MULTIVARIATE NON-INVASIVE MEASUREMENTS OF SKIN

DISORDERS

By

Josefina Nyström

Akademisk avhandling Som med tillstånd av rektorsämbetet vid Umeå Universitet för erhållande av Teknologie doktorsexamen vid Teknisk-Naturvetenskapliga fakulteten, framlägges till offentlig granskning vid Kemiska institutionen, Umeå Universitet sal KB3A9, KBC, fredagen den 6:e oktober 2006, kl. 10.30.

Fakultetsopponent: Doc Lars Nørgaard, Department of Dairy and Food Science, The Royal Veterinary and Agricultural University, Rolighedsvej 30, DK-1958 Frederiksberg C., Denmark.

Title Multivariate Non-Invasive Measurements of Skin Disorders Author Josefina Nyström, Department of Chemistry, Analytical Chemistry, Umeå University, SE-90187 Umeå, Sweden Abstract The present thesis proposes new methods for obtaining objective and accurate diagnoses in modern healthcare. Non-invasive techniques have been used to examine or diagnose three different medical conditions, namely neuropathy among diabetics, radiotherapy induced erythema (skin redness) among breast cancer patients and diagnoses of cutaneous malignant melanoma. The techniques used were Near-InfraRed spectroscopy (NIR), Multi Frequency Bio Impedance Analysis of whole body (MFBIA-body), Laser Doppler Imaging (LDI) and Digital Colour Photography (DCP). The neuropathy for diabetics was studied in papers I and II. The first study was performed on diabetics and control subjects of both genders. A separation was seen between males and females and therefore the data had to be divided in order to obtain good models. NIR spectroscopy was shown to be a viable technique for measuring neuropathy once the division according to gender was made. The second study on diabetics, where MFBIA-body was added to the analysis, was performed on males exclusively. Principal component analysis showed that healthy reference subjects tend to separate from diabetics. Also, diabetics with severe neuropathy separate from persons less affected. The preliminary study presented in paper III was performed on breast cancer patients in order to investigate if NIR, LDI and DCP were able to detect radiotherapy induced erythema. The promising results in the preliminary study motivated a new and larger study. This study, presented in papers IV and V, intended to investigate the measurement techniques further but also to examine the effect that two different skin lotions, Essex and Aloe vera have on the development of erythema. The Wilcoxon signed rank sum test showed that DCP and NIR could detect erythema, which is developed during one week of radiation treatment. LDI was able to detect erythema developed during two weeks of treatment. None of the techniques could detect any differences between the two lotions regarding the development of erythema. The use of NIR to diagnose cutaneous malignant melanoma is presented as unpublished results in this thesis. This study gave promising but inconclusive results. NIR could be of interest for future development of instrumentation for diagnosis of skin cancer. Keywords Multivariate Data Analysis, Non-invasive techniques, Clinical studies, Principal Component Analysis, Partial Least Squares, Partial Least Squares Discriminant Analysis, Wilcoxon Signed Rank Sum Test, Near-InfraRed spectroscopy, Multi Frequency Bio Impedance Analysis of whole body, Laser Doppler Imaging, Digital Colour Photography

ISBN: 91-7264-154-1

MULTIVARIATE NON-INVASIVE

MEASUREMENTS OF SKIN DISORDERS

Josefina Nyström 2006 Umeå University Department of Chemistry Analytical Chemistry

COPYRIGHT © 2006 JOSEFINA NYSTRÖM ISBN: 91-7264-154-1

PRINTED IN SWEDEN BY VMC–KBC UMEÅ UNIVERSITY, UMEÅ 2006

Contents

LIST OF PAPERS_______________________________________________________1 ABBREVIATIONS______________________________________________________3 NOTATIONS _________________________________________________________3 MEDICAL DICTIONARY _________________________________________________4

1. INTRODUCTION ___________________________________________________5

2. DATA ANALYSIS ___________________________________________________9

2.1 DATA PRE-TREATMENT ____________________________________________10 2.1.1 Multiplicative Scatter Correction, MSC____________________________11 2.1.2 Savitzky-Golay Smoothing Derivative _____________________________11 2.1.3 Orthogonal Signal Correction, OSC ______________________________12

2.2 PRINCIPAL COMPONENT ANALYSIS, PCA _______________________________12 2.3 PARTIAL LEAST SQUARES, PLS ______________________________________15 2.4 MODEL FIT ______________________________________________________15 2.5 UNIVARIATE DATA ANALYSIS– COMPARING TWO GROUPS __________________17

3. INSTRUMENTATION ______________________________________________19

3.1 NEAR-INFRARED SPECTROSCOPY, NIR_________________________________19 3.2 MULTI FREQUENCY BIO IMPEDANCE ANALYSIS OF WHOLE BODY, MFBIA-BODY_21 3.3 LASER DOPPLER IMAGING, LDI ______________________________________23 3.4 DIGITAL COLOUR PHOTOGRAPHY, DCP ________________________________24

44.. RREESSUULLTTSS _________________________________________________________27

4.1 DIABETES _______________________________________________________27 4.2 RADIOTHERAPY INDUCED ERYTHEMA__________________________________30 4.3 DIAGNOSES OF MELANOMA USING NIR -UNPUBLISHED RESULTS_____________33

55.. CCOONNCCLLUUSSIIOONNSS AANNDD FFIINNAALL TTHHOOUUGGHHTTSS ____________________________37

55.. AAPPPPEENNDDIIXX II -- OVERVIEW OF THE STUDIES _________________________39

66.. RREEFFEERREENNCCEESS _____________________________________________________41

77.. AACCKKNNOOWWLLEEDDGGEEMMEENNTTSS ___________________________________________45

List of paper

1

List of papers This thesis is based on the papers listed below. They are referred to in the text by the corresponding Roman numerals (I-V). I Geladi P., Nyström J., Eriksson J.W., Nilsson A., Lithner F.,

Lindholm-Sethson B., “A multivariate NIR study of skin alterations in Diabetic patients as compared to control subjects”. Journal of Near Infrared Spectroscopy, 2000, 8, 217-227.

