SERS-based viral load quantification of hepatitis B virus from PCR products

Surface-enhanced Raman spectroscopy for the characterization of filtrate portions of hepatitis B blood serum samples using 100 kDa ultra filtration devices

The blood serum of patients infected by the Hepatitis B virus contains high molecular weight fractions and low molecular weight fractions (LMWF) of biomarker proteins of the disease. The LMWF including the associated peptidome and metabolome, is recognized as a critical molecular population with high potential for research on disease-associated biomarkers. This fraction of biomarkers can be suppressed by HMWF, proteins such as albumin, and immunoglobulins hence difficult to be detected. The purpose of this study is to separate HMWF) and LMWF using 100 kDa centrifugal filtration devices resulting in two parts including residue (HMWF) and filtrate parts (LMWF) of blood serum followed by the analysis of the later part employing surface-enhanced Raman spectroscopy (SERS). This strategy can enhance this optical technique's capability to characterize the biochemical changes caused by the infection of HBV and the diagnosis of the disease. The silver nanoparticles (Ag-NPs) were employed as a SERS substrate to distinguish between filtrate parts of the blood serum of HBV patients and healthy individuals based on their specific SERS peaks. The SERS spectral features associated with the filtrate parts of HBV patients' blood serum are well differentiated from the healthy volunteers. Principle component analysis (PCA) was applied on the SERS spectral data sets of HBV patients and healthy individuals and found extremely beneficial for the classification of their SERS spectral groups. Moreover, partial least square regression analysis (PLSR) has shown excellent performance in the quantitative analysis of the viral load values of the HBV patients using their SERS spectral data sets.

Surface-enhanced Raman spectroscopy for the characterization of filtrate portions of blood serum samples of myocardial infarction patients using 30 kDa centrifugal filter devices

Myocardial infarction (MI) is the leading cause of death and disability worldwide. It occurs when a thrombus forms after an atherosclerotic plaque bursts, obstructing blood flow to the heart. Prompt and accurate diagnosis is crucial for improving patient survival. Surface-enhanced Raman spectroscopy (SERS) offers quick response and excellent resolution for qualitative and quantitative analysis of body fluids, making it a valuable diagnostic tool. This study explores SERS for identifying proteins in blood serum samples from MI patients, focusing on cardiac Troponin I (cTnI), a key biomarker. Due to the small size and low concentration of cTnI, its SERS signal may be weak or absent. To address this, 30 kDa filtering devices are used to obtain filtrate portions of serum samples from cTnI-positive patients and healthy individuals. SERS spectral analysis of these filtrates identifies key SERS bands associated with biomolecular changes related to cTnI levels. Principal component analysis (PCA) effectively differentiates SERS spectra from healthy and MI-positive patients. Partial least squares regression (PLSR) quantifies cTnI levels based on SERS features, with a model showing R2 value of 0.79 and RMSEC of 1.37, validating its accuracy.

SERS profiling of blood serum filtrate components from patients with type II diabetes using 100 kDa filtration devices

Blood carries some of the most valuable biomarkers for disease screening as it interacts with various tissues and organs in the body. Human blood serum is a reservoir of high molecular weight fraction (HMWF) and low molecular weight fraction (LMWF) proteins. The LMWF proteins are considered disease marker proteins and are often suppressed by HMWF proteins during analysis. This issue is addressed by using a filtration device to isolate the filtrate portion from blood serum samples having biomarker proteins up to the size of the cutoff value of the filtration device. In this research, 100 kDa filter devices are employed to obtain the filtrate portions from blood serum samples of type II diabetes mellitus patients and healthy volunteers, followed by characterization using surface-enhanced Raman spectroscopy (SERS) with silver nanoparticles (Ag NPs) as the SERS substrate. By using this approach, the collected filtrate is expected to contain marker proteins at a size of <100 kDa, which are associated with type II diabetes. These marker proteins are in a narrow size range (cutoff value of 100 kDa). Hence, they may be more easily identified by their characteristic SERS spectral features as compared to their analysis in the respective whole blood serum samples due to the exclusion of larger size proteins. These proteins that are present in the filtrate portions of type II diabetes may include adiponectin, C-reactive protein, insulin, leptin, RBP4, IL-6, TNF-α, Fibroblast Growth Factor 21 (FGF21), albumin, transthyretin, alpha-antitrypsin, transferrin, apolipoprotein A-1 (ApoA-1) and fetuin-A. Some prominent SERS bands are observed at 356 cm-1, 435 cm-1, 490 cm-1, 548 cm-1, 596 cm-1, 729 cm-1, 746 cm-1, 950 cm-1, 1330 cm-1, 1362 cm-1, 1573 cm-1 and 1689 cm-1, which differentiate type II diabetes patients from healthy individuals. Moreover, the SERS spectral data sets of various samples are classified using two chemometric approaches: principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). The validation of the PLS-DA analysis classification model is indicated with 81% accuracy, 79% specificity, and 85% sensitivity, having a value of AUC = 0.75.

