raman spectroscopy based diagnosis of dengue virus

Bilal Khan

2018

Department of Physics and Applied Mathematics

Pakistan Institute of Engineering and Applied Sciences

Nilore, Islamabad, Pakistan

Raman Spectroscopy based Diagnosis of

Dengue Virus Infection in Human Blood

Serum

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Reviewers and Examiners

Foreign Reviewers

1. Dr. Georges Wagnieres,

Head Photo-medicine Group, Laboratoire Leenaards-Jeantet d’imagerie fonctionnelle

et métabolique, Ecole Polytechnique Fédérale de Lausanne, Switzerland.

2. Mark Cronin-Golomb, Associate Professor

Department of Biomedical Engineering, Science and Technology Center,

Massachusetts, USA.

3. Carlos R. Stroud, Professor of Optics & Professor of Physics

The Institute of Optics, University of Rochester, Rochester, New York, USA.

Thesis Examiners

1. Dr. Muhammad Aslam Baig, Professor

National Center for Physics (NCP), Islamabad.

2. Dr. Farhan Saif, Professor

Department of Electronics, Quaid-i-Azam University, Islamabad.

3. Dr. Shahid Manzoor, CSO

Physics Department, CIIT, Islamabad.

Head of the Department (Name): Dr. Muhammad Yousaf Hamza

Signature with Date: _________________________________

Certificate of Approval

This is to certify that research work presented in this thesis titled “Raman Spectroscopy

based Diagnosis of Dengue Virus Infection in Human Blood Serum” was conducted by

Mr. Bilal Khan under the supervision of Dr. Mushtaq Ahmed.

No part of this thesis has been submitted anywhere else for any other degree. This thesis is

submitted to Department of Physics and Applied Mathematics in partial fulfillment of the

requirements for the degree of Doctor of Philosophy in the field of Physics.

Student Name: Bilal Khan Signature: ----------------------------

Examination Committee:

Examiners Name, Designation & Address Signature

Internal Examiner 1 Dr. Muhammad Aslam Baig

Internal Examiner 2 Dr. Farhan Saif

Internal Examiner 3 Dr. Shahid Manzoor

Supervisor Dr. Mushtaq Ahmed

Co-Supervisor Dr. Masroor Ikram

Department Head Dr. Muhammad Yousaf Hamza

Dean Research PIEAS Dr. Mutawarra Hussain

Thesis Submission Approval

This is to certify that the work contained in this thesis entitled Raman Spectroscopy based

Diagnosis of Dengue Virus Infection in Human Blood Serum, was carried out by Bilal

Khan, and in my opinion, it is fully adequate, in scope and quality, for the degree of Ph.D.

Furthermore, it is hereby approved for submission for review and thesis defense.

Supervisor: _____________________

Name: Dr. Mushtaq Ahmed

Date: April 02, 2018

Place: PIEAS, Islamabad.

Co-Supervisor: __________________

Name: Dr. Masroor Ikram



Head, Department of Physics and Applied Mathematics: ___________________

Name: Dr. Muhammad Yousaf Hamza



Raman Spectroscopy based Diagnosis of

Dengue Virus Infection in Human Blood

Serum

Bilal Khan

Submitted in partial fulfillment of the requirements

for the degree of Ph.D.

2018

Department of Physics and Applied Mathematics

Pakistan Institute of Engineering and Applied Sciences

Nilore, Islamabad, Pakistan

ii

Dedications

To my loving mother, visionary father, caring mother-in-law,

encouraging wife

&

four loving daughters; Aayzah, Sonaiha, Maysha and Ainasaba.

iii

Author’s Declaration

I Bilal Khan hereby declare that my PhD thesis titled “Raman Spectroscopy based

Diagnosis of Dengue Virus Infection in Human Blood Serum“ is my own work and has

not been submitted previously by me or anybody else for taking any degree from Pakistan

Institute of Engineering and Applied Sciences (PIEAS) or any other university / institute in

the country / world.

At any time if my statement is found to be incorrect (even after my graduation), the

university has the right to withdraw my PhD degree.

__________________

(Bilal Khan)

April 02, 2018

PIEAS, Islamabad.

iv

Plagiarism Undertaking

I Bilal Khan solemnly declare that research work presented in the thesis titled “Raman

Spectroscopy based Diagnosis of Dengue Virus Infection in Human Blood Serum” is

solely my research work with no significant contribution from any other person. Small

contribution / help wherever taken has been duly acknowledged or referred and that complete

thesis has been written by me.

I understand the zero tolerance policy of the HEC and Pakistan Institute of

Engineering and Applied Sciences (PIEAS) towards plagiarism. Therefore, I as an author of

the thesis titled above declare that no portion of my thesis has been plagiarized and any

material used as reference is properly referred/cited.

I undertake that if I am found guilty of any formal plagiarism in the thesis titled above

even after the award of my PhD degree, PIEAS reserves the rights to withdraw/revoke my

PhD degree and that HEC and PIEAS has the right to publish my name on the HEC/PIEAS

Website on which name of students are placed who submitted plagiarized thesis.

__________________

(Bilal Khan)

April 02, 2018

PIEAS, Islamabad.

v

Copyrights Statement

The entire contents of this thesis entitled Raman Spectroscopy based Diagnosis of Dengue

Virus Infection in Human Blood Serum by Bilal Khan are an intellectual property of

Pakistan Institute of Engineering & Applied Sciences (PIEAS). No portion of the thesis

should be reproduced without obtaining explicit permission from PIEAS.

vi

Table of Contents

Dedications............................................................................................................................ ii

Author’s Declaration .......................................................................................................... iii

Plagiarism Undertaking ..................................................................................................... iv

Copyrights Statement .......................................................................................................... v

Table of Contents ................................................................................................................ vi

List of Figures ...................................................................................................................... ix

List of Tables ....................................................................................................................... xi

Abstract ............................................................................................................................... xii

List of Publications ........................................................................................................... xiv

List of Abbreviations and Symbols .................................................................................. xv

Acknowledgments ............................................................................................................ xvii

1 Introduction ................................................................................................................ 1

1.1 Overview .............................................................................................................. 1

1.2 Objective .............................................................................................................. 3

1.3 Chemical Diagnostic Techniques ........................................................................ 4

1.4 Optical Diagnostic Techniques ............................................................................ 4

1.4.1 Elastic Scattering Spectroscopy (ESS) ......................................................... 4

1.4.2 Diffuse Reflectance Spectroscopy (DRS) .................................................... 4

1.4.3 Differential Path-Length Spectroscopy (DPS) ............................................. 5

1.4.4 Near Infrared Spectroscopy .......................................................................... 5

1.4.5 Fluorescence Spectroscopy ........................................................................... 5

1.4.6 Raman Spectroscopy .................................................................................... 6

1.5 Thesis Layout ....................................................................................................... 6

2 Diagnosis of DENV Infection and Raman Spectroscopy ........................................ 7

vii

2.1 DENV Infection ................................................................................................... 7

2.1.1 DENV Structure ........................................................................................... 7

2.1.2 DENV Strains and ‘Immune Protection’ ...................................................... 7

2.1.3 Diagnostics Techniques ................................................................................ 8

2.1.4 Dengue in the Future .................................................................................. 11

2.2 Raman Spectroscopy .......................................................................................... 11

2.2.1 History of Raman Scattering ...................................................................... 11

2.2.2 Raman Shift ................................................................................................ 12

2.2.3 Classical Description .................................................................................. 12

2.2.4 Quantum Description .................................................................................. 16

2.2.5 Raman Signal Intensity ............................................................................... 18

2.2.6 Quantitative and Qualitative Analysis ........................................................ 19

2.2.7 Recent Advances in Raman Spectroscopy ................................................. 20

2.2.8 Diagnostic Applications ............................................................................. 21

2.2.9 Advantages ................................................................................................. 22

2.2.10 Disadvantages............................................................................................ 23

2.3 Summary ............................................................................................................ 23

3 Materials and Methods ............................................................................................ 25

3.1 Collection of Samples ........................................................................................ 25

3.2 Experimental Setup ............................................................................................ 25

3.3 Preprocessing Methods ...................................................................................... 27

3.4 Statistical Analysis ............................................................................................. 29

3.4.1 Multivariate Analysis Techniques .............................................................. 29

3.4.2 PLS Regression........................................................................................... 29

3.5 Molecular Analysis ............................................................................................ 33

4 Results and Discussions ............................................................................................ 34

4.1 NS1 based Screening ......................................................................................... 34

viii

4.1.1 Results and Discussion: .............................................................................. 34

4.2 IgM based Screening .......................................................................................... 40

4.2.1 Results and Discussions: ............................................................................ 40

4.3 IgG based Screening .......................................................................................... 47

4.3.1 Results and Discussions ............................................................................. 47

4.4 Lactate as Biomarker ......................................................................................... 57

4.4.1 Acquiring Raman Spectra ........................................................................... 57

4.4.2 Raman spectral analysis.............................................................................. 58

4.4.3 Results and Discussion ............................................................................... 60

5 Conclusions and Future Prospects .......................................................................... 63

5.1 Raman Spectroscopy based Diagnosis of DENV Infection ............................... 64

5.1.1 NS1 based Study ......................................................................................... 64

5.1.2 IgM based Study ......................................................................................... 64

5.1.3 IgG based Study ......................................................................................... 65

5.1.4 Lactate as a Biomarker ............................................................................... 65

5.2 Future Prospective ............................................................................................. 65

References ........................................................................................................................... 67

ix

List of Figures

Figure 2-1 A typical Raman spectrum. ................................................................................... 12

Figure 2-2 Diatomic molecule as a mass on a spring. ............................................................ 13

Figure 2-3 Bond length of a diatomic molecule during a vibration. ....................................... 15

Figure 2-4 Polarizability as a function of vibrational displacement about equilibrium. ......... 15

Figure 2-5 Energy level diagram. ........................................................................................... 16

Figure 2-6 Conservation of energy for Raman scattering (Stokes). ....................................... 17

Figure 2-7 Energy level diagram for Raman scattering. ......................................................... 18

Figure 3-1 Schematic diagram of a typical fiber optic probe based Raman system. .............. 26

Figure 3-2 Experimental setup of the Raman system. ............................................................ 27

Figure 3-3 Preprocessing of the Raman spectrum from a raw spectrum to final a

preprocessed Raman spectrum is shown here: a: Raw Raman spectrum, b: denoised and

smoothed Raman spectrum, c: baseline corrected Raman spectrum, d: vector-normalized

Raman spectrum....................................................................................................................... 29

Figure 3-4 Mathematical description of PLS regression. ....................................................... 30

Figure 3-5 Area under ROC curve and its interpretation. ....................................................... 32

Figure 4-1 Raman spectra of sera samples used in NS1 based screening study. .................... 35

Figure 4-2 Calibration curve of model for NS1 based screening. .......................................... 36

Figure 4-3 Regression coefficients of PLS model for NS based screening. ........................... 36

Figure 4-4 Raman spectra of sera samples used in IgM based screening study. .................... 40

Figure 4-5 RMSECV curve calculated by using number of PCs from 1 to 20. ...................... 41

x

Figure 4-6 Curves obtained by employing methods of Kaiser, Scree and parallel factor

analysis for IgM based screening. ............................................................................................ 41

Figure 4-7 Calibration curve for IgM based screening along with predictions for testing data

set. ............................................................................................................................................ 42

Figure 4-8 Sensitivity, specificity and accuracy of the IgM based screening model at

different cut-off values. ............................................................................................................ 43

Figure 4-9 ROC curve for IgM based screening. .................................................................... 43

Figure 4-10 Regression vector along with average spectra of negative, mild IgM positive and

strong IgM positive samples. ................................................................................................... 44

Figure 4-11 Patch area display of Raman spectra used in IgG based screening. .................... 48

Figure 4-12 RMSE curve for PCs optimization for IgG based model. ................................... 48

Figure 4-13 Eigen values based curves for optimization for IgG based model. ..................... 49

Figure 4-14 Calibration curve of PLS model developed for IgG based screening. ................ 50

Figure 4-15 Sensitivity, specificity and accuracy of the PLS model for IgG at different cut-

off values. ................................................................................................................................. 50

Figure 4-16 Receiver operator characteristic (ROC) curve for IgG based screening model. . 51

Figure 4-17 Regression vector along with average spectra of negative, mild IgG positive and

strong IgG positive samples. .................................................................................................... 52

Figure 4-18 Sketch of experiment setup. ................................................................................ 58

Figure 4-19 Vector normalized mean Raman spectra of healthy and dengue infected sera

(upper) along with the mean difference between the normal and infected samples (lower). .. 58

Figure 4-20 Vector normalized Raman spectra of lactic acid solution. .................................. 59

Figure 4-21 Vector normalized mean Raman spectra of healthy sera, dengue infected sera, 50

mM/L and 100 mM/L of lactic acid solution in healthy sera. ................................................. 60

xi

List of Tables

Table 2-1 Cross-sections of most common optical processes [87]. ........................................ 19

Table 2-2 σ(νex) of Raman scattering for CHCl3 at different incident wavelengths [87]. ....... 19

Table 4-1 Positive regression coefficient obtained by PLS model for NS1 based screening

along with molecular description. ............................................................................................ 38

Table 4-2 Negative regression coefficient obtained by PLS model for NS1 based screening

along with molecular description. ............................................................................................ 39

Table 4-3 Prominent Raman bands highlighted by the strongly positive or strongly negative

values of regression coefficients of IgM based model. ............................................................ 46

Table 4-4 Prominent Raman bands which have been highlighted by the strongly negative

values of regression coefficients of this model are tabulated for their bio-molecular

assignment................................................................................................................................ 54

Table 4-5 Prominent Raman bands which have been highlighted by the strongly positive

values of regression coefficients of this model are tabulated for their bio-molecular

assignment................................................................................................................................ 55

xii

Abstract

Dengue virus (DENV) infection is a mosquito born infectious disease. Its diagnostic is utmost

important for treatment, as the symptoms of disease are quite similar to other diseases.

Current pathological diagnostics methods available are reverse transcriptase polymerase

chain reaction (RT-PCR) and enzyme linked immunosorbent assay (ELISA). RT-PCR is

used to detect the virus itself while ELISA is used to detect non-structural protein-1 (NS1)

and antibodies like immunoglobulin-M (IgM) and immunoglobulin-G (IgG). Existing

methods e.g. virus isolation, RT-PCR and ELISA have certain disadvantages like more time

consuming, false-positive/false-negative results and expensive as compared to Raman

spectroscopy. Raman spectroscopic technique provides molecular signatures, minimum

running cost and online results. Raman spectra of biological samples combined with a

suitable statistical data-mining technique like partial least squares (PLS) regression can be

used to devise a new method for diagnosis of DENV infection in human blood sera. In

present studies, this technique is successfully applied for the diagnostic of DENV infection

based on three steps. A graphical user interface (GUI) was specially designed and its code

was developed in MATLAB (Mathworks 2009a) programming language to implement PLS

for the presented research work.

