Raman spectroscopy based differentiation of typhoid and dengue fever in infected human sera

Naseer, K.A.; Amin, Ayyaz; Saleem, Muhammad; Qazi, Javaria

doi:10.1016/j.saa.2018.08.008

Cited by 35 publications

(15 citation statements)

References 32 publications

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“…[22][23][24][25][26] By using a laser to focus directly on the serum sample, previous studies have demonstrated that Raman spectroscopy may differentiate virus infections from healthy individuals by detecting the characteristic peaks of Raman spectrum, which respond to specific carbohydrates of the viral glycoprotein. [10,12,27,28] Because SARS-CoV-2 is a virus causing severe infectious disease, for protection, our method seals the serum sample in a polypropylene biological tube for preservation. The laser light goes through the transparent tube wall before approaching the serum sample, and it greatly reduces the signal-to-noise ratio of the Raman signal for the serum sample.…”

Section: Training and Validation Of Modelmentioning

confidence: 99%

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An efficient primary screening of COVID‐19 by serum Raman spectroscopy

Yin

Lü

et al. 2021

J Raman Spectroscopy

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The outbreak of COVID‐19 coronavirus disease around the end of 2019 has become a pandemic. The preferred method for COVID‐19 detection is the real‐time polymerase chain reaction (RT‐PCR)‐based technique; however, it also has certain limitations, such as sample‐dependent procedures with a relatively high false negative ratio. We propose a safe and efficient method for screening COVID‐19 based on Raman spectroscopy. A total of 177 serum samples are collected from 63 confirmed COVID‐19 patients, 59 suspected cases, and 55 healthy individuals as a control group. Raman spectroscopy is adopted to analyze these samples, and a machine learning support‐vector machine (SVM) method is applied to the spectrum dataset to build a diagnostic algorithm. Furthermore, 20 independent individuals, including 5 asymptomatic COVID‐19 patients and 5 symptomatic COVID‐19 patients, 5 suspected patients, and 5 healthy patients, were sampled for external validation. In these three groups—confirmed COVID‐19, suspected, and healthy individuals—the distribution of statistically significant points of difference showed highly consistency for intergroups after repeated sampling processes. The classification accuracy between the COVID‐19 cases and the suspected cases is 0.87 (95% confidence interval [CI]: 0.85–0.88), and the accuracy between the COVID‐19 and the healthy controls is 0.90 (95% CI: 0.89–0.91), while the accuracy between the suspected cases and the healthy control group is 0.68 (95% CI: 0.67–0.73). For the independent test dataset, we apply the obtained SVM model to the classification of the independent test dataset to have all the results correctly classified. Our model showed that the serum‐level classification results were all correct for independent test dataset. Our results suggest that Raman spectroscopy could be a safe and efficient technique for COVID‐19 screening.

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Section: Training and Validation Of Modelmentioning

confidence: 99%

“…Because it is a simple process of sample preparation and it is free of sample loss, Raman spectroscopy is highly recommended for diagnosing a diverse range of diseases, including dengue fever, hepatitis C virus, cancer, HIV, Alzheimer's disease, and endometriosis. [ 10–16 ] Raman can also be used to detect RNA viruses. [ 17 ]…”

Section: Introductionmentioning

confidence: 99%

An efficient primary screening of COVID‐19 by serum Raman spectroscopy

Yin

Lü

et al. 2021

J Raman Spectroscopy

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“…When support vector machine (SVM) learning models coupled with polynomial kernel order 1 were applied, DENV was predicted with a predictive accuracy of 85% and a sensitivity of 73% [53]. In a recent study, RS was used to differentiate between bacteria (Salmonella Typhi) and virus (DENV) infections in human blood serum, with 12 distinct Raman bands linked to typhoid-infected samples and 4 to DENV [54].…”