II Nyström J., Lindholm-Sethson B., Stenberg L., Ollmar S., Eriksson

J.W., Geladi P. “Combined near-infrared spectroscopy and multifrequency bio-impedance investigation of skin alterations in diabetes patients based on multivariate analyses”. Medical & Biological Engineering & Computing, 2003, 41, 324-329.

III Nyström J., Geladi P., Lindholm-Sethson B., Rattfelt J., Svensk A-C., Franzen L. “Objective Measurements of Radiotherapy induced Erythema”. Skin Research and Technology: 2004, 10, 242-250.

IV Nyström J., Geladi P., Lindholm-Sethson B., Larson J., Svensk A-

C., Franzén L. ”Objective measurement of Radiation Induced Erythema by nonparametric hypothesis testing on indices from multivariate data”. Submitted to Chemometrics and Intelligent Laboratory systems.

V Nyström J., Svensk A-C., Lindholm-Sethson B., Geladi P., Larson

J., Franzen L. “Objective evaluation of Essex lotion and Aloe vera effect on Radiotherapy induced Erythema in Breast Cancer Patients treated with High-Energy Electrons”. Submitted to Acta Oncology.

Reprinted with kind permissions from NIR Publications (Paper I), Springer (Paper II) and Blackwell Publishing (Paper III).

List of paper

2

List of papers not included in this thesis Lindholm-Sethson B., Nyström J., Geladi P., Nelson A., “Gramicidin A interaction at a dioleoyl phosphatidylcholine monolayer on a mercury drop electrode”. Analytical and Bioanalytical Chemistry, 2003, 375, 350-355. Lindholm-Sethson B., Nyström J., Geladi P., Koeppe R., Nelson A., Whitehouse C., “Are biosensor arrays in one membrane possible? A combination of multifrequency impedance measurements and chemometrics”. Analytical and Bioanalytical Chemistry, 2003, 378, 478-485. Geladi P., Sethson B., Nyström J., Lillhonga T., Lestander T., Burger J., “Chemometrics in spectroscopy: Part 2. Examples”. REVIEW ARTICLE. Spectrochimica Acta Part B: Atomic Spectroscopy, 2004, 59 (9) 1347-1357.

Abbreviations & Notations

3

Abbreviations CMM Cutaneous Malignant Melanoma ECF ExtraCellular Fluid ICF IntraCellular Fluid LDI Laser Doppler Imaging LMM Lentigo Maligna Melanoma MFBIA-body Multi Frequency Bio Impedance Analysis of whole body MSC Multiplicative Scatter Correction NIR Near-InfraRed Spectroscopy OSC Orthogonal Signal Correction PCA Principal Component Analysis PLS Partial Least Squares PLS-DA Partial Least Squares Discriminant Analysis PRESS Predicted Residual Error Sum of Squares PRESSTS Predicted Residual Error Sum of Squares for the Test Set R2 Goodness of fit RGB Red, Green, Blue SS Sum of Squares SSM Superficial Spreading Melanoma UV Unit Variance Notations X Data matrix [IxK] Y Response matrix [IxM] I Number of calibration observations J Number of test set observations K Number of variables A Number of components E Residual Matrix for data matrix X, [IxK] F Residual matrix for response matrix Y, [IxM] B Regression coefficients for PLS models, [KxM] ta Score vector for component a in X [Ix1] pa Loading vector for component a in X, [Kx1] ca Loading vector for component a in Y, [Mx1] wa PLS Weight vector for component a, [Kx1]

Medical dictonary

4

Medical dictionary benign nevus - harmless pigmented lesion of the skin cutaneous - skin related erythema - abnormal skin redness histopathology - microscopic examination of diseased

tissue insulin - hormone that regulates the

carbohydrate metabolism invasive - medical procedure that penetrates the

skin lentigo maligna melanoma - type of melanoma that primarily

shows non-invasive growth but can enter a vertical phase

muscle atrophy - loss of strength or mass in the muscle neuropathy - nerve damage or nerve deterioration nodular - less common type of melanoma,

which has a rapid vertical growth oedema - swelling of tissue caused by

accumulation of extracellular fluid radiotherapy - use of ionised radiation for cancer

treatment superficial spreading melanoma - most common form of cutaneous

melanoma, which spreads in a horizontal direction but can enter a vertical phase

umbilicus - navel

Introduction

5

11.. IInnttrroodduuccttiioonn

ertain conditions and diseases are currently diagnosed subjectively by nurses and physicians. The examination

procedures are often time-consuming and diagnoses may differ from time to time. An example of this is the diabetic healthcare where people suffering from diabetes often develop pathological changes in their feet. Following a preset foot journal, medical specialists examine the feet once or twice a year to monitor deterioration of nerve function over time. Parameters such as vibration sensibility, calf reflex, muscle atrophy, skin changes and ulcers are measured. The measured parameters are summarised in a neuropathy value. The used neuropathy scale comprises three steps; no change, slight changes, and severe changes. Furthermore, neuropathy changes can also be diagnosed by skin biopsy. Biopsies are very uncomfortable and, moreover, the healing process can be prolonged since the patients affected by neuropathy often have reduced ability to heal wounds. Another example of subjective judgement is found during postoperative radiotherapy treatment of breast cancer patients. 30 to 50 % of all persons suffering from cancer in Sweden are treated with radiotherapy (Degerfält 1998). A common side effect of this treatment is the development of various skin reactions such as dry skin, itch and erythema, i.e. skin redness. In the most severe cases ulcers may arise, which may cause an abrupt end to any commenced radiotherapy treatment. The patients are recommended to use skin lotions in the irradiated areas in order to reduce the side effects. The lotions are considered to bring positive effects to the treated skin, but so far the results in existing studies are mostly based on subjective evaluations, where physicians or nurses have visually inspected the reddened skin (De Conno et al. 1991; Heggie et al. 2002; Rubin et al. 1972). Non-objective diagnosis is also carried out for suspicious moles. The fastest increasing of all cancers in Sweden is Cutaneous Malignant Melanoma, CMM (Socialstyrelsen 2004). As for the situation today,

