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CHAPTER 1
INTRODUCTION
1.1 OVERVIEW
This introductory chapter outlines the reasons for conducting this
research, research problems raised from the existing knowledge and the
objectives of this work. It also covers the related research work mentioned in
the literature. It concludes with the scope of the research and outline of the
thesis.
1.2 NEED FOR IMPROVED POINT OF CARE DEVICES
Disease can be defined as any condition where the normal
functioning of the body is impaired, leading to a change in normal state of
health of an individual. One such important disease is the infectious disease,
which accounts for more than 60% of human diseases. There are several
agents causing infections in humans such as, bacteria, fungi, protozoa and
viruses. Many of these microorganisms have developed drug resistance,
making the treatment more difficult. They damage their host and if untreated,
may eventually cause death. It is well known that one bacterium can cause
several infections and one infectious disease can be due to many bacteria.
For the cause of a disease, an infectious microorganism must
colonise the host surface and invade the sterile tissues of a susceptible
individual and produce an injury resulting in the development of signs and
symptoms. A combination of the effects of this damage to the host and the
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response to the injury will give the symptoms of the disease – such as
inflammation, fever, pain, etc. The precise symptoms that are observed
depend on the species of infecting microorganism, the physiological processes
within the host that are affected and the host response. Disease is, therefore,
caused by a complex series of interactions between the infecting
microorganism and its host. Every year, infectious diseases are still the major
cause for millions of deaths in the world and hence, it is an important
prerequisite for rapid and accurate diagnosis to ensure appropriate therapy.
1.3 MOTIVATION
Among various specimens received by clinical laboratories,
detection of microorganisms in sterile body fluid has an important diagnostic
and therapeutic implication. Infection of sterile body sites such as blood,
urinary system, brain, etc., requires rapid diagnosis and initiation of proper
antibiotic treatment so as to avoid complications which include death of the
patient. The different sterile body fluids are blood, cerebrospinal fluid,
peritoneal dialysis effluents, urine etc (Hawkey 2004). The importance of
rapid identification of microorganisms in sterile body fluids is illustrated by
two examples given below, which motivated this research.
Neonatal mortality is due to the infection occurring in utero or
immediately after birth of the baby. This problem is commonly referred to as
“Neonatal sepsis”. Fifty percent of home delivered babies and 20% of hospital
delivered babies develop sepsis (Bang 1999, Panigrahi 2006). This sepsis may
manifest as Meningitis, pneumonia or septicemia in the baby (Jeeva Sankar
2008). Sepsis is the most common (80-90 percent) primary diagnosis for
admission in hospitals (Panigrahi 2006). In about 85% of cases symptoms
appear within 24 hours of birth and almost within 78 hours for other cases
(Singh 1994, Takkar 1974). Neonatal sepsis is always an emergency, rapid
confirmatory diagnosis is very vital which helps to initiate accurate treatment
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to save the baby (Vergnano 2005). The common bacteria causing neonatal
sepsis are Escherichia coli, Klebsiella pneumoniae, Group B streptococci,
Citrobacter, Acinetobacter, listeria monocytogenes and Pseudomonas
aeruginosa (Sugandhi 1993).
Another important disease is Urinary Tract Infection (UTI) of
neonates, which is a dangerous and unrecognized forerunner of systemic
sepsis. UTI is the most frequent infectious disease in children and is another
important cause for morbidity and mortality. It is very common in neonates
and infants, with a reported prevalence of 0.1% to 1% in neonates, increasing
to 14% in febrile neonates and 53% in infants (Theodoras 2006). UTI results
in more serious complications including kidney failure if it is untreated at the
right time (Acharya 1992). A significant proportion of children with UTI have
underlying Vesico Ureteral Reflux (VUR) that predisposes the renal scarring
(reflux nephropathy), which is an important cause of hypertension and
chronic renal failure. It is believed that early recognition and appropriate
management of VUR prevent the development of renal insufficiency
(Sushmita 2004). The common bacteria causing UTI are Escherichia coli,
Klebsiella, Enterobacter, Citrobacter, Pseudomonas aeruginosa and
alkalingenes facecalis (Theresa 2001).
Thus rapid detection and early diagnosis of these two diseases are
very important in healthcare centres. Conventionally infectious diseases are
diagnosed by growing (culture) the causative agent/s in the clinical
microbiology laboratory. This is a gold standard method for microbial
identification and requires growth of microorganisms in selective media. The
most important prerequisite for culture is that the sample should be collected
in a “sterile container” and transported to the laboratory without delay. The
clinical microbiology laboratories employ the conventional method of
growing the bacteria into visible Colony Forming Units (CFU) from the
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clinical samples by culturing. Later, after a period of growth the bacteria is
identified by using different identification tests and at the same time the
antibiotic susceptibility of the identified bacteria is tested by employing Kirby
Bauer disc diffusion method (Wilson 2001). Thus totally it takes 48 to 72
hours for isolation, identification and antibiotic sensitivity testing of the
microorganisms (De Boer 1999). Also the colony forming units are not
related to actual activity of the microorganisms but show the existence of
pathogens in the sample. Thus this method causes substantial delay and also it
is labour intensive and measures only viable microorganisms. Despite these
drawbacks, they are used as standard methods as they are sensitive and give
qualitative information on the number of microorganisms present in the
sample. Nowadays automated methods for culturing are available for reducing
turnaround time. Still these methods require a minimum of 24 hours for
releasing the earliest test result (Garcia Prats 2000).
