ANIMAL MODELS FOR THE EFFECT OF AGE ON SUSCEPTIBILITY TO INHALED PARTICULATE MATTER
- 1 January 2000
- journal article
- research article
- Published by Taylor & Francis in Inhalation Toxicology
- Vol. 12 (9) , 863-900
- https://doi.org/10.1080/08958370050123216
Abstract
Epidemiological findings of associations between ambient particulate matter (PM) and respiratory and cardiovascular mortality and morbidity have fostered increased laboratory research aimed at understanding the key PM components, mechanisms, and dose-response relationships responsible for the effects. Because the health impacts are largely observed in subpopulations having characteristics known or presumed to confer increased susceptibility to PM, there is a need for identifying, developing, and using animal models of these susceptibility factors. Age, during both development and senescence of the cardiorespiratory system and its defenses, is one of the PM susceptibility factors cited frequently. This review is intended as a summary of current knowledge regarding age-related differences in the structure and function of the respiratory and pulmonary vascular systems of humans and animals. Its purpose is to facilitate the selection of appropriate animal models for research on the various facets of potential age-related susceptibility of the human respiratory tract to the effects of inhaled PM. The selection of models is a difficult challenge because no single animal species adequately models the full range of human respiratory anatomy, physiology, and age-related changes. With careful selection among the many species, strains, and comparative ages, however, animals can be selected to model most, if not all, of the individual factors hypothesized to confer increased susceptibility of humans to inhaled PM. The existing information does not provide an adequate basis for selecting models to test all of the current age-related susceptibility hypotheses. However, the information summarized in this report should facilitate the investigator's review of potential models.Keywords
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