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环境因素对利用可见-近红外光谱法检测绵羊粪便中血液作为捻转血矛线虫感染检测指标的影响。

Influence of environmental factors on the detection of blood in sheep faeces using visible-near-infrared spectroscopy as a measure of Haemonchus contortus infection.

机构信息

The Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4067, Australia.

Commonwealth Scientific and Industrial Research Organisation (CSIRO) Agriculture and Food, St. Lucia, QLD, 4067, Australia.

出版信息

Parasit Vectors. 2020 Nov 23;13(1):591. doi: 10.1186/s13071-020-04468-6.

DOI:10.1186/s13071-020-04468-6
PMID:33228768
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7684953/
Abstract

BACKGROUND

Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible-near-infrared (Vis-NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis-NIR spectroscopy in quantifying blood in faeces.

METHODS

Visible-NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387-609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores.

RESULTS

Principal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated 'healthy' SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57-94%, specificity 44-79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood.

CONCLUSION

This study demonstrates the potential of Vis-NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

摘要

背景

现有的寄生性胃肠道线虫,捻转血矛线虫的诊断方法既耗时又需要专业知识,限制了其在现场的应用。实用的农场诊断工具可以方便及时的治疗决策,从而防止生产和羊群福利的损失。我们之前证明了可见-近红外(Vis-NIR)光谱技术能够高精度地检测和定量绵羊粪便中的血液。在这里,我们报告了我们对绵羊类型和环境变化是否影响 Vis-NIR 光谱定量粪便中血液的预测准确性的研究。

方法

从澳大利亚南澳大利亚州(SA)和新南威尔士州不同环境和绵羊类型的无蠕虫绵羊粪便中采集 Vis-NIR 光谱,并添加不同浓度的绵羊血液。使用主成分分析(PCA)对光谱进行分析,并使用偏最小二乘回归(PLSR)围绕血红蛋白(Hb)波长区域(387-609nm)构建校准模型。使用模型预测来自 SA 的添加粪便和来自昆士兰州(QLD)的自然感染绵羊粪便中的 Hb 浓度。使用 Hemastix®测试条和 FAMACHA©诊断测试评分对 QLD 的样本进行定量。

结果

主成分分析表明,位置、绵羊种类和混合与个体样本是影响 Hb 预测的因素。该模型成功地将“健康”SA 样本与需要驱虫治疗的样本区分开来,具有中等至良好的预测准确性(灵敏度 57-94%,特异性 44-79%)。该模型对自然感染 QLD 样本中的血液没有预测性,这可能部分归因于样本之间粪便背景和血液化学的可变性,或者用于血液定量的验证方法的差异。然而,对 QLD 样本的 PCA 分析,识别出了含有高量和低量血液的样本之间的差异。

结论

本研究表明 Vis-NIR 光谱技术有潜力估计来自不同类型绵羊和环境背景的粪便中的血液浓度。然而,在这里开发的校准模型没有捕获足够的环境变化,无法准确预测从与校准模型不同的环境中收集的粪便中的 Hb。因此,有必要建立更能代表捻转血矛线虫流行地区的样本的模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a491/7684953/eee6ee8f7c28/13071_2020_4468_Fig5_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a491/7684953/f492e4970cde/13071_2020_4468_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a491/7684953/8a173f58bc50/13071_2020_4468_Fig2_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a491/7684953/eee6ee8f7c28/13071_2020_4468_Fig5_HTML.jpg

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