efficiency and immunosuppressive microenvironment. Herein, we fabricated a library of small molecule redox-labile nanoparticles (NPs) (i.e., diPTX-2C NPs, diPTX-2S NPs, and diPTX-2Se NPs) by the self-assembly of dimer paclitaxel (PTX) prodrug, and then utilized these NPs with the traditional Chinese medicine (TCM) Qi-Yu-San-Long-Fang (Q) for effective chemoimmunotherapy on Lewis lung carcinoma (LLC)-bearing mice models. Under the high concentration of glutathione (GSH) and H₂O₂, diPTX-2Se NPs could specifically release PTX in cancer cells and exert a higher selectivity and toxicity than normal cells. In LLC tumor-bearing mice, oral administration of Q not only effectively downregulated programmed death ligand-1 (PD-L1) expression, but also remodeled the immunosuppressive tumor immune microenvironment via the increase of CD4⁺ T and CD8⁺ T cell proportion and the repolarization of M2 into M1 macrophages in tumor tissues, collectively achieving superior synergistic treatment outcomes in combination with intravenous PTX prodrug NPs. Besides, we found that the combination regimen also demonstrated excellent chemoimmunotherapeutic performances on low-dose small established tumor and high-dose large established tumor models. This study may shed light on the potent utilization of Chinese and Western-integrative strategy for efficient tumor chemoimmunotherapy.

Objective : To investigate the factors affecting the outcome of high-risk human papillomavirus ( HR- HPV) infection in patients with normal cervix examined by colposcopy in Hefei area and the relationship between persistent HR-HPV infection and cervical cytology． Methods : Data of colposcopy patients were collected from 478 HR-HPV infected patients with normal cervix through colposcopy．Their age，number of sexual partners，contracep- tive methods and other relevant basic information were recorded．Vaginal interferon use，HR-HPV infection at year 1 and year 2，and cervical liquid-based cytology test ( LCT) results were tracked，univariate and multivariate ana- lyses were performed based on basic information，and ROC curves were plotted． Results : The HR-HPV clearance rate at 1 year was 59. 41% ，and the clearance rate at 2 years was 66. 75%．The other 12 types of infection ( 31， 33，35，39，45，51，52，56，58，59，66，68) were more common than the 16 and 18 types．Univariate and mult- ivariate analyses showed that age＞50 years，number of sexual partners ≥2，and history of cervical conectomy in- creased the risk of persistent HR-HPV infection ( χge = 21. 676，P ＜0. 001; χumber of sexual partners = 8. 262，P =0. 004; χistory of cervical conectomy = 11. 267，P = 0. 001 ) ． The risk of HR-HPV infection was significantly lower when condom or vaginal interferon was used ( χondom use = 10. 885，P = 0. 001; χnterferon use = 4. 099，P = 0. 043) ．The area under the ROC curve (AUC) of combined diagnosis of HR-HPV persistent infection was higher than that of single diagnosis，and the AUC of combined diagnosis was 0. 737．Persistent HR-HPV infection was an independent risk factor for abnormal LCT，and the AUC predicted by the model was 0. 755．No cancer was found in patients with persistent HR-HPV infection for 2 years，and the proportion of abnormal LCT was higher than that in patients with negative HR-HPV．The difference was statistically significant ( χ2 = 39. 64，P＜0. 001) ． Conclusion The combined ROC model constructed for patients＞50 years old，with multiple sexual partners，history of cervical surgery， no vaginal interferon use，and no condom use has certain value in predicting persistent HR-HPV infection，and per- sistent HR-HPV infection has predictive value in predicting LCT abnormalities．

Objective To investigate the predictive value of combined radiomic features derived from chest CT scans with clinical characteristics for epidermal growth factor receptor(EGFR)gene mutations in non-small cell lung cancer(NSCLC).Methods A multi-center case-control study was conducted on the clinical data and CT images of 1 070 NSCLC patients from the radiology departments of the 3 medical institutions between January 2013 and October 2023.The 719 NSCLC patients from the First Affiliated Hospital of Army Medical University were randomly divided into a training set and an internal validation set in a ratio of 7∶3;The 173 patients in the Eastern Theatre General Hospital and the 178 patients in Army Medical Centre of PLA were assigned into the external validation set 1 and 2,respectively.Least absolute shrinkage and selection operator(LASSO)regression was employed to identify the optimal radiomic features,which were subsequently used to construct a radiomics model.Univariate and multivariate logistic regression analyses were applied to identify clinical features associated with EGFR mutation,thereby developing a clinical model.The radiomic and clinical features were subsequently combined to develop a comprehensive model.All the 3 classification models were built using random forest(RF)machine learning.The area under curve(AUC),accuracy,sensitivity and specificity were utilized to evaluate the predictive performance of the models.Calibration curve was plotted to assess the goodness of fit of the comprehensive model,while decision curve analysis was performed to assess the clinical utility of the model.Results The AUC value of the radiomics model was 0.762 4(95%CI:0.692 4～0.825 1),0.745 4(95%CI:0.671 1～0.814 3),and 0.724 7(95%CI:0.639 7～0.801 6),respectively,in the internal validation set,external validation set 1,and external validation set 2;The AUC value of the clinical prediction model was 0.691 7(95%CI:0.627 9～0.757 6),0.652 5(95%CI:0.576 7～0.729 1),and 0.779 2(95%CI:0.712 5～0.847 3),respectively in the above sets in turn;The comprehensive model constructed based on clinical features and radiomic features showed the best predictive efficacy,with an AUC value of 0.818 0(95%CI:0.757 7～0.874 3),0.782 4(95%CI:0.703 1～0.848 2),and 0.796 6(95%CI:0.718 1～0.868 6),respectively in the above sets.Calibration curve analysis indicated that the comprehensive model had a good fit,while decision curve analysis revealed that the model provided a favorable net benefit.Conclusion Our comprehensive model constructed based on chest CT radiomic features and clinical characteristics shows superior predictive performance for EGFR gene mutations in NSCLC across multiple center datasets,which may be helpful for clinical decision-making for treatment strategies.

