We observed the pulmonary responses and changes in TXB2 and 6-keto-PGF1a concentrations in plasma and lung lymph in endotoxin induced lung injury. The typical pulmonary responses appeared after the injury. The TXB2 concentrations in plasma and lung lymph increased by 24.5 and 30.7 times and those of 6-keto-PGFla by 10.6 and 15.7 times, respectively, as compared with baseline. The TXB2 and 6-keto-PGF1a concentrations in lymph were 68 and 75 percent higher than those in plasm a respectively. The results indicated that TXA2 and-PGI2 synthesis increased in the lung after injury and that TXA2 and PGI2 were important media in the lung injury.

The purpose of this study was to determine the role of lipid peroxidation by measuring the induced-chemiluminescence (ICL) of plasma and lung lymph in endotoxin-induced acute lung injury in awake sheep. The ICL of aortic plasma and lung lymph were significantly increased, but the ICL of venous plasma was not significantly increased after endotoxin infusion. The peak level of ICL was increased and its time was delayed after endotoxin. We conclude that lipid peroxidation plays an important role in endotoxin-induced acute lung injury, and ICL of arterial plasma is a sensitive marker in reflecting oxidant damage in lung.

Objective:To detect the expression and methylation of TIA1 in breast cancer and to study its correlation with multi-slice spiral CT signs.Methods:50 patients with breast cancer were collected from Feb.2019 to Mar. 2020. The expression levels of TIA1 in breast cancer tissues and in peritumoral tissues were estimated by quantitative real-time polymerase chain reaction. Bioinformatics software MethPrimer was used for predicting TIA1 promotor and confirmed the existence of cPG island. Methylation-specific PCR (MSP) was performed to detect TIA1 DNA promoter methylation. All patients were examined by multi-slice CT. CT images were analyzed through observing the tumor size, shape, calcification area, lymph node metastasis and margin. The correlation between CT signs and TIA1 methylation status was further analyzed.Results:The expression levels of TIA1 in breast cancer tissues were lower than in peritumoral tissues (0.50±0.12, 0.95±0.10, P=0.00) , while TIA1 DNA promoter methylation rate was higher than in peritumoral tissues (64%, 42%, χ2=4.86, P<0.05) .There were no significant differences in TIA1 DNA promoter methylation rate among patients with different tumor shape and micro calcifications. TIA1 DNA promoter methylation rate in patients with mass diameter≥2 cm were significantly higher than those in patients with mass diameter<2 cm (78.57%, 45.45%, P<0.05) , and TIA1 DNA promoter methylation rate in patients with lymph node metastasis was higher than those without lymph node metastasis (79.17%, 50%, P<0.05) . TIA1 DNA promoter methylation rate in patients with burr at edge of mass was higher than those without burr at edge of mass (80.77%, 45.83%, P<0.05) . Conclusion:There is a correlation between CT imaging signs and TIA1 DNA promoter methylation rate in patients with breast cancer, which can provide more reference for the judgment of malignant degree and prognosis of patients with breast cancer.

Objective To detect the spatial clustering and high risk areas of pulmonary tuberculosis (PTB) in Sichuan province in 2018 and,to compare the effects of application on both SaTScan 9.4.1 software and FleXScan 3.1.2 software to detect the PTB spatial clusters.Methods Geographic information database was established by using the incidence data of PTB and demographic data reported in the'China disease prevention of infectious disease reporting information management system'in Sichuan province in 2018.Spatial clustering analysis was conducted using the Poisson model in software SaTScan 9.4.1 and FleXScan 3.1.2 to detect the high risk areas of PTB by software ArcGIS 10.5.Differences of clusters locations and scopes in the two scanning methods were compared.Results The reported incidence rate of PTB was 57.34/100 000 (47 601 cases) in Sichuan province in 2018,presenting an obvious clustering distribution.SaTScan and FleXScan scanned 8 and 10 clusters showed statistically significant differences (P＜0.05),with log-likelihood ratio (LLR) as 24.62-2 416.05 and 1.48-2 618.96,respectively.Results from scanning of the two methods showed that the most likely clusters appeared in the Daliangshan and Xiaoliangshan of Liangshan Yi ethnic aggregation areas.The other clustering areas would include some minority areas in the western Sichuan plateau,detected by both two methods differences in the shape and scope of the clustering were detected by both methods.The clustering scopes detected by SaTScan covered some counties,in which the actual incidence was not high.FleXScan could distinguish the clusters and detect more irregular shaped clusters.Conclusions Obvious spatial clusters of PTB distribution were found in Sichuan in 2018.Areas of Daliangshan,Xiaoliangshan and the minority areas in Western Sichuan plateau appeared at high risk,suggesting these were the key areas for prevention and control.FleXScan seemed more conducive in accurately distinguishing the "cold spot" areas in the highly aggregated areas,and more suitable for the application of spatial clustering detection for TB,in Sichuan province.

Objective To analyze the spatial and temporal distribution of smear positive pulmonary tuberculosis (PTB) in Liangshan Yi autonomous prefecture in Sichuan province from 2011 to 2016. Methods The registration data of PTB in 618 townships of Liangshan from 2011 to 2016 were collected from"Tuberculosis Management Information System of National Disease Prevention and Control Information System". Software ArcGIS 10.2 was used to establish the geographic information database and realize the visualization of the analysis results. Software OpenGeoda 1.2.0 was used to conduct the analyses on global indication of spatial autocorrelation (GISA) and local indication of spatial autocorrelation (LISA). Software SaTScan 9.4.1 was used for spatio-temporal scanning analysis. Results From 2011 to 2016, the registration rate of smear positive PTB in Liangshan declined from 56.97/100000 (2666 cases) to 21.11/100000 (1038 cases). The global spatial autocorrelation coefficient Moran's I ranged from 0.25 to 0.45 and the difference was significant (all P=0.000). Local autocorrelation analysis showed that"high-high"area covered 43, 34, 37, 34, 42 and 61 townships from 2011 to 2016, respectively, mainly in Leibo county. Spatial temporal clustering analysis found one class Ⅰ clustering in the area around Bagu township of Meigu county and two class Ⅱ clustering in the areas around Liumin and Hekou township of Huili county, respectively (all P=0.000). Conclusion Obvious spatial temporal clustering of smear positive PTB distribution was found in Liangshan from 2011-2016. Hot spot areas with serious smear positive PTB epidemic and high spread risk were mainly found in northeastern Liangshan, including townships in Leibo and Meigu counties. Targeted TB prevention and control should be conducted in these areas.