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Overview of Sepsis and Sepsis Biomarker Detection Overview of Sepsis and Sepsis Biomarker Detection

Souvik Kundu Iowa State University

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Overview of Sepsis and Sepsis Biomarker Detection

by

Souvik Kundu

A creative component submitted to the graduate faculty

in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

Major: Electrical and Computer Engineering

(Microelectronics & Photonics)

Program of Study Committee: Prof. Ratnesh Kumar, Major Professor

Prof. Gary Tuttle Prof. Long Que

The student author, whose presentation of the scholarship herein was approved by the

program of study committee, is solely responsible for the content of this dissertation. The Graduate College will ensure this creative component is globally accessible and will not

permit alterations after a degree is conferred.

Iowa State University

Ames, Iowa

2019

Copyright © Souvik Kundu, 2019. All rights reserved.

ii

DEDICATION

I would like to dedicate this master’s creative component report to my parents without whose

support I would not have been able to complete this work. I would also like to thank my friends

ETG for their support and ECpE department of Iowa State University financial assistance during

the execution of this work.

iii

TABLE OF CONTENTS

Page

LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

CHAPTER 1. Introduction to SEPSIS and Global Impact . . . . . . . . . . . . . . . . . . . 1

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Definition, Cause and Manifestation of Sepsis . . . . . . . . . . . . . . . . . . . . . . 2

1.2.1 Why Sepsis is Fatal ? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Role of Lymphocytes in Sepsis Mechanism . . . . . . . . . . . . . . . . . . . . . . . . 4

1.4 Sepsis Flow of Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.5 Sepsis Impact Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

CHAPTER 2. Traditional Sepsis Diagnosis Systems and their Limitations . . . . . . . . . . 11

2.1 Understanding Sepsis Diagnosis from Biomarker’s point of View : Road towards

label-free sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2 Introduction to Traditional Sepsis Diagnosis Systems . . . . . . . . . . . . . . . . . . 12

2.3 Limitations of Traditional Sepsis Diagnosis Systems . . . . . . . . . . . . . . . . . . 14

CHAPTER 3. Alternatives to Traditional Blood Culture based Sepsis Diagnostic Methods . 17

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.2 Label free Method of Sepsis Detection using Specific Biomarkers . . . . . . . . . . . 17

3.3 Non-Traditional (Label Free) Sepsis Detection Schemes . . . . . . . . . . . . . . . . 18

3.3.1 Microfluidic Based Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

iv

3.3.2 Machine Learning Based Approach . . . . . . . . . . . . . . . . . . . . . . . . 19

3.3.3 Electrochemical Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.3.4 Field-Effect Transistor Based Approach . . . . . . . . . . . . . . . . . . . . . 22

3.3.5 Optical Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.3.6 Miscellaneous Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

CHAPTER 4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

v

LIST OF TABLES

Page

2.1 Sepsis Biomarker Concentrations . . . . . . . . . . . . . . . . . . . . . . . . 12

2.2 Current Sepsis Diagnosis Techniques . . . . . . . . . . . . . . . . . . . . . . 15

3.1 Survey on Microfluidic and Lab on Chip Sensors for Sepsis Diagnosis 27

3.2 Survey on Electrochemical Sensors for Sepsis Diagnosis . . . . . . . 28

3.3 Survey on Optical Sensors for Sepsis Diagnosis . . . . . . . . . . . . . 29

3.4 Survey on FET, Mass and Machine Learning based sensors for

Sepsis Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

vi

LIST OF FIGURES

Page

1.1 The interrelationship between systemic inflammatory response syndrome

(SIRS), sepsis, and infection (1). . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Flow of events during immune response in Sepsis and Non-Sepsis case . . . . 6

1.3 (a) Release of Biomarkers during Inflammation Process. (b) Activation and

apoptosis of T-cells during non-sepsis and sepsis scenario respectively. (c)

Balance and imbalance between SIRS and CARS during sepsis and severe

sepsis. Figures re-referenced from (2) . . . . . . . . . . . . . . . . . . . . . . 6

1.4 (a)Sepsis vs Other Diseases (source: www.sepsis.com) (b) Rise in mortality

with increase in the number of systemic inflammatory response syndrome

symptoms (source : https://www.medscape.org/viewarticle/498564 ) . . . . . 8

1.5 (a) Sepsis trials are predominantly conducted in high-income countries (b)

Rise in awareness world-wide (3) . . . . . . . . . . . . . . . . . . . . . . . . 8

1.6 Time to adequate antibiotics in hours, and relation to mortality rate. (4) . . 9

2.1 Traditional infection diagnosis technology in clinics (a) ELISA (b) FRET . . 13

2.2 Time line for the diagnosis of bloodstream infections conventional - (marked

in blue) and recent (marked in - red) technologies available for the diagno-

sis of bloodstream pathogens. Case I : positive blood cultures (a); Case

II : whole blood (b). Blue vertical line represents the time during which

blood cultures became positive. Diamond signs represent theoretical times

required for the detection of pathogens with each technology(5) . . . . . . . 13

3.1 Microfluidic device to estimate neutrophil motility from a drop of blood (6) 19

vii

3.2 Electrochemical Sensing and Screen Printed Electrode (7) . . . . . . . . . . 21

3.3 Electrolyte Gated Organic FET (8) . . . . . . . . . . . . . . . . . . . . . . . 23

3.4 Detection of CRP using SPR Technology (a) without nano enhancers (b)

with nano enhancers (9) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.5 Optical Fiber Sensor (10) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.6 Acoustic Biosensor (11) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.1 Determining factors for the incidence of sepsis. (Figure referred from (5)) . 32

viii

ACKNOWLEDGMENTS

I would like to take this opportunity to express my gratitude to those who helped me with

different aspects of conducting research and the writing of this creative component report. First

and foremost, Dr. Ratnesh Kumar for his guidance and support throughout this research and the

writing of this report. I would also like to thank my committee members Dr. Gary Tuttle and

Dr. Long Que for their support and valuable inputs during the execution of this work. I would

additionally like to thank Dr. Shawana Tabassum for her assistance and knowledge sharing with

me at the initial stages of my graduate research.

ix

ABSTRACT

Sepsis being a fatal physiological state due to imbalance in immune system due to infection,

and most common cause for millions of deaths in the non-coronary intensive care unit world wide,

requires special attention in it’s diagnostic methods and cure. Therefore understanding of literature

related to sepsis is of utmost importance. With the advent of inter-disciplinary research the study

and diagnosis of sepsis problem is not limited to medical field, rather it requires interventions and

active participation of other fields of science and technology. However, often subject matter from

an interdisciplinary research is expounded in an abstruse manner and hence it becomes elusive

for a researcher from different research domain to understand it, leading to loss of quality and

efficiency in research. In this survey report, the material is presented in a form that facilitates easy

comprehension for the non-medical researchers and has been focused on introducing sepsis, it’s

causes, extent, comparison of diagnosis techniques : conventional labeled and label-free detections;

with special emphasis on sepsis biomarkers to help researchers from multi-disciplinary domain to

develop and fabricate devices and ideas to compliment the existing sepsis diagnosis system present

in the medical field. A future direction of sepsis diagnosis along with the implementation of novel

techniques for sepsis biomarker quantification is also reported.

