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Creative Components Iowa State University Capstones, Theses and Dissertations
Spring 2019
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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