C

Introduction

6

people have suspicious moles examined by their general practitioner to reassure themselves that skin cancer has not developed. One problem is however, that it is hard to distinguish a harmless mole, a so-called benign nevus, from a CMM. An oncologist specialised in melanoma can give the correct diagnosis for 95 % of the various kinds of moles while the accuracy rate for a less-experienced doctor is only around 50 % (McIntosh et al. 2001). This thesis presents alternative methods that could be used to objectively study different conditions on the skin by using non-invasive techniques. In addition to relieving patients from any discomfort, they will save time and give earlier and more rapid diagnoses. The three examined topics presented here: neuropathy among diabetics, radiotherapy induced erythema and diagnosis of suspicious moles have been studied in order to find new and objective non-invasive approaches. The methods used for the studies are Near-InfraRed Reflectance spectroscopy (NIR), Multi Frequent Bio Impedance Anlysis of whole body (MFBIA-body), Digital Colour Photography (DCP) and Laser Doppler Imaging, (LDI). NIR, MFBIA-body and DCP generate multivariate data, which have been analysed with multivariate techniques, namely Principal Component Analysis, PCA (Wold et al. 1987), and Partial Least Squares Discriminant Analysis, PLS-DA (Geladi et al. 1986). LDI generates univariate data, which is analysed with univariate statistics. The extent of neuropathy in diabetic patients was studied with NIR in paper I and gave promising results. Therefore, a new study was set up with a larger patient group. This time MFBIA-body was measured additionally and the outcome from this study is presented in paper II. Paper III is based on a preliminary study where NIR, DCP and LDI were tested and established as viable methods for measuring radiotherapy induced erythema. In paper IV, these techniques were validated further and they were also used to investigate what effect the lotion treatment has on the development of erythema. The Wilcoxon signed rank sum test (Miller et al. 1993) was used to find significance limits for the measurement methods in this paper. Paper V is a clinical presentation of the results presented in paper IV. The reader is referred to Appendix I for an overview of the size of studies.

Introduction

7

Finally, unpublished results from a pilot study on diagnosis of suspicious moles are presented. This study was performed in order to investigate the use of NIR spectroscopy as a screening tool for detection of CMM. The aims of the thesis were:

• To find objective non-invasive methods that could relieve patients from painful procedures and unnecessary operations.

• To demonstrate the value of using multivariate data analysis for clinical data.

8

Data Analysis

9

22.. DDaattaa AAnnaallyyssiiss

or many scientific studies, e.g. in the medical, biological or chemical research areas, large amounts of data need to be

measured in order to follow the studied process or condition. The measured data can be assembled in a matrix, X, which size depends on the number of studied observations, I, and the number of measured variables, K, see figure 1. The studied observations should preferably be recorded in a randomized run order, which has also been the case in the current thesis. The number of variables often exceeds the number of observations and correlations between variables are common. Large data sets have historically and especially in the healthcare, been analysed with univariate methods by looking at one variable at the time or by calculating an index based on known factors. In this way, the analyst presets the conditions based on a priori knowledge. This is not an ideal solution if the data is to be analysed objectively. Furthermore, the univariate methods cannot deal with variable collinearity, i.e. variables sharing the same information.

Figure 1. Data matrix, X, consist of I observations and K variables. The multivariate methods Principal Component Analysis, PCA (Wold et al. 1987; Jackson 1991), and Partial Least Squares, PLS (Geladi et al. 1986), have been applied to the measured data in this thesis. These methods compress the information found in the large datasets into a few orthogonal components that describe most of the variance in the data. An advantage these techniques provide is that the data interpretation is simplified due to the more parsimonious representation of components instead of collinear variables. The use of multivariate techniques handles

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Data Analysis

10

noise as well, since only relevant components with important variation are analysed. This will be discussed further in section 2.4, where the procedure of how to extract significant components is described. 2.1 Data Pre-treatment Before the data is analysed it is often subjected to pre-treatment. The pre-treatment aims to prepare the data so that an informative multivariate model can be found more easily. The data matrix is most of the time mean-centred with respect to the variables prior to the calculation of PCA or PLS. This is referred to as variable mean-centring and means that the values of a certain variable are subtracted with its mean. The use of variable mean-centring implies that the PCA or PLS model will not be affected by the variable means. Different types of scaling can also be used as pre-treatment and depends on the type of data. Unit Variance scaling, UV-scaling, can be a good approach if there are large differences in variance between the variables and especially if the variables have different units and are supposed to be equally important. In UV-scaling the variables are set to equal variance, which is accomplished by multiplying each variable with the inverse of the standard deviation of the variable, i.e. 1/sk. UV-scaling is often used in combination with variable mean-centring. However, for spectral data, such as the NIR data presented in all papers of the current thesis, UV-scaling enlarges the spectral noise. Variable mean-centring is therefore often the only pre-treatment method used for spectral data. When spectral data is recorded, the reflected light is more or less diffusely reflected (Burns et al. 2001; Osborne et al. 1993), which gives rise to baseline errors and wavelength dependencies. This so called light scattering may vary between observations depending on for example the particle size distribution or the density of the measured sample. In order to reduce these effects Multiplicative Scatter Correction, MSC, or Savitzky–Golay smoothing derivative can be applied to spectral data.

