Department of Pathophysiology, Faculty of Medicine, University of Belgrade, Dr. Subotica 9, 11129, Belgrade, Serbia.
Department of Immunology and Physiology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, UAE.
Sci Rep. 2024 Aug 23;14(1):19595. doi: 10.1038/s41598-024-70559-4.
This study focuses on developing machine learning models to detect subtle alterations in hepatocyte chromatin organization due to Iron (II, III) oxide nanoparticle exposure, hypothesizing that exposure will significantly alter chromatin texture. A total of 2000 hepatocyte nuclear regions of interest (ROIs) from mouse liver tissue were analyzed, and for each ROI, 5 different parameters were calculated: Long Run Emphasis, Short Run Emphasis, Run Length Nonuniformity, and 2 wavelet coefficient energies obtained after the discrete wavelet transform. These parameters served as input for supervised machine learning models, specifically random forest and gradient boosting classifiers. The models demonstrated relatively robust performance in distinguishing hepatocyte chromatin structures belonging to the group exposed to IONPs from the controls. The study's findings suggest that iron oxide nanoparticles induce substantial changes in hepatocyte chromatin distribution and underscore the potential of AI techniques in advancing hepatocyte evaluation in physiological and pathological conditions.
本研究旨在开发机器学习模型，以检测由于氧化铁纳米粒子暴露而导致的肝细胞染色质组织的细微改变，假设暴露将显著改变染色质纹理。总共分析了来自小鼠肝组织的 2000 个肝细胞核感兴趣区域（ROI），对于每个 ROI，计算了 5 个不同的参数：长运行强调、短运行强调、运行长度非均匀性，以及离散小波变换后获得的两个小波系数能量。这些参数作为监督机器学习模型（特别是随机森林和梯度提升分类器）的输入。这些模型在区分暴露于 IONP 的组和对照组的肝细胞染色质结构方面表现出相对稳健的性能。该研究结果表明，氧化铁纳米粒子诱导肝细胞染色质分布发生实质性变化，并强调了人工智能技术在推进生理和病理条件下肝细胞评估中的潜力。