1

CHAPTER 1. Introduction to SEPSIS and Global Impact

1.1 Introduction

The word ’sepsis’ is a word derived from the ancient Greek word [σηψις], which means de-

composition of animal- or plant-based organic materials by bacteria(12). Apart from that, it was

also mentioned in Homeric poems as ’sepo’ [σηπω], meaning ’I rotted’. Even between 460-370

BC Hippocrates had represented the term sepsis with the word ’sepidon’, which meant ’distortion,

dissolution of a web structure’. Interestingly, the term was also popular among the great minds

including Aristotle, Plutarch, and Galen with similar meaning and is in use for over 2700 years(13).

In modern medical science, Sepsis can broadly be defined as an unbalanced immune response of an

organism to an infection that eventually ends up injuring its own organs or tissues. If this response

is not detected at an early stage, it may result in septic shock i.e. widespread inflammation all over

the body which ultimately leads to multiple organ failures and deaths. There are more than 1.7

million cases of sepsis in the United States each year and around 270,000 results in death. Moreover,

out of all the deaths that occur in hospitals, 1 in 3 are due to sepsis. Sepsis is also a leading cause

for hospital readmission. Its occurrence is unpredictably and progress frighteningly quickly. There-

fore, understanding how to best diagnose and manage sepsis is of utmost importance. However,

often the definition of sepsis and it’s variants (infection, bacteremia, septicemia, septic syndrome,

septic shock) which are closely related are misunderstood. Hence confusion prevails among both

researchers and clinicians. Therefore, there was an urgent need for standardization of terms related

to sepsis and their appropriate definitions, which was established in The ACCP/SCCM Consensus

Conference at Chicago in 1991 (1). The conference aimed at providing general guidelines for future

investigations related to sepsis, so that researchers are able to compare and improve various existing

and therapeutic protocols.

2

Figure 1.1 The interrelationship between systemic inflammatory response syndrome(SIRS), sepsis, and infection (1).

1.2 Definition, Cause and Manifestation of Sepsis

Broadly, sepsis is a clinical response that arises due to an infection. However, even in the absence

of infection, an identical response can also be noted. Thus a new phrase was coined to describe the

inflammatory process that is independent of the cause is known as – systemic inflammatory response

syndrome (SIRS) (1). SIRS is a broader or general term used to address a large number of clinical

conditions including infectious insults and non-infections insults (pancreatitis, ischemia, multiple

trauma, tissue injuries, hemorrhagic shock, immune-mediated, organ injury, etc.). Therefore in

this context Sepsis is basically that diseased condition when SIRS occurs as a result of a confirmed

infection to the host. This active infection can be bacterial, fungal, parasitic, viral, etc. The

definition of Sepsis is better understood by means of the illustration in Fig.1.1. Sepsis syndrome

was identified over a century ago and was based on lots of clinical data. However, there is no

concrete definition of sepsis. The definitions of sepsis have evolved over a period of time. In

2001 the definitions of sepsis and septic shock were revised to include the threshold values of

organ damage. However, in the last couple of years,, the modern definition of sepsis has changed

dramatically and is defined as a life-threatening organ dysfunction caused by a dysregulated host

response to infection. The consensus document describes organ dysfunction as an acute increase

in total Sequential Organ Failure Assessment (SOFA) (13). Further, in case of Sepsis there are

3

several clinical conditions that arise related to rise in body temperature above 38◦C or less than

36◦C, a heart rate and respiratory rate of more than 90 beats/min and 20 breaths/min respectively,

an acute alternations in WBC count i.e. either greater than 12000 per cu. mm. or less than 4000

per cu. mm. Therefore, to summarize the sepsis process we may infer that it starts with an

inflammatory response with respect to the presence or invasion of a microorganism, that leads to

sepsis if the above mentioned clinical conditions are satisfied, and eventually end up confirming

severe sepsis due to multiple organ dysfunctions. Finally, the host suffers from the septic shock

which is basically sepsis-induced with hypotension despite adequate fluid resuscitation along with

the presence of perfusion abnormalities that may include, but are not limited to, lactic acidosis,

oliguria, or an acute alteration in mental status (1).

1.2.1 Why Sepsis is Fatal ?

From 1.2 it is well understood that the fatality due to sepsis is not directly caused by the

invading microorganisms or pathogens; rather the resulting clinical condition is caused by dysreg-

ulation of the host immune response that leads to multiple organ dysfunction, coagulopathy, and

hypotension(14). In the past years, many research was conducted to understand the root cause

of sepsis(14)(15)(16). To determine the root cause of sepsis; understanding of the complex triad

of infection, inflammation, and coagulation is very important and also the difference between the

immune response during normal infection and during the time of sepsis is required. During sepsis,

there is multi-infection immune response imbalance. The imbalance is caused by both inflamma-

tory and anti-inflammatory immune response.The sequential flow of events regarding the inception

of infection, dysregulation of immune response and finally which leads to imbalance is narrated

in - 1.4. In (17) authors have claimed that when an external pathogen invades, an attempt is

made by the host defense system in order to contain the foreign organisms in spreading and mul-

tiplying inside the host body. This event is followed by an inflammatory response that activates

the coagulation process and fibrin deposition. However, during the exaggerated response by the

immune system, it leads to a situation where the coagulation causes another diseased condition in

4

a severe form that leads to microvascular thrombosis and organ dysfunction which is also known

as dissemated intravascular coagulation (DIC) (18). Actually, this microvascular thrombosis is an

adaptive response to infection which prevents the intruding pathogens present in the tissues from

getting spread via the systemic circulatory system. Thus due to this thrombosis i.e. by clogging

the path between the tissue and circulatory system temporarily, it prevents the entry of pathogen

in body tissue and the in the meantime the immune system with the help of leukocytes removes

the pathogens or bacteria and repairs the damaged tissues. However, during acute infection the

microvascular thrombosis becomes generalized, there is prolonged clotting then extensive tissue

ischemia (i.e. inadequate blood supply to an organ) may cause organ failure and death. This phe-

nomenon is supported by studies on a post-mortem of patients with sepsis, as they demonstrate the

presence of microvascular thrombosis in many organs including the lung, adrenals, liver, gut, kidney

and even brain (19). Researchers, therefore, have found an obvious relation between inflammation

and coagulatory response in the host system(20)(21). Again, they have also acknowledged the im-

portance of endothelial activation for microvascular dysfunction, which is one of the hallmarks of

sepsis(22)(23). The illustration of the flow of events during sepsis is given in Fig.1.4 and narrated

in section 1.4.