First step: Raman spectra of ELISA confirmed NS1 positive and negative sera samples

are discriminated by PLS regression. Analysis of regression coefficients, which differentiate

these groups, shows an increasing trend for phosphatidylinositol, ceramide and amide-III, and

a decreasing trend for thiocyanate in the DENV infected serum.

Second step: Raman spectra of samples, with known value of ELISA based AI of IgM

are discriminated by PLS regression. Analysis of regression coefficients revealed that

concentration of asparagine, glutamate, galactosamine etc. were found to increase while

concentration of fructose, cholesterol, cellobiose, and arabinose were found to decrease with

increasing values of antibody index (AI) of IgM.

Third step: Raman spectra of samples, with known value of ELISA based AI of IgG are

discriminated by PLS regression. Analysis of regression coefficients revealed that myristic

acid, coenzyme-A, alanine etc. were found to increase, while amide III, collagen, proteins,

xiii

fatty acids, phospholipids and fucose were found to decrease with increasing values of AI of

IgG. Raman spectroscopy provides not only the diagnosis of DENV infection, but it also

enables the detailed insight of the abnormalities appearing in molecular composition of a

sample.

Importantly, Raman spectra @ 532 nm excitation were used to investigate the possible

use of lactate as biomarker for DENV infection. It was found that spectral difference in

healthy and infected samples is due to an elevated level of lactate in DENV infected group.

xiv

List of Publications

Journal Publications

M. Bilal, M. Saleem, M. Bilal, M. Khurram, and S. Khan, “Raman spectroscopy

based discrimination of NS1 positive and negative dengue virus infected serum,”

Laser Phys. Lett., vol. 13, no. 9, p. 95603, (2016).

M. Bilal, M Saleem, M. Bilal, T Ijaz, S. Khan, R. Ullah, A. Raza, M. Khurram, W.

Akram, and M. Ahmed, “Raman spectroscopy-based screening of IgM positive and

negative sera for dengue virus infection,” Laser Phys., vol. 26, no. 11, p. 115602,

(2016).

M. Bilal, M. Saleem, M. Bial, S. Khan, R. Ullah, H. Ali, M. Ahmed, and M. Ikram,

“Raman spectroscopy based screening of IgG positive and negative sera for dengue

virus infection,” Laser Phys. Lett., vol. 14, no. 11, p. 115601, (2017).

M. Bilal, R. Ullah, S. Khan, H. Ali, M. Saleem, and M. Ahmed, “Lactate based

optical screening of dengue virus infection in human sera using Raman spectroscopy,”

Biomed. Opt. Express, vol. 8, no. 2, p. 1250, (2017).

M Bilal, M. Saleem, S. T. Amanat, H. A. Shakoor, R. Rashid, A. Mahmood and M.

Ahmed, “Optical diagnosis of malaria infection in human plasma using Raman

spectroscopy,” J. Biomed. Opt., vol. 20, no. 1, p. 17002, (2015).

S. Khan, R. Ullah, A. Khan, A. Sohail, N. Wahab, M. Bilal, M. Ahmed, “Random

Forest-Based Evaluation of Raman Spectroscopy for Dengue Fever Analysis,” Appl.

Spectrosc., vol. 71, no. 9, p. 2111-2117, (2017).

S. Khan, R. Ullah, M. Saleem, M. Bilal, R. Rashid, I. Khan, A. Mahmood and M.

Nawaz, “Raman spectroscopic analysis of dengue virus infection in human blood

sera,” Opt. - Int. J. Light Electron Opt., vol. 127, no. 4, pp. 2086–2088, (2016).

S. Khan, R. Ullah, A. Khan, N. Wahab, M. Bilal, and M. Ahmed, “Analysis of

dengue infection based on Raman spectroscopy and support vector machine (SVM),”

Biomed. Opt. Express, vol. 7, no. 6, p. 2249, (2016).

xv

List of Abbreviations and Symbols

ADE Antibody-dependent enhancement

AI Antibody/Antigen index

ANN Artificial neural networks

AUC Area under ROC curve

BRC Breast cancer

CARS Coherent anti-Stokes Raman spectroscopy

CCD Charged coupled devices

DENV Dengue virus

DF Dengue fever

DHF Dengue hemorrhagic fever

DPS Differential path-length spectroscopy

DSS Dengue shock syndrome

ELISA Enzyme linked immunosorbent assay

EM Electromagnetic

ESS Elastic scattering spectroscopy

GUI Graphical user interface

IgG Immunoglobulin-G

IgM Immunoglobulin-M

IR Infra-red

LOC Lab-on-chip

LOO Leave one out

NIH National institute of health

NILOP National Institute of Lasers and Optronics

NIR Near infra-red

NORI Nuclear medicine oncology & radiotherapy institute

NS1 Non-structural protein-1

PCA Principal component analysis

PCs Principal components

PLS Partial least squares

xvi

PRS Polarized Raman spectroscopy

QCMD Quality control for molecular diagnostics

RF Random forest

RMC Rawalpindi medical college

RMSECV Root mean squared error in cross validation

RMSEP Root mean squared error in predictions

RNA Ribonucleic acid

ROC Receiver operating characteristic

RRS Resonance Raman spectroscopy

RT-PCR Reverse transcriptase polymerase chain reaction

SD Standard deviation

SERS Surface enhanced Raman spectroscopy

SVM Support vector machine

TEC Thermo electric cooler

xvii

Acknowledgments

First and foremost, I am heartedly thankful to Almighty Allah (جل جلاله) for His countless

blessings including the opportunity of the present research work and its completion. I offer

my sincere gratitude to the Holy Prophet Hazrat Muhammad (صلى الله عليه وسلم), the sole pride of mankind

and icon of mercy.

I would like to pay heartfelt gratitude to my supervisor Dr. Mushtaq Ahmed and co-

supervisor Dr. Masroor Ikram and Dr. Muhammad Saleem. Unequivocally, their dedication

and devotion to work has been a source of immense inspiration for me. I must appreciate

their guidance, cooperation and generosity for study and research related issues and beyond.

I would like to thank specially to Dr. Muhammad Saleem (NILOP) for his kind

supervision and guidance from very first day in every stage of the present research work. He

was the driving force for me and kept me on track by his vision and support. Most

importantly, I am cordially thankful to my visionary father and caring mother who always

prayed and scarified to flourish my career. I also appreciate the exceptionally supporting role

of my wife, mother-in-law and siblings during my studies. I am thankful to Mr. Rub Nawaz

Khan for all his support, encouragement and prayers at crucial stages of my life.

I thank Dr. Rahat Ullah for his extra ordinary support in the completion of the present

research work along with Dr. Saranjam Khan, Dr. Hina Ali. I would like to thank Dr.

Muhammad Khurram and Dr. Faiza for providing biological samples and expert serology

opinion. I am thankful to my friends Dr. Banat Gul and Mr. Abdul Basit for their guidance,

advices, and valuable suggestions and comments. I am thankful to all the teachers and staff of

PIEAS, NILOP and RMC for helping me in my study and experimental work. I am also

thankful to Mrs. Uzma Shazia, Mr Iqbal, Mrs. Fatima Batool and Mr. Muhammad Irfan.

Finally, financial support from Higher Education Commission of Pakistan under PhD

5000 fellowship program (Phase-II) is highly acknowledged.

1

1 Introduction

1.1 Overview

Dengue virus (DENV) infection is a mosquito born infectious disease. Dengue fever

(DF) is the most significant arborviral disease in the world today, which is caused by

DENV. Dengue fever may develop into dengue hemorrhagic fever (DHF) and dengue

shock syndrome (DSS) in some severe cases. This virus belongs to the family

flaviviridae, and it is based on a capsulated single strand of ribonucleic acid (RNA).

There are four serotypes of this virus. Its RNA contains the coding for seven non-

structural and three structural proteins [1]. Dengue virus infected subjects are reported

with various symptoms which are somehow similar to other diseases like flu, malaria,

chickengunya and typhoid. According to an estimate by world health organization

(WHO), there are 390 million cases of DENV infections per year. Among these,

500000 cases are about hospitalizations while 25000 cases of death are estimated. It is

reported that a majority of these infections (70–80 %) are of asymptotic nature [2]–

[4]. Its vector is found throughout the subtropical and tropical areas of the world.

Female aedes aegypti mosquito is responsible for its spreading because it carries

the DENV in its mid-gut for about 7-14 days for incubation. Later it moves towards

salivary glands of the mosquito and it is mixed with saliva. Dengue virus is

transmitted to the host when its carrier mosquito bites the host i.e. human. The blood

stream, epithelial tissues and dendritic cells are infected initially at the site of biting.

This virus moves into the bone marrow, liver and other parts of the body for its

replication in the first few days [5], [6]. It produces structural and non-structural

proteins for its fast replication and facilitation respectively. Mild fever starts after 4-7

days of mosquito bite which is known as DF. Later on, the immune system of a

human body responds to DENV infection by producing anti-dengue antibodies

immunoglobulin-M (IgM) and Immunoglobulin-G (IgG). IgM is produced after 5-6

days of fever and IgG is produced after 7-10 days of fever [7]–[9]. In the first five

days of fever, non-structural protein (NS1) is the only biomarker which can be used

Introduction

2

for diagnosis of DENV infection when there is no antibody produced. Later on IgM

and IgG can be detected for diagnosis of DENV infection.

Virus isolation [10]–[13] and reverse RT-PCR [14]–[16] are costly, time

consuming and highly sophisticated methods of diagnosis which cannot be used for

screening of DENV infection as a routine test. However, ELISA is a technique which

is being commonly used for the diagnosis of DENV infection (a detailed description

is given in section 2.1.3). It screens the serum for positive and negative samples on

the basis of NS1, IgM and IgG. It is primarily based on chemical complexes like

enzyme-antigen-complex and enzyme-antibody-complex. It yields a numeric value

which represents the antigen/antibody index (AI). A sample with the value of AI

above cut-off value of that specific ELISA kit is declared as positive for that specific

antigen/antibody [17]. It is evident that impurity in reagents, used in ELISA, may

result in false-positive or false-negative results. This invites the researchers to

investigate other techniques which should be more economical, quick, reliable and

robust.

Light based diagnostic methods, known as optical diagnosis, are being

investigated in various research centers for the diagnosis of various diseases. Light

based spectroscopic techniques have the potential to analyze biological samples in a

variety of ways to characterize them for the diagnosis of diseases. These

spectroscopic techniques have been described in section 1.4 along with their potential

for diagnostic applications. These techniques include infra-red (IR) spectroscopy,

fluorescence spectroscopy, elastic scattering spectroscopy (ESS), near infra-red (NIR)

spectroscopy, differential path-length spectroscopy (DPS) and Raman spectroscopy. It

is important to mention that in resonance phenomenon based techniques tunable

wavelengths are required for different molecules, however in Raman spectroscopy a

non-resonant Raman signal can be recorded with a single wavelength from different

molecules present in the sample at a time. Raman spectroscopy has certain other

advantages of sensitivity, specificity, quickness, no-sample preparation and cost

effectiveness, which encouraged the investigation of this technique to be used as a

possibly new diagnostic tool for DENV infection.

Dengue virus infection alters the biochemistry of infected blood; therefore,

Raman spectroscopy can be used for assessment of these biochemical changes

Introduction

3

effectively. Raman spectra of human blood sera having disease e.g. malaria [18],

dengue [18]–[23], Hepatitis-C [24], diabetes, female breast cancer, nasopharyngeal

cancer and other types of cancers [25]–[27] have been investigated with the help of

statistical models to predict diseases. With the help of such type of models, partial

least squares (PLS) regression algorithm has been employed to quantify various

analytes in body fluids [28], [29]. Raman spectroscopy in combination with

multivariate analysis can be an excellent tool for the diagnosis of DENV infection

which is fast, reliable, accurate, more efficient and economical. In this contribution,

Raman spectra of positive and control sera, on the basis of NS1, IgM and IgG, were

used with a PLS regression routine to develop multivariate models in MATLAB

(Mathworks 2009a) environment for the prediction of infected samples. The model

yields a vector of regression coefficients at corresponding Raman shifts. These

regression coefficients can be analyzed to identify the biological molecules which

play certain roles in this disease.

The present research work is intended to investigate the prospects of Raman

spectroscopy with 785 nm and 532 nm excitation wavelengths for the diagnosis of

DENV infection in human blood serum. This study comprised of four main goals

which are; NS1 based screening, IgM based screening, IgG based screening and

investigation of the role of lactate as a new biomarker for DENV infection. The

results of all these studies are found to be very encouraging for further studies and

implementation of Raman spectroscopy based diagnosis of DENV infection.

1.2 Objective

DENV produces certain types of biological molecules for its reproduction and

propagation. The molecule known as NS1 helps in the propagation of DENV.

Immune system of human body protects it by producing antibodies like IgM and IgG

against pathogen i.e. DENV. Diagnosis of DENV infection is usually done by ELISA

of IgM, IgG and NS1. It is a chemical test with certain disadvantages of cost, time and

accuracy. In presented study, Raman spectra of samples with known ELISA based

results about NS1, IgM and IgG were used to develop PLS models. These models

were aimed at screening of the positive and negative sera samples by using their

Raman spectra for NS1, IgM and IgG. Results of these Raman spectroscopy based

screenings combined with clinical symptoms will help the physicians to diagnose the

Introduction

4

DENV infection in a comparatively quick, cost-effective and accurate manner.

Lactate is a potential biomarker for diagnosis of DENV infection. Elevated levels of

lactate in human blood serum can also be examined by using Raman spectroscopy. It

will further strengthen the diagnosis of DENV infection.

Every chemical compound has its own characteristic Raman spectrum which

works as its fingerprint. High level of specificity of Raman spectroscopy is capable of

increasing the accuracy, specificity and sensitivity of diagnosis of DENV infection

which is demonstrated successfully in the presented research work. Raman

spectroscopy doesn’t require any sample preparation and it does not require any

chemical, thus the proposed technique is quick, cost effective and accurate.

1.3 Chemical Diagnostic Techniques

Virus isolation, RT-PCR and ELISA are the chemical methods which are in practice

for the diagnosis of DENV infection. In virus detection [10]–[13], the DENV itself is

separated from the sample of infected subject and it takes almost a week in this

process. RT-PCR [14]–[16] is used to determine the viral load quantitatively by a

repeated process and it usually takes one day in this process. Enzyme linked

immunosorbent assay (ELISA) [30], [31] based detection of NS1, IgM and IgG are

the most commonly used techniques at the moment.

1.4 Optical Diagnostic Techniques

1.4.1 Elastic Scattering Spectroscopy (ESS)

When a tissue is exposed to light, the light is reflected, scattered or absorbed. These

scattering events may take place for a number of times in an elastic manner such that

the light coming out of the tissue has the same energy as that of the incident one.