Data Analysis

11

2.1.1 Multiplicative Scatter Correction, MSC Multiplicative Scatter Correction, MSC (Geladi et al. 1985), is a pre-treatment method used to remove light scatter and nonlinearities from the recorded spectra; effects which are typically found in NIR spectroscopy. This pre-treatment method was used for the NIR data matrices in paper I. MSC is accomplished by regression the spectra against an average spectrum as shown in equation (1).

ikkiiik exbax ++= (1) where ai represents the additive effect, bi the multiplicative effect, x the average spectra and eik the spectral effects not modelled by the linear equation. The constants ai and bi are used to correct each individual spectrum, x*

ik, as shown in equation (2).

iiik*ik )/ba(xx −= (2)

2.1.2 Savitzky-Golay Smoothing Derivative Savitzky-Golay (Martens et al. 1996; Press et al. 1992; Savitzky et al. 1964) is a smoothing low-pass filter used to reduce noise. In the simplest cases, this is achieved by replacing the actual data point value with an average obtained from the surrounding data points. This so-called moving-window average reduces “high-frequency” ripple noise and is useful for data obtained from high throughput scanning instruments. A better approximation is obtained by fitting a higher order polynomial to the data points in the window. Savitzky-Golay first derivative was applied to the NIR data in paper II of the present thesis.

Data Analysis

12

2.1.3 Orthogonal Signal Correction, OSC Orthogonal Signal Correction, OSC (Wold et al. 1998), is a pre-treatment method used to find more parsimonious, i.e. simple, PLS models. OSC removes unwanted systematic variation in X, which is orthogonal to Y. The description of OSC below requires that the reader is somewhat familiarised with multivariate modelling in general. The uninitiated reader is referred to sections 2.2 and 2.3 before reading the remaining part of the OSC theory. OSC is accomplished by calculating a score vector, tosc, which is orthogonal to the response matrix, Y. The resulting OSC component, toscp’osc, consisting of a score and a loading vector, is subtracted from the original X matrix. Several OSC components, assembled as ToscP’osc can be deduced from matrix X as shown in equation (3). The desired PLS calculation is then performed on the filtered data matrix Xp, see equation (4).

EPTX +′= OSCOSC (3)

EX =p (4) The OSC approach presented by Wold et al. (Wold et al. 1998) has the disadvantage of not using any kind of validation when it decides the orthogonal components. This may lead to over-fitting the final PLS model and even lessen the efficiency of it (Trygg et al. 2002). Trygg et al. have proposed an improved orthogonal correction method, namely Orthogonal Projections to Latent Structures, O-PLS (Trygg et al. 2002), in order to avoid problems with over-fitting. 2.2 Principal Component Analysis, PCA Principal Component Analysis, PCA (Wold et al. 1987; Jackson 1991), is a method that allows large data matrices to be expressed as a few principal components, PC’s. These can be seen as directions in the

Data Analysis

13

multivariate X-space that explain as much of the variance as possible. The PCA decomposition of a matrix, X, implies that original variables are described by a number of extracted principal components instead. The first principal component is a vector that starts at the origin of the coordinate system and points in the direction of the largest variation in the data space. The second component is orthogonal to the first component and explains the second largest variation in the data. The number of calculated significant components is equivalent to the rank of the data matrix, i.e. the number of uncorrelated variables. PCA modelling decomposes the mean centred data matrix, X, into a score matrix T, a loading matrix P and a residual matrix E, see equation (5). EptptptEPTX +′++′+′+=+′= AA2211 ... (5) As shown in the equation above, each principal component consists of a score vector, t, and a loading vector, p. The residual matrix, E, consists of noise that is ideally what is left of matrix, X, after the significant PC’s are extracted. Classes and outliers among the observations can be found by plotting two score vectors against each other, ti/tj. Classes are observations that have similar properties and they can be known in advance or they can be found after the model has been calculated. Outliers are observations that do not belong to any of the known classes, for example measurements that are corrupted. Three groups can be seen in figure 2a from paper III, where the patients separate according to the radiotherapy treatment. The first component, t1, causes the three groups to separate. The score plot also shows that there is one outlier, namely E10a. This object deviated from the others since it was the only measurement made on scabbed skin.

Data Analysis

14

outlier-4

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Figure 2a. NIR PCA score plot from paper III. t1 separates the observations into three groups; (reference skin), (skin irradiated with photons) and (skin irradiated with high energy electrons). a, b, and c indicates the 1st, 2nd and 3rd measurement occasions respectively.

The corresponding loading plot can be used to find variables that are of importance for the separation of the classes or trends found in the score plot. The loadings can be visualised in loading scatter plots as pi/pj or in a loading line-plot, see figure 2b. Loading line-plots are frequently used for spectral data since they often consist of a large number of extremely correlated variables. Spectral regions, which are important for the separation seen in the first component in the score plot (figure 2a), have high values in p1.

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Data Analysis

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2.3 Partial Least Squares, PLS Partial Least Squares, PLS, (Geladi et al. 1986; Wold et al. 2001) is a multivariate regression method often used to find correlation between a data matrix, X, and a response matrix, Y. In other words, PLS uses the information in the X matrix to predict the response matrix Y. This is accomplished by the calculation of weight vectors, wa, that maximise the covariance between X and Y. Each weight vector is used to calculate a score vector, t. Loading vectors in X and Y, p and c, can then be calculated. A latent component in both X and Y can be formed as tp’ and tc’. These are used in the decompositions of X and Y, which are described by equations (6) and (7).

EPTX +′= (6)

FCTY +′= (7) The response, Y, should be explained as well as possible without modelling noise, i.e. F should be as small as possible. Regression coefficients, B, are obtained during the PLS model calculation (Wold et al. 2001). These can be used for the prediction of new observations according to equation (8).

