Number of Ganglion Cells in Human Colon Wall via Two Different Methods of Full-Thickness Sections and Fragmented Tissues

Document Type : Original Article (s)

Authors

1 Associate Professor, Department of Pathology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

2 Student of Medicine, School of Medicine AND Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract

Background: The most important way to diagnose Hirschsprung's disease is biopsy of rectum and its hystopathologic study by several slices (to 50 slices); detection of the disease depends on ganglion cells. Recently, there are two methods for detection of these cells; full-thickness sections and fragmented tissues.  This study aimed to compare these to methods in detecting ganglion cells; which was not done before.Methods: In this cross-sectional study done in Alzahra hospital (Isfahan, Iran) during 2011-2012, 25 samples of colon wall in patients with detected carcinoma were studied. A 1-cm slice from each sample was fixed in formalin. Another 1-cm slice from each sample was crushed and fixed on filter paper in formalin. The samples were prepared by tissue processor machine and colored by Hematoxylin and Eosin method. Both groups were coded by second person and analyzed by a unique pathologist. The number of ganglion cells was determined and recorded.Findings: The mean ± SD of counted ganglion cells was 33.96 ± 11.80 in full-thickness sections and 25.64 ± 16.90 in fragmented tissues methods. According to independent sample t-test, the difference between two methods was statistically meaningful (P = 0.049).Conclusion: According to current study, fragmented tissues method is better than the full-thickness sections method in detection of ganglion cells for diagnosis of Hirschsprung's disease.

Keywords

References

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Journal of Isfahan Medical School
Volume 31, Issue 254 - Serial Number 254
July and August 2013
Pages 1522-1528

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History

  • Receive Date: 27 May 2013
  • Revise Date: 06 May 2024
  • Accept Date: 06 April 2022

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