Development of an unbiased methodology to quantify pathological changes in the respiratory tract

Patton, Veronica Ann (2021) Development of an unbiased methodology to quantify pathological changes in the respiratory tract. MVM(R) thesis, University of Glasgow.

Full text available as:
Download (1MB) | Preview


Histopathological analysis applies to a variety of situations (diagnostics, sudden deaths, controversial cases, infectious diseases, intoxications amongst others) and, if achieving a diagnosis is considered the ultimate aim in most of the cases, when it comes to more exquisitely experimental fields, the simple assessment of presence/absence of alterations is considered not sufficient.
For this reason, the need of a rigorous evaluation of the samples had initially pushed to the development of scoring systems (aka ‘grading’) which however remain only partially beneficial and produce data of exclusively semi-quantitative nature. The advent of several image analysis software programs has overcome this limitation and opened the way to convert images into proper measurements which in turn are suitable to further statistical analysis.

In my thesis, I wanted to develop a consistent method to quantify pathological changes. For this purpose, I decided to test ImageJ on a thoroughly characterised and largely used ex vivo organ culture (EVOC) system represented by horse tracheal explants infected with equine influenza virus (EIV). Whether the traditional histopathological scoring should be regarded as a valid complementary tool, ImageJ allowed to consistently measure different stainings (pixel quantification) and nuclear parameters (particles count; nuclear area) and to avoid the need of a second operator to validate the analysis.

In conclusion, quantification of pathological changes by means of image analysis software not only is feasible, but also provides with unbiased data and highlights even the more subtle changes that would otherwise be unapparent by eye via traditional scoring approach.

Item Type: Thesis (MVM(R))
Qualification Level: Masters
Subjects: R Medicine > RB Pathology
S Agriculture > SF Animal culture > SF600 Veterinary Medicine
Colleges/Schools: College of Medical Veterinary and Life Sciences > School of Veterinary Medicine
Supervisor's Name: Rupp, Dr. Angie and Murica, Professor Pablo
Date of Award: 2021
Depositing User: Theses Team
Unique ID: glathesis:2021-82386
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 18 Aug 2021 08:19
Last Modified: 18 Aug 2021 08:24
Thesis DOI: 10.5525/gla.thesis.82386

Actions (login required)

View Item View Item


Downloads per month over past year