1.3 Role of Lymphocytes in Sepsis Mechanism

A lymphocyte is a subtype of WBC which mainly comprise of Natural Killer(NK) cells(24), T-

cells (thymus)(25) and B-cells(bone-marrow) (26). T cells are involved in cell-mediated immunity

i.e. they provide immunity by activating phagocytes, antigen-specific cytotoxic T-lymphocytes, and

releases various cytokines in response to an antigen(foreign organism). B cells respond to pathogens

by producing a large number of antibodies for neutralizing foreign bodies like bacteria and viruses.

NK cells are generally part of the innate or inborn immune system and play a vital role in the

tumors and virally infected cells. In this section, we will discuss how these lymphocytes along with

dendritic cells(DCs)(27) become dysfunctional during sepsis. Recent study (28) infers that during

sepsis there is extensive apoptosis of T and B cells accompanied by profound immunosuppression.

5

An increased number of T-suppressor cells was also noted. Thus for lethality associated with sepsis,

it begins with apoptotic deletion of T and B cells followed by defective DCs, and this marks the onset

of immunosuppression. Therefore this defective innate immune system leads to a loss in its ability

to engulf bacteria, which corresponds to prolonged coagulation and results in the development of

multiorgan failure (MOF) and finally death. Studies also reveal that the development of sepsis

can also lead to redox imbalance in WBCs (leukocytes) and organs due to the build-up of reactive

oxygen species (ROS). This is followed by an inflammatory response (SIRS), including a sustained

immune response and other immune activation states in endothelial cells and leukocytes. The

detailed analysis and pathways are narrated in several papers including (29; 30; 31; 32; 33; 16). In

this report we present a concise pathway of an immune response corresponding to bacterial invasion

for better understanding of sepsis flow of events described in the next section.

1.4 Sepsis Flow of Events

In order to understand the complex flow of events in case of sepsis lets us take an example

of bacterial infection. In this section the flow of events i.e. the immune response of the body

with respect to the inception of bacterial infection is narrated in a rather simplified manner in

order to facilitate easy grasping of the complex biological phenomenon for non-medical researchers.

Therefore, the complex jargon has been avoided and emphasis has been put on the sequential and

logical flow of events that take place during sepsis and non-sepsis cases.It is worth mentioning

here that the flow of events during the immune response of the body is nearly similar in case of

most of the infections or injuries. Fig.1.2 illustrates the immune response with respect to

bacterial infection. There are two types of molecular patterns called damage-associated molecular

patterns(DAMPs) and pathogen-associated molecular patterns(PAMPs) that are released into the

bloodstream from the affected tissues when the body suffers from either an injury or an infec-

tion respectively. Bacterial cell walls are composed of lipo-polysaccharide (LPS) which are also

known as endotoxin. These toxin molecular patterns that are present inside a bacterial cell is

released when the cell disintegrates. These patterns are known as PAMPs and are received by

6

Figure 1.2 Flow of events during immune response in Sepsis and Non-Sepsis case

Figure 1.3 (a) Release of Biomarkers during Inflammation Process. (b) Activation andapoptosis of T-cells during non-sepsis and sepsis scenario respectively. (c) Bal-ance and imbalance between SIRS and CARS during sepsis and severe sepsis.Figures re-referenced from (2)

7

toll like receptors (TLR) that resides on the cell surface. This binding between LPS and TLR

leads to the release of pro-inflammatory cytokines like tumor necrosis factor (TNF), interleukins

(IL-1, IL-12, IL-6, HLA(MHC), etc as illustrated in Fig.1.3(a). This phenomenon is known as

systemic immune response syndrome(SIRS) that leads to inflammation and coagulation. Almost

simultaneously an anti-inflammatory response is produced in the body that tries to compensate the

inflammatory process called compensatory anti-inflammatory response syndrome. This leads to the

production of anti-inflammatory cytokines called IL-10, soluble inhibitors, s-TNF, IL-1 receptors,

etc. So, in general, during the invasion of any infection this complex triad of inception, inflamma-

tion and coagulation occur with a balance between SIRS and CARS. The balance between them

is disturbed when instead of activation of T-cells by macrophages, apoptosis of T-cells occurs. As

shown in Fig.1.3(c) in patients with severe sepsis HLA-DR expression on macrophages is decreased,

and they have increased expression of the negative co-stimulatory molecule CTLA-4 (cytotoxic T

lymphocyte-associated antigen-4)(34) as well as another molecule associated with apoptosis of T-

cells called PD-1 (programmed cell death)(35). Generally, T-cells express a positive co-stimulatory

molecule called CD28. Along with the T-cell antigen receptor (TCR) it recognizes antigen in the

context of the antigen presenting cells Class II MHC, which activates the T-cell. However, de-

creased expression of CD28 and enhanced expression of the alternative ligand CTLA-4 (also called

CD152) leads to apoptosis of T-cells(36). Now, since there is a lack of T-cells the elimination of

bacterial infection takes more time. This leads to prolonged coagulation which tries to prevent the

migration of infection to various organs of the body but eventually, due to this delayed coagulation,

there is an insufficient supply of blood and nutrients to the organs which leads to organ failure and

tissue toxicity. This is the root cause of fatality in sepsis patients.

1.5 Sepsis Impact Statistics

In order to showcase the impact of sepsis throughout the world we hereby present a statistical

illustration of the present scenario. There are over 31.5 million people who develop sepsis each year

worldwide. Among them, 19.4 million suffer from severe sepsis and about 5.3 million people die(37).