This substantial delay causes problems for the attending clinician in
selecting the antibiotic treatment which is illustrated as in the Figure 1.1. Thus
there is an urgent need for the rapid identification of the pathogens so as to
start an effective antibiotic treatment which would save the afflicted. In order
to overcome these difficulties and to improve the method of detection for
rapid diagnosis, an alternative but affordable method is to be developed.
Figure 1.1 Conventional time required for diagnosis
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The motivating factor to take up this innovative but daunting task
as a thesis work, was from an article entitled “Olfactory detection of human
bladder cancer by dogs: proof of principle study”, in British Medical Journal
(Willis 2004). The hypothesis of that article was that dogs may be able to
detect malignant tumours on the basis of odour. In that article a woman is said
to have sought medical help as a direct result of her dog’s inordinate interest
in a skin lesion, which subsequently proved to be a malignant melanoma.
Tumours produce volatile organic compounds, which are released
into the atmosphere through, for example, breath and sweat. Some of these
volatile organic compounds are likely to have distinctive odours; even when
present in minute quantities, they would be detectable by dogs, by their
exceptional olfactory acuity. Dogs were assessed for their ability to detect
bladder cancer, by placing one cancer urine sample randomly among six
controls in blind experiments. After repeated training, they were able to detect
the cancerous urine. This study provided a benchmark for this investigation
against which further literature survey was continued in search of smell
emitted by microorganism for their rapid identification.
1.4 DIAGNOSTIC POWER OF SMELL – A HISTORICAL
PERSPECTIVE
In 1986, the odour of different disease was described by Richard
Axel and Linda Buck and it was stated that odour is important in diagnosis,
especially in the emergency room. It was well known in the past that a
number of infectious or metabolic diseases could liberate specific odour
characteristics of the disease stage, which can be noticeable in the sweat,
breath, urine or the stools (Karlik 2004). Any disorder in the normal function
of the body results in the liberation of complex volatile mixtures through the
same media. So, the diagnostic power of smell, the volatiles produced by the
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microorganism and conventional techniques for volatile analysis were further
evaluated in this section.
It has been widely known for centuries that certain medical
conditions can be identified by a particular smell emitted by the sufferer
(Subbarayappa 2001). Though smell is the least appreciated sense it is the
most significant and was used as a diagnosing tool in the ancient period
(Adams 1994). Table 1.1 gives the summary of recorded diseases and
infections and their characteristic odours (Pavlou 2000 a).
Table 1.1 Diseases and their recorded liberated odours
Odour Site / Source Disease
Baked brown bread Skin Typhoid
Stale beer Skin Tuberculosis
Grape Skin / sweat Pseudomonas infection
Rotten apples Skin / sweat Anaerobic infection
Ammoniacal Urine Bladder infection
Amine-like Vaginal discharge Bacterial vaginosis
Foul Stool Rotavirus gastroenteritis
Sweet Sweat Diphtheria
Foul Sputum Bacterial infection
Putrid Breath Diabetes mellitus
Musty / horsey Infant skin Phenylketonuria
Foul Infant stool Cystic fibrosis
Acetone-like Breath Diabetes mellitus
(Adapted from Pavlou 2000 a and Christopher 2004)
In earlier days medical practitioners had not discovered bacterial
pathogenity, but clearly recognized that disease-host interaction could change
the odour of body excretions such as sweat, urine, vaginal fluid and sputum
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(Porter 1997). The potential of diagnostic power of smell is not much
prominent because the odour that is emanating out of the human body may be
tough for the humans to detect, discriminate and identify the disease type. The
forthcoming section analyses the conventional techniques available for
identification of microorganisms by volatiles.
1.4.1 Volatiles by Microorganisms as Biomarkers
As smell can be used as a factor for diagnosing ailment, this section
explores the volatiles emitted by microorganisms. Generally, microorganisms
produce a wide range of alcohols, ketones, aldehydes, esters, carboxylic acids,
lactones, terpenes, sulphur and nitrogen compounds (Needham 2004). These
compounds represent both primary and secondary metabolites. Many factors
are observed to affect the composition of volatiles such as temperature,
oxygen concentration, age of the culture and microbial species. The main
metabolic pathways for secondary metabolite production are presented in
Figure 1.2 (Needham 2004).
Out of these, the volatiles from bacteria generally emanate from the
breakdown of protein as well as carbohydrates consisting of the products of
decarboxylation and deamination of amino acids (Cowell 1997). These
vapours are generally methylamine and ammonia. There can also be sulphur
based such as hydrogen sulphide, methyl mercaptan and dimethyl disulphide.
The Table 1.2 presents the volatile compound liberated by different bacterial
species. Thus, the use of smell in Clinical diagnosis for identifying microbes
has been rediscovered, which has put forth a path for the development of Gas
Chromatography (GC) and Mass Spectrometry (MS) for diagnosis of
infectious diseases.