XBY = (8) A version of PLS, Partial Least Squares Discriminant Analysis, PLS-DA, was used in paper I. PLS-DA is calculated in the same way as PLS but the Y variables are not real responses as they consist of dummy variables defined by class belongings. 2.4 Model fit PCA and PLS models summarise the variance in the data matrices, which is expressed as the goodness of fit, R2. The more variance the models

Data Analysis

16

explain the higher the R2 values will be. The highest value R2 can reach is one and this means that the residual matrix, E, has no variance. The goodness of fit can be calculated for mean centred X and Y matrices, see equation (9) and (10).

))/SS(SS(1XR 2 XE−= (9)

))/SS(SS(1YR 2 YF−= (10) In the equations above, Sum of Squares, SS, is a summation of all squared elements of a matrix. If too few components are calculated, the model will be under-fitted, i.e. the model will not describe all the variation that is of importance. On the other hand, if the data is over-fitted, the model will describe unwanted variation such as noise. Cross-validation (Wold 1978) was used in order to calculate the number of significant components, which can be obtained by omitting certain parts of the data matrix during the model calculation. The calculated model is used to predict the parts that were omitted. The cross-validation procedure is repeated until all objects have been omitted once. The Predicted Residual Error Sum of Squares, PRESS, is obtained by comparing the observed true values for the omitted parts with the predicted values. Q2 is a measure of the prediction ability of a model and is obtained when the PRESS values are divided by the sum of squares, see equation (11) and (12).

)PRESS/SS(1XQ2 X−= (11)

)PRESS/SS(1YQ2 Y−= (12) The observations used for building PCA or PLS models are called a calibration set. The test set on the other hand consists of observations, which have not been used to build the model. A test set should be used for examination of model prediction ability instead of an internal validation, e.g. cross-validation. The use of an external test set gives a more independent validation of the model, where the model calculation is by no means influenced by the observations used for the prediction. The test set observations should preferably have been measured on a different occasion compared to those included in the calibration set. It can be

Data Analysis

17

difficult to get enough patients to make it possible to use an external test set for clinical data, where the number of patients is often limited. In this thesis, this was also the case in paper I. It was however possible to increase the credibility of the test set validation in this paper by using two test sets in separate PLS-DA models. Root Mean Squared error of Prediction, RMSEP, is a measure of the ability of a model to predict new samples, see equation (13).

/JPRESSRMSEP TS= (13) PRESSTS stands for Predicted Residual Error Sum of Squares of the Test Set and J is the number of observations. 2.5 Univariate data analysis– Comparing two groups The most common way to test if two groups are separated from each other is to use the Student’s t-test (Miller et al. 1993; Box et al. 1978), which shows if the means of two groups are significantly different. The t-test works well if the data is normally distributed, but this is seldom the case when working with clinical data. A nonparametric test (distribution free), namely the Wilcoxon Signed Rank Sum Test, WSRST (Box et al. 1978; Miller et al. 1993) was used instead of the Student’s t-test in papers IV and V of the present thesis. WSRST can be used when the data is approximately symmetrical but not normally distributed. The stated null hypothesis is that there is no median difference between the pairs. An example of how to calculate WSRST is given in paper IV of this thesis.

18

Instrumentation

19

33.. IInnssttrruummeennttaattiioonn 3.1 Near-InfraRed Spectroscopy, NIR

ear-InfraRed Spectroscopy, NIR (Burns et al. 2001; Osborne et al. 1993) measures the first, second and third overtones caused

by vibration of chemical bonds between C-H, O-H and N-H. This technique, usually carried out in a fast and accurate manner, is used in many different areas, e.g. in pharmaceutical (Reich 2005), forest (Michell et al. 1996; Schimleck et al. 2000) and food industries (Reid et al. 2006; McClure 2003). The NIR wavelengths range between 780 and 2500 nm (12 820 to 4000 cm-1). Three replicate NIR spectra from paper II are shown in figure 3.

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Instrumentation

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NIR instruments are divided into three main categories based on their different optical principals; dispersive, interferometric and non-thermal (Osborne et al. 1993). Two types of scanning instruments have been used in this thesis, namely dispersive and interferometric. Interferometric instruments have several advantages compared to dispersive; e.g. higher energy throughput at the same resolution (better signal to noise ratio) and also more accurate wave number calibration. Dispersive instruments on the other hand are less sensitive for probe presentation, and are therefore preferably used for online measurements. The reader is referred to the literature (Burns et al. 2001) for more information about NIR instrumentation and for insights concerning advantages and drawbacks for the instrument types. A Bruker Vector 22/N instrument was used in paper I while a Bruker Matrix instrument was used in papers III-V. Both are interferometric instruments based on a Michelson Interferometer. The interferometer generates an interferogram by scanning a movable mirror over a fixed distance. Fourier transform is used to encode the interferogram into a spectrum (Ingle et al. 1988). The two Bruker instruments have a working range of 15 000 to 4000 cm-1 (660 to 2500 nm). The Vector 22/N was equipped with an InAs detector and the Matrix with a InGaAs detector. The maximum resolution of the instruments is 2 cm-1, but for the studies presented in this thesis the resolution was manually set to 4 cm-1. The measurements were performed with a fibre-optic sampling probe for solids and powders, see figures 4a and b. A FossNIRsystem process analytical instrument was used in paper II. This is a dispersive instrument, which uses a moving grating to split the radiation into a selection of wavelengths (Ingle et al. 1988). The wavelength resolution of the instrument is 2 nm. This instrument has two detectors, which enable the instrument to measure almost the entire visible range (400 to 780 nm). A silicon detector records the reflected light between 400 and 1100 nm and a sulphide detector detects the light between 1100 and 2500 nm. These measurements were made with a fibre

Instrumentation

21

optic remote reflectance probe that is often used for monitoring processes. The probe has a measurement diameter of 2 cm, see figure 4c.

c)c)a)a) b)b) Figure 4. Fibre optic probes used in the present thesis for NIR measurements. a) and b) Fibre-optic sampling probe used in papers I and III-V. c) Fibre optic remote reflectance probe used in paper II.

