Surface enhanced Raman spectroscopy of RNA samples extracted from blood of hepatitis C patients for quantification of viral loads.

Hepatitis C viral RNA Partial least square regression analysis Principal component analysis Silver nanoparticles Surface enhanced Raman spectroscopy

Journal

Photodiagnosis and photodynamic therapy
ISSN: 1873-1597
Titre abrégé: Photodiagnosis Photodyn Ther
Pays: Netherlands
ID NLM: 101226123

Informations de publication

Date de publication:
Mar 2021
Historique:
received: 24 09 2020
revised: 23 11 2020
accepted: 14 12 2020
pubmed: 22 12 2020
medline: 15 5 2021
entrez: 21 12 2020
Statut: ppublish

Résumé

Raman spectroscopy is a promising technique to analyze the body fluids for the purpose of non-invasive disease diagnosis. To develop a surface-enhanced Raman spectroscopy (SERS) based method for qualitative and quantitative analysis of HCV from blood samples. SERS was employed to characterize the Hepatitis C viral RNA extracted from different blood samples of hepatitis C virus (HCV) infected patients with predetermined viral loads in comparison with total RNA of healthy individuals. The SERS measurements were performed on 27 extracted RNA samples including low viral loads, medium viral loads, high viral loads and healthy/negative viral load samples. For this purpose, silver nanoparticles (Ag NPs) were used as SERS substrates. Furthermore, multivariate data analysis technique, Principal Component Analysis (PCA) and Partial Least Square Regression (PLSR) were also performed on SERS spectral data. The SERS spectral features due to biochemical changes in the extracted RNA samples associated with the increasing viral loads were established which could be employed for HCV diagnostic purpose. PCA was found helpful for the differentiation between Raman spectral data of RNA extracted from hepatitis infected and healthy blood samples. PLSR model is established for the determination of viral loads in HCV positive RNA samples with 99 % accuracy. SERS can be employed for qualitative and quantitative analysis of HCV from blood samples.

Sections du résumé

BACKGROUND BACKGROUND
Raman spectroscopy is a promising technique to analyze the body fluids for the purpose of non-invasive disease diagnosis.
OBJECTIVES OBJECTIVE
To develop a surface-enhanced Raman spectroscopy (SERS) based method for qualitative and quantitative analysis of HCV from blood samples.
METHODS METHODS
SERS was employed to characterize the Hepatitis C viral RNA extracted from different blood samples of hepatitis C virus (HCV) infected patients with predetermined viral loads in comparison with total RNA of healthy individuals. The SERS measurements were performed on 27 extracted RNA samples including low viral loads, medium viral loads, high viral loads and healthy/negative viral load samples. For this purpose, silver nanoparticles (Ag NPs) were used as SERS substrates. Furthermore, multivariate data analysis technique, Principal Component Analysis (PCA) and Partial Least Square Regression (PLSR) were also performed on SERS spectral data.
RESULTS RESULTS
The SERS spectral features due to biochemical changes in the extracted RNA samples associated with the increasing viral loads were established which could be employed for HCV diagnostic purpose. PCA was found helpful for the differentiation between Raman spectral data of RNA extracted from hepatitis infected and healthy blood samples. PLSR model is established for the determination of viral loads in HCV positive RNA samples with 99 % accuracy.
CONCLUSION CONCLUSIONS
SERS can be employed for qualitative and quantitative analysis of HCV from blood samples.

Identifiants

pubmed: 33348077
pii: S1572-1000(20)30506-8
doi: 10.1016/j.pdpdt.2020.102152
pii:
doi:

Substances chimiques

Photosensitizing Agents 0
Silver 3M4G523W1G
RNA 63231-63-0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

102152

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Auteurs

Saira Nasir (S)

Department of Chemistry, University of Agriculture Faisalabad, 38040, Pakistan.

Muhammad Irfan Majeed (MI)

Department of Chemistry, University of Agriculture Faisalabad, 38040, Pakistan. Electronic address: irfan.majeed@uaf.edu.pk.

Haq Nawaz (H)

Department of Chemistry, University of Agriculture Faisalabad, 38040, Pakistan. Electronic address: hachemist@yahoo.com.

Nosheen Rashid (N)

Department of Chemistry, University of Central Punjab, Lahore, Faisalabad Campus, Pakistan.

Saqib Ali (S)

Department of Chemistry, University of Agriculture Faisalabad, 38040, Pakistan.

Sidra Farooq (S)

Department of Chemistry, University of Agriculture Faisalabad, 38040, Pakistan.

Muhammad Kashif (M)

Department of Chemistry, University of Agriculture Faisalabad, 38040, Pakistan.

Sidra Rafiq (S)

Department of Chemistry, University of Agriculture Faisalabad, 38040, Pakistan.

Saira Bano (S)

Department of Chemistry, University of Agriculture Faisalabad, 38040, Pakistan.

Muhammad Naeem Ashraf (MN)

Department of Chemistry, University of Agriculture Faisalabad, 38040, Pakistan.

Muhammad Abubakar (M)

Department of Chemistry, University of Agriculture Faisalabad, 38040, Pakistan.

Shamsheer Ahmad (S)

Department of Chemistry, University of Agriculture Faisalabad, 38040, Pakistan.

Asma Rehman (A)

National Institute for Biotechnology and Genetic Engineering (NIBGE), P. O. Box 577, Jhang Road Faisalabad, Pakistan.

Imran Amin (I)

PCR Laboratory, PINUM Hospital, Faisalabad, Pakistan.

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Classifications MeSH