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Figure 1.2 Pathways involved in the production of different secondary
metabolites
(Adapted from Pasanen 1996, Evans 2000).
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Table 1.2 Volatiles emanated by different bacterial species
BacteriaMetabolic Products / Volatiles known to be
produced
Enterococcus faecalis Pyruvate, lactic acid
Enterobacter species Butanediol, ethanol, CO2, 3-methyl-1-butanol,
ammonia
Escherichia coli Lactic acid, acetic acid, succinic acid, formic
acid, ethanol, butanediol
klebsiella pneumonia Butanediol, ethanol, CO2
Proteus mirabilis Lactic acid, acetic acid, succinic acid, formic
acid, ethanol, butanediol, isobutylamine,
isopentylamine, ethylamine, isobutanol, 1-
undecene, methyl ketones
Pseudomonas
aeruginosa
Pyruvate
Serratia species Butanediol, ethanol, CO2
Staphylococcus aureus Isobutanol, isopentyl acetate, 1-undecene, methyl
ketones, ammonia, ethanol, trimethylamine,
2,5,dimethylpyrazine isoamylamine, 2-
methylamine, acetic acid
Streptococcus Lactic acid, alcohols
(Adapted from Pavlou 2000b, Gardner 2000, Kodogiannis 2002)
1.4.2 Conventional Methods for Diagnosing Bacteria by Volatiles
Gas Chromatography (GC) and GC with Mass Spectrometry
(GC-MS) were used during 1950’s, to separate and identify volatile
biomarkers for the use of odour analysis in clinical application for identifying
bacterial species (Lieblich 1984).
Gas chromatography is widely used as an analytical tool for
separating relatively volatile components such as alcohols, ketones, aldehydes
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and many other organic and inorganic compounds. The Mass
spectrometry (MS) is an analytical technique for the determination of the
elemental composition of a sample. It is also used for elucidating the chemical
structures of molecules, such as peptides and other chemical compounds. The
MS principle consists of ionizing chemical compounds to generate charged
molecules or molecule fragments and measuring their mass-to-charge ratios.
The Gas Chromatography - Mass Spectrometry (GC-MS) is a method that
combines the features of gas chromatography and mass spectrometry to
identify different substances within a test sample.
During the 19th
century, it was found that production of volatiles
was not restricted to humans alone but also for microorganisms. In 1837, the
production of benzaldehyde by microorganisms was reported and in 1923
naturally liberated microbial odours were reviewed. It was identified in 1984
that acetylcholine and trimethyl amine can act as biomarker for UTI (Fend
2004). The GC-MS analysis of pathogenic bacteria such as Pseudomonas
aeruginosa, Proteus mirabilis, Klebsiella pneumoniae, Staphylococcus aureus
and Clostridium septicum revealed that all of them produced complex odour
pattern (Larsson 1978).
Thus, GC-MS has been used in many clinical applications, such as
in the analysis of urine. The characteristic odours of a culture often give a
clue to the identification of one or more organisms present and trained
microbiologists can often identify a microbial culture by smell alone.
Traditionally, volatile species were determined by sample extraction followed
by GC-MS analysis. However, this approach requires some knowledge of the
molecules involved. Several variables, such as pH, acidity, carbohydrate
content, temperature and protein levels, need to be kept within a narrow
range.
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Although the introduction of GC-MS enabled the sufficient study
of possible disease markers, it has never appeared as a fully evaluated routine
instrument for clinical diagnosis (Christopher 2004). The application of this
technology is very limited due to reasons such as high capital costs, laborious
and time-consuming methods. It also requires significant expertise and
involves complexity of volatiles detected (Manolis 1983, Phillips 1997,
Zhang 2003, Christopher 2004). However, the knowledge generated by GC-
MS has notably enriched the understanding of liberation of smell by
microorganisms and in meticulous the potential role of Volatile Organic
Compounds (VOC) as diagnostic markers (Christopher 2004). The
development of instruments for routine clinical application for microbial
detection without the drawbacks of GC-MS is undertaken in this work.
1.5 PROPOSED TECHNOLOGY
Jellum et al (1973) has stated, “If one is able to identify and
determine the concentration of all compounds inside the human body,
including high molecular weight as well as low molecular weight substances,
one would probably find that almost every known disease would result in
characteristic changes in the biochemical composition of the cells and the
body fluids”. The starting point of this challenge is clinical chemistry which
helps in accomplishing both prevention and efficient treatment of diseases.
As the standard approach of analysis of organic compounds causing
odour, which employed analytical chemistry instruments such as gas
chromatography and mass spectrometry had their own drawbacks, research
was pursued for developing mechanical systems that can mimic the human
senses of smell. Thus, the drive to artificially replicate the biological sense of
smell was on (Figure 1.3).
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Figure 1.3 Comparison of biological olfaction with artificial olfaction
(Adapted from Turner 2004 and Christopher 2004)
Exploitation of metabolic profile as sources of information for
diagnosis is strictly connected with all possible ways of accessing it and
measuring it by suitable instruments. Recently attempts have been made to
apply artificial olfactory or Electronic noses to exploit the metabolic profile in
clinical practice (Amico 2008).
