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利用便携式可持续生物光子学平台和人工智能算法检测基孔肯雅热病毒感染的唾液样本。

Salivary detection of Chikungunya virus infection using a portable and sustainable biophotonic platform coupled with artificial intelligence algorithms.

机构信息

Innovation Center in Salivary Diagnostic and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, ARFIS, Av. Pará, 1720, Campus Umuarama, Uberlândia, Minas Gerais, CEP 38400-902, Brazil.

Rodents Animal Facilities Complex, Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil.

出版信息

Sci Rep. 2024 Sep 16;14(1):21546. doi: 10.1038/s41598-024-71889-z.

DOI:10.1038/s41598-024-71889-z
PMID:39278957
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11402986/
Abstract

The current detection method for Chikungunya Virus (CHIKV) involves an invasive and costly molecular biology procedure as the gold standard diagnostic method. Consequently, the search for a non-invasive, more cost-effective, reagent-free, and sustainable method for the detection of CHIKV infection is imperative for public health. The portable Fourier-transform infrared coupled with Attenuated Total Reflection (ATR-FTIR) platform was applied to discriminate systemic diseases using saliva, however, the salivary diagnostic application in viral diseases is less explored. The study aimed to identify unique vibrational modes of salivary infrared profiles to detect CHIKV infection using chemometrics and artificial intelligence algorithms. Thus, we intradermally challenged interferon-gamma gene knockout C57/BL6 mice with CHIKV (20 µl, 1 X 10 PFU/ml, n = 6) or vehicle (20 µl, n = 7). Saliva and serum samples were collected on day 3 (due to the peak of viremia). CHIKV infection was confirmed by Real-time PCR in the serum of CHIKV-infected mice. The best pattern classification showed a sensitivity of 83%, specificity of 86%, and accuracy of 85% using support vector machine (SVM) algorithms. Our results suggest that the salivary ATR-FTIR platform can discriminate CHIKV infection with the potential to be applied as a non-invasive, sustainable, and cost-effective detection tool for this emerging disease.

摘要

目前,基孔肯雅热病毒(CHIKV)的检测方法涉及侵入性和昂贵的分子生物学程序,作为金标准诊断方法。因此,迫切需要寻找一种非侵入性、更具成本效益、无试剂和可持续的方法来检测 CHIKV 感染,以维护公共卫生。便携式傅里叶变换红外光谱仪与衰减全反射(ATR-FTIR)平台结合,可用于通过唾液来区分系统性疾病,然而,唾液在病毒疾病中的诊断应用研究较少。本研究旨在使用化学计量学和人工智能算法,确定唾液红外光谱特征的独特振动模式,以检测 CHIKV 感染。因此,我们用 CHIKV(20 µl,1 X 10 PFU/ml,n = 6)或载体(20 µl,n = 7)经皮挑战干扰素-γ基因敲除 C57/BL6 小鼠。在第 3 天(由于病毒血症峰值)收集唾液和血清样本。通过实时 PCR 在感染 CHIKV 的小鼠血清中确认 CHIKV 感染。使用支持向量机(SVM)算法的最佳模式分类显示出 83%的灵敏度、86%的特异性和 85%的准确性。我们的结果表明,唾液 ATR-FTIR 平台可以区分 CHIKV 感染,有可能作为一种非侵入性、可持续和具有成本效益的新兴疾病检测工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a98/11402986/5151fe6d4db5/41598_2024_71889_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a98/11402986/5151fe6d4db5/41598_2024_71889_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a98/11402986/5151fe6d4db5/41598_2024_71889_Fig1_HTML.jpg

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