A nucleic acid-based surface-enhanced Raman scattering of gold nanorods in N-gene integrated principal component analysis for COVID-19 detection

A rapid, simple, sensitive, and selective point-of-care diagnosis tool kit is vital for detecting the coronavirus disease (COVID-19) based on the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strain. Currently, the reverse transcriptase-polymerase chain reaction (RT-PCR) is the best technique to detect the disease. Although a good sensitivity has been observed in RT-PCR, the isolation and screening process for high sample volume is limited due to the time-consuming and laborious work. This study introduced a nucleic acid-based surface-enhanced Raman scattering (SERS) sensor to detect the nucleocapsid gene (N-gene) of SARS-CoV-2. The Raman scattering signal was amplified using gold nanoparticles (AuNPs) possessing a rod-like morphology to improve the SERS effect, which was approximately 12-15 nm in diameter and 40-50 nm in length. These nanoparticles were functionalised with the single-stranded deoxyribonucleic acid (ssDNA) complemented with the N-gene. Furthermore, the study demonstrates method selectivity by strategically testing the same virus genome at different locations. This focused approach showcases the method's capability to discern specific genetic variations, ensuring accuracy in viral detection. A multivariate statistical analysis technique was then applied to analyse the raw SERS spectra data using the principal component analysis (PCA). An acceptable variance amount was demonstrated by the overall variance (82.4 %) for PC1 and PC2, which exceeded the desired value of 80 %. These results successfully revealed the hidden information in the raw SERS spectra data. The outcome suggested a more significant thymine base detection than other nitrogenous bases at wavenumbers 613, 779, 1219, 1345, and 1382 cm-1. Adenine was also less observed at 734 cm-1, and ssDNA-RNA hybridisations were presented in the ketone with amino base SERS bands in 1746, 1815, 1871, and 1971 cm-1 of the fingerprint. Overall, the N-gene could be detected as low as 0.1 nM within 10 mins of incubation time. This approach could be developed as an alternative point-of-care diagnosis tool kit to detect and monitor the COVID-19 disease.

The Label-Free Detection and Identification of SARS-CoV-2 Using Surface-Enhanced Raman Spectroscopy and Principal Component Analysis

The World Health Organization (WHO) declared in a May 2023 announcement that the COVID-19 illness is no longer categorized as a Public Health Emergency of International Concern (PHEIC); nevertheless, it is still considered an actual threat to world health, social welfare and economic stability. Consequently, the development of a convenient, reliable and affordable approach for detecting and identifying SARS-CoV-2 and its emerging new variants is crucial. The fingerprint and signal amplification characteristics of surface-enhanced Raman spectroscopy (SERS) could serve as an assay scheme for SARS-CoV-2. Here, we report a machine learning-based label-free SERS technique for the rapid and accurate detection and identification of SARS-CoV-2. The SERS spectra collected from samples of four types of coronaviruses on gold nanoparticles film, fabricated using a Langmuir-Blodgett self-assembly, can provide more spectroscopic signatures of the viruses and exhibit low limits of detection (<100 TCID50/mL or even <10 TCID50/mL). Furthermore, the key Raman bands of the SERS spectra were systematically captured by principal component analysis (PCA), which effectively distinguished SARS-CoV-2 and its variant from other coronaviruses. These results demonstrate that the combined use of SERS technology and PCA analysis has great potential for the rapid analysis and discrimination of multiple viruses and even newly emerging viruses without the need for a virus-specific probe.

