Objective:To explore the causal relationship between gut microbiota and lung function (FEV1/FVC) using two-sample Mendelian randomization method; To explore its application in the TCM field.Methods:This was a Mendelian randomization study. The GWAS data of gut microbiota from the MiBioGen consortium study and the GWAS data of lung function (FEV1/FVC) published by IEU OpenGWAS in the public database were used, and instrumental variables were extracted according to prespecified thresholds. The inverse variance weighted method (IVW) was mainly used for analysis. The results were evaluated according to the effect indicator β value and 95% CI. When the IVW method was statistically significant, further sensitivity analysis was performed. Leave-one-out test, heterogeneity test, horizontal gene pleiotropy test and MR-Egger regression intercept analysis were used to verify the stability and reliability of the results. Results:A total of 10 causal relationships between gut microbiota and lung function (FEV1/FVC) were determined using the IVW method: family. BacteroidalesS24.7group ( β=-0.029, P=0.015), family. ClostridialesvadinBB60group ( β=-0.028, P=0.040), family. Streptococcaceae ( β=-0.056, P=0.042), genus. LachnospiraceaeFCS020group ( β=0.025, P=0.029), genus. Lactococcus ( β=-0.024, P=0.038), genus. Peptococcus ( β=0.025, P=0.049), genus. RuminococcaceaeUCG011 ( β=-0.030, P=0.038), genus. Ruminococcus2 ( β=0.028, P=0.033), genus. Terrisporobacter ( β=-0.030, P=0.018), phylum. Cyanobacteria ( β=0.027, P=0.039). Leave-one-out analysis showed that the results were stable, and the effects of heterogeneity and horizontal gene pleiotropy on causal effect estimation could be removed. Conclusion:The gut microbiota may play a role in the changes of lung function, which to a certain extent confirms the TCM theory of "exterior-interior relationship between the lung and large intestine", and can provide certain reference for the research direction of TCM.

Objective:To evaluate the residual risk (RR) of transfusion transmitted HIV (TT-HIV) after the implementation of nucleic acid amplification test (NAT) in blood screening test among blood centers in China.Methods:The data of blood donors and HIV infection markers from 2017 to 2020 were collected from 28 blood centers via the Platform of Comparison of blood establishments Practice in Chinese Mainland. The new infection rate/window period mathematical model was used for two types of blood screening strategies, namely, two rounds ELISA plus individual NAT take turn with pooling NAT (2ELISA+ ID-NAT/MP-NAT) and two ELISA plus one round pooling NAT (2ELISA+ MP-NAT), and the RR of HIV infection was estimated also based on first donors (FDs) and repeated donors (RDs) in different blood donation years. T-test analyses were conducted for comparing TT HIV RR among FDs and RDs in different blood donation years with two blood screening strategies, and the variation trend of RR in HIV test was observed.Results:From 2017 to 2020, the RR of FDs in 2ELISA+ ID-NAT/MP-NAT blood screening strategy was 2.869/10 6 person-year, 3.795/10 6 persons-year, 3.879/10 6 person-year, and 2.890/10 6 person-year respectively. The RR of RDs was 1.797/10 6 person-year, 1.502/10 6 person-year, 1.857/10 6 person-year, and 1.483/10 6 person-year respectively. Significant difference exists between RR of FDs and RDs, with F=9.898 and p<0.05. In 2ELISA+ MP-NAT strategy, the RR of FDs was 3.508/10 6 person-year, 1.868/10 6 person-year, 2.204/10 6 person-year, and 1.765/10 6 person-year respectively. The RR of RDs was 0.948/10 6 person-year, 0.926/10 6 person-year, 0.748/10 6 person-year, and 0.682/10 6 person-year respectively. Statistical difference existed between RR of FDs and RDs, with F=17.126 and P<0.05. There was no significant difference between the RR of FDs in these two strategies with F=3.493 and P>0.05, while there was a difference between the RR of RDs in these two strategies with F=24.516 and P<0.05, and a difference between the RR of total donors (TDs) in these two strategies F=20.216 and P<0.05. Conclusions:The RR of TT HIV significantly decreased after the introduction of NAT into blood test among blood centers in China. There were some differences in the RR of HIV testing among different blood screening strategies. There could be significant differences in the RR of HIV testing among different groups of blood donors. Compared with FDs, RDs is the low risk group for HIV.