Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista, Rod. Prof. Paulo Donato Castellane, 14884-900 Jaboticabal, São Paulo, Brazil.
Embrapa Pecuária Sudeste, Rodovia Washington Luiz, Km 234, 13560-970 São Carlos, SP, Brazil.
Int J Parasitol. 2023 Feb;53(2):119-127. doi: 10.1016/j.ijpara.2022.12.004. Epub 2023 Jan 16.
Haemonchus contortus is the most prevalent and important gastrointestinal nematode (GIN) in small ruminants. Since it reduces the packed cell volume (PCV), causing anemia, early diagnosis can be used for targeted selective treatment (TST) of sheep, reducing antiparasitic drug use and anthelmintic resistance. This study aimed to predict PCV values through near-infrared spectroscopy (NIRS) and to develop a classification and diagnosis model of H. contortus infection using PCV values, eggs per gram of feces (EPG) counts and mean daily weight gain (DWG). A total of 1728 spectra were collected from blood samples of 216 lambs with a portable NIR spectroscope. In parallel, other parameters indicative of infection were measured: PCV by hematocrit, FAffa MAlan CHArt (FAMACHA) scores, EPG and DWG. To evaluate the relationship between NIRS spectra and the evaluated parameters, principal component analysis (PCA) was used for an exploratory analysis, regression by the partial least squares method (PLS) for the prediction of PCV values via NIRS, and PCA linear discriminant analysis (PCA-LDA) as a classification model for diagnosis. The absorption peaks in the NIRS region associated with the excitation of overtones of nitrogen-hydrogen (N-H) functional groups of proteins had a strong impact on the principal components (PCs), indicating that blood proteins, especially hemoglobin, can be estimated by the NIRS technique. The model for predicting PCV by PLS presented a standard error of prediction of 2.53, root-mean-square error of 2.48, and coefficient of determination of 0.84, indicating good correlation between the PCV values predicted by the model and the PCV obtained by hematocrit. The PCA-LDA model presented 93.33% sensitivity and 82.18% accuracy, both higher than those of the FAMACHA method, as was expected for resilient Morada Nova lambs. The multivariate models associated with the NIRS technique reported here can be used in the future as a quick and versatile tool for H. contortus infection diagnosis and TST application in lambs.
捻转血矛线虫是反刍动物中最普遍和最重要的胃肠道线虫（GIN）。由于它降低了红细胞压积（PCV），导致贫血，早期诊断可用于绵羊的靶向选择性治疗（TST），减少抗寄生虫药物的使用和驱虫剂耐药性。本研究旨在通过近红外光谱（NIRS）预测 PCV 值，并利用 PCV 值、粪便每克虫卵数（EPG）和平均日增重（DWG）开发捻转血矛线虫感染的分类和诊断模型。使用便携式近红外光谱仪从 216 只羔羊的血液样本中采集了 1728 个光谱。同时，还测量了其他感染指标：红细胞压积、FAMACHA 评分、EPG 和 DWG。为了评估 NIRS 光谱与评估参数之间的关系，使用主成分分析（PCA）进行了探索性分析，使用偏最小二乘法（PLS）进行了 NIRS 预测 PCV 值的回归，以及 PCA 线性判别分析（PCA-LDA）作为诊断的分类模型。NIRS 区域中的吸收峰与蛋白质的氮-氢键（N-H）基团的泛音激发有关，对主成分（PC）有很大影响，这表明血液蛋白，特别是血红蛋白，可以通过 NIRS 技术来估计。PLS 预测 PCV 的模型的预测标准误差为 2.53，均方根误差为 2.48，决定系数为 0.84，表明模型预测的 PCV 值与红细胞压积获得的 PCV 值之间具有良好的相关性。PCA-LDA 模型的灵敏度为 93.33%，准确率为 82.18%，均高于 FAMACHA 方法，这与 Morada Nova 羔羊的弹性预期相符。与 NIRS 技术相关的多元模型将来可以作为一种快速、通用的捻转血矛线虫感染诊断工具，并在羔羊中应用 TST。