Characterization of structural changes occurring in insulin at different time intervals at room temperature by surface-enhanced Raman spectroscopy

BACKGROUND

Insulin storage above the temperature recommended by food and drug administration (FDA) causes decrease in its functional efficacy due to degradation and aggregation of its protein based active pharmaceutical ingredient (API) that results poor glycemic control in diabetic patients. The aggregation of protein causes serious neurodegenerative diseases such as type-2 diabetes, Huntington disease, Parkinson's disease, and Alzheimer's disease. Surface-enhanced Raman spectroscopy (SERS) has been employed for the denaturation study of many proteins at the temperature above the recommendations of food and drug administration (FDA) (above 30 °C) which indicates potential of technique for such studies.

OBJECTIVE

SERS along with multivariate discriminating analysis techniques-based analysis of degradation of liquid pharmaceutical insulin protein after regular intervals of time at room temperature to analyze the structural changes in this protein during the storage of insulin pharmaceutical at room temperature.

METHODS

Silver nanoparticles (Ag-NPs) prepared by chemical reduction method are used as SERS active substrate for the surface enhancement of the insulin spectral signal. SERS spectral measurements of insulin were collected from eight different samples of insulin in the time range of 7 pm to 7 am first at fridge temperature (5 °C), second after half hour and next six with the time difference of 2 h each time at room temperature. The acquired SERS spectral data was preprocessed and analyzed. SERS structural transformations detection and discrimination potential in insulin was further confirmed by applying multivariate discriminating analysis techniques including principal component analysis (PCA) and Partial least square regression analysis (PLSR).

RESULTS

SERS significantly detects the structural changes produced in insulin even after 2 h of insulin placement at room temperature. PCA successfully differentiates the insulin spectral data obtained after regular intervals of time according to PC-1 (77 %) explained variance. Application of PLSR model provides quantitative confirmation of SERS efficiency, by providing insulin data regression coefficients plot, efficient prediction of time with calibration data set having 0.77 mean square absolute error of calibration (RMSAEC), validation data set with 0.80 mean square absolute error of prediction (RMSAEP) and 0.98 coefficient of determination (R2) for both calibration and validation data set.

CONCLUSION

SERS is proved as a highly sensitive and discriminating technique to detect and discriminate insulin structural changes after regular intervals of time at room temperature.

Surface-enhanced Raman spectroscopy for the characterization of different anatomical subtypes of oral cavity cancer

BACKGROUND

The prognosis for oral cancer patients is still very poor worldwide. Early detection and treatment therapy remain the key issue to be addressed for improved patient survival. The characteristic Raman spectral features associated with the biochemical changes in the blood serum samples can be used for the diagnosis of diseases, particularly for oral cancer. Surface-enhanced Raman spectroscopy (SERS) is a promising technique for non-invasive and early detection of oral cancer by analyzing molecular changes in body fluids.

OBJECTIVES

To detect oral cavity anatomical subsites (buccal mucosa, cheek, hard palate, lips, mandible, maxilla, tongue and tonsillar region) cancers by using blood serum samples, SERS with principal component analysis is used.

MATERIAL AND METHOD

SERS is employed with silver nanoparticles for the analysis and detection of oral cancer serum samples by comparing with healthy serum samples. SERS spectra are recorded by Raman instrument and preprocessed using the statistical tool. Principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA) are used to discriminate between oral cancer serum samples and control serum samples.

RESULTS

Some major SERS peaks are observed at 1136 cm^-1 (Phospholipids) and 1006 cm^-1 (Phenylalanine) remain higher in intensities for oral cancer spectra as compared to healthy spectra. The peak at 1241 cm^-1 (amide III) is observed only in oral cancer serum samples while absent in healthy serum samples. Higher protein and DNA contents were detected in SERS mean spectra of oral cancer. Moreover, PCA is used to identify the biochemical differences in the form of SERS features which is used to differentiate between oral cancer and healthy blood serum samples, while PLS-DA is used to build differentiation model of oral cancer serum samples and healthy control serum samples. PLS-DA provides successful differentiation with 94% specificity and 95.5% sensitivity.