Objective To evaluate the predictive value of deep learning features from chest CT images combined with clinical and radiomics features for epidermal growth factor receptor(EGFR)mutations in non-small cell lung cancer(NSCLC).Methods This case-control study retrospectively analyzed clinical and imaging data of 1 070 NSCLC patients from radiology departments at three hospitals(January 2013 to October 2023).Patients were divided into:a training set(n=502)and internal validation set(n=217)via 7∶3 randomization of 719 cases from the First Affiliated Hospital of Army Medical University;external validation set 1(n=173)from General Hospital of Eastern Theater Command;external validation set 2(n=178)from Daping Hospital of Army Medical University.Deep learning features were extracted using a 2.5D convolutional neural network(CNN)with ResNet101 backbone,radiomics features were derived from CT images,and clinical risk factors were identified to construct models.An integrated model combined deep learning,clinical,and radiomics features.All four models were developed using random forest(RF)classifiers.Calibration curves assessed goodness-of-fit,and decision curve analysis(DCA)evaluated clinical utility.Results The deep learning model achieved AUCs of 0.833 7(95%CI:0.770 6～0.884 7),0.815 1(0.741 6～0.882 8),and 0.810 1(0.745 2～0.873 6)in the internal and two external validation sets,respectively.Clinical models yielded AUCs of 0.731 0(0.660 2～0.802 1),0.746 0(0.666 4～0.824 9),and 0.813 4(0.743 1～0.883 6);radiomics models showed AUCs of 0.762 4(0.692 4～0.825 1),0.745 4(0.671 1～0.814 3),and 0.724 7(0.639 7～0.801 6).The integrated model demonstrated optimal performance with AUCs of 0.905 5(0.857 0～0.945 4),0.832 7(0.763 3～0.896 4),and 0.889 0(0.834 4～0.934 3).DCA indicated significant net benefit for EGFR prediction at threshold probabilities of 0.15～0.85 using the integrated model.Conclusion Deep learning features from CT images effectively predict EGFR mutation status in NSCLC.The integrated model combining deep learning,clinical,and radiomics features further enhances predictive performance.

Objective:To develop a rapid assessment scale for healthy aging suitable for the Chinese population.Methods:Based on existing healthy aging assessment scales, national standards, and expert consensus, an initial Healthy Aging Rapid Assessment Scale was drafted through two rounds of expert consultation. A pre-survey was conducted with 3 220 subjects recruited from Guangzhou between July 2023 and July 2024. Items were screened through item analysis and exploratory factor analysis to form the final scale. Reliability and validity of the final scale were validated across five cities: Guangzhou, Dongguan, Shenzhen, Baoding, and Chuxiong.Results:The initial version comprised 36 items, while the finalized scale contained 18 items across three dimensions: metabolic health, mental health, and cognitive health. Test-retest reliability ranged from 0.71 to 0.81 across all study sites. The Spearman-Brown coefficient varied between 0.91-0.96, Cronbach′s α between 0.77-0.83, comparative fit index (CFI) between 0.90-0.98, goodness-of-fit index (GFI) between 0.90-0.99, and root-mean-square error of approximation (RMSEA) between 0.03-0.09. For the three dimensions, reliability and validity metrics demonstrated consistency: Spearman-Brown coefficients 0.87-0.99, Cronbach′s α 0.77-0.83, CFI 0.90-0.98, GFI 0.90-0.99, and RMSEA 0.03-0.09 across four regions.Conclusion:The developed Healthy Aging Rapid Assessment Scale for the Chinese population exhibits robust reliability and validity.