An Electronic nose (E-nose) is a machine that is designed to detect
and discriminate among complex odours using a sensor array. The sensor
array consists of broadly tuned (non-specific) sensors that are treated with a
variety of odour - sensitive biological or chemical materials. An odour
stimulus generates a characteristic fingerprint (or smell-print) from the sensor
array. Patterns or fingerprints from known odours are used to construct a
database and train a pattern recognition system so that unknown odours can
subsequently be classified and identified. Thus, E-nose consists of three
functional components that operate serially on an odorant sample: a sample
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handler, an array of gas sensors and a signal-processing & recognizing system
(Bhuyan 2001).
1.6 RECENT AND RELATED WORKS
When attempting to go through the applications of E-nose
technology for infectious diseases in medicine, it becomes apparent that there
is one clear delineator: application of E-nose in-vitro and in-vivo
measurements for diagnosing infectious diseases. Using these two very broad
subject headings, this part attempts to provide in detail evaluation of
utilization of this technology for diagnosing the diseases by the following
feature aspects viz, type of sample used, the number of samples utilized for
experimental procedure, type of sensors, type of multivariate data analysis
and other technical specifications together with percentage of recognition.
1.6.1 In-vitro Diagnostics
The diagnosis performed from assays in a controlled environment
outside a living organism is called in-vitro diagnostics. Among the
experiments conducted using E-nose technology in-vitro measurements, most
of the studies are related to infectious diseases to investigate pathological
microorganisms. Using this technology, various infections have been reported
and are as discussed below:
1.6.1.1 Detection of general pathogens
Gibson et al (2000) demonstrated an experiment to discriminate
bacteria in culture by using conducting polymers (Bloodhound Sensors Ltd in
collaboration with oxoid Ltd). This work indicated that it was possible to
simultaneously detect bacteria and identify them by smell. The rapidity of the
culturing and sampling to produce the results was reduced to a single working
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day, with a 4-6 hrs incubation period. Identification of four bacteria
Escherichia coli, Proteus mirabilis, Pseudomonas aeruginosa and
Staphylococcus aureus was carried out to 95% of confidence match.
In the same year, McEntegart et al (2000) detected and
discriminated the coliform bacteria with gas sensor arrays. A culture of
Enterobacter aerogenes is readily discriminated from an Escherichia coli
strain using principal components analysis. The data were generated by an
array of eight Quartz Micro Balance (QMB), eight Metal Oxide
Semiconductor (MOS) and four electrochemical gas sensors. Two strains of
Escherichia coli were not discriminated under ideal conditions. The
conclusion of this report was that the sensitivity was good for concentration of
bacteria sample in 5x108/ml. For improving sensitivity level, type of sensors,
sampling system and pattern classification should be enhanced.
1.6.1.2 Gastroesophageal Infection
Helicobacter pylori are the most common agent causing
gastrointestinal bacterial disease worldwide. It is now recognized to be the
principal cause of chronic gastritis, gastric or duodenal ulceration and is
considered to be the most significant gastric carcinogen. Pavlou et al (2000b)
tried to discriminate Helicobacter pylori and several other gastroesophageal
isolates. All bacteria were isolated from patients by sniffing and are cultured
on blood agar plates. The bacteria used for study were Staphylococcus aureus,
Enterococcus faecalis, Klebsiella species, Proteus mirabilis, Escherichia coli
and Helicobacter pylori. All bacteria were incubated at 37 C for 5 hours
except Helicobacter pylori which was incubated for 72 hrs. It was analysed
using conducting polymers (Bloodhound sensors) and by genetic based back
propagation algorithm by neural analyst software. Totally 53 data were taken,
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out of which 33 were subjected for training and 20 for testing. The prediction
rate was 94%.
1.6.1.3 Intra abdominal Infection
Pavlou et al (2002b) recognized the anaerobic bacterium isolates in-
vitro using E-nose. They examined the discrimination of clinical anaerobic
isolates of Clostridium species, Bacteroides fragilis and sterile cultures.
Twenty six swab specimens were collected aseptically from intra-abdominal
infections and wounds in anaerobic jars for overnight at 37 C. After 16 hrs of
incubation, each sample was placed in a sampling bag and the head space was
sampled by Bloodhound sensors. Genetic Algorithm (GA), Back Propagation
(BP) neural network, Principal Component Analysis (PCA), Discriminant
Function Analysis (DFA) and cross validation are employed for data
processing. It was possible to obtain very good differentiation between
clostridium species, Bacteroides fragilis and sterile media. Eight samples
were used as unknowns and were analysed successfully. Clear discrimination
could be obtained between the bacteria with 94% recognition.
1.6.1.4 Infections in cell culture
A rapid detection method for bacterial infections in bioprocesses is
a crucial factor. Routine checks for contamination are made usually once a
day, by time consuming incubations of media samples in a bio reactor. In
2002, detection of bacterial infections in a mammalian cell culture process
was realised using a sensor array by Bachinger. The sensor array is employed
with 10 MOS Field Effect Transistor (MOSFET) sensors. The bacterial
strains of Bacillus cereus and Pseudomonas aeruginosa were used for this
study. It was incubated at 30 C for 24 - 48 hrs. By using this technology, it
was able to report the contamination of cell culture 2 days before by the
indication of pO2 signal indicating the same contamination.
