近红外光谱和机器学习算法用于快速和非侵入性检测Trichuris
Tharanga N Kariyawasam1, Silvia Ciocchetta2, Paul Visendi3
1School of the Environment, Faculty of Science, The University of Queensland, Brisbane, Queensland, Australia.
PLoS neglected tropical diseases
|November 13, 2023
概括
近红外光谱学 (NIRS) 和人工神经网络 (ANN) 可以快速检测使用各种样本在小鼠中的鞭虫感染. 这种技术对在人类中诊断土壤传播的虫 (STH) 具有前景.
科学领域:
- 寄生虫学的寄生虫学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 土壤传播的虫 (STH),如Trichuris trichiura (鞭虫),影响全球数百万人.
- 廉价和快速的诊断工具对于STH检测至关重要.
- 这项研究探讨近红外光谱学 (NIRS) 用于STH诊断.
研究的目的:
- 为了评估NIRS与机器学习相结合,用于检测Trichuris muris.
- 评估NIRS在便,血液,血清样本和小鼠的非侵入性样本上的性能.
主要方法:
- 小鼠被感染T. muris在低剂量和高剂量,与对照组.
- 在感染后6周内,NIRS被用于扫描样本和小鼠.
- 人工神经网络 (ANN) 算法被开发和验证,以区分感染和未感染的小鼠.
主要成果:
- NIRS和ANN成功地将感染者与未感染的小鼠区分开来,早在感染后2周.
- 检测是有效的,无论样品类型 (便,血液,血清,非侵入性).
- 结果与血清学和寄生虫学发现相关.
结论:
- 这是第一个证明NIRS在诊断人类STH感染方面的潜力.
- NIRS提供了一个有前途的非侵入性诊断方法.
- 这种技术可以用于人类群体中大规模的STH监测.
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