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医学寄生虫和节肢动物诊断的最新技术

State-of-the-Art Techniques for Diagnosis of Medical Parasites and Arthropods.

作者信息

Ruenchit Pichet

机构信息

Department of Parasitology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.

出版信息

Diagnostics (Basel). 2021 Aug 26;11(9):1545. doi: 10.3390/diagnostics11091545.


DOI:10.3390/diagnostics11091545
PMID:34573887
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8470585/
Abstract

Conventional methods such as microscopy have been used to diagnose parasitic diseases and medical conditions related to arthropods for many years. Some techniques are considered gold standard methods. However, their limited sensitivity, specificity, and accuracy, and the need for costly reagents and high-skilled technicians are critical problems. New tools are therefore continually being developed to reduce pitfalls. Recently, three state-of-the-art techniques have emerged: DNA barcoding, geometric morphometrics, and artificial intelligence. Here, data related to the three approaches are reviewed. DNA barcoding involves an analysis of a barcode sequence. It was used to diagnose medical parasites and arthropods with 95.0% accuracy. However, this technique still requires costly reagents and equipment. Geometric morphometric analysis is the statistical analysis of the patterns of shape change of an anatomical structure. Its accuracy is approximately 94.0-100.0%, and unlike DNA barcoding, costly reagents and equipment are not required. Artificial intelligence technology involves the analysis of pictures using well-trained algorithms. It showed 98.8-99.0% precision. All three approaches use computer programs instead of human interpretation. They also have the potential to be high-throughput technologies since many samples can be analyzed at once. However, the limitation of using these techniques in real settings is species coverage.

摘要

多年来,诸如显微镜检查等传统方法一直被用于诊断寄生虫病以及与节肢动物相关的医学病症。一些技术被视为金标准方法。然而,它们有限的灵敏度、特异性和准确性,以及对昂贵试剂和高技能技术人员的需求都是关键问题。因此,人们不断开发新工具以减少缺陷。最近,出现了三种最先进的技术:DNA条形码技术、几何形态计量学和人工智能。在此,对与这三种方法相关的数据进行综述。DNA条形码技术涉及对条形码序列的分析。它用于诊断医学寄生虫和节肢动物,准确率为95.0%。然而,这项技术仍然需要昂贵的试剂和设备。几何形态计量分析是对解剖结构形状变化模式的统计分析。其准确率约为94.0 - 100.0%,与DNA条形码技术不同的是,它不需要昂贵的试剂和设备。人工智能技术涉及使用训练有素的算法对图片进行分析。它的精度为98.8 - 99.0%。所有这三种方法都使用计算机程序而非人工解读。它们还有成为高通量技术的潜力,因为可以一次性分析许多样本。然而,在实际应用中使用这些技术的局限性在于物种覆盖范围。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2908/8470585/32a7bed2caab/diagnostics-11-01545-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2908/8470585/dce831c0bf2e/diagnostics-11-01545-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2908/8470585/da0d17c8ac27/diagnostics-11-01545-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2908/8470585/50bdcc931db7/diagnostics-11-01545-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2908/8470585/32a7bed2caab/diagnostics-11-01545-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2908/8470585/dce831c0bf2e/diagnostics-11-01545-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2908/8470585/da0d17c8ac27/diagnostics-11-01545-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2908/8470585/50bdcc931db7/diagnostics-11-01545-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2908/8470585/32a7bed2caab/diagnostics-11-01545-g004.jpg

相似文献

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State-of-the-Art Techniques for Diagnosis of Medical Parasites and Arthropods.

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[3]
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[4]
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[5]
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[6]
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本文引用的文献

[1]
Mining Public Metagenomes for Environmental Surveillance of Parasites: A Proof of Principle.

Front Microbiol. 2021-6-30

[2]
Deep learning approaches for challenging species and gender identification of mosquito vectors.

Sci Rep. 2021-3-1

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Respir Med Case Rep. 2020-11-19

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Ecol Evol. 2020-8-18

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Comparison of Skin Scraping and Standard Superficial Skin Biopsy in the Laboratory Diagnosis of Scabies.

Turkiye Parazitol Derg. 2020-9-4

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Parasitol Res. 2020-8-15

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Clin Microbiol Infect. 2020-10

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Species Composition of Anopheles (Diptera: Culicidae) in Selected Forested Tourist Areas of Nigeria Endemic for Malaria.

J Med Entomol. 2020-11-13

[10]
Detection of Intestinal Protozoa in Trichrome-Stained Stool Specimens by Use of a Deep Convolutional Neural Network.

J Clin Microbiol. 2020-5-26

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