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1.6.1.5 Ear Nose Throat and Eye Infection
Despite the robustness of eye, it is exposed to a harsh environment
where it is continually in contact with infectious airborne organisms. They
provide an environment in which contaminating bacteria can cause an
infection. The number of organisms responsible for infection of the eye is
relatively small; nevertheless the consequences are always potentially serious
as the eye may become irreversibly damaged. Thus a rapid diagnosis method
is essential for proper treatment. Rittaban et al (2002), discriminated the
microorganisms causing eye infections. The bacterial samples used in this
experiment are among the most common bacterial pathogens responsible for
eye infection i.e. Staphylococcus aureus, Haemophilus influenza,
Streptococcus pneumonia, Escherichia coli, Pseudomonas aeruginosa and
Moraxella catarrhalis. All bacteria were grown on blood or lysed blood agar
in standard Petri dishes at 37 C in a humidified atmosphere of 5% CO2 in air.
After overnight culturing, the bacteria were suspended in sterile saline
solution (0.5M NaCl) to a concentration of approximately 108 colony forming
units per ml. A tenfold dilution was sniffed using the E-nose. For this
experiment the E-nose used was cyranose 320, with 32 polymer sensors
configured as an array. For the eye bacteria tests, the cryanose 320 was
introduced manually to a sterile glass vial containing a fixed volume of
bacteria in suspension (4 ml). The operation was repeated ten times for each
one of the three dilution of each of the six bacteria species, to give a total of
180 readings. All data were normalized using a fractional difference model
and then normalized. The data analysis and pattern recognition were done by
Principal Component Analysis (PCA), Fuzzy Clustering Means (FCM) and
Self Organizing Map (SOM) to assess clustering within the data set. The
result of PCA was accounted for 74%. SOM gave 96% accuracy for bacterial
classification. The six-bacterial sets were also analysed using three supervised
ANN classifiers, namely Multi Layer Perceptron (MLP), Probabilistic Neural
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Network (PNN) and Radial Basis Function (RBF) paradigms. PNN was able
to correctly classify 94% of the response vectors whereas the RBF networks
level of correct classification was up to 98%.
The same procedure was adapted in by Ritaban et al (2004), for
four bacteria set from swab samples causing Ear Nose Throat infections. The
pattern recognition technique was able to predict the four different Ear Nose
Throat infecting bacteria classes with 98% accuracy. Similar work was
reported in the year 2005 and 2006 by Ritaban for Ear Nose Throat infection
classification in hospital environment.
1.6.1.6 Respiratory infections
Lai et al (2009) identified common upper respiratory bacterial
pathogens using cyranose 320. Swabs of bacteria were obtained from in-vitro
samples. Data from the 32-element sensor array were subjected to PCA for
depiction in two-dimensional space and differences in odorant patterns were
assessed by calculating Mahalanobis distances. The E-nose was able to
distinguish between control swabs and bacterial samples. Furthermore,
calculation of the Mahalanobis distances among the various bacteria
demonstrated distinct odorant classes. This work demonstrates that the E-nose
could differentiate among various common bacterial pathogens of the upper
respiratory tract, including Staphylococcus aureus, Streptococcus
pneumoniae, Haemophilus influenza and Pseudomonas aeruginosa. This
technology could provide a rapid means to identify organisms causing upper
respiratory infections.
Gardner et al (2000) applied an E-nose to identify bacteria that
cause upper respiratory infections. Infections in 180 swab samples infected
with Staphylococcus aureus, Legionalla pneumphilia and Escherichia coli
which are confirmed through culture test were taken for analysis. The samples
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were incubated only for 10 minutes. Volatile patterns were produced by alpha
fox 2000 Metal Oxide Semiconductor (MOS) sensor array and analysed by
Linear Discriminant Analysis. Hundred per cent of Staphylococcus aureus and
92% of Escherichia coli samples were correctly identified.
1.6.1.7 Leg Infection
Green Wood et al (1997) attempted the bacterial detection in
venous ulcers and subsequently in burns management using Aromascan A32S
instrument (Osmetech Plc). Firstly, standard wound surface swabs and wound
biopsies in non healing venous ulcers were compared with results generated
from the instrument. In 13 out of 15 patients, the aroma maps correlated not
only with the presence of the different groups of bacteria found after
conventional microbial testing but also with the subsequent abolition of
bacteria with appropriate antibiotic therapy leading to wound healing in the
majority. Parry et al (1995) reported the identification of streptococcal
infection in leg ulcer. Persaud (2005 a) also carried out similar type of work.
1.6.1.8 Tuberculosis
Pavlou et al (2004) tried to identify and discriminate
Mycobacterium tuberculosis, Mycobacterium avium, Mycobacterium
scrofulaceum and Pseudomonas aeruginosa for diagnosing Tuberculosis by
E- nose technology. Totally 46 samples were taken for first experiment and
61 samples were taken for second experiment. They were incubated for 5 to 6
hrs at 35 C. Blood Hound Sensors were used for headspace analysis. The
volatile patterns were further analysed by PCA and then with NN. Genetic
algorithm was invoked for optimization. First experiment gave 100%
recognition rate whereas second experiment gave 96% recognition rate.