Scattering centers can either be normal or pathological. All the scattered photons are

collected and recorded as a spectrum. By using the refractive indices of cellular

components, the changes can be identified [32]–[34].

1.4.2 Diffuse Reflectance Spectroscopy (DRS)

In this technique the same wavelength of light has been observed after scattering

from the sample [35]. It is same case of Rayleigh scattering. Scattering strength

Introduction

5

depends upon the refractive index of the sample. By using refractive index of the

cellular matrix it can determine the size and density of the matrix. Cancer is a disease

which causes these types of changes and DRS can be used for its diagnosis [36].

1.4.3 Differential Path-Length Spectroscopy (DPS)

DPS is an optical technique which is used to determine intrinsic optical properties in-

vivo in a minimally invasive manner. It differs from ESS in the way that it fixes the

path length and visitation depth of the scattered photons [37]. The signal obtained has

combined information about morphology and biochemistry of the biological sample.

Its system is based on two spectrometers which are used for illumination and

collection of signals with the help of fibers. A halogen lamp is used as a white light

source. It has been applied in the medical field for breast tissue [38], bronchial tree

[39] and oral mucosa [40].

1.4.4 Near Infrared Spectroscopy

The spectrum in the range 800-2500 nm is known as near infrared (NIR). It penetrates

deep into biological tissues. It probes the chemical bonds of biological molecules

[41]. Differentiation of biological samples on the basis of various functional groups

and chemical bonds is achieved by using the NIR spectrum. It has been applied on

human tissues to grade the various kinds of neoplasia [42].

1.4.5 Fluorescence Spectroscopy

All the tissues contain fluorophores which make the tissue to fluoresce when light is

made incident on it. The fluorescence spectrum contains the information of all the

fluorophores present in the sample which help in differentiation between the two

different groups of samples with varying composition [41], [43]–[45]. In cases where

malignant tissues cannot be identified by white light, fluorescence spectroscopy is

useful. An early diagnosis of laryngeal cancer is a remarkable achievement with this

technique [46].

Introduction

6

1.4.6 Raman Spectroscopy

A non-resonant type of scattering in which the scattered photon has different energy

than that of the incident one is known as Raman scattering. Raman spectroscopy is a

vibrational spectroscopic technique where the interaction of electromagnetic (EM)

waves with a molecule changes its polarizability which results in the Raman bands.

Each band corresponds to a specific energy transition between the vibration energy

levels. A Raman spectrum consisting of these specific Raman bands is known as the

molecular-fingerprint of a target molecule. Raman spectroscopy is a sensitive

technique with high specificity. It is described in details in next chapter. It can be used

effectively for various applications in different fields of science and technology [47]–

[49].

1.5 Thesis Layout

Thesis of the present research work is presented in five chapters. Over view of the

problem addressed in the present research is described briefly in chapter 1 with a

description of DENV infection and its diagnosis. Moreover, brief information

regarding various optical spectroscopic techniques has also been provided along with

Raman spectroscopy and its efficacy regarding diagnosis of DENV infection in the

same chapter. In chapter 2, the first part is dedicated to the description of details about

DENV infection and the sequence of the events that occur during this disease. Then

various most frequently used diagnostic techniques have been described along with

their advantages and drawbacks. In the second part of chapter 2, Raman spectroscopy,

its theoretical and mathematical description on the basis of Classical Physics and

Quantum Physics, related instrumentation and its advantages are discussed. Chapter 3

contains the information regarding the process of samples collection, experimental

setup for Raman spectrum acquisition, statistical methods for model development,

processing of the Raman spectra and biological molecules based analysis of

regression coefficients of the developed model by PLS regression. All the published

articles of the present research work are presented in chapter 4 where experimental

works, analysis, results and discussions are given for NS1 based study [50], IgM

based study [51], IgG based study [52] and lactate based study [53]. Finally, an

overall conclusion of the present research work is given in Chapter 5.

7

2 Diagnosis of DENV Infection and Raman

Spectroscopy

2.1 DENV Infection

DENV infection is a mosquito borne epidemic disease of tropical and subtropical

areas of the world with a high degree of mortality, even in twenty-first century. It is

being investigated with great intensity; however, still there are various unidentified

causing factors and dynamics of this disease. Some of the factors responsible for its

worldwide spread are population growth and transportation of the modern world [54].

Symptoms of DF include flu, headache, diarrhea, loss of appetite, muscle/joint pain,

neurological manifestation, bleeding tendency, thrombocytopenia and skin rash [55].

The DENV is responsible for this disease.

2.1.1 DENV Structure

Encapsulated RNA of DENV, which is 11 kb in length, encodes ten types of proteins.

Three of them are structural proteins while seven are non-structural. One of these non-

structural proteins is known as NS1. It has an important role from diagnostic point of

view. Four sera types of DENV are known as DEN-1, DEN-2, DEN-3 and DEN-4.

They are transmitted by female aedes aegypti mosquito [56]. Unfortunately, there is

no specific vaccine available to control it. It is reported that DENV is not neutralized

by antibodies produced against it by human body immune system. It is an extremely

alarming fact to know that these antibodies may play a negative role by antibody-

dependent enhancement (ADE) of DENV infection [57]–[59].

2.1.2 DENV Strains and ‘Immune Protection’

Initial fever in this disease is categorized as DF while its severe cases are categorized

as DHF and DSS [60]. If a patient is supported with therapy then a study shows that

only 1-5 % cases of DF have resulted in death. Only few of these cases move towards

Diagnosis of DENV Infection and Raman Spectroscopy

8

DHF and DSS where plasma leakage is the first sign. In certain cases this shock leads

to death of the patient [61].

After the bite of a mosquito, it usually takes 4-7 days to show the first sign as

fever. In these initial days the virus replicates itself rapidly and it rises to a certain

level that a biting mosquito can be infected. This stage of high concentration on

DENV in the body is known as viremia [61]. During viremia, NS1 is found abundant

in the serum. The immune system responds to it by producing IgM in 5-6 days from

the day of first mosquito bite. After 7-10 days human body immune system produces

IgG which last for a very long time in the body. If a patient is reported with a high

level of IgG in an acute phase of DF then it is termed as secondary infection [7]–[9].

Presence of IgG in the body ensures protection from future DENV infections. But it

protects only against the serotype of DENV of last infection, so the body still remains

vulnerable to other serotypes. A person with infection of two or more serotypes of

DENV at a time increase the chance of DHF and DSS [62]. There is a hypothesis that

if a body contains antibodies which are cross-reactive and non-neutralizing produced

by an earlier infection, then secondary infection is facilitated by these agents with the

help of Fc (a portion of the immunoglobulin molecule) receptors [63].

2.1.3 Diagnostics Techniques

Virus isolation

It is a usual practice for virus isolation that samples of tissue, blood, serum or plasma

are collected well within 5 days, when viremia occurs. These samples are required to

be cooled at 4-8 oC if the storage period is less than a day; otherwise, i.e. for longer

periods these samples should be cooled well at about -70 oC. For DENV isolation, it

is a usual practice to culture the cells with the help of host cells [64], [65]. Isolation of

DENV was performed by Kimura and Hotta in 1943. Four techniques have been

successfully used in different studies [10]–[13]. According to one of these studies [9]

aedes albopictus mosquito cells were cultured in a minimum essential medium

(MEM). The cells were cultured at 28 °C for 7 days. Culture supernatants were

collected and checked for the presence of dengue virus. The serotypes of the isolated

dengue viruses were determined by RT-PCR [9]. This whole process may take 7-14

days and the results are positive only if the samples are well stored and transported

such that it could not affect the viability of DENV in these samples [65].


9

This technique has certain issues like high cost, expert staff, sophisticated

equipment, reagent purity and most importantly it takes about 7 days. All these

drawbacks make it unsuitable for a routine-based diagnosis technique in the days of

an outbreak of DENV infection.

Genome detection

RT-PCR method enables the detection of RNA of DENV in serum, plasma, cells,

tissues etc. It takes one day to perform this test. Serotyping can also be done with this

technique [66]. Conventional RT-PCR has been reported to have sensitivity and

specificity 48.4-100 % and 100 % respectively [15], [16], [66]. Sensitivity is low if

the sample is not of acute phase, which is a drawback of this technique. Moreover,

frequent false-positive results, costly equipment and the need of skilled technician

also make it inconvenient to be adopted as a common diagnosis technique for DENV

infection [31], [67].

This technique has been reported to be successfully used in some studies [68],

[69]. A serum sample was used for isolation of RNA. Process of incubation for

reverse transcriptase was done 53 °C for 10 minutes. Then the process of polymerase

chain reaction was conducted in 30-40 amplification cycles.

ELISA based Antigen Detection

As mentioned in section 2.1.1, NS1 appears in seven days of a mosquito bite, so it

becomes the first biomarker of activity regarding replication and viremia of DENV.

Importantly, its concentration is found to be higher if the subject is a potential case of

DHF [31]. Some of the techniques like immuno-histochemical, immuno-fluorescence

assay (IFA) and radio-immuno-assay (RIA) have been investigated for detection of

NS1, but the success rate was very low as compared to ELISA [30].

In the year 2000, Young and his coworkers succeeded in capturing NS1 in acute

phase serum samples with a method that is known as ELISA. It is a very complex

chemical process which has been explained in great detail by K. Bundo K and A.

Igarashi [70]. Firstly, a layer of adsorbed antigen/antibody is formed in the well plate.

This is called coating. Then the blocking is performed and after that the process of

detection is completed. It is important to note here that this process takes place only at

the surface so it is required to wash the well plate after every step so that unbounded

chemical should be removed properly. Dilution is advised to be avoided for better


10

results. Various layers of antibodies can help in amplification of the final signal so it

is advised to use this technique of multiple layers. Tools for these techniques are

commercially available in kits with ELISA format. They have potentially replaced the

methods of virus isolation and RT-PCR for the screening of DENV infection.

Sensitivity of these kits has been reported to be 94.7-98.3 % and 67.1-77.3 % for

primary infections and secondary infections respectively. However, specificity is 100

% for both type of infections [30]. Low sensitivity of NS1 in case of secondary

infection is a potential drawback as these infections lead to DHF and DSS which is of

prime importance from doctors’ point of view.

ELISA based Antibodies Detection

There are several methods for the detection of antibodies in the serum e.g.

hemagglutination inhibition (HAI) [7], complement fixation (CF) and neutralization

test (NT) [71]. However, these methods are not very commonly used due to several

drawbacks and complexities involved in their procedures. Accurate detection of IgM

and IgG at an earliest possible stage is very useful. Different companies like Standard

Diagnostics, South Korea and Alere, Australia have introduced ELISA based kits for

IgM and IgG detection. These kits are commercially available. Several studies have

been performed on these kits to determine the performance of these kits especially in

secondary infections [16], [72]–[74]. These studies showed that sensitivity and

specificity of these kits for IgM detection ranged in 20.22-99 % and 52-100 %

respectively. Moreover, sensitivity and specificity of these kits for IgG detection

ranged in 78-88.9 % and 63.5-100 % respectively. Cross-reactivity due to Japanese

encephalitis, yellow fever and secondary infections were causing a huge problem in

the reliability of results by these kits as they produce false-positive results quite often

[75]. Frequent use of ELISA based IgM and IgG detection kits around the world is

aimed at discriminating the primary infection from secondary infection.

Virus detection and RT-PCR are not viable solutions for routine based diagnosis

in clinics because these techniques are time consuming, costly and require highly

skilled technicians. ELISA is the technique which is being used as a routine test for

DENV infection due to low cost, quick and robust nature as compared to virus

detection and RT-PCR. However, WHO reports that due to impurities in the reagents

used in ELISA, it results in false-positive and false-negative outcomes [76]. An

ELISA kit is designed to be used for samples in a batch, which means that using this


11

technique for a lone sample is not feasible economically as the kit is valid to be used

during a very short time after it is opened.

It is a fact that the test that provides highly specific and sensitive diagnostic

results are usually more sophisticated and require costly equipment, highly skilled

technicians and more time. Rapid diagnostic tests are simpler, quicker, robust and

cheap, but their sensitivity and specificity are low. Hence, the sensitivity and

specificity are inversely related with an easy and rapid diagnostic test [77].

2.1.4 Dengue in the Future

It is feared by some of the researchers that the DENV infection has the potential to

increase due to various supporting factors e.g. increased travelling, poverty, poor

hygiene and poor sanitation conditions. Its control, diagnosis, management and

prevention will be the hot topic of future research. It is important to explore all the

factors responsible for DENV infection so that the objectives of WHO about a healthy

world could be achieved [78]–[83].

2.2 Raman Spectroscopy

2.2.1 History of Raman Scattering

Smekal was the first to postulate the scattering of light in an inelastic manner in 1923

[84]. Landsberg and Mandelstam saw unexpected frequency shifts in scattering from

quartz [85] in1928. In the same year, Sir Chandrasekhara Venkata Raman became the

pioneer, who demonstrated it practically by using filtered light of the sun and a

telescope along with his co-worker Krishnan [86]. After a span of two years he was

awarded The Nobel Prize for physics in 1930. This type of inelastic scattering is

named after him as ‘Raman effect’ or ‘Raman scattering’. With the invention of laser

systems in 1960s, this field of research grew rapidly. In 1977 SERS was discovered

and single molecule detection was reported in 1997. Research in this field saw a boom

with the availability of thermo electric cooler (TEC) cooled detectors of high

resolution and grating based monochoromators. Fiber optics helped in the application

of this phenomenon in various fields.


12

2.2.2 Raman Shift

A Raman spectrum is obtained by recording the intensity of scattered photons against

their Raman shift in cm-1. Raman shift for each photon can be calculated by Eq. 2-1

where 𝜆𝑖 and 𝜆𝑠 are the wavelengths of incident and scattered photons respectively in

centimeters.

𝑅𝑎𝑚𝑎𝑛 𝑠ℎ𝑖𝑓𝑡 (𝑐𝑚−1) = 1

𝜆𝑖−

1

𝜆𝑠 2-1

A representative Raman spectrum is shown in Fig. 2-1.

Figure 2-1 A typical Raman spectrum.

2.2.3 Classical Description

Brief introduction of Raman scattering is given in section 1.4.6. Classically, a di-

atomic molecule can be assumed to be made of two masses m1 and m1 attached with

each other by means of a spring with spring constant K as shown in Fig. 2-2. Here,

masses m1 and m2 represents two atoms while spring represents the chemical bonding

between these two atoms. Displacement of masses m1 and m2 are represented by x1

and x2 respectively.


13

Figure 2-2 Diatomic molecule as a mass on a spring.

