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吸气,呼气:为什么动物模型中的颗粒物暴露如此严重?

Inhale, exhale: Why particulate matter exposure in animal models are so acute?

机构信息

Programa de Pós-Graduação Em Ecologia de Ecossistemas, Universidade Vila Velha, Rua Comissário José Dantas de Melo, 21, Boa Vista, CEP 29102-920, Vila Velha, ES, Brazil; Departamento de Tecnologia Industrial, Centro Tecnológico, Universidade Federal Do Espírito Santo, Av. Fernando Ferrari, 514, Goiabeiras, CEP 29060-970, Vitória, ES, Brazil.

Laboratory of Immunobiology, Universidade Vila Velha, Rua Comissário José Dantas de Melo, 21, Boa Vista, CEP 29102-920, Vila Velha, ES, Brazil.

出版信息

Environ Pollut. 2019 Aug;251:230-237. doi: 10.1016/j.envpol.2019.04.084. Epub 2019 Apr 30.

DOI:10.1016/j.envpol.2019.04.084
PMID:31082607
Abstract

Ecotoxicological studies that try to describe the effects of particulate matter (PM) on human health are important in order to gain a deeper understanding of their effects in disease outcomes. Because exposure protocols are not easily comparable, evaluating human PM exposure is a difficult task. Thus, interpreting ambiguous or conflicting results from different experiments could lead to misleading conclusions about the true nature of PM effects. To address these issues, we compiled a collection of relevant research articles in order to compare present PM exposure methods and extract data related to concentration, inhalation rates (IR), and doses. We also compare the experimental exposure levels reported in these articles to PM levels around the world. In particular, our dataset covers reported results from 75 research articles. To allow for comparison between protocols, we used this data to fit a normalization equation that depends upon concentration, exposure time, dose, inhalability, and physiological parameters. Based on the collected research papers, instillation is the prevalent exposure method. Also, the median PM IR from these experiments is three orders of magnitude higher than the PM IR found in environmental conditions (EAP). Experiments employing inhalation of concentrated PM show IR results that are two orders of magnitude higher than EAP; these results are cause for concerns, since the PM exposure were acute, sudden, and higher than the worst-case exposure scenarios reported by the world megacities. We also found that different PM exposure protocols are sources for the observed variability in physiological response results found from animal models. We discuss these findings and make suggestions for future exposure methodologies. Such considerations should be valuable for quantifying PM exposure in disease outcomes.

摘要

为了更深入地了解颗粒物 (PM) 对人类健康的影响,进行描述其影响的生态毒理学研究非常重要。由于暴露方案不容易比较,评估人类 PM 暴露是一项艰巨的任务。因此,解释来自不同实验的模糊或相互矛盾的结果可能会导致对 PM 影响的真实性质产生误导性结论。为了解决这些问题,我们编译了一系列相关的研究文章,以便比较目前的 PM 暴露方法并提取与浓度、吸入率 (IR) 和剂量相关的数据。我们还将这些文章中报告的实验暴露水平与世界各地的 PM 水平进行了比较。特别是,我们的数据集涵盖了 75 篇研究文章的报告结果。为了能够在方案之间进行比较,我们使用这些数据拟合了一个依赖于浓度、暴露时间、剂量、可吸入性和生理参数的归一化方程。根据收集到的研究论文,滴注是普遍采用的暴露方法。此外,这些实验的 PM IR 中位数比环境条件 (EAP) 中的 PM IR 高出三个数量级。吸入浓缩 PM 的实验显示的 IR 结果比 EAP 高出两个数量级;这些结果令人担忧,因为 PM 暴露是急性的、突然的,并且高于世界大城市报告的最坏情况暴露情况。我们还发现,不同的 PM 暴露方案是动物模型中观察到的生理反应结果变异性的来源。我们讨论了这些发现,并为未来的暴露方法学提出了建议。这些考虑因素对于量化疾病结果中的 PM 暴露应该是有价值的。

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Inhale, exhale: Why particulate matter exposure in animal models are so acute?吸气,呼气:为什么动物模型中的颗粒物暴露如此严重?
Environ Pollut. 2019 Aug;251:230-237. doi: 10.1016/j.envpol.2019.04.084. Epub 2019 Apr 30.
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Inhale, exhale: Why particulate matter exposure in animal models are so acute? Data and facts behind the history.吸气,呼气:为何动物模型中颗粒物暴露如此严重?历史背后的数据与事实。
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引用本文的文献

1
Effects of Different Particulate Matter Exposure Methods on Systemic Inflammation.不同颗粒物暴露方法对全身炎症的影响。
Iran J Public Health. 2022 May;51(5):1180-1182. doi: 10.18502/ijph.v51i5.9434.
2
Effect of Aerobic Exercise on Oxidative Stress and Inflammatory Response During Particulate Matter Exposure in Mouse Lungs.有氧运动对小鼠肺部暴露于颗粒物期间氧化应激和炎症反应的影响。
Front Physiol. 2022 Feb 3;12:773539. doi: 10.3389/fphys.2021.773539. eCollection 2021.
3
Inhale, exhale: Why particulate matter exposure in animal models are so acute? Data and facts behind the history.
吸气,呼气:为何动物模型中颗粒物暴露如此严重?历史背后的数据与事实。
Data Brief. 2019 Jul 8;25:104237. doi: 10.1016/j.dib.2019.104237. eCollection 2019 Aug.