CONCLUSIONS

SERS can be used for the diagnosis of oral cancer and to identify metabolic changes that occur during disease development.

Surface-enhanced Raman spectroscopy of centrifuged blood serum samples of diabetic type II patients by using 50KDa filter devices

Blood serum contains essential biochemical information which are used for early disease diagnosis. Blood serum consisted of higher molecular weight fractions (HMWF) and lower molecular weight fractions (LMWF). The disease biomarkers are lower molecular weight fraction proteins, and their contribution to disease diagnosis is suppressed due to higher molecular weight fraction proteins. To diagnose diabetes in early stages are difficult because of the presence of huge amount of these HMWF. In the current study, surface-enhanced Raman spectroscopy (SERS) are employed to diagnose diabetes after centrifugation of serum samples using Amicon ultra filter devices of 50 kDa which produced two fractions of whole blood serum of filtrate, low molecular weight fraction, and residue, high molecular weight fraction. Furthermore SERS is employed to study the LMW fractions of healthy and diseased samples. Some prominent SERS bands are observed at 725 cm^-1, 842 cm^-1, 1025 cm^-1, 959 cm^-1, and 1447 cm^-1 due to small molecular weight proteins, and these biomarkers helped to diagnose the disease early stage. Moreover, chemometric techniques such as principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are employed to check the potential of surface-enhanced Raman spectroscopy for the differentiation and classifications of the blood serum samples. SERS can be employed for the early diagnosis and screening of biochemical changes during type II diabetes.

Raman spectroscopy for viral diagnostics

Raman spectroscopy offers the potential for fingerprinting biological molecules at ultra-low concentration and therefore has potential for the detection of viruses. Here we review various Raman techniques employed for the investigation of viruses. Different Raman techniques are discussed including conventional Raman spectroscopy, surface-enhanced Raman spectroscopy, Raman tweezer, tip-enhanced Raman Spectroscopy, and coherent anti-Stokes Raman scattering. Surface-enhanced Raman scattering can play an essential role in viral detection by multiplexing nanotechnology, microfluidics, and machine learning for ensuring spectral reproducibility and efficient workflow in sample processing and detection. The application of these techniques to diagnose the SARS-CoV-2 virus is also reviewed.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-023-01059-4.

Raman spectroscopy in chronic heart failure diagnosis based on human skin analysis

This work aims at studying Raman spectroscopy in combination with chemometrics as an alternative fast noninvasive method to detect chronic heart failure (CHF) cases. Optical analysis is focused on the changes in the spectral features associated with the biochemical composition changes of skin tissues. A portable spectroscopy setup with the 785 nm excitation wavelength was used to record skin Raman features. In this in vivo study, 127 patients and 57 healthy volunteers were involved in measuring skin spectral features by Raman spectroscopy. The spectral data were analyzed with a projection on the latent structures and discriminant analysis. 202 skin spectra of patients with CHF and 90 skin spectra of healthy volunteers were classified with 0.888 ROC AUC for the 10-fold cross validated algorithm. To identify CHF cases, the performance of the proposed classifier was verified by means of a new test set that is equal to 0.917 ROC AUC.

Comparison of surface-enhanced Raman spectral data sets of filtrate portions of serum samples of hepatitis B and Hepatitis C infected patients obtained by centrifugal filtration

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) is an efficient technique which has been used for the analysis of filtrate portions of serum samples of Hepatitis B (HBV) and Hepatitis C (HCV) virus.

OBJECTIVES

The main reason for this study is to differentiate and compare HBV and HCV serum samples for disease diagnosis through SERS. Hepatitis B and hepatitis C disease biomarkers are more predictable in their centrifuged form as compared in their uncentrifuged form. For differentiation of SERS spectral data sets of hepatitis B, hepatitis C and healthy person principal component analysis (PCA) proved to be a helpful. Centrifugally filtered serum samples of hepatitis B and hepatitis C are clearly differentiated from centrifugally filtered serum samples of healthy individuals by using partial least square discriminant analysis (PLS-DA).