Hooke’s law can be written for this type of system as

𝑚1𝑚2

𝑚1 + 𝑚2(

𝑑2𝑥1

𝑑𝑡2+

𝑑2𝑥2

𝑑𝑡2) = −𝐾(𝑥1 + 𝑥2) 2-2

Here (m1m2/[m1+m2]) represents the reduced mass of the system which can be

represented by μ. Moreover, (x1+x2) represent the total displacement and it can be

replaced by q. Now the above equation can be rewritten in simple form as

𝜇 (𝑑2𝑞

𝑑𝑡2) = −𝐾𝑞 2-3

This second order differential equation can be solved for q, which yields a

solution as

𝑞 = 𝑞𝑜𝑐𝑜𝑠(2𝜋𝜈𝑚. 𝑡) 2-4

In this solution, νm represents the vibrational frequency of the molecule shown

in Fig. 2-2. Its value can be determined by

𝜈𝑚 =1

2𝜋√

𝐾

𝜇 2-5

Equation 2-3 shows that the vibration of a molecule follows a cosine pattern

whereas equation 2-4 depicts that the strength of a chemical bond among the atoms is

directly proportional the frequency of vibration. Reduced mass is however inversely

related to the vibrational frequency. It can be concluded that molecules of different

chemicals will have their own characteristic pattern of these vibrations. Polarizability

of a molecule α is another important term. This property of a molecule shows the

tendency of a molecule to polarize its electronic cloud under the influence of external

electric field. Polarizability is a function of displacement q. In case of Raman

scattering the external electric field is provided by the electric field of an excitation

source of light which is usually a laser beam. The dipole moment P induced due to an

applied field E in a molecule with polarizability α is given by

𝑃 = 𝛼𝐸 2-6

As the electric field is provided by the incident EM wave, in which E oscillates

in a cosine pattern with amplitude Eo and frequency νo, given by


14

𝐸 = 𝐸𝑜cos(2𝜋𝜈𝑜. 𝑡) 2-7

This equation is used in equation 2-6 to get

𝑃 = 𝛼 𝐸𝑜𝑐𝑜𝑠(2𝜋𝜈𝑜. 𝑡) 2-8

Using an approximation which is known as small amplitude approximation, it is

possible to write α as a linear function of q

𝛼 = 𝛼𝑜 + (𝜕𝛼

𝜕𝑞)

𝑞=0

. 𝑞 + ⋯ 2-9

Making use of this expression for α in equation 2-8

𝑃 = 𝛼𝑜 𝐸𝑜𝑐𝑜𝑠(2𝜋𝜈𝑜. 𝑡) + (𝜕𝛼

𝜕𝑞)

𝑞=0

. 𝑞𝑜𝑐𝑜𝑠(2𝜋𝜈𝑚. 𝑡) 𝐸𝑜𝑐𝑜𝑠(2𝜋𝜈𝑜. 𝑡) 2-10

First part on the left side shows the elastic scattering known as Rayleigh scattering,

while the second term represents inelastic scattering which is known as Raman

scattering. Interestingly the last term has two terms of cosine functions which are

being multiplied. This product can be converted into a sum of two cosine terms by

using a trigonometric formula 2.Cos(A).Cos(B)=Cos[(A+B)/2]+Cos[(A-B)/2], which

yields

𝑃 = 𝛼𝑜 𝐸𝑜𝑐𝑜𝑠(2𝜋𝜈𝑜. 𝑡) +1

2(

𝜕𝛼

𝜕𝑞)

𝑞=0

𝑞𝑜𝐸𝑜 . 𝑐𝑜𝑠(2𝜋{𝜈𝑜 − 𝜈𝑚}. 𝑡)

+1

2(

𝜕𝛼

𝜕𝑞)

𝑞=0

𝑞𝑜𝐸𝑜. 𝑐𝑜𝑠(2𝜋{𝜈𝑜 + 𝜈𝑚}. 𝑡) 2-11

Here second term on the left side of the equation represents the oscillating electric

field with frequency which is νo-νm, i.e. less than the incident photon. This represents

Stokes Raman scattering. While, the third term on left hand side represents the

oscillating electric field with frequency νo+νm, i.e. greater than that of the incident

photon. This represents the anti-Stokes Raman scattering.

It is important to note here that the condition which is found to be necessary for

Raman scattering is ∂α/∂q≠0. This condition says that the change of position of atoms,

in a specific vibrational mode, must result in the change of polarizability. In order to

understand this point, consider a diatomic molecule; say AB, in Fig. 2-3. Let L be the

length of bond A-B in equilibrium position. Assume that the maximum displacement

of its atoms from the mean position in a specific vibrational mode is qo.


15

Figure 2-3 Bond length of a diatomic molecule during a vibration.

Figure 2-4 Polarizability as a function of vibrational displacement about

equilibrium.

At the position of maximum compression the atoms of the molecule are so much

close to each other that they are least affected by the externally applied electric field

i.e. incident EM wave. As these atoms start to go away from each other the value of

polarizability α starts to increase and reaches to its equilibrium value αo when atoms

reach back to their equilibrium position. It moves on to maximum elongation and at

that point the molecule can easily be perturbed by externally applied electric field.

Here α has maximum value. In Fig. 2-4 the values of α against the separation of atoms


16

is shown for understanding of the condition for Raman scattering to occur. The

Raman scattered light will have frequencies νo-νm and νo+νm. This condition can be

considered to be the selection rule of Raman scattering α.

2.2.4 Quantum Description

According to quantum mechanics a molecule has quantized electronic, vibrational and

rotational energy levels. These energy levels are shown in Fig. 2-5.

Figure 2-5 Energy level diagram.

Atoms of a diatomic molecule vibrate about the mean position and they behave

just like a harmonic oscillator which has quantized energy levels i.e. Ej=hνj[j+(1/2)].

Here j represents the quantized vibrational energy level as shown in Fig. 2-6.

Therefore, it can be said that the difference of energies of levels E1 and E0 is ΔEm. i.e.

νm= ΔEm /h [47]–[49]. According to Boltzmann distribution function in a collection of

molecules than will be a certain number density of molecules in a state with j=0

which will be more than the number density of the state with j=1 and so on. This is

linked with the temperature of the molecules. Due to interaction of a photon with a

molecule, a transition in the vibrational energy level takes place and Raman scattering


17

takes place as a result of this transition. The EM wave of this photon sets up an

oscillation g dipole moment and molecule-photon complex jumps to a virtual energy

level. Energy level of this virtual state is not equal to any electronic state, but is

greater than the vibrational level, so the molecule remains in the ground electronic

state. During this interaction some quanta of energy is kept by the molecule which

corresponds to the vibrational mode of that molecule and surplus energy is taken

away by the Raman scattered photon as shown in Fig. 2-6.

Figure 2-6 Conservation of energy for Raman scattering (Stokes).

If a molecule jumps from lower vibrational level to a higher vibrational level, then the

energy of Raman scattered photon will be h(νo-νm) i.e. Stokes Raman scattering (Δj=

1). Whereas, if a molecule jumps from higher vibrational level to lower vibrational

level, then the Raman scattered photon will the carry the energy h(νo+νm) i.e. anti-

Stokes Raman scattering (Δj= -1). As depicted by the factor in Boltzmann distribution

of molecules, it is evident that the number density of molecules in lower vibrational

energy level will be more than the number density in higher vibrational energy level,

it can be said that the Stokes Raman scattering and anti-Stokes Raman scattering will

take place simultaneously but the intensity of latter will be lower comparatively [51]–

[53]. The same phenomenon is shown in Fig. 2-7, along with Raleigh scattering, IR

absorption and fluorescence to have a comparative understanding with Raman

scattering.


18

Figure 2-7 Energy level diagram for Raman scattering.

2.2.5 Raman Signal Intensity

Typically, out of 107 incident photons only 1 photon is scattered inelastically. This

gives an idea that how much low intensity of Raman signal can be detected in

collected scattered light. It is important to have a view of the parameters that

determine the strength of Raman signal. The relation of intensity of Raman signal ФR

with different parameters is given by

𝛷𝑅 ∝ 𝜎(𝜈𝑒𝑥)𝜈𝑒𝑥4 𝐸𝑜𝑛𝑖𝑒

−𝐸𝑖𝑘𝑇

Here νex is excitation frequency, σ(νex) is Raman scattering cross-section for a

particular wavelength, Eo, is the irradiance of the incident beam, ni is the number

density of the target sample and the last exponential term is Boltzmann factor for state

i. Raman scattering cross-section is compared with cross-sections of different

processes in the Table 2-1.

It is important to note that the intensity of Raman signal is inversely

proportional to the fourth power of wavelength of the incident beam as shown in

Table 2-2. By using a shorter wavelength and higher power more intense Raman

signal can be recorded from the same concentration of scatterers in a sample.


19

Table 2-1 Cross-sections of most common optical processes [87].

Cross-section of process σ(νex) (cm2)

Surface enhanced resonance Raman scattering 10-15

Surface enhanced Raman scattering 10-16

Absorption of UV 10-18

Emission of fluorescence 10-19

Absorption of IR 10-21

Resonant Raman scattering 10-24

Rayleigh scattering 10-26

Raman scattering 10-29

Table 2-2 σ(νex) of Raman scattering for CHCl3 at different incident wavelengths

[87].

λex (nm) σ(νex) ( x 10-28 cm2)

532.0 0.66

435.7 1.66

368.9 3.76

355.0 4.36

319.9 7.56

282.4 13.06

2.2.6 Quantitative and Qualitative Analysis

From 1928 to the end of the last century, the components used for Raman

spectroscopy were neither cheap not that much efficient as they are at the moment.

This led to a delay in the application of Raman spectroscopy for the most part of the

last century in various fields. Now with stable monochromatic laser systems in the

form of diode lasers, high resolution TEC cooled CCD detectors, fiber optics based

probes and more intelligent and programmable applied multivariate statistical analysis

methods; not only qualitative analysis but also quantitative analysis can be performed.

Hence, by using Raman spectroscopic analysis, the concentration of particular specie

in a sample can be determined.


20

2.2.7 Recent Advances in Raman Spectroscopy

Surface Enhanced Raman Spectroscopy (SERS)

SERS is a technique in which the strength of the Raman signal is enhanced to the

order of 1014-1015. The use of specially constructed nano-surfaces and nano-particle is

getting famous among the researchers who are interested in detection and analysis of

chemical present in trace amounts in samples. Gold and silver are being used for this

technique while copper and aluminum is also under investigation. Kits for SERS are

commercially available as well. It is interesting that the exact enhancement

phenomenon of this technique is still a topic of debate. However, chemical and

electric filed enhancements are two well-known factors considered responsible.

Electric field theory takes into account the mechanism of surface plasmon resonance

whereas; chemical theory explains it on the bases of charge-transfer-complexes [88].

Coherent Anti-Stokes Raman Spectroscopy (CARS)

It uses the interaction of a pump laser beam with frequency ωp and a Stokes laser

beam with frequency ωs. Frequency of CARS signal is given by 2ωp- ωs. When the

beat frequency of a pump beam and Stokes matches with the frequency of a particular

Raman band, then it results in an enhanced anti-Stokes Raman signal, which is known

as CARS [89]. It has some advantages which can exploit the signal from a particular

Raman band of a molecule of interest from the target sample for diagnosis or analysis.

Setup for this type of spectroscopy is not simple to be established.

Resonance Raman Spectroscopy (RRS)

In this type of Raman spectroscopy, a tunable dye laser is used to specifically target

the transition of interest out of all the available transitions in a large biological

molecule where fluorescence competes and subdues the strength of the Raman signal

of interest. Due to intentional involvement of resonance between an incident laser

frequency and the frequency of target transition it is known as resonant Raman

spectroscopy [90].

Polarized Raman Spectroscopy (PRS)

It utilizes a specialized setup where an excitation beam with a specific polarization is

made incident on the sample. The Raman scattered light is passed through filters to


21

collect the Raman signal of a specific polarization at the detector. This technique can

provide us information about orientation of the bonds in a molecule [91].

2.2.8 Diagnostic Applications

In recent years Raman spectroscopy has been used in medical field, especially for the

diagnosis of various diseases, including infectious diseases and cancer. Importantly,

few of them were in-vivo studies [92]–[94]. At University Hospital, Groningen, a

group of researchers have shown that Raman spectroscopy can clearly differentiate

the three type of cell layers [95]. Excised breast tissues were successfully classified by

Raman spectroscopy according to the stage of cancer progression which correlated

very closely to those of clinical results obtained by histo-pathological methods [96]–

[98]. In a recent research by a group at National Institute of Lasers and

Optronics (NILOP), it has been demonstrated that Raman spectroscopy can be

used to screen female BRC cases from whole blood samples [99]. A similar

kind of research was carried by another group which also showed promising

results for further research in this field [100]. Barrett's esophagus was investigated

for changes that are accompanied by the carcinogenesis by Raman spectroscopy

[101]. Thyroid cell lines were also successfully screened for malignancy by using

Raman spectroscopy [102]. Hepatitis C is infectious disease which is screened by

Raman spectroscopy by a group at NILOP recently [24]. Moreover, Raman

spectroscopy is also used by that group for screening of nasopharyngeal cancer in the

human blood sera [103]. Raman spectroscopy can become a suitable detection probe

with lab-on-chip (LOC) devices to produce an efficient clinical diagnostic test [104],

[105].

A study reported the detection of NS1 by using SERS. It is reported to be

highly sensitive technique which is capable of distinguishing between NS1 of Zika

virus and DENV [106]. Dr. Fengwei Bai’s group has used gold-nanoparticles to detect

antibodies against DENV [109]. They have reported it to be a quick and sensitive

assay as compared to RT-PCR [107]. A group of scientists at NILOP, Pakistan is

working on diagnosis of DENV infection by Raman spectroscopy at wavelengths of

785 nm and 532 nm. Various multivariate statistical classification techniques e.g.

principal component analysis (PCA), PLS, support vector machine (SVM) and


22

random forest (RF) analysis are applied on Raman spectra, and have reported good

results.

2.2.9 Advantages

Some of the advantages of Raman spectroscopy are described here briefly.

No sample preparation: A Raman spectrum can be acquired from a biological

sample without any specific physical or chemical preparation. This allows us to use

this technique in-vivo.

Non-destructive: With proper wavelength selection and power optimization, this

technique can be used for non-destructive application, which is prime objective for

analysis of any biological sample, from the medical point of view.

Aqueous samples: IR spectroscopy is influenced intensely due to the presence of

water which is an essential part of biological samples, but in case of Raman

spectroscopy, it does not affect the Raman signal and is a better choice for analysis.

Specificity: The Raman spectrum of a chemical will have specific bands for that

particular chemical only. Hence a specific chemical has a specific Raman spectrum.

This spectrum is termed as a molecular fingerprint of that particular compound.

Organic and inorganic samples: The Raman spectroscopy can analysis both types of

samples no matter whether they are organic or inorganic.

