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颗粒物诱导的肺部疾病状况对微流控肺芯片的生物学影响。

Biological Influence of Pulmonary Disease Conditions Induced by Particulate Matter on Microfluidic Lung Chips.

作者信息

Jabbar Faiza, Kim Young-Su, Lee Sang Ho

机构信息

Advanced Micro Mechatronics Lab, School of Engineering, Jeju National University, Jeju, Republic of Korea.

BioSpero, Inc., Jeju, Republic of Korea.

出版信息

Biochip J. 2022;16(3):305-316. doi: 10.1007/s13206-022-00068-x. Epub 2022 Jul 8.

Abstract

UNLABELLED

Particulate matter (PM10)-induced respiratory illnesses are difficult to investigate in trans-well culture systems. Microphysiological systems offer the capacity to mimic these phenomena to analyze any possible hazards that PM10 exposure poses to respiratory system of Humans. This study proposes an on-chip healthy human lung distal airway model that efficiently reconstitutes in vivo-like environmental conditions in a microfluidic device. The lung-on-chip model comprises a TEER sensor chip and portable microscope for continuous monitoring. To determine the efficacy of our model, we assessed the response to exposure to three PM environmental conditions (mild, average, and severe) and analyzed the relevant in vivo physiological and toxicological data using the airway model. Our results revealed significant increases in the levels of the IL-13, IL-6, and MUC5AC pathological biomarkers, which indicate increased incidences of on-chip asthma and chronic obstructive pulmonary disease conditions. Overall, we deduced that this model will facilitate the identification of potential therapeutics and the prevention of chronic life-threatening toxicities and pandemics such as COVID-19. The proposed system provides basic data for producing an improved in organ-on-chip technology.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13206-022-00068-x.

摘要

未标注

在跨膜培养系统中很难研究颗粒物(PM10)诱发的呼吸道疾病。微生理系统能够模拟这些现象,以分析PM10暴露对人类呼吸系统可能造成的任何危害。本研究提出了一种芯片上的健康人肺远端气道模型,该模型能在微流控装置中高效重建类似体内的环境条件。芯片肺模型包括一个跨上皮电阻(TEER)传感器芯片和用于连续监测的便携式显微镜。为了确定我们模型的有效性,我们评估了对三种PM环境条件(轻度、中度和重度)暴露的反应,并使用气道模型分析了相关的体内生理和毒理学数据。我们的结果显示,白细胞介素-13(IL-13)、白细胞介素-6(IL-6)和粘蛋白5AC(MUC5AC)等病理生物标志物水平显著升高,这表明芯片上哮喘和慢性阻塞性肺疾病的发病率增加。总体而言,我们推断该模型将有助于识别潜在的治疗方法,并预防慢性危及生命的毒性反应和大流行疾病,如2019冠状病毒病(COVID-19)。所提出的系统为改进芯片器官技术提供了基础数据。

补充信息

在线版本包含可在10.1007/s13206-022-00068-x获取的补充材料。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85c5/9263805/1b78fac3ede8/13206_2022_68_Fig1_HTML.jpg

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