8

Figure 1.4 (a)Sepsis vs Other Diseases (source: www.sepsis.com) (b) Rise in mortality withincrease in the number of systemic inflammatory response syndrome symptoms(source : https://www.medscape.org/viewarticle/498564 )

Figure 1.5 (a) Sepsis trials are predominantly conducted in high-income countries (b) Risein awareness world-wide (3)

Further, it has been estimated that there are about 3 million cases of sepsis among neonates per

year globally and about 1.2 million children suffer from mortality rates as high as 11 – 19 % (38).

Due to puerperal sepsis, about 75,000 women die every year around the globe(39). With mortality

rates between 28% and 50%(40), sepsis is not only lethal but also expensive. It is considered as one

of the most exorbitant condition to treat in the hospitals and clinics in the US, with an average

cost for treating 3.1 million population sums up to US$24 billion per year(41; 40). The

estimated number of deaths in the US due to sepsis is higher than the number of deaths from

prostate cancer, AIDS and breast cancer combined (42). A statistical overview and comparison

9

Figure 1.6 Time to adequate antibiotics in hours, and relation to mortality rate. (4)

among infectious diseases and mortality rates are illustrated in Fig.1.4. Underlying these stern

numbers a dominant factor is the absence of an accurate, and prompt sepsis diagnostic method at

the point of care (43). For sepsis patients, early detection of the onset of sepsis is critical; since for

each and every hour of delay in exercising an appropriate antimicrobial medication to the patients

results in roughly 7.6% decrease in survival rates. Mortality rates within each hour of antibiotic

delay is shown in Fig.1.6 (4). Again, even if the patients survive and gets discharged, they still

have a continuing risk of mortality(44). This calls for an urgent need for faster techniques for early

detection of sepsis(45). Apart from early detection a general awareness also needs to be instilled in

the populations and concerned authorities. With that view in mind, World Sepsis Day is celebrated

across the globe since 2012 on 13th September every year. Statistical analysis of Google search data

on sepsis worldwide depicts a considerable amount of rising awareness regarding sepsis among the

world population as illustrated in Fig.1.5(b). However, since the treatment of sepsis is an expensive

majority of sepsis trials registered on ClinicalTrials.gov (the US clinical trials registry, which is the

largest in the world) and anzctr.org.au (the Australian New Zealand Clinical Trials Registry) are

in high-income countries as shown in Fig.1.5(a)(46). Therefore with every passing day with the

increase in awareness and development of novel techniques for sepsis diagnosis the world is striving

10

towards mitigation of sepsis problem and thus calls for a fast, accurate and economic point-of-care

device for sepsis diagnosis.

11

CHAPTER 2. Traditional Sepsis Diagnosis Systems and their Limitations

2.1 Understanding Sepsis Diagnosis from Biomarker’s point of View : Road

towards label-free sensing

As discussed in section 1.2 any insult including burns, pathogen attacks, severe surgery, etc.

can give rise to SIRS. Simultaneously, another anti-inflammatory response is initiated to dampen

the inflammatory process known as Compensatory Anti-inflammatory Response Syndrome (CARS)

(47). Continuing the discussion from section 1.2.1, that there is multiple organ failure (MOF) during

sepsis which turns out to be fatal, we further assert that after the necrosis or death of cells in a

specific organ or tissue, alarmins are releases by those damaged cells. These alarmins, also known as

damage-associated molecular patterns (DAMPs) and they initiate a sterile inflammation associated

with non-infectious SIRS. Similarly, pathogen-associated molecular patterns (PAMPs) initiate and

perpetuate the infectious pathogen-induced inflammatory response(48). Thus DAMPS and PAMPS

are host biomolecules that initiate and extend the inflammatory response of the immune system.

Sepsis is initiated by two factors simultaneously :

• recognition of multiple infection-derived microbial products (16)

• endogenous danger signals by complement and specific cell-surface receptors on cells (im-

mune, epithelial and endothelial) whose primary job is to continuously sample their local

environment and do surveillance (49).

Now, the PAMPs and DAMPs can either bind to complements or various types of receptors on the

cell surface can induce a complex intracellular signaling system with redundant and complementary

activities(50) as illustrated in Fig.1 of (16). This triggering of multiple signaling pathways leads to

the expression of various genes which are responsible for inflammation. Further, the recruitment

of various pro-inflammatory intermediates can also lead to the initiation of expression of multi-

12

ple early activation of genes which includes cytokines(IL-1, TNF, IL-12, type I IFNs, IL-18) and

they are associated with inflammation. These cytokines initiate a cascade of other inflammatory

chemokines and cytokines (including IL-6, IFN, IL-8, etc. ), as well as the polarization and sup-

pression of components of adaptive immunity(16); thereby having an intense effect on coagulation

in the vascular and lymphatic endothelium, which results in the increased expression of selectins

and adhesion molecules (51). Thus due to series of alternation in the pro- and anti-coagulant

proteins (activated protein C, thrombomodulin, PAI-1, etc.) results in the transformation of the

endothelial tissues from the anti-coagulant state (healthy) to a pro-coagulant (sepsis) state. Fur-

ther, the vascular permeability is also increased due to loss of endothelial tight junctions caused

by the induction of pro-inflammatory proteases (52). This further leads to multiple organ failure

and cause death due to sepsis. Thus from the above discussion, we can conclude that tracking of

various biomarkers like IL-6, IL-10, CCL2, CXCL10, etc. can lead towards the early diagnosis or

indication of the presence of sepsis. Table 2.1 lists the biomarkers corresponding to sepsis and their

respective concentration(53).

