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开发用于测量新冠病毒抗体水平周期性变化的新型光谱学和机器学习方法。

Development of novel spectroscopic and machine learning methods for the measurement of periodic changes in COVID-19 antibody level.

作者信息

Guleken Zozan, Tuyji Tok Yeşim, Jakubczyk Paweł, Paja Wiesław, Pancerz Krzysztof, Shpotyuk Yaroslav, Cebulski Jozef, Depciuch Joanna

机构信息

Uskudar University, Faculty of Medicine, Department of Physiology, Turkey.

Department of Medical Microbiology, Cerrahpaşa Medical Faculty, İstanbul University-Cerrahpaşa, Turkey.

出版信息

Measurement (Lond). 2022 Jun 15;196:111258. doi: 10.1016/j.measurement.2022.111258. Epub 2022 Apr 26.

DOI:10.1016/j.measurement.2022.111258
PMID:35493849
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9040476/
Abstract

In this research, blood samples of 47 patients infected by COVID were analyzed. The samples were taken on the 1st, 3rd and 6th month after the detection of COVID infection. Total antibody levels were measured against the SARS-CoV-2 N antigen and surrogate virus neutralization by serological methods. To differentiate COVID patients with different antibody levels, Fourier Transform InfraRed (FTIR) and Raman spectroscopy methods were used. The spectroscopy data were analyzed by multivariate analysis, machine learning and neural network methods. It was shown, that analysis of serum using the above-mentioned spectroscopy methods allows to differentiate antibody levels between 1 and 6 months via spectral biomarkers of amides II and I. Moreover, multivariate analysis showed, that using Raman spectroscopy in the range between 1317 cm and 1432 cm, 2840 cm and 2956 cm it is possible to distinguish patients after 1, 3, and 6 months from COVID with a sensitivity close to 100%.

摘要

在这项研究中，对47名感染新冠病毒的患者的血样进行了分析。样本在检测到新冠病毒感染后的第1个月、第3个月和第6个月采集。通过血清学方法测量了针对严重急性呼吸综合征冠状病毒2（SARS-CoV-2）N抗原的总抗体水平和替代病毒中和情况。为了区分抗体水平不同的新冠患者，使用了傅里叶变换红外（FTIR）和拉曼光谱法。通过多变量分析、机器学习和神经网络方法对光谱数据进行了分析。结果表明，使用上述光谱法分析血清能够通过酰胺II和I的光谱生物标志物区分1至6个月的抗体水平。此外，多变量分析表明，在1317厘米至1432厘米、2840厘米至2956厘米范围内使用拉曼光谱法，可以以接近100%的灵敏度区分感染新冠病毒1个月、3个月和6个月后的患者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/05d8037f0781/gr11_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/86f08ef88449/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/9e798557cd36/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/9fc0caa43cf0/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/b56de4363a65/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/ce868d453ea4/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/eb6e930291c7/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/d6b756e5622d/gr7_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/7fbe71a691e0/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/6c993fec7bbd/gr9_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/4fbf228058bb/gr10_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/05d8037f0781/gr11_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/86f08ef88449/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/9e798557cd36/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/9fc0caa43cf0/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/b56de4363a65/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/ce868d453ea4/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/eb6e930291c7/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/d6b756e5622d/gr7_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/7fbe71a691e0/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/6c993fec7bbd/gr9_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/4fbf228058bb/gr10_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db94/9040476/05d8037f0781/gr11_lrg.jpg

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