Wide Concentration Range: With recent advancements it has been made possible to

detect the presence of a single molecule in the sample. Moreover, there is no need to

dilute the sample if a sample has a very high concentration of certain chemical.

Windows compatibility: Raman spectroscopy can be used to analyze the samples,

which are contained in common transparent containers.

Quick: It does not require any sample preparation and any sort of time wasting

process which make it a quick method to obtain a Raman spectrum and quickly

analyze it with modern day laptops with the help of available software. Real time

analysis is a huge plus for its application during a chemical process and surgery of

infected areas especially tumor removal.


23

Mixture of Molecules: If a mixture of large molecules is excited by a laser beam then

then the recorded Raman spectrum will contain Raman peaks of all the molecules

present in that sample. Therefore, information about all the molecules can be analyzed

by a single Raman spectrum. It makes the study of complete picture of the biological

samples easier to characterize.

2.2.10 Disadvantages

Low excitation probability: As described in previous sections that Raman scattering

cross-section is of the order 10-29 which is extremely low as compared to other

processes, so it requires very high grade optics and TEC cooled detectors to collect as

much Raman signal as possible.

Fluorescence: During the process of detection of a Raman signal, the fluorescence

signal is also recorded. As the strength of the fluorescence signal is more than Raman

signal, therefore it causes problems for purely Raman scattering based spectrum

acquisition and analysis. At higher wavelengths, the fluorescence signal decreases,

but it causes a decrease in Raman signal as well because of λ-4 dependence.

Raman signal at longer distances: The Raman signal is extremely hard to collect at

longer distance as compared to the fluorescence signal. So this technique can only be

applied efficiently for closely focused locations.

Overlapping of Peaks: When a sample contains large number of molecules, then it is

highly likely that some of the Raman peaks which lie very close to each other in the

spectrum may overlap with each other. Such peaks are either suppressed or appear as

shoulder peaks, which makes the analysis somehow difficult.

2.3 Summary

A lot of efforts have been made for the diagnosis of DENV infection. Chemicals

methods have been developed improved and are being used for screening purpose all

over the world at the moment. However, sensitivity and false-positive results are still

a big concern and it invites the researchers to develop more sensitive and accurate

diagnostic techniques, especially at an earliest possible stage. These new methods

should be cheap because this disease is affecting developing countries very badly. The

time factor is also important for two reasons. Firstly, quick screening of a suspected


24

person is important because during the outbreak of DENV infection, the inflow of

suspected subjects at health care units increases more than its capacity due to waiting

for the results of their tests. Secondly, DENV infection is not cured, but is managed to

sustain the good heath of infected person with intake of various medicines and a

prescribed diet plan [108]. The earlier a person is diagnosed with DENV infection, the

more are the chances for his betterment and survival. New diagnostic techniques

should also be simpler to be used so that it should not require a highly skilled

technician for its operation.

All these needs can potentially be met if Raman spectroscopy could be applied

for this cause. Due to several advantages, Raman spectroscopy is a potential candidate

to be developed as a technique for the diagnosis of DENV infection. With extremely

low running cost, no chemical consumption, no sample preparation, software based

automatic acquisition of Raman spectrum and model based automatic diagnosis of

DENV infection in a quick way will help the affected developed countries a lot in

controlling the mortality rate.

25

3 Materials and Methods

3.1 Collection of Samples

There are different body fluids which may have been used for this screening

study but the choice of serum was made to avoid the problem of thermal degradation

of sample due to laser excitation. Serum has almost all the biological molecules which

may have any role in DENV infection. Moreover the sample of serum is much easier

to acquire, handle and store for analysis as compared to saliva, urine, tears etc. In

short, the serum is rich in biological molecules, easily acquired and handled. Samples

of blood serum were collected from Rawalpindi Medical College (RMC), Rawalpindi,

Pakistan and its allied hospitals. A number of studies have been conducted during the

present research work and for each case, a different set of samples were collected and

analyzed. A number of hospitals played their role in the present research e.g. Nuclear

Medicine Oncology & Radiotherapy Institute (NORI) Islamabad, Meyo Hospital

Lahore and PAEC Hospital Islamabad. Blood samples were collected from suspected

subjects, who were reported with symptoms of DENV infection, before any clinical

confirmation and medication. A sample of 3 ml non-heparinized blood from arm-pit

was drawn into a tube, recommended for serum extraction. Serum was extracted by

means of a centrifuge machine at RMC. Serum was poured into two centrifuge tubes,

one for ELISA based analysis of NS1, IgM and IgG while other for Raman

spectroscopy at NILOP. From sample extraction phase to the final stage of sample

disposal, it was ensured that all the safety guidelines of the National Institute of

Health (NIH) [109] must be followed in letters and spirit.

3.2 Experimental Setup

A Raman system is composed of three basic units, which are: an excitation source of

light, a spectrometer and a detector. A light source is usually a laser of appropriate

wavelength and adjustable power. In addition to these basic units, microscope and

fiber optics are also used. Fiber optics is used for guiding excitation light to the

sample and collection of Raman signals from the sample.

Materials and Methods

26

Figure 3-1 Schematic diagram of a typical fiber optic probe based Raman

system.

A filter is used to stop Rayleigh signal usually. With the help of grating

spectrometer it is made possible that the photons of different wavelength fall at

different pixels of a TEC cooled CCD detector according to calibration. A laptop is

interfaced with CCD to collect, record and plot the Raman spectrum. A schematic

diagram of a typical Raman system is shown in Fig. 3-1.

The Raman system used in the present research work is shown in Fig. 3-2. It

was manufactured by Agiltron, USA with commercial name PeakSeeker Pro-785. It

consists of a diode laser @785 nm wavelength, an optical fiber probe, a high pass

filter grating based monochromator and TEC cooled CCD detector. An optical fiber

probe contained an excitation fiber as well as several collection fibers. A high-pass

filter was used to collect the Stokes Raman scattering signal only. A TEC cooled

CCD detector was used with controlled temperature at -20 oC to minimize the thermal

current in the detector. The system was coupled with a computer interface. The

graphical user interface (GUI) of software was installed at the computer which was

used to operate the Raman system. Laser power can be set in the range of 5-300 mW

and integration time in the range of 1-30 seconds. Focusing of excitation and

collection was achieved by coupling the probe into an integrated microscope (RMS-

785 by Agiltron, USA).


27

Figure 3-2 Experimental setup of the Raman system.

A camera attached on top of this microscope provided the option of visualizing

the focusing point as well as capturing the image of focused surface before and after

the recording of a Raman spectrum to access any sort of change or thermal

degradation of a sample for power optimization. A 10x objective was used throughout

the present research work. A metallic substrate sheet of aluminum was preferred over

other substrates like glass and quartz due to lower background and fluorescence

contribution. Raman spectra were recorded in the range of 300-1800 cm-1 with a

resolution of 10 cm-1. The Raman spectrum was calibrated by using a silicon wafer

with characteristic peak at 520 cm-1.

3.3 Preprocessing Methods

All the molecules present in the sample produce their characteristic Raman

fingerprint. This spectral overlapping of nearby bands makes it difficult to visually

analyze them qualitatively and quantitatively. In addition, the presence of natural

fluorophores in the sample produces fluorescence background that makes the

characterization of the biological samples more complex and difficult. A multivariate

model for the discrimination analysis can help us minimize such difficulties [110].

PLS regression is a multivariate method which enables us to discriminate even minute

level of variations found in the spectra of biological samples. This technique is


28

preferably applied on preprocessed spectral data. Preprocessing is used to eliminate

fluorescence background, electronic noise and the substrate effects to an optimum

level. Preprocessing methods are employed by MATLAB (Mathworks 2009a), prior

to their use in the development of a multivariate model. User friendly GUI is

developed in the MATLAB (Mathworks 2009a) environment for carrying out all

preprocessing, regression model development and analysis.

Denoising: A typical Raman spectrum is recorded in raw form in Fig. 3-3(a). First of

all, electronic noise from the Raman spectra has been removed. The denoising of

spectra is performed by using ‘wden’ function in MATLAB. Wavelet decomposition

and reconstruction method [111] is employed by this function to eliminate noise. In

this method, a spectrum is decomposed into orthogonal wavelets of level 10. A soft

thresholding is used based on the Stein's principle of unbiased risk. The spectrum is

reconstructed by using wavelet coefficients. Stein's unbiased risk estimator

reduces the background in the spectrum; as a result, pixel intensity levels below the

threshold are minimized.

Smoothing: In the second step, denoised spectra were smoothed using a digital

moving average filter, Savitzky-Golay [112], [113]. It was applied over a span of

seven points with 4rd order polynomial fitting. The combination of these smoothening

functions removes the noise more efficiently by preserving the Raman bands in the

spectra. A denoised and smoothed Raman spectrum is shown in Fig. 3-3(b)

Baseline correction: In the third step, fluorescence background is removed by

baseline correction using ‘msbackadj’ function with a window of 200 cm-1 and a

polynomial of 4th degree for estimation of the baseline [114], [115]. It ensured that

Raman features narrower than 200 cm-1 were preserved, while wider ones are

considered as the fluorescence background and therefore filtered out. A baseline

corrected spectrum is shown in Fig. 3-3(c).

Normalization: Finally, the resulted Raman spectrum is vector normalized as shown

in Fig. 3-3(d). For development of multivariate model, a suitable spectral range is

also selected. All these processes were employed automatically by a code written in

MATLAB (Mathworks 2009a) to ensure the true, unbiased and robust nature of

preprocessing. Finally, the processed spectra were employed as trainee data set in the

PLS regression algorithm for the development of model.

http://www.svi.nl/BackGround

http://www.svi.nl/VoXel


29

Figure 3-3 Preprocessing of the Raman spectrum from a raw spectrum to final a

preprocessed Raman spectrum is shown here: a: Raw Raman spectrum, b:

denoised and smoothed Raman spectrum, c: baseline corrected Raman

spectrum, d: vector-normalized Raman spectrum.

3.4 Statistical Analysis

3.4.1 Multivariate Analysis Techniques

Statistical techniques are based on the method of machine based learning from a set of

data (variables) with known results (responses). This data is known as trainee data.

Regression is applied on this data to develop a vector of regression coefficients which

is known as beta vector or regression vector of the model. There are several

techniques which are in use for analysis and prediction of responses by using

variables of unknown samples. A number of multivariate statistical methods are used

e.g. PCA, PLS regression, artificial neural networks (ANN), logistic regression, SVM,

random forest analysis, random forest regression, etc. During the present research

work PLS is mainly implemented, however, SVM and random forest regression have

also been used to assess their usefulness.

3.4.2 PLS Regression

PLS regression predicts responses (Y) from variables (X) [116]–[118]. In the present

study, variables are the intensities of Raman bands of a spectrum of all the samples


30

placed in a matrix X such that each row represents the Raman spectrum of a sample.

Thus a matrix X with order n x p will contain data of n samples with p Raman bands.

Here p depends on the number of pixels of CCD used for recording of a spectrum,

which are 1024 in number for PeakSeeker Pro-785.

Figure 3-4 Mathematical description of PLS regression.

The responses Y are the elements of a column matrix where the clinical result

of each sample in matrix X is correspondingly placed in a matrix Y. It can be either

the value of AI of IgM/IgG or a value ‘0’/’1’ to show clinically positive or negative

result of a sample. In order to correlate the responses (Y) with variables (X) a

regression vector is needed as shown in Fig. 3-4. The principal components (PCs) are

determined which contribute to the majority of the variance found in the data. Only

few of these PCs are used to develop the regression vector such that they are capable

enough to predict the responses with the help of variables for every sample of the

trainee data set [119]. This objective is achieved through a process of decomposition

of X and Y according to equations 3-1 and 3-2.

X

t

XX ELSX 3-1

Y

t

YY ELSY 3-2

SX and SY represent the projections of X and Y respectively. Whereas LX and LY

represent the loadings of X and Y. Regression vector correlates X and Y in such a


31

way, that a minimum number of PCs are utilized to predict the responses of trainee

data set as accurately as reasonable achievable with over fitting.

Model development

A GUI is used to develop the PLS model. All the spectral data along with their

clinical results is loaded in the GUI. The number of samples to be used in testing data

set is provided to this GUI as an input parameter. It automatically selects the specified

number of samples, in a random manner, to be used for testing of the developed

model. Spectral data of remaining samples, known as trainee data set, is used for

development of PLS model. This segregation of the samples into trainee and testing

data set in a random, unbiased and automatic manner ensures unbiased nature of the

developed model. Numbers of PCs to be used are chosen by using the methods of

Kaiser, Scree and Parallel factor. Firstly, it is aimed at ensuring that the number of

PCs should be less than one third of the number of samples of positive/control group

(with the lowest number of samples). Secondly, it is done to avoid over-training of the

model. Two plots are used for these purposes which are drawn with the help of eigen

values of the data and root mean square of error [120].

Model Evaluation

Only that model is accepted for further testing phase which gives the best

performance according to leave one out (LOO) cross validation method. In this

method data of one trainee sample is left out of the model development and the model

is developed to predict the left out sample. This prediction is plotted on y-axis in the

calibration plot where its clinical result is taken along x-axis. This sample is placed

back into the trainee data set and the process is repeated for all the samples one by

one. In this way a curve is obtained, which is known as the calibration curve. From

these values of predicted responses by model and the corresponding clinical

responses, a correlation coefficient known as R-square (r2) value is calculated. An

ideal value of r2 is 1, whereas its value around 0.9 is considered acceptable for a good

prediction model [121]. Root mean square error in cross validation (RMSECV) is

also calculated. Standard deviation (SD) in error is another parameter which is

calculated to determine the goodness of fit for the model [122].


32

Model Testing

After the successful development of a PLS regression model, it is passed through a

testing phase to evaluate its performance for those hidden samples of testing data set,

which were not included in the phase of developing and optimizing of the model. The

Raman spectrum of each of the sample in testing data set is multiplied in a specific

manner with the regression vector and it yields a value of prediction. A predicted

value above cut-off is taken as predicted positive, whereas a predicted value below

cut-off is declared predicted negative. Now, on the basis of these predictions false-

positive, true-positive, false-negative and true-negative results were calculated. These

results are used to determine sensitivity, specificity and accuracy of the developed

model.

Figure 3-5 Area under ROC curve and its interpretation.

Sensitivity is calculated by dividing the number of correct positive prediction by

the total number of positive samples in the testing data set. Specificity is calculated by

dividing the number of correct negative predictions by the total number of negative

samples in the trainee dataset. Accuracy is determined by dividing the total number of


33

correct predictions by the total number of predictions made. Root mean square in

error of predictions (RMSEP) is also calculated. Similarly, the standard deviation of

error in predictions is also calculated to assess the performance of the model

according to the criteria that it should as low as possible around zero [121], [122].