Table 2.1 Sepsis Biomarker Concentrations

BiomarkerConcentrationin Blood(normal)[pg/mL]

Concentrationin Blood(Sepsis) [pg/mL]

CRP <3 >3

PCT <10 10-10000

IL-6 <1 1-5000

2.2 Introduction to Traditional Sepsis Diagnosis Systems

Diagnosis of sepsis by traditional methods includes the process of culturing the blood, urine,

CSF, bronchial fluid samples. Generally, CRP or leukocyte counts acts as an indicator or clinical

sign for sepsis. Blood cultures are done in continuous-monitoring blood culture systems(CMBCS)

and following a set of pre-approved guidelines (54). Fully automated systems are prevalent

for incubating the blood samples along with detection and analysis of CO2 released and O2 ex-

13

Figure 2.1 Traditional infection diagnosis technology in clinics (a) ELISA (b) FRET

Figure 2.2 Time line for the diagnosis of bloodstream infections conventional - (markedin blue) and recent (marked in - red) technologies available for the diagnosisof bloodstream pathogens. Case I : positive blood cultures (a); Case II : wholeblood (b). Blue vertical line represents the time during which blood culturesbecame positive. Diamond signs represent theoretical times required for thedetection of pathogens with each technology(5)

14

hausted during the culture process. The sensing is generally done using fluorescent sensors which

are popularly known as labeled sensing techniques(55). Apart from labeled sensing, there are sev-

eral other techniques to estimate the level of gases released which includes calorimetric analysis

(56), automated growth detection techniques, hybrid techniques including - lysis centrifugation-

intrinsic fluorescence method, rapid intrinsic fluorescence method for fast and direct identification

of pathogens in blood cultures (57). Among the traditional pathogen detection techniques, there

are some relatively new detection schemes that include - Multiplexed PCR + hybridization or mi-

croarray, PCR + Mass Spectroscopy, Broad Spectrum PCR. These are exercised on whole blood

mainly. Also, specialized techniques are employed nowadays for identification and susceptibility

testing of positive blood culture. Use of techniques like MALDI-TOF, Molecular POCT, and their

combination or Multiplexed PCR along with mass spectroscopy is employed to increase the accu-

racy in the quantification of the pathogens. Table 2.2 reports the state of the art techniques for

Sepsis detection prevalent. A schematic diagram comparing the old and new traditional methods

of sepsis identification is depicted in Fig.2.2(5). These methods involving analysis of blood culture

is currently the gold standard for any bloodstream infections like sepsis. However, there are several

limitations associated with the traditional diagnosis(58) which are briefly discussed in the next

section.

2.3 Limitations of Traditional Sepsis Diagnosis Systems

• Limitations associated with acquiring adequate blood volume for blood culture. Study (59),

(60) confirms that the diagnostics yield improves with increase in extracted blood volume

and also insufficient volume often yields in negative results. Therefore for several pediatric

patients with certain critical clinical conditions; it is not always possible to extract sufficient

volume of blood from them. This makes the traditional blood culture methodology acts as a

bottleneck for them.

• There is always a time associated with loading of blood culture bottles into the automated in-

strument for measuring microbial activities(61),(62). Ideally, to reduce false-negative sample

15

Table 2.2 Current Sepsis Diagnosis Techniques

Device Company Technology Sample Time DiagnosisFDA

ApprovedPOC

EPOC SiemensBlood gas

(Analyzer)

Whole

blood1min qSOFA Yes Semi

Microbiology

- Septi-Chek

Becton

DickinsonBlood culture

Whole

blood38 hrs

Identify

PathogensYes No

Oxoid signalThermoFisher

ScientificBlood culture

Whole

blood24 hrs

Identify

PathogensYes No

QuickFISH AdvanDx Fluorescence

Positive

blood

culture

1.5 hrsIdentify

PathogensYes No

hemoFISHMiacom

diagnosticsFluorescence

Positive

blood

culture

0.5 hrIdentify

PathogensYes No

Verigene Luminex PCR

Positive

blood

culture

3.5 hrsIdentify

PathogensNo No

FilmArrayBiofire

diagnosticsPCR

Positive

blood

culture

1 hrIdentify

PathogensNo No

HYPLEX BAG PCR

Positive

blood

culture

3 hrsIdentify

PathogensNo No

ACCU-PROBE Gen-probeChemi-

luminescent

Positive

blood

culture

3 hrsIdentify

PathogensNo No

PLEX-ID BAC Abbott PCR

Positive

blood

culture

6 hrsIdentify

PathogensNo No

Staph SRBecton

DickinsonPCR

Positive

blood

culture

3 hrsIdentify

PathogensYes No

StaphPlex Qiagen PCR

Positive

blood

culture

5 hrsIdentify

PathogensNo No

MALDI-TOF bioMrieux

Matrix

assisted

laser

desorption

Positive

blood

culture

2 hrsIdentify

PathogensNo No

Magicplex Seegene PCRWhole

blood3.5 hrs

Identify

PathogensNo No

SeptiFast Roche PCRWhole

blood6 hrs

Identify

PathogensYes No

16

and minimize the detection time, the blood cultures need to be loaded immediately; which

seldom happens.

• Limitations are also associated with slow-growing pathogens (63) in culture media. Some

pathogens multiply and express themselves slowly and this results in a low signal of micro-

bial activities in the culture media which reduces the signal to noise ratio. This limitation

gets further deteriorated if there is a prevalence of previous anti-microbial therapy that was

exercised on the patient at some earlier time.

• Blood Culture tests take about 24 to 72 hours to confirm the prevalence of infection, pathogen

identification and anti-microbial susceptibilities(64) (65); and by the time a positive result

arrives, the patient may start suffering from severe sepsis or sepsis shock along with multiple

organ failure. From there the recovery becomes almost impossible.

• Even the anomalous counts for leukocytes or CRP may be misleading as that might be an

outcome of some other clinical condition or disease rather than sepsis thereby increasing the

false negative rate(66).

So, it is evident that several diagnostic and therapeutic dilemma prevails in case of the tradi-

tional sepsis diagnosis process. So, an unambiguous diagnosis for the differential test of sepsis is of

paramount significance.

17

CHAPTER 3. Alternatives to Traditional Blood Culture based Sepsis

Diagnostic Methods

3.1 Introduction

The limitations of the traditional diagnosis discussed in the previous chapter need to be ad-

dressed by the intervention of several interdisciplinary studies. In the past decades, many novel

techniques have been discussed in order to mitigate the existing limitations and parallel enhance-

ment of the limit of detection. Some detection techniques focused on detecting the sepsis biomarker

or a combination of biomarkers rather than directly detecting the pathogens, whereas some re-

searchers preferred to study the motion or motility of various blood components in a sepsis patient

and compare them with a healthy human. Detection techniques include microfluidic sensor fabrica-

tion, plasmonic biosensors, chemical sensors, optical biosensors, eventually paving the way towards

label-free detection techniques. This facilitated the feasibility of fabrication of point of care devices

with arguable limit of detections and requires a low volume of blood samples. In the following sub-

sections, we will discuss the basis of biomarker detection for sepsis and report some inter-disciplinary

state-of-the-art label-free detection schemes which ultimately may lead to a point-of-care solution

to sepsis detection.

