Pycoca: A Quantifying Tool of Carbon Content in Airway Macrophage for Assessment the Internal Dose of Particles

Abstract

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 and reduces the identification error. Consistency assessment with the gold standard reveals that PyCoCa exhibits outstanding prediction ability with the ICC values of over 0.80. Subsequent sensitivity analysis suggests that PyCoCa achieves excellent performances regarding accuracy and robustness under different exposure environment and with different staining condition of macrophage. 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.

Keywords: Carbon particles quantification, Macrophage, Mask R-CNN, Exposure assessment