3.2 Multi Frequency Bio Impedance Analysis of whole body, MFBIA-body Multi Frequency Bio Impedance Analysis of whole body, MFBIA-body, (Yanovski et al. 1996) measures the electrical impedance of body tissues. This is accomplished by injecting an alternating current in a selected range of frequencies to the foot and the hand while measuring the potential and phase shift at the wrist and the ankle, see figure 5.

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Figure 5. Measurement sites for MFBIA-body and NIR. Figure taken from paper II. 1. Measurement electrode, 2. Injection electrodes for MFBIA-body. a,b, c and d are measurement areas for NIR.

Instrumentation

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The measured impedance increases with decreasing frequencies due to the capacitive nature of the lipid membranes in the body. At high frequencies the signal can penetrate the cell membranes so that the alternating current can pass through both the ExtraCellular Fluid, ECF, and the IntraCellular Fluid, ICF, of the body (Cornish et al. 1996). At low frequencies the alternating current can only pass through the ECF. It is possible to extract reasonable estimations of the Total Body Fluid, TBF, and the ECF/ICF ratio with a correct choice of frequencies (a few Hz to 1 MHz) and a good empirical or theoretical model. Since oedemas are common among many diseases and cause changes in the ECF/ICF ratio, MFBIA-body data contains information, which is related to such conditions. One of the most widely accepted models to analyse the biological tissue is based on the equivalent circuit, which is shown in figure 6a. A plot of the data in the complex impedance plane gives a semicircle, where the R0 and R∞ can be estimated, see figure 6b. These values are then fed into various algorithms together with information on weight, sex and age to obtain an estimate of the body composition (Cornish et al. 1996).

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Figure 6. a) A simplified equivalent circuit for the dielectric properties of the human body. b) A complex plane plot of the equivalent circuit.

Some researchers have developed biophysical models based on the Cole and Cole theory (Cole et al. 1941) and the Hanais theory (Fenech et al. 2001). However it was not within the scope of this thesis to perform calculations according to these models. Instead the impedance data was

Instrumentation

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analysed with PCA, which enables the data to be studied without the limitations of an assumed model. PCA also considers correlation between patients, something that classical models cannot do. A Hydra ECF/ICF model 4200 from XITRON was used to measure MFBIA-body for diabetic patients for the study presented in paper II. This instrument measures the impedance in the frequency range of 5 kHz to 1 MHz. The measurements were made at 50 frequencies equally spaced according to a logarithmic scale. In addition to NIR spectroscopy, MFBIA-body was used in paper II to study neuropathy among diabetic patients. 3.3 Laser Doppler Imaging, LDI Laser doppler (Seifalian et al. 1995; Simonen 1997) is a frequently used technique for studying different conditions in healthcare. The most frequently used laser doppler technique emits light through a transmitting optical fibre attached to the skin. When the light hits the red blood cells it is backscattered with a shift caused by the movement of the cells. The reflected light is sent through an additional fibre bundle and is recorded by a photo detector. A laser doppler imaging instrument, PIM I from Perimed, was used in papers III-V. This instrument is equipped with a laser (HeNe λ=632 nm) placed in the scanner head, see figure 7.

Figure 7. The laser doppler scanning head used in the studies presented in papers III-V.

Instrumentation

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The detector is placed in the laser doppler head and records the reflected light as the laser beam moves over the measured area in a rectangular manner. The scanner head is situated around 15-20 cm above the measured area. The perfusion values obtained for each measured area were averaged into a mean value, which was considered to be more representative as superficial blood vessels affect individually measured points. Three replicate perfusion images were recorded in each area, and in addition to calculation of an average image, the standard deviation for the replicates was calculated. 3.4 Digital Colour photography, DCP Photography is a common technique for study radiotherapy induced erythema (Haghdoost et al. 2001; Maiche et al. 1994). The photographs are often studied visually and then classified subjectively according to different skin reaction rating scales (Russell et al. 1994). Digital colour photography has been used in papers III-V to objectively measure erythema. A digitalised photograph consists of pixels in two directions, which are measured for a number of colours or channels. The photographs analysed in the present thesis are RGB (Red, Green, Blue) images with three colour channels. The cameras used were a Fuji FinePix S1 Pro (paper III) and a Fuji FinePix S2 Pro (papers IV and V), which are both equipped with 6.1 Mpixel RGB CCD-sensors with 8 bit colour depth for each of the three channels. As an internal reference, a white paper ruler was used in paper III, while a white plastic ruler was used in papers IV and V, see figure 8. This allows corrections to be made for unwanted exposure variation and also for changes caused by differences in light conditions. In paper III, a Skin Redness Index, SRI, based on the average values for the Red and Green channels of the recorded skin and the corresponding reference was calculated. An SRI value was derived for each photo according to equation (14).

Instrumentation

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refrulerg,rulerr,sking,skinr,

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)/I)/(I/I(I)/I)/(I/I(I

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In papers IV and V, the average values of all three channels were calculated after the images had been adjusted with the internal reference. PCA was used for visualisation of the results and the Wilcoxon signed rank sum test was used to calculate the detection limit of erythema.

Figure 8. A typical photograph of skin and

the ruler used as reference in papers III-V.

