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Asthma research in mice: An overview of current models and their methodological variability

敏化 哮喘 卵清蛋白 医学 免疫学 动物模型 免疫系统 内科学
作者
Yan‐Jiao Chen,Cai-Tao Chen,Gabriel Shimizu Bassi,Yong‐Qing Yang
出处
期刊:International Reviews of Immunology [Taylor & Francis]
卷期号:44 (3): 127-140 被引量:7
标识
DOI:10.1080/08830185.2024.2431507
摘要

Studies in murine experimental models have made significant contributions to the understanding of asthma pathophysiology and the discovery of innovative therapeutic approaches. Nonetheless, there is a plethora of options available for selecting mouse strains, sensitization methods, challenge routes and doses, as well as approaches to evaluating host response in murine asthma model protocols. Due to the diversity of models employed, comparing results across different studies proves exceedingly challenging. The study conducted a search of pertinent PubMed articles from 2022 to April 15th, 2024. After relevant publications had been selected, the characteristics of each study were extracted, including animal strains, animal sex, sensitization methods, challenge methods, and reported outcome measures. The modeling parameters of Ovalbumin (OVA)-induced asthma model, and House Dust Mite-induced asthma model were analyzed. Additionally, we extracted data on the dose of OVA sensitization, alum administration, challenge OVA dose, and alum/sensitization OVA ratio from seven included studies. Subsequently, we conducted an analysis to determine the correlation between each of these factors and the lung resistance index (RI). This study presents an overview of the current mouse asthma models, offering valuable methodological guidance for researchers. Furthermore, this study highlights that certain parameters like sensitization dose, challenge dose, and so on, exert specific effects on the asthma lung resistance. However, there is a lack of standardized criteria and guidelines in this regard. The effects and underlying mechanisms of parameters on asthma responses remain unclear, necessitating further investigation into model parameters.
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