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Fend et al (2006) investigated the potential of an E- nose
comprising 14 conducting polymers to detect different Mycobacterium
species and Pseudomonas aeruginosa in the headspaces of cultures. Totally
330 sputum samples were taken from culture-proven and human
immunodeficiency virus-tested Tuberculosis and non- Tuberculosis patients.
The data were analyzed using PCA, DFA and artificial neural networks. The
E-nose found the differences between different Mycobacterium species and
between mycobacteria and other lung pathogens both in culture and in spiked
sputum samples. The detection limit in culture and spiked sputa was found to
be 1 × 104
mycobacteria ml1. After training of the neural network with 196
sputum samples, 134 samples were used to challenge the model. The E-nose
correctly predicted 89% of culture-positive patients. The specificity and
sensitivity of the described method were 91% and 89% respectively,
compared to culture test.
1.6.1.9 Bacterial Vaginosis
Bacterial vaginosis is a particularly ill-defined phenomenon with
uncertain symptoms. It is commonly thought to arise as a result of fluctuation
of the normal vaginal flora. The most common organisms associated with
bacterial vaginosis are: Gardnerella vaginalis, Bacteroides Prevotella
species, Mobiluncus species and Mycoplasma hominis. E- nose was
incorporated by Hay et al (2003) to detect positive patients suffering from
bacterial vaginosis. The optimum method for sampling was determined on
372 samples. The headspace is transferred across the Osmetech sensor array
where the signal is transduced and recorded for processing. The sensitivity
and specificity of were 81.45% and 76.1%. Results could be produced from
the Osmetech Microbial Analyser within 20 minutes whereas the culture test
prediction takes at about five days. By PCA, a clear discrimination was
obtained between bacterial vaginosis positive and negative patients.
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Chandiok et al (1997) analysed vaginal swabs from 68 women
attending a genitourinary clinic using AromaScan system. After training the
system recognised patterns generated from four patients with and four patients
without bacterial vaginosis. The positive predictive value of the test was
61.5%. These results indicate that the AromaScan technology may be of value
as a screening test for bacterial vaginosis.
1.6.1.10 Urinary Tract Infection
Using E-nose the volatile patterns emitted by urine samples were
examined by Pavlou et al (2002a). They conducted two experiments with 25
and 45 samples from patients for specific bacterial contaminants using agar
culture technique. The bacteria types used in this study were Escherichia coli
(E.coli), Proteus mirabilis and staphylococcus. All samples were incubated at
37 C for 4 to 5 hrs. The volatile patterns were produced by Bloodhound
sensors. Sensor data processing employed a hybrid intelligent system of
genetic algorithms, back propagation neural networks and multivariate
techniques such as non-parametric Principal component analysis, parametric
discriminant function analysis and cross validation. Genetic supervision was
used for optimization which consisted of models of evolutionary combination
of all input sensor parameters. The UTI prediction rate was 100%.
E-nose was also used by Aathithan et al (2001) and Guernion et al
(2001) for analysing urine by sensing volatile organic compounds and
significant results were reported.
Kodogiannis et al (2002) identified urine volatile compounds as
diagnostic markers. Forty five urine samples were collected and incubated for
5 hrs at 37 C. These samples were infected by E.coli, proteus and
staphylococcus. Bloodhound sensors were used for generating volatile
patterns and analysed by Radial Basis Function (RBF). The soft combination
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of neural classifiers resulted in 93.75% accuracy over the testing data set.
Similar work was carried out by Kodagiannis and Wadge (2005).
Yates et al (2005) demonstrated the headspace analysis of blood
and urine samples for robust bacterial identification. Blood and urine samples
of disparate forms were analysed using a Cyrano Science C320 E-nose
together with an Agilent 4440 Chemosensor. The large dimensional data sets
resulting from these devices present computational problems for parameter
estimation of discriminant models. A variety of data reduction and pattern
recognition techniques were employed in an attempt to optimize the
classification process. A 100% successful classification rate for the blood data
from Agilent 4440 was achieved by RBF neural network. A successful
classification rate of 80% was achieved for the urine data from C320 which
were analysed using a novel nonlinear time series model.
1.6.1.11 Blood Infection
Lykos et al (2001) proposed sensorial analysis as an alternative
method to identify bacteria from blood cultures of patients with bacteremia
and septicemia (caused by E.coli, Pseudomonas aeruginosa, Staphylococcus
aureus and Enterococcus faecalis) instead of the conventional, sub culturing
procedures done on diagnostic plate media. Each culture was thawed,
aseptically inoculated onto the Trypticase Soy Agar with 5% Sheep Blood
Plate and incubated overnight at 35oC in air. The culture was transferred again
onto the sheep blood plate and incubated overnight at 35oC in air. A
suspension of each culture, equivalent to 0.5 McFarland Standard, was made
using sterile 0.15M NaCl. One ml of the suspension was aseptically
transferred into a 125 ml Erlenmeyer flask that contained 25 ml of Trypticase
Soy Broth, or Brain Heart Infusion Broth. It was again incubated for 18 hrs
and analysed by electro chemical sensors. The data analysis was done by
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LabVIEW and PCA was done for confirming the differentiation between the
samples.