3.2 Label free Method of Sepsis Detection using Specific Biomarkers

Biomarkers are measurable substances whose concentration increases or decreases with respect

to diseases, infections, or environmental exposures to an organism. The level of specific biomarker

or a combination of biomarkers gives an indication of presence of a medical condition or disease.

A large number of biomarkers that are related to sepsis have been reported in past few decades.

However, the accuracy and effectiveness of such biomarkers can not be evaluated until and unless

they are compared with respect to similar baselines among studies. In (67) authors have conducted

18

a large systematic review and meta-analysis in order to evaluate the reported biomarkers in last two

decades by retrieving informations from journals like PubMed and Embase. They have identified

seven most common biomarkers that includes - procalcitonin(PCT), C-reactive protein, interlukin 6,

soluble triggering receptor expressed on myeloid cells-1, prepsin, lipopolysaccharide binding protein

(LPS) and CD64. Although, biomarkers provide a decent correlation with the severity of sepsis

in population based studies, but due to the lack of specificity of biomarkers and different early

inflammatory response for different patients; they have failed to prove it’s effectiveness in case of

individual patients. Thus distinguishing sepsis from other similar type of clinical conditions which

are not sepsis is critical. According to recent literature reviews (68) sepsis biomarkers about 178

biomarkers have been retrieved. But no biomarker shows sufficient sensitivity and specificity to

sepsis (69) with one exception in the use of PCT (70) (71). However, a combination of biomarkers

can lead to better specificity and sensitivity (72). Here we will discuss some state of the art

non-traditional (label free) sepsis detection systems.

3.3 Non-Traditional (Label Free) Sepsis Detection Schemes

Study suggests non-traditional detection schemes can be employed effectively to combat sepsis.

Several means of detection from microfluidic, machine learning, plasmonic, optical sensors have

been discussed in this section.

3.3.1 Microfluidic Based Approach

Irima et. al. in (6) have reported a novel diagnosis procedure where the motility of neutrophils

present in blood is measured in a microfluidic assay. This novel device as illustrated in Fig.3.1

required only a drop of diluted whole blood for diagnosis. 5 motility parameters were studied and a

hybrid score was calculated to estimate the prevalence of sepsis. They have also applied supervised

machine learning algorithms to actually narrow down the total number of control parameters and

thereby increasing the efficiency of the overall diagnosis process. They observed that the motility

of neutrophils from sepsis patients exhibited higher motility compared to neutrophils from healthy

19

Figure 3.1 Microfluidic device to estimate neutrophil motility from a drop of blood (6)

human blood sample. The complete detection process took about 6hrs to complete which is much

less than conventional detection schemes. The observation time can be further reduced by training

the model with a larger training database.

3.3.2 Machine Learning Based Approach

With the advent of data analytics and machine learning algorithms, researchers now have a

better way of predicting sepsis from a set of observations from past diagnosis and tests and their

outcomes. For example three classification methods, which includes logistic regression (LR), sup-

port vector machines (SVM), and logistic model trees (LMT), were used to predict onset of sepsis

in adult Intensive Care Unit (ICU) patients using vital signs and blood culture results in (73). In

(74) a random forest-improved fruit fly optimization algorithm-kernel extreme learning machine,

was used to effectively diagnose the sepsis. They have enhanced the diagnosis accuracy and to

identify the most important biomarkers authors performed feature selection using random forest

20

algorithm. Their research concluded that there is an increase in acetic acid in sepsis group, however

the linoleic acid and cholesterol is decreased. Thus by means of machine learning algorithms a bet-

ter decision making in clinical domain can be obtained for accurate detection of sepsis. As sepsis

is very common in new born babies diagnosis of sepsis at an early stage is extremely important.

In (75) authors utilize three physiological attributes(76) to predict sepsis which includes - heart

rate, respiratory rate and blood oxygen saturation. These variables were chosen by the experienced

pediatricians of NICU of Monash Children Hospital in order to predict the clinicians treatment in

preterm infants with suspected late-onset sepsis. Machine Learning algorithms including Multi-

layer Perceptron Neural Network (NN) , Logistic Regression (LR), Support Vector Machine (SVM)

with Gaussian kernel, and ensemble learning models including - Random Forest (RF) and Gradient

Boosting Decision Tree (GBDT) were used and result predicts that RF and GBDT outperformed

LR, SVM, and NN. Authors also claim that their method can accurately predict the onset of sepsis

24 hours ahead which provides clinicians more opportunity to restrict the infection before it starts

to cause harm to the new born baby. Now-a-days hospitals are employing the Artificial Intelli-

gence to monitor the onset of sepsis as early detection can save life. Duke University Hospital, in

Durham, N.C., has officially launched Sepsis Watch, that identifies incipient sepsis cases and raises

the alarm(77). Several other machine and deep learning ( convolutional-LSTM )(78) (79) based

prediction algorithms are also reported which predicts sepsis with comparable efficiency. However,

Recent Temporal Patterns (RTPs) used in conjunction with SVM classifier outperforms some other

state-of-the-art ML and DL techniques(80). Also, Cloud based systems have been proposed that

works in congujation with ML and AI.

3.3.3 Electrochemical Approach

Electrical transducers are widely used due to their high sensitivity, simplicity and amenability to

inexpensive miniaturization. The increasing need for a patient-centered, efficient and inexpensive

diagnostic system has resulted in the emergence and development of point-of-care (POC) sepsis

diagnosis. Min et al. have reported the development of a POC platform, termed IBS (integrated

21

Figure 3.2 Electrochemical Sensing and Screen Printed Electrode (7)

biosensor for sepsis) for rapid and reliable sepsis identification (81). A portable platform compris-

ing of a disposable kit (to capture sepsis biomarker interleukin-3 (IL-3) on magnetic beads and

label it for subsequent electrochemical measurements), an electrical detection system (to measure

electrical current for IL-3 quantification), a microcontroller unit for signal processing and a blue-

tooth module for wireless communication, all packaged into a single monolithic device, outperforms

the conventional enzyme-linked immunoadsorbent assays (ELISA) by providing >5 times faster

response, >10 times more sensitivity and an order of magnitude larger dynamic detection range.