A curve is drawn by calculating the true-positive rate against false-positive rate

at different cut-off values starting from lowest to the highest value according to

clinical responses. This curve is known as receiver operating characteristic (ROC)

curve. A typical ROC curve along with performance determining criteria is shown in

Fig. 3-5. Ideally, the area under ROC curve (AUC) should be 1 but a value above 0.8

is reasonably good for a prediction model [123]–[125].

3.5 Molecular Analysis

After successful development and testing of a reasonably good acceptable model, it is

important to translate these statistics based results into disease related bio-molecular-

based results. To achieve this goal, it is important to know the meaning and

importance of highly positive regression coefficient and highly negative regression

coefficients. A regression coefficient against a corresponding Raman shift with a

considerably high value on percent base means that the concentration of the biological

molecule, associated with that particular Raman shift, increases as the severity of the

disease increases. Similarly, a regression coefficient against a corresponding Raman

shift with considerably negative value on percent base means that the concentration of

the biological molecule, associated with that particular Raman shift, decreases as the

severity of the disease decreases. Assignment of Raman bands to particular biological

molecules are done with the help of a database available in literature [126], [127]. It is

then tried to support these finding based on the regression model with other clinical

studies performed to determine the role of such molecules in relation to disease under

consideration. Molecular analysis has been summarized in chapter 4.

34

4 Results and Discussions

To investigate the possible use of Raman spectroscopy as a diagnostic tool for

DENV infection in human blood sera, it was decided to start the Raman spectroscopy

based screening of NS1 positive and negative samples. Results and discussion is

given in section 4.1. Then it was intended to use Raman spectroscopy for screening of

sera samples on the basis of antibody index (AI) of IgM and IgG quantitatively.

Results and discussions of these two studies are presented in sections 4.2 and 4.3

respectively. Moreover, laser @532nm was used to investigate the possibility of

lactate as a potential biomarker for diagnosis of DENV infection, results and

discussions of this part is presented in section 4.4.

4.1 NS1 based Screening

As discussed chapter 1 and chapter 2, in RNA of DENV produces NS1. In the present

study, Raman spectra of NS1 positive and NS1 negative sera were used with a PLS

regression routine to develop a multivariate model for the optical screening of NS1

positive samples in the samples which were suspected of DENV infection. In total

218 blood sera samples from subjects of different ages and genders have been used.

Among all these samples, 95 were NS1 positive and 123 were NS1 negative. For

model development 178 samples were used which contained 80 NS1 positive and 98

NS1 negative samples. For testing of the developed model 40 samples were used.

According to clinical test based on ELISA, 15 samples were NS1 positive and 25

samples were NS1 negative. All the process of sample collection, Raman spectrum

acquisition and preprocessing was performed as described in sections 3.1 - 3.3.

4.1.1 Results and Discussion:

Eight number of PCs were used for the development of PLS regression model. Raman

spectra of all the samples of NS1 positive and NS1 negative group are recorded in

Fig. 4-1. It is a patched area type graph where green color shows NS1 negative group

while red color shows NS1 positive group. Average plot of each group is also shown.

Results and Discussions

35

The model was duly calibrated and the calibration curve produced is shown in Fig. 4-

2. Value of r2 was determined to be 0.9, which is quite promising. Values of

RMSECV and SD for cross-validation were found to be 0.15 each, authenticating the

predictions made by the model. The results of unknown suspected samples were also

plotted against their clinical results in Fig. 4-2. Importantly, the region between o.4 to

0.6 was declared as grey region where the result of a predicted sample is termed as

inconclusive. Value of RMSEP and SD for testing data set was determined to be 0.2

each. Accuracy, sensitivity, specificity and AUC were determined to be 100 %, 100

%, 100 % and 1 respectively.

Figure 4-1 Raman spectra of sera samples used in NS1 based screening study.


36

Figure 4-2 Calibration curve of model for NS1 based screening.

Figure 4-3 Regression coefficients of PLS model for NS based screening.


37

Important regression coefficients at corresponding Raman bands are shown in Fig. 4-3

where values were strongly positive or strongly negative. A list of important

regression coefficients along with the role of their associated biological molecules

have been given in Table 4-1 and Table 4-2. Few of these molecules have been

discussed here for their role with respect to DENV infection.

Positive regression coefficient at 776 cm-1 depicts that phosphatidylinositol

[128] is found in higher concentration in NS1 positive subjects as compared to

negative. Its high concentration is supported by the fact that expression of NS1

takes place at the surface of infected cells [129] and human defense system

targets NS1 in response to infection. Cellular glycosyl-phosphatidylinositol is

used by DENV for signal transduction capacity as a result of binding of NS1

to specific antibody [130].

Positive regression coefficient at 1127 cm-1 is reported for Raman band of

ceramide [131] whose level is reported to be high [132] in NS1 positive

samples.

At 736 cm-1 a negative correlation exists which is reported to be one of the

three characteristic Raman band of thiocyanate [133]. It is a potentially useful

therapeutic agent with host defense and antioxidant properties [134].

At 1454 cm-1, the positive correlation exist for the Raman band of protein

which has a structural role [135] and the present study shows its higher

concentration in NS1 positive group where structures of viruses are

synthesized.

At 1045 cm-1, proline [136] is reported to have high concentration in case of

DENV infection [132].

Raman bands at 1224, 1254, 1273 and 1283 cm-1 are characteristic Raman

bands of amide-III [127] which showed positive correlation with NS1 positive

group.


38

Table 4-1 Positive regression coefficient obtained by PLS model for NS1 based

screening along with molecular description.

Raman

shift

Strength of

regression

coefficients

(%)

Molecular assignments

1174 86.4 DNA bases and protein

789 70.2 C5-O-P-O-C3 phosphodiester bonds in DNA

1283 62.4 Differences in collagen content, amide III

776 46.4 Phosphatidylinositol

1224 42.3 PO2− in nucleic acids and amide-III (β sheet structure of

protein)

1503 42.1 NH3

1454 38.0 Protein and phospholipids

1127 37.7 Protein and ceramide

1273 35.4 DNA/RNA bases and amide-III (proteins)

1254 33.8 DNA/RNA bases and amide-III (proteins)

1045 37.7 Proline

1363 33.1 Tryptophan

608 29.9 Cholesterol

942 29.3 Skeletal modes (polysaccharides, amylose, amylopectin)

1344 28.6 Protein

1466 27.6 CH deformation (DNA/RNA and proteins and lipids and

carbohydrates)

1487 24.4 Collagen, NH3

1410 23.5 Amino acids, aspartic and glutamic acid

806 21.8 DNA: O-P-O symmetric stretching

847 20.8 Saccharides (α-glucose, maltose)

1645 20.2 Amide I (α-helix of Protein)


39

Table 4-2 Negative regression coefficient obtained by PLS model for NS1 based

screening along with molecular description.

Raman

shift

Strength of

regression

coefficients

(%)

Molecular assignments

1164 100.0 Tyrosine

1029 99.0 O-CH3 stretching of methoxy groups, keratin (protein

assignment)

780 74.0 Ring breathing of nucleotide bases

1230 71.0 DNA/RNA bases and amide III

1093 70.8 Phosphate backbone vibration as a marker mode for the

DNA concentration

1010 68.5 Tryptophan ring breathing

1055 60.6 RNA/DNA

1018 58.9 Ribose of RNA/DNA

760 56.2 Ethanolamine group, phosphatidyl-ethanolamine,

tryptophan (proteins)

1676 55.4 Amide-I (β-sheet) and DNA/RNA bases

1097 54.2 Phosphodioxy (PO2−) groups

1421 53.0 DNA/RNA bases and proteins

1294 51.6 Methylene twisting and amide III (protein band)

1666 47.3 α-Helical structure of amide-I (collagen assignment) and

DNA/RNA bases

1354 39.1 DNA/RNA bases

1067 38.0 PO2− stretching (DNA/RNA)

973 37.1 Ribose vibration, one of the distinct RNA modes

841 36.4 Saccharide (α)

858 36.4 DNA bases

1075 34.5 PO2− stretching (DNA/RNA)

1201 33.3 DNA/RNA

1117 33.0 Glucose

1682 32.9 One of absorption positions for the C=O stretching

vibrations of cortisone

1378 31.0 Paraffin, lipid assignment

1436 29.1 DNA,/RNA bases

883 27.8 Proteins, including collagen-I

1432 27.7 DNA/RNA and proteins

832 27.4 Asymmetric O-P-O stretching DNA bases


40

4.2 IgM based Screening

Diagnostic methods in practice are based on detection of DENV itself or its related

antibodies like IgM, which are produced as a response against DENV infection by the

human immune system, as discussed in Chapter 1 and Chapter 2 in details.

In the present study, a multivariate model has been developed to predict

quantitative values of AI of IgM in the dengue suspected samples. This model is

developed by utilizing the PLS regression. Raman spectra of 78 samples have been

used as the trainee data set. Fig. 4-4 displayed the patch area display and average of

pre-processed spectra of 78 samples used in the model development. ELISA based

cut-off value for AI of IgM was 9. Out of 78 DENV infected samples, 37 have IgM

values of AI above cut-off (≥ 9) (red colored) and 41 below cut-off (< 9) (green

colored). Testing data set contained 30 samples which were used for blind testing of

the developed model. Predicted values of AI of IgM by the model were found in

excellent agreement with the ELISA results.

Figure 4-4 Raman spectra of sera samples used in IgM based screening study.

4.2.1 Results and Discussions:

The maximum number of PCs to be used for model development was determined

according to the methods given in section 3.4. Firstly, the RMSE curve was obtained


41

as shown in Fig. 4-5. Secondly, methods of Kaiser, Scree and parallel factor [120] are

employed as shown in Fig. 4-6.

Figure 4-5 RMSECV curve calculated by using number of PCs from 1 to 20.

Figure 4-6 Curves obtained by employing methods of Kaiser, Scree and parallel

factor analysis for IgM based screening.


42

It produced three curves; the real data eigenvalues (blue color), percentile of

eigenvalues (red color) and mean of eigenvalues (green color). According to both of

these figures it is assured that the choice of 3 PCs is authenticated and acceptable to

avoid over-training of the model. The value of r2 for this model has been found to be

0.929. The second parameter is the RMSECV that is also used to validate the

outcomes of statistical model. In this model, RMSECV was calculated to be 2.17.

Based on LOO cross validation method, calibration curve has been plotted for IgM

using 78 samples, as shown in Fig. 4-7. The predicted values of AI of IgM are shown

in Figs. 4-7. Evidently, the predicted values of AI of IgM are quite promising and an

excellent correlation has been found with clinical results. The RMSEP of AI of IgM

for the blind samples has been found to be 3.25, which shows a reasonable accuracy

of the model.

Figure 4-7 Calibration curve for IgM based screening along with predictions for

testing data set.

Another important statistical parameter is SD in errors of the predicted values

used to evaluate the accuracy of multivariate model. Standard deviation in errors of

LOO predicted values of AI of IgM for 78 trainee samples were found to be 2.18,

whereas in 30 blind suspected samples, it was 3.31. Sensitivity, specificity and

accuracy have been calculated and plotted in Fig. 4-8 at the corresponding cut-off


43

values. Importantly, these parameters have been calculated from the predictions of 30

blindly tested samples. Accuracy, sensitivity and specificity were calculated to be

96.67 %, 90 % and 100 % respectively. An ROC curve, shown in Fig. 4-9, is also

produced and AUC was found to be 0.985.

Figure 4-8 Sensitivity, specificity and accuracy of the IgM based screening model

at different cut-off values.

Figure 4-9 ROC curve for IgM based screening.


44

Analysis of Regression Coefficients

To visualize the relevance of coefficients of regression (plotted as regression vector)

with spectral variations caused by IgM during infection, all the Raman spectra were

divided into three groups as shown in Fig. 4-10. Spectra with an AI value of IgM

below 9 are placed in group “IgM negative” and their average spectrum is plotted in

green color. Similarly, the spectra with an AI value of IgM between 9 and 20 are

placed in group “mild IgM positive” and their average spectrum is plotted in blue

color, while the spectra with AI value of IgM above 20 are placed in group “strong

IgM positive” and their average spectrum is plotted in red color. At the bottom of that

plot, regression curve is also displayed for comparison in black color. It was

established that regression curve has pointed out some Raman bands with positive and

negative regression coefficients. These trends were also confirmed by analyzing the

average spectra of these groups visually. Dengue virus infection causes the rising and

reducing of some molecular levels and inducing of new ones (IgM) which form the

basis of the regression vector of the model.

Figure 4-10 Regression vector along with average spectra of negative, mild IgM

positive and strong IgM positive samples.

Molecules associated with these Raman bands have been identified through

existing literature. Raman shift at 1594 cm-1 is assigned to asparagine [137] which has

been reported for its role in DENV propagation in the host [138]. Raman shifts at

1537, 1075 and 1318 cm-1 are assigned to glutamate which is reported to accumulate


45

due to DENV infection and its effects on the nervous system [139]. Raman shifts at

1514, 1387 and 1318 cm-1 are assigned to galactosamine which is reported to be an

integral part of IgM [140]. Raman shifts at 1387, 1437 and 1065 cm-1 are assigned to

palmitic acid which is reported for its high concentration due to IgM activation and

stress in the body [141] and It is also reported for its role in apoptosis in liver due to


Raman shifts at 1387, 1018, 1075, 732 and 1359 cm-1 are assigned to dextrose

which is reported to be found in elevated levels in DENV infected subjects [143].

Raman shifts at 1018, 1437, 1507, 1473 and 1097 cm-1 are assigned to myristic acid

which is found in higher concentrations in serum due to IgM [141]. Raman shifts at

1018 and 1065 cm-1 are assigned to vaccenic acid which is converted to linoleic acid

[144] in the body and is reported for its role in the immune system [142]. Raman

shifts at 1437 and 1378 cm-1 are assigned to arginine which is reported to have high

concentration in DENV infection [145]. Raman shift at 1075 cm-1 is reported for

triglyceride which has been reported for its elevated level in serum because of IgM

[146]. Raman shifts at 1473, 1318 and 1097 cm-1 are assigned to

phosphoenolpyruvate which is reported to have high concentration in DENV infection

[147]. Raman shift at 732 cm-1 is assigned to phosphatidylserine, which is reported to

mediate the entry of DENV in target cells [148]. Raman shift at 626 cm-1 is assigned

to fructose which has shown a decreasing trend in the present study because it is

consumed to form fructose-bisphosphate-aldolase which takes part in glycolysis

which is necessary for DENV infection [149]. Raman shifts at 885 cm-1 are assigned

to cellobiose. Raman shifts at 936 and 843 cm-1 are assigned to arabianose. Both of

these moles have shown a decreasing trend when the IgM level rises in the body.

