☆ 4.7 Article

Identification and assessment of pulmonary Cryptococcus neoformans infection by blood serum surface-enhanced Raman spectroscopy

SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY (2021)

Journal

SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY

Volume 260, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD

DOI: 10.1016/j.saa.2021.119978

Keywords

Blood serum; Pulmonary cryptococcal infection; PLS-LDA; Surface-enhanced Raman spectroscopy

Funding

National Natural Science Foundation of China [61605025, 31870140]
Program for the Introduction of High-End Foreign Experts [GXL20200218001]
Fundamental Research Funds for the Central Universities [N2119002, N142005001, N172002001]
Natural Science Foundation of Liaoning Province [20180550787]
K.C. Wong Magna Fund in Ningbo University, and Liaoning Revitalization Talents Program [XLYC1807001]

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Abstract

In this study, blood serum surface-enhanced Raman spectroscopy (SERS) method was investigated for the identification and assessment of pulmonary C. neoformans infection, showing great clinical potential for accurate diagnosis and evaluation.

Cryptococcus neoformans (C. neoformans) is a causative agent for acute pulmonary infection, which can further develop to lethal meningoencephalitis if untreated. The meningoencephalitis infection can be prevented, if timely treatment on pulmonary cryptococcal infection can be implemented based on its early diagnosis and accurate assessment. In this study, blood serum surface-enhanced Raman spectroscopy (SERS) method was investigated on identification and assessment of pulmonary C. neoformans infection. The serum SERS measurements were collected from the mice infected with C. neoformans and the healthy mice, in which the infected mice were further divided into four subgroups according to the duration of infection. Based on those SRES measurements, biochemical differences were analyzed among those different groups to investigate the potential biomarkers for identifying and assessing the pulmonary C. neoformans infection. Furthermore, partial least square (PLS) analysis followed by linear discriminant analysis (LDA) model was employed to identify pulmonary cryptococcal infection and to assess the degrees of infection with the accuracies of 96.7% and 85.3%, respectively. Therefore, our study Cryptococcus neoformans (C. neoformans) is a causative agent for acute pulmonary infection, which can further develop to lethal meningoencephalitis if untreated. The meningoencephalitis infection can be prevented, if timely treatment on pulmonary cryptococcal infection can be implemented based on its early diagnosis and accurate assessment. In this study, blood serum surface-enhanced Raman spectroscopy (SERS) method was investigated on identification and assessment of pulmonary C. neoformans infection. The serum SERS measurements were collected from the mice infected with C. neoformans and the healthy mice, in which the infected mice were further divided into four subgroups according to the duration of infection. Based on those SRES measurements, biochemical differences were analyzed among those different groups to investigate the potential biomarkers for identifying and assessing the pulmonary C. neoformans infection. Furthermore, partial least square (PLS) analysis followed by linear discriminant analysis (LDA) model was employed to identify pulmonary cryptococcal infection and to assess the degrees of infection with the accuracies of 96.7% and 85.3%, respectively. Therefore, our study has demonstrated the great clinical potential of using serum SERS technique for an accurate identification and assessment of pulmonary cryptococcal infection. (c) 2021 Elsevier B.V. All rights reserved.

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Label-free diagnosis of breast cancer based on serum protein purification assisted surface-enhanced Raman spectroscopy

Yamin Lin, Jiamin Gao, Shuzhen Tang, Xin Zhao, Mengmeng Zheng, Wei Gong, Shusen Xie, Siqi Gao, Yun Yu, Juqiang Lin

Summary: A new serum protein analysis method combined with SERS technology was used for breast cancer detection. The diagnostic model established through multivariate statistical analysis showed promising performance in classifying breast cancer, providing a potential new approach for diagnosis.