Further, using human clinical samples (n=62), sensitivity and specificity of 91.3% and 82.4% are

achieved respectively. In addition, survival analysis on patients confirmed the role of IL-3 in organ

failures resulting from septic shock. The current total cost of the device breaks down to about $50

for the IBS reader and $5 per test for the reagent use. A scale-up production is expected to further

reduce these prices, thus providing IBS competitive cost advantages over ELISA ($11) or lateral

flow strips ($10-$20). The limitations in differentiating sepsis from other noninfectious causes

of SIRS have been addressed through multiplexed detection of a panel of biomarkers which results

in improved decision making regarding treatment and drug administration. In this regard, in a

recent study Selvam et al. reported the first-of-its-kind electrochemical impedance spectroscopy

22

(EIS) based nanochannel sensor system built with a nanoporous nylon membrane integrated onto

microelectrodes (82). The covalent binding of biomarkers onto the electrode surface forms an elec-

trical double layer which is transduced as impedance changes and recorded via EIS. The sensor is

demonstrated to detect three sepsis biomarkers PCT, LPS and lipoteichoic acid (LTA) in pooled

human serum as well as in human whole-blood samples with LODs of 0.1 ng ml−1, 1 µg ml−1 and 1

µg ml−1 respectively. Other electrochemical sensors using highly-oriented pyrolytic graphite (83),

host-guest nanonet electrode (84) and enzyme conjugated acrylic microspheres and gold nanopar-

ticles composite coated onto a carbon-paste screen printed electrode (7) have also been developed

for sepsis biomarker detection as shown in Fig.3.2.

3.3.4 Field-Effect Transistor Based Approach

Field-effect transistors are gaining attention for infectious disease detection because of their

low voltage operation (<1V), inherent gain amplification, biocompatibility and miniaturization

(85). Seshadri et al. developed a label-free immunosensor based on electrolyte-gated organic field-

effect transistor (EGOFET) for PCT detection (8). The schematic diagram is depicted in Fig.3.3.

Monoclonal antobodies were immobilized on the poly-3-hexylthiophene (P3HT) organic semicon-

ductor (OSC) surface that formed the transistor electronic channel. The antibody immobilization

and analyte-receptor binding events induced distinct changes in the transistor figure of merits i.e.

threshold voltage, drain current and carrier mobility. The antibody functionalized to the OSC

channel induced alterations in charge carrier transfer path in the channel which translated into

changes in carrier mobility. On the other hand, the net negative charge on the target PCT acted as

traps for holes induced in the OSC, eventually reducing the drain current and shifting the device

threshold voltage. The reported EGOFET could detect PCT concentrations. spanning from 0.8

pM to 4.7 nM with a detection limit of 2.2 pM.

23

Figure 3.3 Electrolyte Gated Organic FET (8)

3.3.5 Optical Approach

Driven by the need to conduct in-situ measurements of sepsis or perform the diagnosis on a

tightly constrained footprint, optical detection have proved to be an appealing platform. Zubiate

et al. developed a high sensitive CRP measurement technique utilizing the lossy mode resonance

(LMR) of optical fibers which corresponds to the coupling of core mode to a lossy mode in a

thin-film (86). (10) also reports similar fiber optic sensor with lossy mode resonance (LMR). The

experimental setup is illustrated in Fig.3.5. A side polished D-shaped fiber was coated

by a thin indium tin-oxide film subsequently functionalized with layers of CRP-selective aptamers.

The shifts in LMR wavelength were tracked in response to different concentrations of CRP solu-

tions ranging from 0.0625 mgL−1 to 1 mgL−1. The fabricated sensor could detect the minimum

CRP concentrations of 0.0625 mgL−1 which is far below the clinical threshold value of 1 mgL−1,

thus demonstrating its huge potential in clinical diagnosis of sepsis. Surface plasmon resonance

(SPR) is another promising label-free technique for selectively identifying sepsis. In (9) authors

have extended the application of SPRi systems in order to detect biomarkers which have ultralow

24

Figure 3.4 Detection of CRP using SPR Technology (a) without nano enhancers (b) withnano enhancers (9)

25

Figure 3.5 Optical Fiber Sensor (10)

levels in blood. Therefore, the advantage of aptamer technology is combined with nanomaterials

and microwave-assisted surface functionalization. They have implemented a sandwich assay by

intrducing aptamer-modified quantum dots (QDs), and have measured 7 zeptomole (at 5 fg/mL) of

C-reactive protein (CRP) in spiked human serum. Fig.3.4 illustrates the set up. In (87), a thin gold

film is coated on the exposed region of the fiber core to excite the surface plasmons at the interface

between the gold coating and the above dielectric. Afterwards, the SPR sensor is modified with

a polydopamine, followed by the immobilization of anti-CRP monoclonal antibodies. The shifts

in the SPR dip appearing at the output signal are measured and the sensitivity is observed to be

1.17nm µg −1 mL. The optimum reaction time between the anti-CRP and CRP are observed to be

60 mins which is far less than the conventional schemes.

3.3.6 Miscellaneous Approach

Apart from machine learning based algorithms there are several other means of sepsis diagnosis

schemes where the methodology is applicable directly in vitro and in vivo real-time detection

of infections with optimal cost- and time-efficiency. Voltametric diagnosis (88) of E. Coli done

26

Figure 3.6 Acoustic Biosensor (11)

on sepsis infected blood plasma is one such scheme. In (11) researchers have investigated and

developed a quartz crystal microbalance biosensor for the detection of folate binding protein (FBP),

which is another biomarker for sepsis. 30nM detection limit was achieved when the proteins were

immobilized on gold-coated quartz sensor. Fig.3.6 depicts the working principle of the acoustic

biosensor for the detection of FBP.

27

Table 3.1 Survey on Microfluidic and Lab on Chip Sensors for Sepsis DiagnosisPrinciple Sample Biomarker Interface Linearity & LOD Reference

Micro-fluidic

Drop

of

Blood

Counting

of

Neutrophils

N/A N/AIrima

et al.(2018)

Micro-fluidic Blood nCD64 cell counts

619 +/-

340

cells/

chip

Zhang

et al. (2018)

PoC

Microfluidic

Biochip

Blood nCD64 cell counts

102 in

10uL

of

Blood

Hassan

et al.(2018)

Lab

on a

Chip

Buffer IL-3

magneto-

electrchemical

sensing

10pg/mLMin

et al. (2018)

28

Table 3.2 Survey on Electrochemical Sensors for Sepsis DiagnosisPrinciple Sample Biomarker Interface Linearity & LOD Reference

Chrono-

amperometry

Human

BloodIL-3

Antibodies,

oxidizing

enzyme

(HRP)/

mediators

10 pg/mLMin

et al. (2018)