Raman shifts at 608 and 957 cm-1 are assigned to cholesterol which is reported for

their relation with IgM. Individuals, who have low cholesterol levels, produce higher

concentration of IgM [150]. A summary of these assignments is given in Table 4-3.


46

Table 4-3 Prominent Raman bands highlighted by the strongly positive or

strongly negative values of regression coefficients of IgM based model.

Raman

shift (cm-1)

Trend

with AI

of IgM

Assigned

molecule Information from literature

1594 positive Asparagine It plays an essential role in DENV

propagation.

1537,

1075, 1318 positive Glutamate

DENV infection induces its accumulation

by creating nervous disorder.

1514,

1387, 1318 positive Galactosamine It is a part of IgM.

1387,

1437, 1065 positive Palmitic acid

Its concentration increases with high level

of IgM in serum. It is also reported to

have a role in apoptosis in liver due to

DENV infection.

1387,

1018,

1075, 732,

1359

positive Dextrose It is found in elevated levels in DENV

infected subjects.

1018,

1437,

1507,

1473, 1097

positive Myristic acid Its concentration increases with high level

of IgM in serum.

1018, 1065 positive Vaccenic acid

Vaccinic acid is converted into linoleic

acid. Linleoic acid plays its role in

immune system.

1437, 1378 positive Arginine Its concentration rises due to DENV

infection.

1396,

1473,

1318, 1097

positive Phosphoenol-

pyruvate

Elevated levels of phosphoenolpyruvate

are reported in DENV infection.

1075, 1300 positive Triglycerides IgM levels show positive relationships

with triglycerides.

732 positive Phosphatidyl-

serine

It mediates the entry of DENV in target

cells.

626 negative Fructose

Due to DENV infection glycolysis takes

place, and it results in reduced levels of

fructose as it is consumed and its level

decreases.

608, 957 negative Cholesterol

Individuals with low cholesterol levels

exhibited higher IgM levels than

individuals without it.

885 negative Cellobiose Not found in already published literature,

having relation with DENV infection

936, 843 negative Arabinose Not found in already published literature,

having relation with DENV infection


47

4.3 IgG based Screening

In the present study, a multivariate model was developed to predict quantitative

values of AI of IgG in the dengue suspected samples. This model is developed by

utilizing the PLS regression, using functions in the MATLAB (Mathworks 2009a).

For training of the model, 79 sera samples have been used. Clinically approved

method, ELISA, has been used for the determination of AI of IgG. These values of AI

of IgG have been provided to the multivariate model for training. For the validation of

model, a blind test was performed using 20 unknown suspected samples. The clinical

results of ELISA for these samples were kept hidden during model development.

Spectral range was chosen from 500-1600 cm-1. Fig. 4-11 displayed patch area display

and average of pre-processed spectra of 79 samples used in the model development.

ELISA based cut-off value for AI of IgG was 9. Out of 79 DENV suspected samples,

36 have IgG values of AI above cut-off (≥ 9) (red colored) and 43 below cut-off (< 9)

(green colored). In addition, Raman spectra of 20 suspected samples were kept hidden

for blind evaluation of model. Optimization of the number of PCs to be used for

model development is done by two methods. According to Fig. 4-12 and Fig. 4-13 it

was concluded that the choice of 3 PCs is authenticated and acceptable to avoid over-

training of the model.

4.3.1 Results and Discussions

Leave one sample out cross validation method has been applied for calibration.

During the LOO process, two parameters have been calculated to check the goodness

of the model; one of these is r2, which explains the variability level of outcomes in the

model and gives a guideline to measure the validity of the model. The value of r2 for

this model has been found to be 0.91, whereas the value of r2 greater than 0.9 is

accepted clinically [121], [151]. In this model, RMSECV for the entire LOO cross

validated predicted values were calculated to be 2.06. It showed the robustness and

authentication of the multivariate model for the prediction of AI of IgG in the samples

[152]. When a suspected Raman spectrum is loaded by GUI platform, it is multiplied

by the corresponding regression vector and produces a predicted value for AI of IgG.

Based on LOO cross validation method, calibration curve has been plotted for IgG

using 79 samples, as shown in Fig. 4-14. The prediction of AI of IgG in 20 suspected

samples, whose clinical ELISA results were kept hidden, are also shown in the same


48

figure. These predicted values were quite promising and an excellent correlation has

been found with clinical results. The RMSEP of AI of IgG for these suspected blind-

tested samples has been found to be 3.25, which shows a reasonable accuracy of the

model [121], [151].

Figure 4-11 Patch area display of Raman spectra used in IgG based screening.

Figure 4-12 RMSE curve for PCs optimization for IgG based model.


49

Figure 4-13 Eigen values based curves for optimization for IgG based model.

Standard deviation of errors in the predicted values used to evaluate the

accuracy of multivariate model. Standard deviation of errors in LOO cross-validation

predicted values of AI of IgG for 79 trainee samples were found to be 2.18, whereas

in 20 blind suspected samples, it was 3.31. These predicted values were awarded zero

point score because these were within one standard deviation [152], and according to

the scoring system of QCMD [153], these results qualify for clinical acceptance.

Sensitivity, specificity, accuracy and area are under ROC curve are very importance

parameters to assess the goodness of fit for a statistical model for medical application

[51]. From the predictions of 20 blindly tested samples; sensitivity, specificity and

accuracy at cut-off have been calculated to be 100 %, 83.3 % and 95 % respectively

as shown in Fig. 4-15. Receiver operating characteristic curve, shown in Fig. 4-16, is

produced by plotting the true-positive rate against false-positive at various cut-off

values from −0.36 to 27.7. Area under ROC curve was found to be approximately 1,

which indicates the reasonable level of accuracy of this model [125].


50

Figure 4-14 Calibration curve of PLS model developed for IgG based screening.

Figure 4-15 Sensitivity, specificity and accuracy of the PLS model for IgG at

different cut-off values.


51

Figure 4-16 Receiver operator characteristic (ROC) curve for IgG based

screening model.

A vector consisting of regression coefficients is yielded by PLS regression. It

is called regression vector. Relevance of coefficients of regression (plotted as

regression vector) with spectral variations caused by IgG during infection is studied

by dividing all the Raman spectra into three groups as shown in Fig. 4-17. Spectra

with AI value below 9 are placed in group “IgG negative” and their average spectrum

is plotted in green color. Similarly, the spectra with AI value between 9 and 20 are

placed in group “mild IgG positive” and their average spectrum is plotted in blue

color, while the spectra with AI value above 20 are placed in group “strong IgG

positive” and their average spectrum is plotted in red color. At the bottom of that plot,

regression curve is also displayed for comparison in black color. It was established

that regression curve has pointed out some Raman bands with positive and negative

trends which are confirmed by analyzing the average spectra of these groups visually.

The Raman bands where regression coefficients are positive indicate those molecules

whose concentrations are increasing with rising levels of biochemical changes

associated with the AI of IgG, whereas, the Raman bands where regression

coefficients are negative indicate the molecules whose concentrations are decreasing


52

with increasing value of AI of IgG. Molecules associated with these Raman bands

have been identified through existing literature [126], [127] and are listed in Table 4-4

and Table 4-5.

Figure 4-17 Regression vector along with average spectra of negative, mild IgG

positive and strong IgG positive samples.

Raman shift at 1245 1272 1287 and 1262 cm-1 are assigned to amide III.

Raman shifts at 873 1330 1454 and 934 cm-1 are assigned to collagen and Raman shift

at 1454 1443 1454 1466 1477 1443 1466 and 1443 cm-1 are assigned to proteins.

These three interrelated biomolecules are found to be positively correlated with AI

value of IgG. Raman shift at 1443 cm-1 is assigned to Fatty acids and Raman shift at

1454 cm-1 is assigned to Phospholipids. Lipid profiling is a topic of interest for the

diagnosis of DENV infection [154]. In the present study, these lipids/fatty acids are

found to be negatively correlated with an increased value of AI of IgG. Raman shift at

529 cm-1 is assigned to Fucose [126] that might be involved in the process of

fucosylation during dengue infection as dengue E protein has two potential

glycosylation sites [155]. Raman shifts at 982, 1018, 1063, 1099 and 1379 cm-1 are

assigned to myristic acid. It was reported to be positively correlated with IgM [51]

and the present study has shown a similar trend with IgG also. Raman shifts at 1095,


53

1575, 633, 721, 1099 and 651 cm-1 are assigned to coenzyme-A. It is required by

DENV to increase the cellular fatty acid synthesis [156]. Raman shifts at 603, 633,

1043, 1055, 1095 and 1191 cm-1 are assigned to glutamate. It is reported that DENV

infection induces glutamate excitotoxicity by releasing more glutamate in synaptic

cleft [157]. In the present study, it is found to be positively correlated with AI of IgG

and it was reported to be positively correlated in IgM study as well. Raman shifts at

1419 cm-1 and 1379 cm-1 are assigned to Alanine. Glutamate is converted into alanine,

which is released into the bloodstream [158]. Raman shifts at 1079 cm-1 and 597 cm-1

are assigned to amide II and amide VI respectively. These types of amides are found

to be positively correlated with IgG in the present study. Raman shifts at 1137 cm-1

and 1095 cm-1 are assigned to arabinose. In IgM study, it was negatively correlated

while in the present study it is found to be positively correlated. The exact role of

arabinose is yet to be discovered, however, it is observed that abdominal pain is a

symptom of DENV infection and arabinose is biomarker of abdominal pain due to

yeast infection [159] and arabinose is used as a culturing medium for viruses [160].

Raman shifts at 1099 cm-1 and 982 cm-1 are assigned to arginine. Its concentration

rises due to DENV infection [145]. Raman shifts at 567 cm-1 and 588 cm-1 are

assigned to vitamin C (ascorbic acid). It is positively correlated with AI of IgG.

Vitamin C plays a role in the immune system [161], [162] and concentration of IgG

rises with vitamin C [163]. Raman shifts at 1191, 1119, 1150, 1154 cm-1 are assigned

to carotene It is found in the present study that carotene is positively correlated with

IgG. Raman shifts at 982 cm-1 and 1594 cm-1 are assigned to fumarate It is related to

immune modulation [164]. Raman shifts at 703, 1063, 1095, 1154 and 1587 cm-1 are

assigned to galactosamine. Its concentration rises due to IgM [51]. This study reveals

that it is also related to IgG as it is found to be positively correlated with IgG. Raman

shifts at 749 cm-1 are assigned to lactic acid. It is reported that a high level of lactic

acid is a potential biomarker for diagnosis of DENV infection [53]. Raman shifts at

1063, 1099, 1419 cm-1 are assigned to Stearic acid. It is up-regulated due to DENV

infection [165]. Raman shifts at 597, 765, 1119, 1575, 1079, 574, 749 and 1363 cm-1

are assigned to tryptophan. It is reported to be elevated during DENV infection [23].

Raman shifts at 1018 and 1079 cm-1 are assigned to vaccenic acid. It is found to be

positively correlated with IgG in the present study. It was also reported in IgM study

that vaccenic acid is converted into linoleic acid. Linoleic acid plays its role in the

immune system. Raman shifts at 1587, 621 and 1182 and 1002 cm-1 are assigned to


54

phenylalanine. Interestingly, it was found that at 1002 cm-1 a strong lowering trend is

shown while other bands of phenylalanine at 1587, 621 and 1182 cm-1 were strongly

positive. This needs to be investigated further in detail. However, it is reported in a

study that its concentration is found to be higher in DENV infected subjects [166].

Normally, phenylalanine is converted into tyrosine by kidney and liver, but due to

malfunction of these organs this conversion is stopped and level of phenylalanine

rises [167].

Table 4-4 Prominent Raman bands which have been highlighted by the strongly

negative values of regression coefficients of this model are tabulated for their

bio-molecular assignment.

Raman

Shift

(cm-1)

Strength

of

Regression

coefficient

(%)

Bio-Molecule Molecular description

1245 100

Amide III

These bands of proteins are found to be

negatively correlated in the present study,

specifically collagen and amide-III.

1272 72

1287 53

1262 61

873 30

Collagen 1330 24

1454 69

934 55

1443 54

Proteins 1466 33

1477 28

1466 33

1443 54 Fatty acids Lipid profiling is a topic of interest for the

diagnosis of DENV infection. 1454 69 Phospholipids

529 28

Fucose Fucose is involved in the process of

fucosylation during dengue infection. 1272 72

1330 24


55

Table 4-5 Prominent Raman bands which have been highlighted by the strongly

positive values of regression coefficients of this model are tabulated for their bio-

molecular assignment.

Raman

Shift

(cm-1)

Strength of

Regression

coefficient

(%)

Bio-Molecule Molecular description

982 59

Myristic acid IgM study has shown similar trend also.

1018 52

1063 47

1099 33

1379 64

1095 34

Coenzyme-A It is required by DENV to increase the

cellular fatty acid synthesis.

1575 32

633 86

721 44

1099 33

1419 23

651 67 Alanine

It is mainly related to liver damage which

occurs in dengue infection. 1379 64

1079 34 Amide II

Amide VI

These types of amides are found to be

positively correlated with IgG in the

present study. 597 70

1095 34 Arabinose

It is observed that abdominal pain is a

symptom of DENV infection and

arabinose is biomarker of abdominal pain. 1137 87

982 59 Arginine

Its concentration rises due to DENV

infection [145]. 1099 33

567 38 Vitamin C

Vitamin C plays a role of antioxidant in

DENV infection [162]. 588 66

1191 88

Carotene It is positively correlated with IgG.

1119 38

1150 76

1154 77

982 59 Fumarate It has a role in immune modulation [164].

1594 42

703 39

Galactosamine It was earlier reported to be high in IgM

study as well [51].

1063 47

1095 34

1154 77

1587 42

603 59

Glutamate It is reported that DENV infection

induces glutamate excitotoxicity.

633 86

1043 32

1055 49

1095 34

1191 88


56

749 43 Lactic acid

It is reported that a high level of lactic

acid is a potential biomarker for diagnosis

of DENV infection [53].

1587 42

Phenylalanine

Interestingly. Raman bands of

phenylalanine are positively correlated

except 1002 cm-1. It needs to be further

investigated.

621 82

1182 100

1002 -52*

1063 47

Stearic acid It is up-regulated due to DENV infection

[165]. 1099 33

1419 23

597 70

Tryptophan It is reported to be elevated during


765 38

1119 38

1575 32

1079 34

574 42

749 43

1363 54

1018 52 Vaccenic acid

Vaccenic acid is converted into linoleic

acid. Linoleic acid plays its role in the

immune system. 1079 34

* This specific Raman band of phenylalanine is found to be negatively correlated with

IgG.


57

4.4 Lactate as Biomarker

In present study, the diagnosis of DENV infection in human blood sera based on an

increase in lactate concentration using Raman spectroscopy is investigated. For

strengthening the claim, different concentrations of lactic acid solution has been

added to normal/healthy sera. The changes occurred in the composition of the serum

sample have been analyzed and discussed accordingly. This will be quite helpful in

the early diagnosis of DENV infection which is of prime importance for management

of the disease.

