Ganglion Cytology: A Novel Rapid Method for the Diagnosis of Equine Dysautonomia

Author:

Piccinelli Chiara1ORCID,Jago Rachel2,Milne Elspeth1ORCID

Affiliation:

1. Department of Veterinary Pathology, Royal (Dick) School of Veterinary Studies and the Roslin Institute, University of Edinburgh, Roslin, Midlothian, UK

2. Equine Veterinary Services, Royal (Dick) School of Veterinary Studies and the Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian, UK

Abstract

Equine dysautonomia (grass sickness) is characterized by autonomic neuronal degeneration and is often fatal. As outbreaks occur, rapid diagnosis is essential but confirmation currently requires histological examination. This study evaluated diagnostic accuracy of cytological examination of cranial cervical ganglion (CCG) scrapings for dysautonomia diagnosis. CCG smears from 20 controls and 16 dysautonomia cases were stained with May-Grünwald Giemsa (MGG), hematoxylin and eosin (HE), and cresyl fast violet (CFV), with HE-stained histological sections of CCG as gold standard for diagnosis. Examining all 3 stains together, the sensitivity and specificity were 100%. Occasional individual smears (4/107, 3.7%) were nondiagnostic due to low cellularity, and in a few individual smears the final diagnosis was correct but more tentative (CFV: 5/33 [15.1%], HE: 2/34 [5.9%], and MGG: 4/36 [11.1%]), due to low cellularity or suboptimal cell morphology. CCG cytology was considered reliable for rapid postmortem diagnosis of equine dysautonomia, particularly using MGG.

Funder

Equine Grass Sickness Fund

Publisher

SAGE Publications

Subject

General Veterinary

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3