METHODOLOGY

Serum sample of HBV, HCV and healthy patients were centrifugally filtered to separate filtrate portion for studying biochemical changes in serum sample. The SERS of these samples is performed using silver nanoparticles as substrates to identify specific spectral features of both viral diseases which can be used for the diagnosis and differentiation of these diseases. The purpose of centrifugal filtration of the serum samples of HBV and HCV positive and control samples by using filter membranes of 50 KDa size is to eliminate the proteins bigger than 50 KDa so that their contribution in the SERS spectrum is removed and disease related smaller proteins may be observed. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are statistical tools which were used for the further validation of SERS.

RESULTS

HBV and HCV centrifugally filtered serum sample were compared and biomarkers including (uracil, phenylalanine, methionine, adenine, phosphodiester, proline, tyrosine, tryptophan, amino acid, thymine, fatty acid, nucleic acid, triglyceride, guanine and hydroxyproline) were identified through PCA and PLS-DA. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were used as a multivariate data analysis tool for the diagnosis of the characteristic SERS spectral features associated with both types of viral diseases. For the classification and differentiation of centrifugally filtered HBV, HCV, and control serum samples, Principal component analysis is found helpful. Moreover, PLS-DA can classify these two distinct sets of SERS spectral data with 0.90 percent specificity, 0.85 percent precision, and 0.83 percent accuracy.

CONCLUSIONS

Surface enhanced Raman spectroscopy along with chemometric analysis like PCA and PLS-DA have been successfully differentiated HBV and HCV and healthy individuals' serum samples.

Contributions of vibrational spectroscopy to virology: A review

Vibrational spectroscopic techniques, both infrared absorption and Raman scattering, are high precision, label free analytical techniques which have found applications in fields as diverse as analytical chemistry, pharmacology, forensics and archeometrics and, in recent times, have attracted increasing attention for biomedical applications. As analytical techniques, they have been applied to the characterisation of viruses as early as the 1970 s, and, in the context of the coronavirus disease 2019 (COVID-19) pandemic, have been explored in response to the World Health Organisation as novel methodologies to aid in the global efforts to implement and improve rapid screening of viral infection. This review considers the history of the application of vibrational spectroscopic techniques to the characterisation of the morphology and chemical compositions of viruses, their attachment to, uptake by and replication in cells, and their potential for the detection of viruses in population screening, and in infection response monitoring applications. Particular consideration is devoted to recent efforts in the detection of severe acute respiratory syndrome coronavirus 2, and monitoring COVID-19.

Surface-enhanced Raman spectroscopic analysis of centrifugally filtered HBV serum samples

BACKGROUND

Raman spectroscopy is an effective tool for detecting and discriminating centrifugally filtered hepatitis B virus serum and centrifugally filtered  control serum.

OBJECTIVES

The purpose of current study is to separate high molecular weight fractions from low molecular weight fractions present hepatitis B serum to increase the disease diagnostic ability of surface enhanced Raman spectroscopy (SERS).

METHODS

Clinically diagnosed centrifugally filtered serum samples of hepatitis B patients are subjected for surface enhanced Raman spectroscopy (SERS) in comparison with centrifugally filtered serum samples of healthy individuals by using silver nanoparticles (Ag-NPs) as SERS substrates. Some SERS spectral features are solely observed in centrifugally filtered serum samples of hepatitis B and some SERS spectral are solely observed in centrifugally filtered serum samples of healthy individuals. The diagnostic ability of SERS is further enhanced with different statistical techniques like principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and partial least square regression analysis (PLSR) have applied.

RESULTS

The disease biomarkers of hepatitis B are more pronounced after their centrifugation as compared with uncentrifuged form. Statistical tools like principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) clearly differentiated centrifugally filtered serum samples of hepatitis B from centrifugally filtered serum samples of healthy individuals. Furthermore, partial least square regression analysis (PLSR) has been applied for predicting unknown viral load of centrifugally filtered serum sample of hepatitis B.

CONCLUSION

SERS technique along with chemometric tools have successfully differentiated centrifugally filtered serum samples of hepatitis B from centrifugally filtered serum samples of healthy individuals. The centrifugal filtration process has increased the differentiation accuracy of PLS-DA in terms of percentage 98% and regression accuracy of PLSR regression analysis in terms of RMSEP (0.30 IU/mL) of this diagnostic method as compared with that of uncentrifuged method.