In total 70 samples of different ages and genders have been used in the present

study. Among these, 20 samples were from healthy volunteers whereas, 50 were

obtained from DENV infected patients. In one part of healthy sera two different

concentration 50 mM/L and 100 mM/L of lactic acid solution (L 1250, Sigma-Aldrich

Chemie GmbH, Germany) were prepared in a control manner for observing their

effects. The sample collection, preparation and storage procedure is same as

mentioned in section 3.1.

4.4.1 Acquiring Raman Spectra

Raman spectra from DENV infected sera, healthy sera, lactic acid solution and lactic

acid solution mixed with healthy sera were recorded. About 15 µl of each sample has

been put on the glass slide and left for some time at room temperature for water

moisture to vaporize. The schematic diagram of the experimental setup is shown in

Fig. 4-18. Raman spectrometer (µRamboss DONGWOO OPRTON, South Korea)

with a spectral resolution of 4 cm-1 was used for recording Raman spectra from all

samples. A diode laser emitting @532 nm has been used for the excitation. The

measured laser power at the sample surface was 40 mW. A microscope objective

having a magnification of 100X has been used, both for focusing the light on the

sample and collection of backscattering light. An acquisition time of 10 seconds has

been used for recording each spectrum. A spectral range from 600 to 1800 cm-1 has

been selected for recording Raman spectra.


58

Figure 4-18 Sketch of experiment setup.

4.4.2 Raman spectral analysis

All the Raman spectra have been smoothed using ‘Savitzky-Golay’ [111] filter with

five points and 3rd order polynomial fitting. The mean vector normalized Raman

spectra of healthy and dengue infected sera as well as the mean difference between

normal and infected samples are shown in Fig. 4-19. In healthy sera three intense

Raman peaks appeared at 1003, 1156 and 1516 cm-1. In dengue infected samples,

Raman peaks appeared at 750, 830, 925, 950, 1003, 1123, 1156, 1333, 1450, 1516,

1580, 1680 and 1730 cm-1.

Figure 4-19 Vector normalized mean Raman spectra of healthy and dengue

infected sera (upper) along with the mean difference between the normal and

infected samples (lower).


59

Furthermore, obvious differences between the normal and dengue infected

samples appeared at 750, 830, 925, 950, 1003, 1123, 1156, 1450, 1516, 1580, and

1730 cm-1 as can be seen in the difference plot in blue color. The detailed assignment

of most of these Raman peaks has been given in another article [22]. Fig. 4-20

illustrated the vector normalized Raman spectra of lactic acid solution with two

intense Raman peaks at 830 cm-1 and 1450 cm-1 along with some medium intensity

peaks at 750, 875, 925, 1040, 1075 and 1730 cm-1.

Figure 4-20 Vector normalized Raman spectra of lactic acid solution.

Fig. 4-21 illustrated the recorded vector normalized mean Raman spectra of healthy

blood sera; dengue infected sera as well as two different concentration of lactic acid

solution mixed with healthy sera. For an obvious differentiation, Raman spectra of

healthy sera samples are shown in green color, dengue infected in red color, whereas

lactic acid solution mixed with healthy sera are shown in blue color (50 mM/L) and

magenta color (100 mM/L).


60

Figure 4-21 Vector normalized mean Raman spectra of healthy sera, dengue

infected sera, 50 mM/L and 100 mM/L of lactic acid solution in healthy sera.

4.4.3 Results and Discussion

The Raman peaks appeared in normal human blood sera have been explained

previously [22], [23]. Raman peak at 1003 cm-1 has been assigned to symmetric ring

breathing mode of phenylalanine and β-carotene, whereas the peaks at 1156 and 1516

cm-1 have been assigned to β-carotene [18], [168], [169]. These Raman peaks are

highly reproducible. In DENV infected sera, these three peaks are either suppressed or

their intensity is decreased. Moreover, new peaks were also arisen at different

frequencies. In dengue infected sera, additional Raman peaks appeared at 750, 830,

925, 950, 1123, 1333, 1450, 1580, 1680 and 1730 cm-1 as shown in Fig. 4-19. The

main contribution to these spectral lines are most probably corresponds to a high

concentration of lactate in DENV infected sera. In the human body, lactate is

produced continuously mostly in muscles. It is then transported to different metabolic

organs via blood which regulates them [170]–[172]. Liver is considered to be the key

organ that converts blood lactate into pyruvate. Around 50-70 % of blood lactate is

extracted by liver and converted into pyruvate [168]. An additional amount of lactate

is cleared by the kidney and some other organs. In normal conditions, with adequate

tissue perfusion, conversion of pyruvate to Acetyl-CoA is occurred largely bypassing

lactate production. In tissue hypoxia/hypo-perfusion, lactate is produced as an end

product of pyruvate in the presence of lactate dehydrogenase enzyme. Lactate exists


61

in two isoforms, L-and D-lactate, such that L-lactate is the primary isomer produced

in the human body. The biochemical impact of DENV infection on the function of

various body organs like liver, kidney, lungs, heart etc. as well as an elevated level

of lactate is well established and reported [173]–[179]. As stated earlier, liver and

kidney are the two main organs in the human body which regulate the lactate level.

Changes in hepatic oxygen supply and intrinsic hepatic disorder affect the

capacity of the liver to metabolize lactate. In such condition, liver becomes a lactate

producing organ rather than using it for gluconeogenesis. Hence, due to dysfunction

of these important body organs in dengue infection, blood lactate level increases. A

good agreement has been observed by comparing the Raman peaks of lactic acid

solution (Fig. 4-20) and dengue infected sera samples (Fig. 4-19). More precisely,

Raman peaks close to 750, 830, 925, 1123, 1450 and 1730 cm-1 appeared both in

lactic acid solution as well as DENV infected samples. Furthermore, a slight blue shift

at wave number 1003 cm-1occurs in dengue infected samples. Possible cause this shift

in dengue is not clear, but it could be due to somewhat different protein composition

next to phenylalanine and β-carotene. For the observation of lactate effects on normal

blood sera, two different concentration of lactic acid solution (50 mM/L and 100

mM/L) has been added to healthy sera in a controlled manner and their Raman spectra

have been recorded as shown in Fig. 4-21. A gradual decrease in the intensity of the

aforementioned three peaks has been observed with an increase in the concentration

of lactic acid solution in the sera samples is clearly visible. So, it can be said that,

apart from the carotenoids deficiency in DENV infection as described earlier [23], the

suppression of Raman peaks at 1003, 1156, and 1516 cm-1 in healthy sera may also be

attributed to an elevated lactate level in the blood. One can use the appearance of

lactate in blood sera as a valuable indicator for the presence of disease. In order to

evaluate lactate as a potential biomarker for dengue diagnosis, in depth studies will be

necessary.

In conclusion, this study presents the screening of DENV infection in human

blood sera based on lactate concentration using Raman spectroscopy. A total of 70

samples, 50 from confirmed DENV infected patients and 20 from healthy volunteers

have been used in the present study. Raman spectra of all these samples have been

acquired in the spectral range from 600 cm-1 to 1800 cm-1 using 532 nm laser as an

excitation source. Spectra of all these samples have been analyzed for assessing the


62

biochemical changes resulting from infection. In DENV infected samples three

prominent Raman peaks have been found at 750, 830 and 1450 cm-1. These peaks are

most probably attributed to an elevated level of lactate due to an impaired function of

different body organs in dengue infected patients. This has been proven by an addition

of lactic acid solution to the healthy serum in a controlled manner. By the addition of

lactic acid solution, the intense Raman bands at 1003, 1156 and 1516 cm-1 found in

the spectrum of healthy serum got suppressed, while new peaks appeared at 750, 830,

925, 950, 1123, 1333, 1450, 1580 and 1730 cm-1. The current study predicts that

lactate may possibly be a potential biomarker for the diagnosis of DENV infection.

63

5 Conclusions and Future Prospects

Raman spectroscopy has great potential as a technique to be used for the diagnosis of

DENV infection. The present research work has shown that it can be used

qualitatively as well as quantitatively for the screening purpose. The dengue virus

produces NS1 and the response of human body results in the production of IgM and

IgG. Raman spectroscopy has successfully been used to screen the sera samples of

subjects, suspected of DENV infection, on the basis of NS1, IgM and IgG. Moreover,

an elevated level of lactate is also shown to be an important biomarker for the

diagnosis of DENV infection.

It is important to mention here that the symptoms of DENV infection are

somehow similar to the other diseases like flu, typhoid, malaria, pneumonia, measles,

enteric fever, leptospirosis, typhus fever [180] etc. Differentiating a healthy group

from an infected group is usually easier because of so many molecular changes that

occur in the blood due to infections of different kinds. However a physician usually

deals with the subjects who are suspected of DENV infection based on the symptoms

which are quite similar to other diseases. Usually, such subjects have to give samples

to be tested for all of these diseases separately to accurately diagnose DENV infected

subjects. In these studies we have selected the suspected subjects which were

presented in the hospital with symptoms similar to the DENV infection. These

subjects may have been suffering from malaria, typhoid, flu etc. Raman spectra of

clinically confirmed DENV infected subjects were used to train the PLS regression

models, which successfully differentiated the DENV infected subjects and the non-

DENV infected samples. This study confirms that the proposed technique has shown

potentials to differentiate DENV infected sample from all the suspected samples.

Conclusions and Future Prospects

64

5.1 Raman Spectroscopy based Diagnosis of DENV

Infection

5.1.1 NS1 based Study

In the present study, a multivariate model has been developed to differentiate NS1

positive and NS1 negative samples in the DENV suspected subjects. Analysis of

DENV suspected samples highlights phosphatidylinositol, ceramide, proline and

thiocyanate at 776, 1127, 1454 and 736 cm-1, respectively. These molecules have been

reported in literature for their role in DENV infection. These molecules have been

identified as potential biomarkers of DENV infection in the present study. Further

research work on these molecules relevant to NS1 may help to develop Raman

spectroscopy as efficient, reliable, and cost effective diagnostic tool for the

recognition of early DENV infection through the prediction of NS1 positive or NS1

negative samples.

5.1.2 IgM based Study

Raman spectroscopy based PLS regression model has been developed using 78

Raman spectra of DENV suspected sera for the prediction of AI of IgM. The model

was operated through GUI platform that loads the Raman spectrum of the suspected

sample. It is multiplied with regression vector of the model and predicts AI of IgM.

The predicted values of AI of IgM are in good agreement with clinical results.

Analysis of regression coefficients revealed that asparagine, glutamate,

galactosamine, palmitic acid, dextrose, myristic acid, vaccenic acid, triglycerides,

phosphoenolpyruvate and phosphatidylserine were found to have an increasing trend

with increasing values of AI of IgM. However, fructose, cholesterol, cellobiose and

arabianose were found to have a decreasing trend with an increasing value of AI of

IgM. The model predicted values were based on reference clinical results; however,

further research is in progress at NILOP to investigate in details for finding the pin-

pointed Raman signatures in the infected samples for IgM.


65

5.1.3 IgG based Study

Raman spectroscopy based PLS regression model has been developed using 79

Raman spectra of DENV suspected sera for the prediction of AI of IgG. The model

was operated through GUI platform that loads Raman spectrum of the suspected

sample, multiplies it with regression vector of the model and predicts AI of IgG. The

predicted values of AI of IgG are in good agreement with the clinical results.

Molecular analysis on the basis of regression coefficients revealed that myristic acid,

coenzyme-A, alanine, arabinose, arginine, vitamin C, carotene, fumarate,

galactosamine, glutamate, lactic acid, stearic acid, tryptophan and vaccenic acid are

positively correlated with the values of AI of IgG. However, amide III, collagen,

proteins, fatty acids, phospholipids and fucose are negatively correlated with values of

AI of IgG. The model predicted values were based on reference clinical results;

however, further research is in progress at NILOP to investigate in details about

finding the pin-pointed Raman signatures in the infected samples for IgG.

5.1.4 Lactate as a Biomarker

Lactate based detection of DENV infection in human blood sera using Raman

spectroscopy was investigated. In dengue infected samples, Raman peaks appeared at

750, 830, 925, 950, 1123, 1333, 1450, 1580, 1680 and 1730 cm-1. In dengue infected

samples, the Raman peaks close to 750, 830, 925, 950, 1123 and 1450 cm-1 are most

probably showing an elevated lactate level which arises due to the impaired function

of important body organs like liver, kidney, lungs etc. Furthermore, it has also been

shown that in DENV infection, the Raman peaks at 1003, 1156, and 1516 cm-1

suppress most probably due to an elevated lactate level in the blood. The research

work at NILOP is still in progress and efforts are underway to provide an alternate

and efficient tool that might help in early detection of different diseases.

5.2 Future Prospective

Cost of Raman system used in presented research work is high as it provides various

options as per requirement of the experiments. However, acquisition of Raman

spectrum does not cost much and results are very quick.


66

Early diagnosis of DENV infection is of prime importance because a DENV

infected patient, who is diagnosed at an early stage, can be treated well in time and its

symptoms are administrated in an efficient way so that the infection may not lead to

DHF and DSS. In this regard a study was conducted [50], where sera samples of

subjects with possible DENV infection were collected and their ELISA based clinical

reports were acquired about NS1, IgM and IgG. The subjects for whom all three tests

were negative were placed in control group while the positive group consisted of only

those samples for which NS1 was positive but IgM and IgG were negative. Hence we

had two groups; one was non-DENV infected and the other with DENV infection but

at an early stage. To differentiate these two groups a PLS model was successfully

implemented with very good results. It showed that the Raman spectroscopy based

PLS model can diagnose DENV infection at an early stage when only NS1 are present

in the serum and antibodies have not yet been produced. Embedding of such a code in

a microcontroller with integrated hand held Raman systems can help diagnose the

DENV infection at an early stage as well.

The GUI used for this research work was designed and developed specifically

in MATLAB (Mathworks 2009a) programming language to yield screening models,

i.e. regression vectors, for screening of serum samples. Such models have potentials

to be used in the code of a microcontroller in a Raman spectrometer which will enable

us to screen the sera samples instantly for NS1, IgM and IgG just like a glucometer,

which is commonly being used for glucose level determination. Such devices would

be able to acquire Raman spectrum of serum of a subject who is suspected of DENV

infection. It will classify a sample as positive or negative on the basis of NS1, IgM

and IgG. Moreover, elevated level of lactate in the serum may also be examined at the

same time. In this way it may help a physician in diagnosis of DENV infected

subjects accurately, quickly and cost effectively. Sooner or later this technique will be

approved for clinical trial. After successful testing of this technique in various

hospitals it can be developed into a hand held diagnostic device just like a glucometer.

67

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