Surface-enhanced Raman spectral analysis for comparison of PCR products of hepatitis B and hepatitis C.
Disease diagnosis
Hepatitis B
Hepatitis C
Multivariate data analysis
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:
Sep 2021
Sep 2021
Historique:
received:
22
04
2021
revised:
15
06
2021
accepted:
12
07
2021
pubmed:
20
7
2021
medline:
16
9
2021
entrez:
19
7
2021
Statut:
ppublish
Résumé
Surface-enhanced Raman spectroscopy is a reliable tool for identification and differentiation of two diseases showing similar symptoms, hepatitis B (HBV) and hepatitis C (HCV). To develop a polymerase chain reaction technique (PCR) based SERS technique for differentiation of two human pathological conditions sharing the same symptoms using multivariate data analysis techniques e.g. principle component analysis (PCA) and partial least square discriminate analysis (PLS-DA). PCR products of HBV and HCV were differentiated by SERS using silver nanoparticles (AgNPs) as a SERS substrate. For this analysis, PCR products of both the diseases with predetermined viral loads were collected and analyzed under SERS instrument and unique SERS spectra of HBV and HCV was compared showing many differences at various points. Diseased classes of HBV and HCV and their negative control classes (viral load less than 1) were compared. PCR products of true healthy DNA and RNA were also compared, which were significantly separated. Moreover, SERS data was analyzed using multivariate data analysis techniques including principle component analysis (PCA) and partial least square discriminate analysis (PLS-DA) and differences were so prominent to observe. SERS spectral data of HBV and HCV showed clear differences and were significantly separated using PCA. Negative control samples of both disorders and their true healthy samples of DNA and RNA were separated according to 1 SERS can be employed for identification and comparison of two human pathological conditions sharing the same symptomology.
Sections du résumé
BACKGROUND
BACKGROUND
Surface-enhanced Raman spectroscopy is a reliable tool for identification and differentiation of two diseases showing similar symptoms, hepatitis B (HBV) and hepatitis C (HCV).
OBJECTIVES
OBJECTIVE
To develop a polymerase chain reaction technique (PCR) based SERS technique for differentiation of two human pathological conditions sharing the same symptoms using multivariate data analysis techniques e.g. principle component analysis (PCA) and partial least square discriminate analysis (PLS-DA).
METHODS
METHODS
PCR products of HBV and HCV were differentiated by SERS using silver nanoparticles (AgNPs) as a SERS substrate. For this analysis, PCR products of both the diseases with predetermined viral loads were collected and analyzed under SERS instrument and unique SERS spectra of HBV and HCV was compared showing many differences at various points. Diseased classes of HBV and HCV and their negative control classes (viral load less than 1) were compared. PCR products of true healthy DNA and RNA were also compared, which were significantly separated. Moreover, SERS data was analyzed using multivariate data analysis techniques including principle component analysis (PCA) and partial least square discriminate analysis (PLS-DA) and differences were so prominent to observe.
RESULTS
RESULTS
SERS spectral data of HBV and HCV showed clear differences and were significantly separated using PCA. Negative control samples of both disorders and their true healthy samples of DNA and RNA were separated according to 1
CONCLUSION
CONCLUSIONS
SERS can be employed for identification and comparison of two human pathological conditions sharing the same symptomology.
Identifiants
pubmed: 34280557
pii: S1572-1000(21)00267-2
doi: 10.1016/j.pdpdt.2021.102440
pii:
doi:
Substances chimiques
Photosensitizing Agents
0
Silver
3M4G523W1G
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
102440Informations de copyright
Copyright © 2021 Elsevier B.V. All rights reserved.