How much is there really? Why stereology is essential in lung morphometry

Weibel, Ewald R; Hsia, Connie C W; Ochs, Matthias (2007). How much is there really? Why stereology is essential in lung morphometry. Journal of applied physiology, 102(1), pp. 459-467. Bethesda, Md.: American Physiological Society 10.1152/japplphysiol.00808.2006

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Quantitative data on lung structure are essential to set up structure-function models for assessing the functional performance of the lung or to make statistically valid comparisons in experimental morphology, physiology, or pathology. The methods of choice for microscopy-based lung morphometry are those of stereology, the science of quantitative characterization of irregular three-dimensional objects on the basis of measurements made on two-dimensional sections. From a practical perspective, stereology is an assumption-free set of methods of unbiased sampling with geometric probes, based on a solid mathematical foundation. Here, we discuss the pitfalls of lung morphometry and present solutions, from specimen preparation to the sampling scheme in multiple stages, for obtaining unbiased estimates of morphometric parameters such as volumes, surfaces, lengths, and numbers. This is demonstrated on various examples. Stereological methods are accurate, efficient, simple, and transparent; the precision of the estimates depends on the size and distribution of the sample. For obtaining quantitative data on lung structure at all microscopic levels, state-of-the-art stereology is the gold standard.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Anatomy

UniBE Contributor:

Ochs, Matthias

Subjects:

600 Technology > 610 Medicine & health

ISSN:

8750-7587

ISBN:

16973815

Publisher:

American Physiological Society

Language:

English

Submitter:

Factscience Import

Date Deposited:

04 Oct 2013 14:46

Last Modified:

18 Mar 2014 10:14

Publisher DOI:

10.1152/japplphysiol.00808.2006

PubMed ID:

16973815

Web of Science ID:

000243600300063

URI:

https://boris.unibe.ch/id/eprint/18955 (FactScience: 1229)

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