Objective:To explore the value of ultrasound imaging characteristics combined with clinical indicators in assessing the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC).Methods:A retrospective analysis was conducted for patients who underwent pancreatic contrast-enhanced ultrasound (CEUS) from September 2017 to October 2023 at Peking Union Medical College Hospital and were diagnosed with PDAC based on pathological findings. Various parameters were recorded, including CA19-9 levels, tumor size, location, morphologic features, echogenicity, presence of internal cystic components, dilatation of the main pancreatic duct, peripheral vascular invasion, CEUS characteristics, presence or absence of liver metastasis, and treatment methods. In April 2024, patient survival information was obtained through telephone follow-up or review of medical records. Based on the results of the cox regression model analysis, a nomogram model of the risk of death was developed. The receiver operating characteristic (ROC) curves were applied to evaluate the predictive efficacy of the model. The calibration curves were plotted to evaluate the accuracy of the model, and clinical decision curves were used to evaluate the clinical benefit of the model.Results:This study included a total of 207 patients with PDAC. As of April 2024, 71 patients were alive and 136 died, with a median survival time of 14 months (95% CI: 12 -17). Multivariate analysis confirmed that the elevated CA19-9 ( HR=1.689, 95% CI: 1.102-2.588), tumor size >4 cm ( HR=1.641, 95% CI: 1.159-2.322), taller-than-wide shapes ( HR=1.450, 95% CI: 1.019-2.065), incomplete hypo-enhancement ( HR=1.618, 95% CI: 1.100-2.380), and liver metastasis ( HR=1.687, 95% CI: 1.175-2.423) were independent risk factors for survival in patients with PDAC. A nomogram model was further constructed for 6-month, 12-month and 3-year survival of patients with PDAC. The areas under the ROC curve were 0.679, 0.705 and 0.815, respectively. The calibration curves suggested that the model was more accurate, and the clinical decision curves showed that the model had a better clinical benefit. Conclusion:The combined use of ultrasound imaging characteristics and clinical indicators could effectively predict the prognosis of PDAC patients. Specifically, tumor size >4 cm, taller-than-wide shapes, incomplete hypo-enhancement, elevated CA19-9, and the presence of liver metastasis are correlated with poorer survival outcomes. The nomogram model constructed on the basis of these factors can be used to assess the survival of patients with PDAC.

Objective:To analyze the expression of tumor-associated transmembrane protein 61 (TMEM61) in cholangiocarcinoma tissues and its influence on prognosis and immune infiltration, as well as the effect on the proliferation of cholangiocarcinoma cells.Methods:In the cholangiocarcinoma gene chip dataset (TCGA-CHOL), differentially expressed genes between cholangiocarcinoma tissues and normal bile duct tissues were screened, and the upregulated TMEM61 gene was selected for further analysis. Based on the TMEM61 expression, cholangiocarcinoma patients higher than the median value were classified as the high-expression group ( n=17), and those lower than the median value were classified as the low-expression group ( n=18). The Kaplan-Meier survival curve was plotted. Functional and pathway enrichment analyses were conducted on differentially expressed genes related to TMEM61, and the correlations between TMEM61 expression and immune cells and immune molecules were respectively analyzed. The expression level of TMEM61 in cholangiocarcinoma tissues was analyzed by immunohistochemistry; The effect of TMEM61 expression on the proliferation of cholangiocarcinoma cells was detected by Western blotting, CCK-8, clone formation assay, etc. Results:Compared with normal tissues, the expression of TMEM61 mRNA in cholangiocarcinoma tissues was significantly upregulated ( t=18.31, P<0.001). The overall survival rate of patients in the high-expression group of TMEM61 was significantly lower than that in the low-expression group, and the difference was statistically significant ( χ2=7.23, P=0.007). The differentially expressed genes related to TMEM61 were involved in cell proliferation, cell cycle and DNA replication, etc. Compared with normal tissues, regulatory T cells ( t=10.21, P<0.001) and M0-type macrophages ( t=5.89, P=0.008) were significantly increased in cholangiocarcinoma tissues. Plasma cells ( t=7.34, P=0.002), γδT cells ( t=9.87, P<0.001), and M2-type macrophages ( t=11.53, P<0.001) were significantly decreased in cholangiocarcinoma tissues. The expression of TMEM61 was correlated with neurociliary protein 1, tumor necrosis factor ligand superfamily member 15 and B7 homologous protein 3 (all P<0.05). The proportion of positive staining area of TMEM61 protein in normal tissues was (10.15±2.27) %, and that in cholangiocarcinoma tissues was (69.43±11.66) %. The difference was statistically significant ( t=14.97, P<0.001). Inhibition of TMEM61 expression led to a decrease in the number of cholangiocarcinoma cell clones and proliferation activity, and the differences were statistically significant (both P<0.01). Conclusion:The expression of TMEM61 is elevated in cholangiocarcinoma tissues and is associated with poor prognosis. The abnormally high expression of TMEM61 affects the infiltration of immune cells and promotes the proliferation of cholangiocarcinoma cells. TMEM61 is expected to become a potential biomarker for the prognosis assessment of cholangiocarcinoma.