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Identifying The Key Drivers Of The Lung Response To Inhaled Geogenic Dusts


Zosky, GR and Wong, RS and Smirk, MN and Perks, K and Iosifidis, T and Ditcham, W and Devadason, SG and Siah, WS and Devine, B and Maley, F and Cook, A, Identifying The Key Drivers Of The Lung Response To Inhaled Geogenic Dusts, Respirology, 23-27 March, 2013, Darwin, Australia, pp. 21. ISSN 1323-7799 (2013) [Conference Extract]

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DOI: doi:10.1111/resp.12045


Aim: To determine the key characteristics of inhaled geogenic PM10 (<10 μm diameter particulate matter) that have the greatest impact on the lung.

Methods: The PM10 fraction was extracted from surface soil samples from 4 communities across Western Australia. BALB/c mice were intranasally exposed to 100 μg of PM10. Control mice received 100 μg of polystyrene beads (2.5 μm) or vehicle alone. Mice were assessed for infl ammation (cellular infl ux, MIP-2, IL-6 and IL-1β), lung volume (plethysmography) and lung mechanics (forced oscillation technique) 6, 24 or 168 hours post exposure. The physical and chemical characteristics of the particles were assessed by cascade impactor and ICP-MS/OES, respectively. Principal component analyses of the outcome measures were used to construct lung impairment scores. Multivariate linear regression models were then used to identify the characteristics of the particles driving the lung responses.

Results: Exposure to geogenic particles caused an acute infl ammatory response (6 hours), an acute impairment in lung mechanics (24 hours) and a long term deficit in lung volume. Both the infl ammatory response and long term deficits in lung volume were associated with the concentration of Fe and variability in particle size (GSD) while the impairment in lung mechanics was associated with Fe and particle size (MMAD).

Conclusions: Despite the complex physico-chemical characteristics of geogenic dusts we were able to identify the concentration of Fe and physical dimensions of the particles as the key drivers of lung responses. Using these data we may be able to predict which communities are at greatest risk of adverse respiratory health due to high particle loads.

Item Details

Item Type:Conference Extract
Keywords:lung disease
Research Division:Biomedical and Clinical Sciences
Research Group:Cardiovascular medicine and haematology
Research Field:Respiratory diseases
Objective Division:Health
Objective Group:Clinical health
Objective Field:Clinical health not elsewhere classified
UTAS Author:Zosky, GR (Professor Graeme Zosky)
ID Code:97224
Year Published:2013
Deposited By:Medicine
Deposited On:2014-12-08
Last Modified:2022-06-29

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