UCD School of Biosystems & Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland.
Environ Pollut. 2024 Dec 1;362:124971. doi: 10.1016/j.envpol.2024.124971. Epub 2024 Sep 16.
Plastic pollution, driven by micro- and nanoplastics (MNPs), poses a major environmental threat, exposing humans through various routes. Despite human colorectal adenocarcinoma Caco-2 cells being used as an in vitro model for studying the intestinal epithelium, uncertainties linger about MNPs harming these cells and the factors influencing adverse effects. Addressing this lacuna, our study aimed to elucidate the pivotal MNP parameters influencing cytotoxicity in Caco-2 cells, employing meta-analysis and machine learning techniques for quantitative assessment. Initial scrutiny of 95 publications yielded 17 that met the inclusion criteria, generating a dataset of 320 data points. This dataset underwent meticulous stratification based on polymer type, exposure time, polymer size, MNP concentration, and biological assays utilised. Subsequent dose-response curve analysis revealed moderate correlations for selected subgroups, such as the (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) MTT biological assay and exposure time exceeding 24 h, with coefficient of determination (R) values of 0.50 (p-value: 0.0065) and 0.60 (p-value: 0.0018) respectively. For the aforementioned two subgroups, the MNP concentrations surpassing 10 μg/mL led to diminished viability of Caco-2 cells. Notably, we observed challenges in employing meta-analysis to navigate this multidimensional MNP dataset. Leveraging a random forest model, we achieved improved predictive performance, with R values of 0.79 and a root mean square error (RMSE) of 0.14 for the prediction of the Log Response Ratio on the test set. Model interpretation indicated that size and concentration are the principal drivers influencing Caco-2 cell cytotoxicity. Additionally, the partial dependence plot illustrating the relationship between the size of MNPs and predicted cytotoxicity reveals a complex pattern. Our study provides crucial insights into the health impacts of plastic pollution, informing policymakers for targeted interventions, thus contributing to a comprehensive understanding of its human health consequences.
塑料污染，由微塑料和纳米塑料（MNPs）驱动，对环境构成重大威胁，通过各种途径暴露于人类。尽管人类结直肠腺癌细胞 Caco-2 被用作研究肠上皮的体外模型，但 MNPs 对这些细胞的危害以及影响不良影响的因素仍存在不确定性。为了解决这一空白，我们的研究旨在阐明影响 Caco-2 细胞细胞毒性的关键 MNP 参数，采用元分析和机器学习技术进行定量评估。对 95 篇出版物进行初步审查后，有 17 篇符合纳入标准，生成了 320 个数据点的数据集。根据聚合物类型、暴露时间、聚合物尺寸、MNP 浓度和使用的生物测定方法，对该数据集进行了细致的分层。随后进行的剂量-反应曲线分析显示，对于选定的亚组，如 3-[4,5-二甲基噻唑-2-基]-2,5-二苯基四唑溴化物（MTT）生物测定和暴露时间超过 24 小时，存在中度相关性，决定系数（R）值分别为 0.50（p 值：0.0065）和 0.60（p 值：0.0018）。对于上述两个亚组，MNP 浓度超过 10μg/mL 会导致 Caco-2 细胞活力降低。值得注意的是，我们在使用元分析处理这个多维 MNP 数据集时遇到了挑战。利用随机森林模型，我们实现了更高的预测性能，在测试集上的 R 值为 0.79，均方根误差（RMSE）为 0.14，用于预测对数响应比。模型解释表明，大小和浓度是影响 Caco-2 细胞细胞毒性的主要驱动因素。此外，大小的偏依赖图说明了 MNPs 与预测细胞毒性之间的关系，揭示了一个复杂的模式。我们的研究为塑料污染对健康的影响提供了重要的见解，为决策者提供了有针对性的干预措施，从而有助于全面了解其对人类健康的影响。
