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该研究将主成分分析、偏最小二乘判别分析等多元统计分析方法用于烟草血浆、尿液和肺组织代谢组学数据的分析,以揭示暴露于不同烟气中大鼠血浆、尿液和肺组织中内源性生物标志物的整体变化情况,筛选潜在生物标志物;将血样、尿样和肺组织代谢轮廓谱分析得到的生物标志物进行整合,运用神经模糊网络模型对标志物进行缩减,并用人工神经网络评价模型预测能力,确定烟气暴露不同时间(7,14,30 d)以及不同烟气暴露对大鼠内源性代谢物变化影响“因果效应”密切相关的关键生物标志物群,明确不同烟气对大鼠机体损伤机制的异同。
In this study, multivariate statistical analysis, such as principal component analysis and partial least-squares discriminant analysis, was used to analyze the metabolomics data of plasma, urine and lung tissue in tobacco to reveal the plasma and urine levels in rats exposed to different flue gases Biomarkers screening for potential biomarkers; integration of biomarkers from blood, urine, and lung tissue metabolite profiling to reduce overall use of neuro-fuzzy network models for marker reduction , And the predictive ability of the model was evaluated by artificial neural network to determine the key organisms closely related to the effect of endogenous metabolites changes in rats at different times (7, 14, 30 d) and different smoke exposures Similarity and Difference of Injury Mechanism of Different Flue Gas on Rat Body.