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

被引:1
|
作者
Dawuti, Wubulitalifu [1 ,2 ]
Dou, Jingrui [1 ,2 ]
Zheng, Xiangxiang [3 ]
Lu, Xiaoyi [4 ]
Zhao, Hui [5 ]
Yang, Lingfei [6 ]
Lin, Renyong [2 ]
Lu, Guodong [1 ,2 ]
机构
[1] Xinjiang Med Univ, Sch Publ Hlth, Urumqi, Peoples R China
[2] First Affiliated Hosp Xinjiang Med Univ, Clin Med Res Inst, State Key Lab Pathogenesis Prevent & Treatment Ce, 137 Liyushan South Rd, Urumqi 830054, Peoples R China
[3] Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing, Peoples R China
[4] Xinjiang Univ, Coll Software, Urumqi, Peoples R China
[5] First Affiliated Hosp Xinjiang Med Univ, Dept Clin Lab, Urumqi, Peoples R China
[6] First Affiliated Hosp Xinjiang Med Univ, Dept Abdominal Ultrasound Diag, Urumqi, Peoples R China
关键词
cystic echinococcosis in sheep; Fourier transform infrared spectra; machine learning algorithms; screening; serum; FT-IR SPECTROSCOPY; OXIDATIVE STRESS; BREAST-CANCER; DIAGNOSIS; ULTRASOUND; BURDEN;
D O I
10.1002/jbio.202200320
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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(-1) 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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页数:9
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