26

Results

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44.. RReessuullttss 4.1 Diabetes

eople suffering from diabetes are divided in two main categories. Type-1 diabetes is more common among children

and young people and is a result of the destruction of the insulin production in the pancreas by an autoimmune reaction. Type-2 diabetes arises when the insulin production and/or the insulin sensitivity is decreased. This type of diabetes is more likely to debut when people get older but it is increasing among young people as well. The most severe and also most common side effect of the disease is the “diabetic foot” (Borssen et al. 1990). It is generally believed that long periods of high glucose values in the blood affect the vessels of microcirculation in the skin and cause an increased permeability, which may lead to the development of oedemas. It is well known from biopsies that one of the reasons for the development of neuropathy among diabetics is the occurrence of extracellular oedemas. This may also give rise to the development of foot ulcers, an affliction that about 15 % of the diabetics experience during their lifetime (Borssen et al. 1990). In the most severe cases, amputation of the lower leg is necessary, which is needless to say highly traumatic. Detection of early symptoms for the “diabetic foot” could prevent such conditions. This was the motivation for the study presented in papers I and II, where the possibility to measure neuropathy with NIR was investigated. In paper II, Multi Frequency Bio Impedance Analysis of whole body, MFBIA-body, was recorded as well. This method was chosen as it gives information about body composition and also about oedemas, which is common among diabetic patients. A PCA score plot (figure 9) from paper I showed that NIR spectra differ between males and females. Consequently, males and females had to be separated for further analysis since it was not possible to filter the data from the effect of gender.

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Figure 9. 3D PCA scoreplot for NIR taken from paper I. Grouping tendency can be seen between the reference males (m), diabetic males (y,) reference females (f) and diabetic females (x).

Four different measurement sites; hand, foot, the lower part of the arm and leg (see figure 5) were recorded for each individual in order to investigate which measurement areas that could be most informative. The candidate areas were limited to the extremities, i.e. parts of the body where diabetics suffer especially from neuropathy. The hand and the foot, belonging to the outer parts of the extremities, were assumed to give the best results. This assumption was proven wrong, however, as the male hand and female foot were the worst areas for predicting neuropathy for men and women respectively. The unexpected results for males can be due to measurement difficulties. The male hand is often covered by hair and has most of the time superficial blood vessels. The poor prediction ability for the female foot can originate from the fact that women use skin lotion to a higher extent than males. The intriguing findings in paper I, motivated a new and larger study, which is presented in paper II. Only males participated in this study and

Results

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the measurement areas were the same as in paper I. This study incorporated a remote reflectance probe for the NIR measurements, see figure 4c. The handling of this probe was rather inconvenient as it was large in size and also heavy. Each patient was therefore told to position the body area in question towards the probe while the measurement was performed. A black blanket was used to cover the probe and the surrounding area from being influenced by ambient light. A PCA score plot based on the NIR measurements showed a separation tendency between the diabetics and the healthy patients that served as references. The score plot for the MFBIA-body data showed no separation whatsoever. The two matrices were then combined and analysed together, see figure 10. They were scaled to equal variance before the analysis was performed. Variables

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The analysis performed on the combined matrices improved the separation between references, diabetics with no or slight neuropathy and diabetics with severe neuropathy, see figure 11.

Results

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4.2 Radiotherapy induced erythema Radiotherapy uses ionized radiation, either by photons (X-ray or gamma radiation) and/or particles (electrons or neutrons). Depending on the diagnosis, breast cancer patients can either undergo breast conserving surgery (partial mastectomy) or have their entire breast removed (total mastectomy). Patients that have undergone partial mastectomy are treated postoperatively with photons. Patients that have undergone total mastectomy are instead treated with electrons. The reason for the different treatments is due to the fact that photons penetrate further into the tissue compared to electrons. Consequently, superficial electron treatment will in a higher extent give rise to erythema. 30 to 50 % of all cancer patients

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in Sweden are treated with radiotherapy. For a good half, the treatment purpose is curative and the rest is treated in a palliative intention of improving the quality of life of the patients (Degerfält 1998). The sensitive target in radiotherapy treatment is DNA, as tumour cells do not have the same ability to repair DNA damage imposed by the treatment as healthy cells do. Tumour cells will die during cell division if the DNA damage is significant (Degerfält 1998). A common side effect of radiotherapy is the development of skin reactions such as dry skin, itch, pigmentation, and erythema. In the most severe cases ulcers develop. If the skin reactions could be reduced it would mean an improved quality of life for the patients. In the 1960s, Winter (Winter 1962) reported that the re-epithelialization of wounds was twice as fast when covered by moist dressings compared to when exposed to air (Hom et al. 1999). This led to a trend towards moisturising where various lotions are used at different treatment centres. No scientific results have so far shown any statistical proof that lotions reduce the extent of erythema, although many studies have been made to investigate their effect (Wickline 2004). The extent of erythema has been subjectively determined by a doctor according to different rating scales in many studies, which also have shown contradicting results (Rubin et al. 1972; De Conno et al. 1991). Attempts have been made to objectively measure the degree of erythema and what effect the skin lotions have on reducing the extent of it. These studies have used the following techniques; dielectric impedance (Nuutinen et al. 1998), visual reflectance spectroscopy (Boström et al. 2001; Turesson et al. 1975; Turesson et al. 1986), laser doppler (Simonen 1997), and digital image analysis of photographs (Mattsson et al. 1996). An objective method that can measure the extent of erythema would allow the future lotion studies to give more accurate and uniform results. The study presented in paper III aimed to find new objective methods to measure erythema. Both electron- and photon treated patients were included in the study. The measurement techniques that were tested included laser doppler imaging, near-infrared spectroscopy and digital colour photography.