1.6.2 In-vivo Diagnostics
In-vivo diagnostics is performed in a living organism. The
objective of in-vivo diagnostics is to depict the biological processes in living
organisms on cellular or molecular level, in most possible manner. Mostly
the non infectious diseases like cancer, diabetes and even renal dysfunction
are reported by this technology and are detected online. Usually all infectious
diseases are diagnosed only in clinical laboratories after culturing. Also
attempts have been made to identify the infectious diseases online for rapid
detection and it was reported for diseases like gastroesophageal disease, Ear
Nose Throat infections, pneumonia, etc., as below:
1.6.2.1 Gastroesophageal Infection
Romano et al (2002) did online measurement on breath samples as
a difference between the sample and synthetic air to identify helicobacter
pyroli. The data set consists of 11 volunteers affected by infection of the
gastric epithelium and 22 volunteers as healthy reference. The gas sensors
used were from LibraNOSE, which consists of eight thickness shear mode
resonators. The data analysis was performed by Linear Discriminant Analysis
(LDA). The recognition rate of Helicobacter pylori was 87.5%.
1.6.2.2 Ear Nose Throat Infection
Shykhon et al (2004) explored the use of an E-nose to identify and
classify pathogens associated with Ear Nose Throat infections. In this study
90 bacterial swab samples were collected and analysed immediately with a
commercial E-nose (Cyranose C320). Similar numbers of swabs were also
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taken from the same site of infection and were sent for microbiology culture.
The E- nose diagnosis was compared with the microbiology diagnosis. It was
found that the E-nose diagnosis was correct in 88.2 per cent of the cases,
which was an encouraging result.
1.6.2.3 Pneumonia
E-nose technology can identify patients with ventilator-associated
pneumonia. Hanson et al (2005) detected pneumonia immediately after
collecting the breath samples by cryanose 320. In this study, 415
mechanically ventilated, critical care patients were screened for the presence
of ventilator associated pneumonia using a clinical pneumonia score. Patients
with high clinical pneumonia scores were enrolled in the study and control
patients who had no evidence of pneumonia were also included. Totally 11
breath samples were taken and passed over the gas sensors to interact with
volatile molecules to produce unique patterns. These patterns were analyzed
using PCA and then classified by Support Vector Machine (SVM), a machine
learning algorithm for pattern recognition. It was assessed for a correlation
between the actual clinical pneumonia scores and the one predicted by the
nose. Hanson found that the nose made clear distinctions between the patients
who were and were not infected. The sensitivity was 98.4%. Finally, 68
samples (34 positive and 34 controls) were analyzed using a leave- one-out
scheme for creating training sets and testing sets. This method, designed to
reflect the generalization property of the SVM classifier, scored a
classification rate of 72%. Furthermore, this study suggested that the
commercial E-nose would be reasonably successful in predicting ventilator
associated pneumonia. Thaller et al (2005) also contributed similar kind of
diagnosis on bacterial sinusitis.
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1.6.2.4 Chronic Rhino Sinusitis
Mohamed et al (2003) examined nasal out-breath samples from
patients with chronic rhino sinusitis (with or without polyposis) and healthy
control volunteers using the E-nose technology. They developed a simple
technique for collecting samples of nasal out-breath in disposable sterile
plastic sacks with a tight closing seal. The PCA was used for discrimination
and all individual E-nose patterns for chronic rhino sinusitis patients were
correctly classified. Two healthy controls were misclassified with 80.0%
success rate. The Artificial Neural Network (ANN) analysis correctly
classified 60.0% of the patterns of both groups.
Bruno et al (2008) analysed the intensity and the quality of the
odorous components present in the air expired by patients affected by rhino
sinusitis, using a new E-nose based on GC and Surface Acoustic Wave
(SAW) analysis. In the GC tracings of the pathologic subjects there were six
peaks, which were not present in control group cases.
1.7 SCOPE OF THIS RESEARCH WORK
From the literature review, it can be stated that there is a
potentiality of utilising E-nose technology for diagnosing neonatal sepsis and
neonatal UTI. As both come under the category of infectious diseases, now
there arises a question of using E-nose technology in-vivo or in-vitro. Table
1.3 gives the summary of earlier work done in clinical diagnosis of infectious
diseases using E-nose technology.
The earlier studies have shown that the recognition rate of
infections using E-nose ranges from as high as 100% to 61.5%. The
difference in detection rate could be attributed to the site of clinical sample,
concentration of the microorganisms, presence of other bacteria in the sample
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and the type of volatile detected. From the Table 1.3 it may be seen that the
percentage of recognition of Ear Nose Throat infection using in-vivo is 88.2%
and of in-vitro it is 98%. Similarly for gastroesophageal infection the
percentage of recognition in-vivo is 87.5% whereas for in-vitro it is 94%. This
illustrates that for infectious diseases, culturing helps in more volatile
generation thereby enhancing percentage of recognition.
