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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

27

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.