Raman Spectroscopy and Its Modifications Applied to Biological and Medical Research

Nowadays, there is an interest in biomedical and nanobiotechnological studies, such as studies on carotenoids as antioxidants and studies on molecular markers for cardiovascular, endocrine, and oncological diseases. Moreover, interest in industrial production of microalgal biomass for biofuels and bioproducts has stimulated studies on microalgal physiology and mechanisms of synthesis and accumulation of valuable biomolecules in algal cells. Biomolecules such as neutral lipids and carotenoids are being actively explored by the biotechnology community. Raman spectroscopy (RS) has become an important tool for researchers to understand biological processes at the cellular level in medicine and biotechnology. This review provides a brief analysis of existing studies on the application of RS for investigation of biological, medical, analytical, photosynthetic, and algal research, particularly to understand how the technique can be used for lipids, carotenoids, and cellular research. First, the review article shows the main applications of the modified Raman spectroscopy in medicine and biotechnology. Research works in the field of medicine and biotechnology are analysed in terms of showing the common connections of some studies as caretenoids and lipids. Second, this article summarises some of the recent advances in Raman microspectroscopy applications in areas related to microalgal detection. Strategies based on Raman spectroscopy provide potential for biochemical-composition analysis and imaging of living microalgal cells, in situ and in vivo. Finally, current approaches used in the papers presented show the advantages, perspectives, and other essential specifics of the method applied to plants and other species/objects.

Amplification-Free Identification and Determination of Nucleic Acids by Surface Plasmon Resonance and Surface-Enhanced Raman Spectroscopy

This review discusses contemporary approaches to designing sensory systems for the identification and determination of nucleic acids (NAs) without amplifying target molecules. Here we summarize the data about methods based on surface plasmon resonance and surface-enhanced Raman spectroscopy, as well as their possibilities, limitations, and prospects for further development.

Surface-enhanced Raman spectral analysis for comparison of PCR products of hepatitis B and hepatitis C

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

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

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

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^st principle component. By analyzing data using partial least square discriminate analysis, differentiation of two disease classes was considered more valid with sensitivity, specificity and accuracy value of 96%, 94% and 98% respectively. Value of area under curve (AUROC) was 0.7527.

CONCLUSION

SERS can be employed for identification and comparison of two human pathological conditions sharing the same symptomology.

Surface-enhanced Raman spectroscopy for comparison of serum samples of typhoid and tuberculosis patients of different stages

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) is a reliable tool for the identification and differentiation of two different human pathological conditions sharing the same symptomology, typhoid and tuberculosis (TB).

OBJECTIVES

To explore the potential of surface-enhanced Raman spectroscopy for differentiation of two different diseases showing the same symptoms and analysis by principal component analysis (PCA) and partial least square discriminate analysis (PLS-DA).

METHODS

Serum samples of clinically diagnosed typhoid and tuberculosis infected individuals were analyzed and differentiated by SERS using silver nanoparticles (Ag NPs) as a SERS substrate. For this purpose, the collected serum samples were analyzed under the SERS instrument and unique SERS spectra of typhoid and tuberculosis were compared showing notable spectral differences in protein, lipid and carbohydrates features. Different stages of the diseased class of typhoid (Early acute and late acute stage) and tuberculosis (Pulmonary and extra-pulmonary stage) were compared with each other and with healthy human serum samples, which were significantly separated. Moreover, SERS data was analyzed using multivariate data analysis techniques including principal component analysis (PCA) and partial least square discriminate analysis (PLS-DA) and differences were so prominent to observe.

RESULTS

SERS Spectral data of typhoid and tuberculosis showed clear differences and were significantly separated using PCA. SERS spectral data of both stages of typhoid and tuberculosis were separated according to 1st principle component. Moreover, by analyzing data using partial least square discriminate analysis, differentiation of two disease classes were considered more valid with a 100% value of sensitivity, specificity and accuracy.

CONCLUSION

SERS can be employed for identification and comparison of two different human pathological conditions sharing same symptomology.