toxicity assessment studies with various experimental models and exposure modalities frequently generate diverse outcomes. In the prevalent experimental, aerosol pollutants are dissolved in culture medium through capture for exposure to two-dimensional planar cellular models in multiwell plates via immersion. However, this approach can generate restricted and inconclusive experimental data, significantly constraining the applicability of risk assessment outcomes. Herein, the cocultivation of lung epithelial and/or vascular endothelial cells was performed using self-designed bionic-lung microfluidic chip housing a gas-concentration gradient generator (GCGG) unit. Exposure experiments involving a concentration gradient of cigarette smoke (CS) aerosol were then conducted through an original assembled real-time aerosol exposure system. Transcriptomic analysis revealed a potential involvement of the cGMP-signaling pathway following online CS aerosol exposure on different cell culture models. Furthermore, distinct responses to different concentrations of CS aerosol exposure on different culture models were highlighted by detecting inflammation- and oxidative stress-related biomarkers (i.e., cell viability, reactive oxygen species, nitric oxide, IL-6, IL-8, TNF-α, GM-CSF, malondialdehyde, and superoxide dismutase). The results underscore the importance of improving chip biomimicry while addressing multi-throughput demands, given the substantial influence of the coculture model on cellular responses triggered by CS. Furthermore, the coculture model exhibited a mutually beneficial protective effect on cells at low CS concentrations within the GCGG unit, yet revealed a mutually amplified damaging effect at higher CS concentrations in contrast to the monoculture model.

中文翻译：

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基于仿生肺微流控芯片的细胞共培养和单一培养对香烟烟雾暴露的反应分析

使用各种实验模型和暴露方式的毒性评估研究经常产生不同的结果。在普遍的实验中，气溶胶污染物通过捕获溶解在培养基中，通过浸没暴露于多孔板中的二维平面细胞模型。然而，这种方法可能会产生有限且不确定的实验数据，严重限制风险评估结果的适用性。在此，使用自行设计的装有气体浓度梯度发生器（GCGG）单元的仿生肺微流控芯片进行肺上皮和/或血管内皮细胞的共培养。然后通过原始组装的实时气溶胶暴露系统进行涉及香烟烟雾（CS）气溶胶浓度梯度的暴露实验。转录组分析揭示了不同细胞培养模型在线 CS 气溶胶暴露后 cGMP 信号通路的潜在参与。此外，通过检测炎症和氧化应激相关生物标志物（即细胞活力、活性氧、一氧化氮、IL-6、IL-8、TNF- α、GM-CSF、丙二醛和超氧化物歧化酶）。鉴于共培养模型对 CS 触发的细胞反应的重大影响，结果强调了改进芯片仿生学同时解决多通量需求的重要性。此外，共培养模型在 GCGG 单元内的低 CS 浓度下对细胞表现出互利的保护作用，但与单一培养模型相比，在较高 CS 浓度下显示出相互放大的损害作用。