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Quantitative microscopy in murine models of lung inflammation.

Abstract

OBJECTIVE

To develop a quantitative means to measure lung inflammation using the murine models of chronic asthma and cystic fibrosis (CF).

STUDY DESIGN

Translational-based medicine often utilizes animal models to study new and innovative therapeutics. In asthma and CF, the animal models focus on airway inflammation and remodeling. The asthma model is based on hypersensitivity-induced airway disease, whereas the CF model focuses on the inflammatory response to infection with Pseudomonas aeruginosa. Qualitative measures of inflammation and lung pathophysiology introduce significant variability and difficulty in interpreting interventional outcomes. The highly sensitive and reproducible quantitative computational program interfaced with Image Pro Microscopy to monitor changes in lung inflammation and lung pathophysiology. The software interfaces with image microscopy and automates the lung section review process.

RESULTS

Results from this program recapitulated data obtained by manual point counting of inflammation, bronchoalveolar lavage differential, and histology. The data show a low coefficient of variation and high reproducibility between slides and sections.

CONCLUSION

Utilization of this new microscopy program will enhance the quantitative means of establishing changes in lung structure and inflammation as a measure of therapeutic intervention with the ability of refining interpretation of in vivo models potentially short-circuiting translation into the clinical setting.

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  • Authors

    Soltzberg J, Frischmann S, van Heeckeren C, Brown N, Caplan A, Bonfield TL

    Source

    MeSH

    Animals
    Asthma
    Chronic Disease
    Cystic Fibrosis
    Disease Models, Animal
    Image Processing, Computer-Assisted
    Leukocytes
    Lung
    Mice
    Mice, Inbred BALB C
    Mice, Knockout
    Microscopy
    Pneumonia
    Severity of Illness Index
    Software

    Pub Type(s)

    Journal Article
    Research Support, N.I.H., Extramural
    Research Support, Non-U.S. Gov't

    Language

    eng

    PubMed ID

    22611751