PyCoCa:A quantifying tool of carbon content in airway macrophage for assessment the internal dose of particles

碳纤维 炭黑 稳健性(进化) 生物系统 材料科学 纳米技术 化学 复合材料 生物 生物化学 基因 复合数 天然橡胶
作者
Xiaoran Wei,Xiaowen Tang,Nan Liu,Yuansheng Liu,Ge Guan,Yi Liu,Xiaohan Wu,Yingjie Liu,Jingwen Wang,Hanqi Dong,Shengke Wang,Yuxin Zheng
出处
期刊:Science of The Total Environment [Elsevier BV]
卷期号:851: 158103-158103 被引量:4
标识
DOI:10.1016/j.scitotenv.2022.158103
摘要

Given the lack of a comprehensive understanding of the complex metabolism and variable exposure environment, carbon particles in macrophages have become a potentially valuable biomarker to assess the exposure level of atmospheric particles, such as black carbon. However, the tedious and subjective quantification method limits the application of carbon particles as a valid biomarker. Aiming to obtain an accurate carbon particles quantification method, the deep learning and binarization algorithm were implemented to develop a quantitative tool for carbon content in airway macrophage (CCAM), named PyCoCa. Two types of macrophages, normal and foamy appearance, were applied for the development of PyCoCa. In comparison with the traditional methods, PyCoCa significantly improves the identification efficiency for over 100 times. Consistency assessment with the gold standard revealed that PyCoCa exhibits outstanding prediction ability with the Interclass Correlation Coefficient (ICC) values of over 0.80. And a proper fresh dye will enhance the performance of PyCoCa (ICC = 0.89). Subsequent sensitivity analysis confirmed an excellent performance regarding accuracy and robustness of PyCoCa under high/low exposure environments (sensitivity > 0.80). Furthermore, a successful application of our quantitative tool in cohort studies indicates that carbon particles induce macrophage foaming and the foaming decrease the carbon particles internalization in reverse. Our present study provides a robust and efficient tool to accurately quantify the carbon particles loading in macrophage for exposure assessment.
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