Histopathological and immunological study of rats liver hydatid cysts isolated from human, sheep, goat and cows

Alsaadawi, Mohenned A.; Al-Safar, Ali Husein Ali; Khudhur, Hasan Raheem; Mazeel, Saif; Hussein, Hussein muhhe; Allawi, Aqil Hussein; Ali, Mansour Jadaan

doi:10.1007/s12639-022-01512-3

Cited by 1 publication

(1 citation statement)

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“…CE in sheep is present in most pastoral and grazing areas of the world and is endemic in South America, Northern Africa, Siberia, Southern and Eastern Europe, Central Asia, and Western China [6]. The CE in sheep causes severe economic losses in livestock industry [7]. In addition, reduction of the infection of CE in sheep is beneficial for the prevention and control of CE by reducing the transmission risk of E. granulosus s.s [8,9].…”

Section: Introductionmentioning

confidence: 99%

Rapid and accurate screening of cystic echinococcosis in sheep based on serum Fourier‐transform infrared spectroscopy combined with machine learning algorithms

Dawuti

Dou

Zheng

et al. 2023

Journal of Biophotonics

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Cystic echinococcosis (CE) in sheep is a serious zoonotic parasitic disease caused by Echinococcus granulosus sensu stricto (s.s.). Presently, the screening technology for CE in sheep is time-consuming and inaccurate, and novel screening technology is urgently needed. In this work, we combined machine-learning algorithms with Fourier transform infrared (FT-IR) spectroscopy of serum to establish a quick and accurate screening approach for CE in sheep. Serum samples from 77 E. granulosus s.s.-infected sheep to 121 healthy control sheep were measured by FT-IR spectrometer. To optimize the classification accuracy of the serum FI-TR method for the E. granulosus s.s.-infected sheep and healthy control sheep, principal component analysis (PCA), linear discriminant analysis, and support vector machine (SVM) algorithms were used to analyze the data.Among all the bands, 1500-1700 cm À1 band has the best classification effect; its diagnostic sensitivity, specificity, and accuracy of PCA-SVM were 100%, 95.74%, and 96.66%, respectively. The study showed that serum FT-IR spectroscopy combined with machine learning algorithms has great potential for rapid and accurate screening methods for the CE in sheep.

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Section: Introductionmentioning

confidence: 99%