High molecular weight hyaluronan attenuates fine particulate matter-induced acute lung injury through inhibition of ROS-ASK1-p38/JNK-mediated epithelial apoptosis

Abstract Background Exposure to fine particulate matter (PM2.5) severely impairs public health. The mechanism of PM2.5-induced lung injury is complex and diverse. Modified Guo-Min Decoction (MGMD) and Yu-Ping-Feng Powder (YPFP) have been found to improve clinical symptoms in respiratory patients during smog weather, but the mechanism remains unclear. This study aimed to investigate the effect and mechanism of YPFP and MGMD against PM2.5-induced lung injury. Methods We established the PM2.5 animal model by intratracheal instilling of PM2.5 suspensions. Rats were administrated MGMD/YPFP/distilled water via gavage every day, and all rats were sacrificed after 28 days. At the end of experiment, BALF and lung tissues were collected. Condition of lung injury, inflammatory cells infiltration, inflammatory cytokines, MUC5AC synthesis and release, and phosphorylation of TLR2-MyD88-NFκB and EGFR-PI3K-AKT signalling pathway were evaluated. Results The results demonstrated that both MGMD and YPFP protected rats from PM2.5-induced damaged structure of lung tissues. The infiltration of neutrophil, monocyte, lymphocyte, and eosinophil was reduced after the treatment of two therapies. The production of pro-inflammatory mediators, MCP-1 and NE, as well as the type2 inflammation-related cytokines, IgE and IL-4, were decreased by MGMD and YPFP. However, the MGMD showed more potent effect on inhibiting IL-4, while YPFP benefited in preventing ICMA-1, IL-1β, and IL-17A. Rare significance was detected in the TLR2-MyD88-NFκB of each group. Treatment with MGMD and YPFP decreased goblet cell hyperplasia and the expressions of MUC5AC. The further investigation demonstrated that YPFP had the effect of simultaneously inhibiting the phosphorylation of PI3K and AKT, whereas MGMD only showed a significant difference in AKT. Conclusions Therefore, both MGMD and YPFP could significantly attenuate PM2.5-induced inflammation of lung and airway mucus hypersecretion. Nevertheless, YPFP had more advantage in preventing type1 inflammation and mucus hypersecretion, while MGMD was more beneficial in reducing type2 inflammation.

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Estimating mean molecular weight, carbon number, and OM∕OC with mid-infrared spectroscopy in organic particulate matter samples from a monitoring network

Atmospheric Measurement Techniques ◽

10.5194/amt-14-4805-2021 ◽

2021 ◽

Vol 14 (7) ◽

pp. 4805-4827

Author(s):

Amir Yazdani ◽

Ann M. Dillner ◽

Satoshi Takahama

Keyword(s):

Molecular Weight ◽

Infrared Spectroscopy ◽

Particulate Matter ◽

Statistical Models ◽

Fine Particulate Matter ◽

Carbon Number ◽

Multivariate Statistical ◽

Mid Infrared ◽

Fine Particulate ◽

Absorbance Profile

Abstract. Organic matter (OM) is a major constituent of fine particulate matter, which contributes significantly to degradation of visibility and radiative forcing, and causes adverse health effects. However, due to its sheer compositional complexity, OM is difficult to characterize in its entirety. Mid-infrared spectroscopy has previously proven useful in the study of OM by providing extensive information about functional group composition with high mass recovery. Herein, we introduce a new method for obtaining additional characteristics such as mean carbon number and molecular weight of these complex organic mixtures using the aliphatic C−H absorbance profile in the mid-infrared spectrum. We apply this technique to spectra acquired non-destructively from Teflon filters used for fine particulate matter quantification at selected sites of the Inter-agency Monitoring of PROtected Visual Environments (IMPROVE) network. Since carbon number and molecular weight are important characteristics used by recent conceptual models to describe evolution in OM composition, this technique can provide semi-quantitative, observational constraints of these variables at the scale of the network. For this task, multivariate statistical models are trained on calibration spectra prepared from atmospherically relevant laboratory standards and are applied to ambient samples. Then, the physical basis linking the absorbance profile of this relatively narrow region in the mid-infrared spectrum to the molecular structure is investigated using a classification approach. The multivariate statistical models predict mean carbon number and molecular weight that are consistent with previous values of organic-mass-to-organic-carbon (OM/OC) ratios estimated for the network using different approaches. The results are also consistent with temporal and spatial variations in these quantities associated with aging processes and different source classes (anthropogenic, biogenic, and burning sources). For instance, the statistical models estimate higher mean carbon number for urban samples and smaller, more fragmented molecules for samples in which substantial aging is anticipated.

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Cold stress provokes lung injury in rats co-exposed to fine particulate matter and lipopolysaccharide

Ecotoxicology and Environmental Safety ◽

10.1016/j.ecoenv.2018.10.064 ◽

2019 ◽

Vol 168 ◽

pp. 9-16 ◽

Cited By ~ 2

Author(s):

Bin Luo ◽

Hongxia Shi ◽

Kai Zhang ◽

Qiaozhen Wei ◽

Jingping Niu ◽

...

Keyword(s):

Particulate Matter ◽

Lung Injury ◽

Cold Stress ◽

Fine Particulate Matter ◽

Fine Particulate

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Estimating mean molecular weight, carbon number, and OM/OC with mid-infrared spectroscopy in organic particulate matter samples from a monitoring network

10.5194/amt-2020-79 ◽

2020 ◽

Cited By ~ 1

Author(s):

Amir Yazdani ◽

Ann M. Dillner ◽

Satoshi Takahama

Keyword(s):

Molecular Weight ◽

Infrared Spectroscopy ◽

Particulate Matter ◽

Statistical Models ◽

Fine Particulate Matter ◽

Carbon Number ◽

Multivariate Statistical ◽

Mid Infrared ◽

Fine Particulate ◽

Absorbance Profile

Abstract. Organic matter (OM) is a major constituent of fine particulate matter which contributes significantly to degradation of visibility, radiative forcing, and causes adverse health effects. However, due to its sheer compositional complexity, OM is difficult to characterize in its entirety. Mid-infrared spectroscopy has previously proven useful in the study of OM by providing extensive information about functional group composition with high mass recovery. Herein, we introduce a new method for obtaining additional characteristics such as mean carbon number and molecular weight of these complex organic mixtures using the aliphatic C–H absorbance profile in mid-infrared spectrum. We apply this technique to spectra acquired non-destructively from Teflon filters used for fine particulate matter quantification at selected sites of Inter-agency Monitoring of PROtected Visual Environments (IMPROVE) network. Since carbon number and molecular weight are important characteristics used by recent models to describe evolution in OM composition, this technique can provide semi-quantitative, observational constraints of these variables at the scale of the network. For this task, multivariate statistical models are trained on calibration spectra prepared from atmospherically relevant laboratory standards and are applied to ambient samples. Then, the physical basis linking the absorbance profile of this relatively narrow region in the mid-infrared spectrum to the molecular structure is investigated using a classification approach. The multivariate statistical models predict mean carbon number and molecular weight that are consistent with previous values of organic-mass-to-organic-carbon (OM/OC) ratios estimated for the network using different approaches. The results are also consistent with temporal and spatial variations in these quantities associated with aging processes, and different source classes (anthropogenic, biogenic, and burning sources). For instance, the models estimate higher mean carbon number for urban samples and smaller, more fragmented molecules for samples in which substantial aging is anticipated.

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