Table 1.3 Summary of research works carried out for diagnosing
infectious diseases
YearDiagnostic
methodDisease Name
Sample
Type
Recognition
rate (in %)
1995 In-vitro Leg infection ulcer 86.7
1997 In-vitro Bacterial vaginosis swab 61.5
2000 In-vitro Gastroesophageal infection Breath 94
2001 In-vitro Blood Infections ATCC std Recognising
2002 In-vitro ENT Infection swab 98
2002 In-vitro General Infection swab 95
2002 In-vitro UTI Urine 100
2002 In-vitro Intra abdominal infection swab 94
2002 In-vitro upper respiratory infection swab 100
2002 In-vivo Gastroesophageal infection breath 87.5
2003 In-vitro Bacterial vaginosis swab 81.45
2003 In-vivo Chronic rhino sinusitis Breath 80
2004 In-vitro Tuberculosis sputum 94
2004 In-vivo ENT Infection swab 88.2
2005 In-vivo Pneumonia Breath 72
2006 In-vitro Tuberculosis sputum 89
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The second point to be determined is the culturing environment and
its parameters. For all infectious diseases with in-vitro diagnostics, the
controlled environment is kept at 37 C and the samples are incubated
according to the growth of microorganism from 4 hours to 16 hours. Thus in-
vitro detection of bacteria by incubating the culture media constantly
for specified period of time influences the concentration of bacteria in the
sample. For all the in-vitro studies the sample dilution ranges from 1x104 ml
-1
to 1x108 ml
-1.
The percentage of recognition still depends on other factors like the
type of gas sensory array and the pattern recognition techniques. Hence by
choosing appropriate sampling techniques and gas sensory array together with
a pattern recognition technique, there is scope for detecting pathogens in
sterile body fluids for diagnosing diseases like neonatal sepsis and UTI.
The common bacteria causing neonatal sepsis are Escherichia coli
(E.coli), Klebsiella pneumoniae, Group B streptococci, Citrobacter,
acinetobacter and listeria monocytogenes, Pseudomonas aeruginosa
(Sugandhi 1993) and the common bacteria causing neonatal UTI are E.coli,
Klebsiella, Enterobacter, Citrobacter, Pseudomonas aeruginosa and
alkalingenes facecalis (Theresa 2001).
The predominating microorganism causing both infections in
neonates is E.coli. This research focuses on identifying E.coli and
discriminating it with the other microorganisms such as Citrobacter,
Pseudomonas aeruginosa etc., which are also the cause for these infections.
1.8 AIMS AND OBJECTIVES
This thesis is divided into three parts. In the first part, the potential
of an Electronic nose as identification tool for E.coli is investigated by finding
appropriate sampling technique and experimentation methods. In the second
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part, different pattern recognition techniques are applied for discriminating
E.coli from other pathogens and also for differentiating the growth phases of
E.coli as lag, log stationary and death phase. The third part, deals with the
implementation of the Pattern Recognition (PARC) system in Field
Programmable Gate Array (FPGA) to make portable device as Neuro chip.
The aim of this research work is to evaluate the potential of E-nose
technology in point-of-care human diseases management: To detect pathogens
in sterile body fluid along with its growth phase and to make a hand held
device.
Objectives
1. To identify the common bacterial agent causing infection of
sterile body fluids. To find out the potential of an E-nose to
discriminate E.coli in different growth phases as a point of care
device.
2. To find the optimal culturing setup for E-nose sensing for early
detection. Also to evaluate the sensor reproducibility of the
samples.
3. To find the optimal pattern recognition architecture through
software simulation
4. To design the identified optimal neural network architecture as
a neurochip with high data precision and to implement in
FPGA. Also to compare the sensitivity and accuracy with
software simulation.
5. To validate the E-nose pattern with conventional identification
methods and to determine the sensitivity and specificity
together with percentage of classification.
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1.9 ORGANISATION OF THE THESIS
The full thesis work has been organized into seven chapters.
Chapter 1 presents the general introduction by outlining the reasons
for conducting this research. It describes the motivation of this investigation,
elaborates the need for improved point of care devices and analysis about the
previous works reported in the literature. It concludes with the statement of
main objective and outline of the thesis.
Chapter 2 reviews about Electronic nose and gives an introduction
on how and why of E-noses. It compares the principles of biological olfaction
with machine olfaction. It describes about sampling unit, gas sensory array,
data processing and pattern recognition methods used so far in the design of
E-nose.
Chapter 3 describes the materials and methods used in this
investigation. This chapter explores the sampling method and the
experimental set up used for this work. Statistical method of pattern
recognition is initially applied to substantiate the possibility of applying
updated recognition techniques.
Chapter 4 explains about the determination of optimal pattern
recognition techniques by soft computing analysis. It uses various artificial
neural network structures with various learning algorithms. Through the
examination of various adaptive learning methods an indication of the content
of the best learning method to perform this application is identified.
Chapter 5 gives the hardware implementation part of pattern
recognition system in FPGA. The architecture was validated for XOR
operation.
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Chapter 6 discusses about the sensitivity, specificity and
classification accuracy obtained in this investigation.
Chapter 7 draws the conclusions from this research work and also
discusses future works in this area.
References and Appendices are given at the end of the thesis.