Raman spectroscopy–based screening of hepatitis C and associated molecular changes

Bilal, Muhammad; Saleem, Muhammad; Khan, Saranjam; Ullah, Rahat; Fatima, Kiran; Ahmed, Mushtaq; Hayat, Abbas; Shahzada, Shaista; Khan, E.U.

doi:10.1088/1612-202x/aa7d37

Cited by 4 publications

(4 citation statements)

References 40 publications

(39 reference statements)

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“…2, section 3.1.) and also show agreement with [34,37] and Bilal et al, findings which have reported the correlation of 718, 872, 1004, 1169, 1250 cm −1 with HCV infection [36].…”

Section: Decision Surface Based Analysis Of Raman Shifts and Its Biocsupporting

confidence: 91%

“…Mannose-binding lectin is one such molecule which on attachment with virus mediates lectin complement pathway that kills virus [35]. 1169 cm −1 peak corresponds to lipids where 1250 cm −1 is assigned to ammonia whose level increases in infected individuals, as liver damaged by HCV infection fails to detoxify ammonia [36]. Lipids are associated with proteins that stimulate destruction process of hepatocytes.…”

Section: Decision Surface Based Analysis Of Raman Shifts and Its Biocmentioning

confidence: 99%

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Analysis of hepatitis C infection using Raman spectroscopy and proximity based classification in the transformed domain

Sohail¹,

Khan²,

Ullah³

et al. 2018

Biomed. Opt. Express

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This work presents a diagnostic system for the hepatitis C infection using Raman spectroscopy and proximity based classification. The proposed method exploits transformed Raman spectra using the proximity based machine learning technique and is denoted as RS-PCA-Prox. First, Raman spectral data is baseline corrected by subtracting noise and low intensity background. After this, a feature transformation of Raman spectra is adopted, not only to reduce the feature's dimensionality but also to learn different deviations in Raman shifts. The proposed RS-PCA-Prox shows significant diagnostic power in terms of accuracy, sensitivity, and specificity as 95%, 0.97 and 0.94 in PCA based transformed domain. The comparison of the RS-PCA-Prox with linear and ensemble based classifiers shows that proximity based classification performs better for the discrimination of HCV infected individuals and is able to differentiate the infected individuals from normal ones on the basis of molecular spectral information. Furthermore, it is observed that characteristic spectral changes are due to variation in the intensity of lectin, chitin, lipids, ammonia and viral protein as a consequence of the HCV infection.

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“…2, section 3.1.) and also show agreement with [34,37] and Bilal et al, findings which have reported the correlation of 718, 872, 1004, 1169, 1250 cm −1 with HCV infection [36].…”

Section: Decision Surface Based Analysis Of Raman Shifts and Its Biocsupporting

confidence: 91%

Section: Decision Surface Based Analysis Of Raman Shifts and Its Biocmentioning

confidence: 99%

Analysis of hepatitis C infection using Raman spectroscopy and proximity based classification in the transformed domain

Sohail¹,

Khan²,

Ullah³

et al. 2018

Biomed. Opt. Express

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“…Surface-enhanced Raman scattering (SERS), with advantages in high sensitivity and specificity, is rapidly expanding its applications in biochemical and biomedical analysis [5][6][7]. When gold or silver nanoparticles are transferred into living cells, SERS signal can be measured by the light interaction with the intracellular biochemical components and nanoparticles [8,9] and provides a powerful tool for sensitive detection of intracellular biological macromolecules (e.g.…”

Section: Introductionmentioning

confidence: 99%

Label-free screening of intrahepatic cholangiocarcinoma cells using electroporation assisted surface-enhanced Raman scattering combined with statistical algorithms

Hong¹,

Zhou²,

Zhang³

et al. 2019

Laser Phys.

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Analysis of biochemical components in living cells is essential for early screening of intrahepatic cholangiocarcinoma (ICC). In this paper, we investigated the feasibility of using electroporation assisted surface-enhanced Raman spectroscopy and multivariate analysis methods to discriminate ICC cells (HCCC-9810 cell line) from normal liver cells (Lo2 cell line) with the aim to develop a label-free and fast method for ICC cell screening. Electroporation technology was employed to deliver silver nanoparticles into living cells and then high quality SERS spectra were successfully measured from 100 ICC cells and 100 normal liver cells. There were significant differences in SERS spectra between ICC cells and normal cells, which indicated special changes in the content of biomolecules including nucleic acids, proteins and lipid in different cell lines. Based on the principal component analysis (PCA) and linear discriminant analysis (LDA) algorithms, high diagnostic sensitivity (97%), specificity (96%) and accuracy (96.5%) were achieved for distinguishing ICC cells and normal cells. This exploratory work demonstrates that the electroporation-assisted SERS technique combined with PCA-LDA diagnostic algorithms has great potential for detecting and screening of cancer cell.

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“…There are several new techniques to solve this problem, such as the fiber-endoscopy with auto fluorescence, aminolaevulinic acid-induced fluorescence, confocal endomicroscopy, etc. Among them, Raman spectroscopy (RS) technology attracts broad attention for its convenience, sensitivity and objectiveness [3][4][5]. RS, a technology based on proton inelastic scattering process, was first described by Raman in 1928.…”

Section: Surface-enhanced Raman Spectroscopy For Classification Of La...mentioning

confidence: 99%

Surface-enhanced Raman spectroscopy for classification of laryngeal cancer and adjacent tissues

Lai¹,

Wan²,

Wu³

et al. 2019

Laser Phys.

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As an optical spectroscopic technique, Raman spectroscopy (RS) has shown promise for in vivo cancer diagnostics in various organs. Common Raman signals are extremely low and easily disturbed by fluorescence. In this study, we used surface-enhanced RS to scan patients' malignant and adjacent tissues, as well as the normal tissues. Raman scattering signals were acquired from 35 normal tissue samples and 39 laryngeal carcinoma tissue samples. Linear discriminant analysis (LDA), based on principal component analysis, was employed to generate diagnostic algorithms for the classification of different laryngeal tissue types. We found there were several variations in surface-enhanced RS (SERS) peaks, and the same with other malignant ones. When using the principle component-LDA (PC-LDA) algorithms, this study showed that the diagnostic sensitivity and specificity were 99.2% and 98.4%, respectively for the identifying cancer tissues from normal samples. Results show that there are significant spectral differences (different peak intensities) between cancer tissues and normal tissues. High classification accuracy can be achieved by using the PC-LDA diagnostic algorithm, which demonstrates the great potential of tissue-SERS analysis combined with PC-LDA diagnostic algorithms for cancer screening in laryngeal cancer.

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Raman spectroscopy–based screening of hepatitis C and associated molecular changes

Cited by 4 publications

References 40 publications

Analysis of hepatitis C infection using Raman spectroscopy and proximity based classification in the transformed domain

Analysis of hepatitis C infection using Raman spectroscopy and proximity based classification in the transformed domain

Label-free screening of intrahepatic cholangiocarcinoma cells using electroporation assisted surface-enhanced Raman scattering combined with statistical algorithms

Surface-enhanced Raman spectroscopy for classification of laryngeal cancer and adjacent tissues

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