Objective:To investigate the independent and combined effects of maternal smoking during pregnancy and offspring genetic susceptibility on overall cancer mortality.Methods:Based on the United Kingdom Biobank ( n=419 228) data, the Cox proportional hazard regression model was used to estimate the effect of maternal smoking during pregnancy on offspring overall cancer (including 16 cancers in men and 18 in women) mortality and its combined effect and interaction with offspring genetic factors. Results:Maternal smoking during pregnancy was significantly associated with a 13% increased risk of overall cancer mortality in men [hazard ratio( HR)=1.13, 95% CI: 1.06-1.20] and 19% increased risk in women ( HR=1.19, 95% CI: 1.11-1.27). Participants with high genetic risk had the highest overall cancer mortality than those with low genetic risk (men: HR=1.42, 95% CI: 1.30-1.55; women: HR=1.38, 95% CI: 1.25-1.52). Compared with participants without maternal smoking during pregnancy and low genetic risk, those with maternal smoking during pregnancy and high genetic risk were associated with a 56% increased risk of overall cancer mortality in men ( HR=1.56, 95% CI: 1.37-1.77) and 59% in women ( HR=1.59, 95% CI: 1.39-1.83). Conclusion:Maternal smoking during pregnancy may increase offspring overall cancer mortality and more severe harm in individuals with high genetic risk.

Objective:To investigate the impact of air pollution on dynamic changes in lung function and further explore the association between genetic factors and lung function and its changes.Methods:Research data were from 14 506 participants in the United Kingdom Biobank with two complete baseline and follow-up lung function tests. Particulate matter [including particulate matter with aerodynamic diameter ≤2.5 μm and ≤10 μm (PM 2.5 and PM 10)], nitrogen dioxide (NO 2), and nitrogen oxides (NO x) concentrations were estimated using land-use regression models. Annual changes in lung function were calculated based on baseline and follow-up lung function tests. Polygenic risk scores (PRS) of lung function [forced expiratory volume in the first second (FEV 1), forced vital capacity (FVC), and the ratio of FEV 1 to FVC (FEV 1/FVC)] were constructed by genetic variations. The association between air pollution concentrations and lung function changes was analyzed by multiple linear regression models, and the impact of genetic factors on lung function and its changes was also assessed. Results:PM 2.5, PM 10, NO 2, and NO x showed a negative correlation with FVC changes [PM 2.5: -6.66 (95% CI: -9.92- -3.40) ml/year; PM 10: -0.40 (95% CI: -0.77- -0.03) ml/year; NO 2: -1.84 (95% CI: -2.60- -1.07) ml/year; NO x: -1.37 (95% CI: -2.27- -0.46) ml/year]. Additionally, PM 2.5, PM 10and NO 2 were also negatively correlated with changes in FEV 1 [PM 2.5: -3.19 (95% CI: -5.79- -0.59) ml/year; PM 10: -3.00 (95% CI: -5.92- -0.08) ml/year; NO 2: -0.95 (95% CI: -1.56- -0.34) ml/year]. PRS of lung function were positively correlated with baseline lung function (FVC, FEV 1, and FEV 1/FVC) and lung function changes (all β>0, all P<0.001). In different PRS stratification analyses, the effect of air pollution on lung function changes remained significant, and there was no apparent heterogeneity. Conclusions:PRS of lung function are significantly associated with baseline and lung function changes. Long-term exposure to air pollution accelerates the decline of lung function indicators such as FVC and FEV 1. The effects of air pollution are consistent in individuals with different genetic risk scores.

This paper discusses the necessity of artificial intelligence(AI)technology empowering the field of traditional Chinese medicine(TCM)in the context of the intelligent era,the connotation and tasks of smart TCM,and the progress of related research and transformation.It closely follows the national orientation,rigid needs and problems,conducts top-level design,and proposes popular AI technologies that can be used in the field of TCM in the future and the research directions that smart TCM will focus on in the fu-ture,in order to further promote the integration of multidisciplinary cross-innovation and help realize the modernization,inheritance and innovation of TCM and lay the foundation.

This paper discusses the necessity of artificial intelligence(AI)technology empowering the field of traditional Chinese medicine(TCM)in the context of the intelligent era,the connotation and tasks of smart TCM,and the progress of related research and transformation.It closely follows the national orientation,rigid needs and problems,conducts top-level design,and proposes popular AI technologies that can be used in the field of TCM in the future and the research directions that smart TCM will focus on in the fu-ture,in order to further promote the integration of multidisciplinary cross-innovation and help realize the modernization,inheritance and innovation of TCM and lay the foundation.