Section: Application Of Rs For the Diagnosis Of Arbovirusesmentioning

confidence: 99%

The application of spectroscopy techniques for diagnosis of malaria parasites and arboviruses and surveillance of mosquito vectors: A systematic review and critical appraisal of evidence

et al. 2021

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Spectroscopy-based techniques are emerging diagnostic and surveillance tools for mosquito-borne diseases. This review has consolidated and summarised recent research in the application of Raman and infrared spectroscopy techniques including near- and mid-infrared spectroscopy for malaria and arboviruses, identified knowledge gaps, and recommended future research directions. Full-length peer-reviewed journal articles related to the application of Raman and infrared (near- and mid-infrared) spectroscopy for malaria and arboviruses were systematically searched in PUBMED, MEDILINE, and Web of Science databases using the PRISMA guidelines. In text review of identified studies included the methodology of spectroscopy technique used, data analysis applied, wavelengths used, and key findings for diagnosis of malaria and arboviruses and surveillance of mosquito vectors. A total of 58 studies met the inclusion criteria for our systematic literature search. Although there was an increased application of Raman and infrared spectroscopy-based techniques in the last 10 years, our review indicates that Raman spectroscopy (RS) technique has been applied exclusively for the diagnosis of malaria and arboviruses. The mid-infrared spectroscopy (MIRS) technique has been assessed for the diagnosis of malaria parasites in human blood and as a surveillance tool for malaria vectors, whereas the near-infrared spectroscopy (NIRS) technique has almost exclusively been applied as a surveillance tool for malaria and arbovirus vectors. Conclusions/Significance The potential of RS as a surveillance tool for malaria and arbovirus vectors and MIRS for the diagnosis and surveillance of arboviruses is yet to be assessed. NIRS capacity as a surveillance tool for malaria and arbovirus vectors should be validated under field conditions, and its potential as a diagnostic tool for malaria and arboviruses needs to be evaluated. It is recommended that all 3 techniques evaluated simultaneously using multiple machine learning techniques in multiple epidemiological settings to determine the most accurate technique for each application. Prior to their field application, a standardised protocol for spectra collection and data analysis should be developed. This will harmonise their application in multiple field settings allowing easy and faster integration into existing disease control platforms. Ultimately, development of rapid and cost-effective point-of-care diagnostic tools for malaria and arboviruses based on spectroscopy techniques may help combat current and future outbreaks of these infectious diseases.

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“…13 Each molecule has a unique scattering patron, which serves as a fingerprint and hence identification entity for a particular molecule or compound in a mixture. In recent years, Raman spectra of blood serum samples were used to diagnose many types of infectious diseases, including dengue virus infection, [14][15][16] hepatitis C, 17,18 typhoid and malaria fever, [19][20][21] HIV infection, 22 tuberculosis, 23 and HBV infection. [24][25][26][27] Therefore, in order to improve the diagnostic capability for infectious diseases, new, reliable, online, and economical methods are needed to be developed for diagnosis and for monitoring the progression of infection.…”

Section: Introductionmentioning

confidence: 99%

Optical diagnosis of hepatitis B virus infection in blood plasma using Raman spectroscopy and chemometric techniques

Saleem

Ali

Khan

et al. 2020

J Raman Spectroscopy

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The potential of Raman spectroscopy has been utilized for the diagnosis of hepatitis B virus (HBV) infection in blood plasma. Raman spectra of 24 diseased and 10 healthy samples were used to develop distinct types of support vector machine (SVM) models, including linear, quadratic, and radial basis function (RBF) using multivariate method of principal component analysis (PCA) to reduce the dimensions of the obtained datasets. To assess the diagnostic power of these algorithms, developed models were tested on independent dataset. RBF‐based PCA‐SVM model achieved the best performance and yielded accuracy of 98.82%, sensitivity of 98.89%, and specificity of 98.80%. The performance of the SVM models was compared with rerated chemometric method of partial least square regression (PLSR), which has been developed by using the same dataset. The PLSR model attained the diagnostic accuracy of 88%, sensitivity of 93%, and specificity of 78% for same dataset. Our developed model has established promising results compared with state‐of‐the‐art approaches. The results reveal the improved performance of the developed chemometric techniques and clinical prediction potential of HBV by PCA‐SVMs in conjunction with Raman spectroscopy.

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Raman spectroscopy based differentiation of typhoid and dengue fever in infected human sera

Cited by 35 publications

References 32 publications

An efficient primary screening of COVID‐19 by serum Raman spectroscopy

An efficient primary screening of COVID‐19 by serum Raman spectroscopy

The application of spectroscopy techniques for diagnosis of malaria parasites and arboviruses and surveillance of mosquito vectors: A systematic review and critical appraisal of evidence

Optical diagnosis of hepatitis B virus infection in blood plasma using Raman spectroscopy and chemometric techniques

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