Electrochemical

Impedance

Spectroscopy

(EIS)

Human

Serum/

Blood

PCT,

LPS and

lipoteichoic

acid

(LTA)

nanoporous

nylon membrane

integrated onto

microelectrodes

0.1ng/ml,

1g/ml,

1g/ml

Selvam

etal.(2017)

Amperometric

Human

Serum/

Blood

Secretory

Phospholipase

Group 2-IIA

(Enzyme)

enzyme

conjugated

acrylic

microspheres

and gold

nanoparticles

composite

coated onto a

carbon-paste

screen printed

electrode (SPE)

0.01100

ng/mL,

5 103 ng/mL

Mansor

et al. (2018)

Microelectrode BufferInterlukin-6

(IL-6)

needle

shaped

micro-electrode

20-100pg/mLRussell

et al. (2019)

Electrochemical

ImmunosensorBuffer PCT

Cu/Mn

Double-Doped

CeO2

Nanocomposites

0.03pg/mlYang

et al.(2017)

Multiplexed

EC Sensor

Infected

Blood

Medium

specific to

bacterial

species

AuNPs

on

SPE

290 CFU/mLGao

et al. (2017)

Electrochemical

Immunosensor

Human

Serum/

Blood

TNFMicroarray

ELISA60pg/mL

Arya

et al. (2017)

29

Table 3.3 Survey on Optical Sensors for Sepsis DiagnosisPrinciple Sample Biomarker Interface Linearity & LOD Reference

SPRHuman

Serum

Folic

Acid

Proteins

Graphene

+

Folic acid

5−500 fM

5fM

Lijit

et al. (2017)

Fluorescence

[FRET]Buffer

Folate

Receptor

Proteins

Ag nanoclusters

coated

DNA /SWCNTs

0.13 ng/mL

33pg/mL

Jiang

et al. (2015)

Optical Fiber

(LMR)

Blood

PlasmaCRP

Core-Claddin

Interface0.0625 - 1 mg/L

Zubiate

et al. (2018)

Plasmonic

(Nano-particles)Buffer CRP

Digital Biomarker

detection

in Microarray

(NP enhanced gold

nano-hole arrays)

27pg/mLBelushkin

et al. (2018)

Total Internal

Reflection

Fluorescence

(TIRF)

Serum

and

Plasma

PCT,

IL-6

microarray based

multiparameter

immunofluorescence

assays

IL-6: 0.27 ng/mL

in serum

0.77 ng/mL

in plasma

PCT: 0.37ng/mL

in serum

1 ng/mL in plasma

Kemmler

et al. (2014)

Fiber Based

ImmunosensorBuffer IL-6

fluorescent

magnetic

nanoparticle

0.1 pg/mLZhang

et al. (2018)

SPR Buffer PCT

KOH treated

gold-coated

SPR chip

4.2 ng/mLVashist

et al. (2016)

Optical,

Electrochemical,

Plasmonic,

Acoustic,

etc

Plasma,

Serum,

and

Buffer

Sepsis

Biomarker

List

State of the Art

Review papersN/A

Oeschger

et al. (2019)

Nano-Plasmonic BufferE. coli

(bacteria)

Bioprinted

Microarray

Based Lens free

Interferometer

Single bacterial

cell in 40 min

Dey

et al. (2019)

30

Table 3.4 Survey on FET, Mass and Machine Learning based sensors for SepsisDiagnosis

Principle Sample Biomarker Interface Linearity & LOD Reference

Quartz Crystal

Microbalance

(QCM) -

D300

QCM unit

Human

Serum

Folate

Binding

Proteins

Au+

folate

/

BSA+

anti-FBP

50pM -

2M

Henne

et al. (2006)

Field

Effect

Transistor

Buffer CRPCMOS

Technology0.1ng/mL

Kim

et al. (2013)

Electrolyte-

Gated

OFET

Buffer PCT

poly-3-

hexylthiophene

(P3HT) /

Antibody

(anti-PCT)

2.2pMSheshadri

et al. (2018)

Machine

Learning

Based

N/A N/A

Prediction

from

variation of

physiological

data

analysis

of

historic data

available

on

sepsis

N/A

Hu

et al. (2018)

Lehmann

et al. (2018)

Strickland

et al. (2018)

Lin

et al.(2018)

Saqib

et al. (2018)

Khoshnevisa

et al.(2018)

31

CHAPTER 4. Summary

There have been threefold intent in narrating this creative component report. First is to include

a perspective of the current status of what is one of those most challenging medical disorders in

regular clinical practice, second is to understand the root cause of the clinical condition and third

is to review existing literature on sepsis diagnosis technologies and identify those areas in which a

fast and robust point of care bio-sensor can be designed .

Apart from the measurement related issues, as discussed in course of this report; the occurrence

of sepsis depends on a combination of system factors, hosts and pathogens, and as illustrated by

the block diagram in Fig.4.1. Even though these factors are depicted as independent entities in

Fig.4.1, they are actually inter-related and hence their interplay is crucial. Starting from social and

demographic factors which include - diet, lifestyle, economic status, sex, race; along with pathogens

that invade a particular individual and the access to health care system is very critical in estimating

the prevalence, extent and subsequent survival of a patient during the septic shock or severe sepsis.

These factors vary and thereby makes the estimation and concrete definition and standardization

quite difficult. Also, clinical data are only available in high-income rate countries thereby limiting

the correct standardization of the treatments and diagnosis.

There has been a great advancement in the diagnosis of sepsis, especially in non-culture based

methods like PCR based, MALDI-TOF, ELISA based technology. These technologies have facil-

itated a great deal of speed in which the infections are identified along with their anti-microbial

activity patterns. But still, it is a fact that until and unless the clinicians can detect the onset of

sepsis early the infected blood samples are not obtained and thus it results in retardation in diag-

nosis and delay in exercising antibiotics. Thus there is a concrete need for point of care device that

can detect sepsis within minutes from the onset of sepsis. From existing literature review narrated

in this creative component report, there is a shred of conclusive evidence that point of care biochip

32

Figure 4.1 Determining factors for the incidence of sepsis. (Figure referred from (5))

can be fabricated having the capability of detecting multiple sepsis biomarkers at a time. Thus

label-free biomarker detection can eventually pave the way for future automatic diagnosis of sepsis

in intensive care units and thereby contribute significantly for the reduction in fatality worldwide.

33

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