Results

32

The introductory study presented in paper III showed that all three measurement methods appear to be viable techniques for measuring erythema. The encouraging results motivated a new and more comprehensive study to be made. Only electron treated patients were included in this study as they are more affected by erythema. Paper IV highlights the data analysis, while the clinical aspects are presented in paper V. There were two goals with this study; to statistically verify the ability to measure erythema with the three techniques used in papers III-V and to test if Essex or Aloe vera lotions have any effect on the development of erythema. The study was based on 50 women that had undergone total mastectomy and were treated postoperatively with radiotherapy using high energy electrons. A reference measurement was performed for each individual prior to the start-up of the radiotherapy treatment. The succeeding measurements were then accomplished once a week during the course of the treatment. Three sites were chosen by a radiophysicist in the treated area so that a uniform dose distribution was obtained. The measurement sites were located according to figure 12. Areas 1 and 3 were treated with Essex or Aloe vera and area 2 was left untreated. The treatment of areas 1 and 3 was interchanged after 25 patients to assure that no blocking effect affected the outcome. Blocking effects can sometimes be observed when irrelevant factors, caused by how the study is set up, influence the results.

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Figure 12. The sites measured with LDI, NIR and DCP presented in paper IV and V. Figure taken from paper IV. Measurements recorded at sites 1-3 before the radiotherapy treatment started served as a reference for NIR and DCP. Measurements recorded at site 4 during the course of treatment served as a reference for LDI.

The results presented in papers IV and V demonstrated that near-infrared spectroscopy and digital colour photography can be used to detect erythema that arises after one week of radiotherapy treatment with high energy electrons (8 Gy). Laser doppler imaging was able to detect erythema developed after two weeks of radiotherapy treatment (18 Gy). However, it was not possible to detect any effect of the lotion treatments regarding the extent of erythema. The significance of each detection limit for the three measurement techniques was tested with the Wilcoxon signed rank sum test. 4.3 Diagnoses of Melanoma using NIR -unpublished results Introduction: Cutaneous Malignant Melanoma, CMM, is the fastest increasing of all cancers in Sweden and every year more than 1800 Swedes are diagnosed with the disease (Socialstyrelsen 2004). All individuals have a number of cutaneous moles and it can be hard to distinguish between benign nevus and a CMM. An early melanoma

Results

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diagnosis is important in order to have a good prognosis. It is of utmost priority to raise the diagnostic precision in the primary care with respect to melanoma diagnosis since doctors not specialised in skin cancer have a fairly low accuracy rate (McIntosh et al. 2001). This was the motivation for the attempts to find new objective methods for classifying malignant melamoma where false negatives are not acceptable. A recent review article (Eikje et al. 2005) shows the efficiency of Fourier Transform Near-InfraRed (FT-NIR) spectroscopy in the diagnosis and classification of melanoma. Materials and methods: The NIR measurements were made with the same MatrixF FT-NIR instrument along with its probe that was used in papers III-V. NIR spectra were recorded from moles situated at different locations on the patients. Reference measurements were taken on healthy skin of the corresponding site or in the area near the lesion. The lesions were surgically removed by the physician and diagnosed by a pathologist after the NIR measurements had been performed. 19 moles suspected to be malignant melanomas were measured on 16 patients, which resulted in a data matrix of size 19x4409. The different diagnoses were divided into three classes; Lentigo Maligna Melanoma, LMM, Nevus and Superficial Spreading Melanoma, SSM. The three replicate spectra of each measurement were checked for outliers and then averaged into mean spectra for both the reference- and the mole measurements. The average reference spectrum taken on healthy skin was subtracted from the average mole spectrum from the same individual. Results and Discussion: Four observations were detected as outliers and were therefore excluded. Two of the outliers were measured in difficult spots namely one underneath the eyebrow and one in the umbilicus. The other two, one ulcerated superficial spreading melanoma and one nodular melanoma, were not comparable with the other observations included in the study. The removal of these outliers did not change the overall interpretation of the model. In the score plot for the two first components (figure 13), there was some tendency that the SSM type of melanoma separated from the Nevus and

Results

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the LMM types. LMM often covers large facial areas and the histopathology deviates (Farrahi et al. 2005). Therefore it cannot be excluded that some measurements were been performed on an area with benign features, since the physician did not specify an exact measurement spot.

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Figure 13. PCA score plot for t1/t2 of the model calculated for the NIR matrix in the melanoma study. denotes LMM, denotes Nevus and denotes SSM. The line shows separation tendency between the SMM and the Nevus/LMM.

The outcome of this study has shown the importance of having close cooperation with a physician in order to avoid inappropriately placed melanomas being included. Also, the physician has to play a leading role during the study to ensure that the exact location is measured especially on a LMM. The results of the study are promising and confirm what already has been reported in the literature (Eikje et al. 2005; McIntosh et al. 2001).

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Conclusion and final thoughts

37

55.. CCoonncclluussiioonnss aanndd ffiinnaall tthhoouugghhttss

his thesis has proposed new approaches for objective non-invasive techniques that could come into practice in clinical

healthcare in the future. Moreover, these techniques have been analysed with multivariate data analysis, which gives a complete overview of the data since all variables are analysed at the same time. This is seldom the case for clinical data where large matrices traditionally are analysed with univariate methods. The increasing amount of collected data in healthcare will require multivariate data analysis to be used more frequently. The results in papers I and II show that NIR can be used to measure neuropathy. Paper II also revealed some interesting findings when the spectra from two techniques, NIR and MFBIA-body, were merged. The analysis of the combined data showed that this approach improved the ability of measuring neuropathy. This suggests that combining different techniques can be a very useful approach in the future. As shown in paper III, NIR seemed to be a viable technique for detecting erythema. However, the analysis in papers IV and V showed less visible separation due to erythema. It was realised that the separation between electron- and photon treated patients seen in the NIR study in paper III, was partly caused by the fact that the measurements were performed on different tissue. The electron treated patients were measured on the chest wall since they had had their breast removed. The photon treated patients as well as the reference measurements were performed on breast tissue. Nevertheless, papers IV and V showed that it is possible to detect erythema after very low radiation doses when using the Wilcoxon signed rank sum test. These findings were impossible to detect by looking at PCA score plots alone. Furthermore, on the basis of the Wilcoxon test, this thesis strongly suggests that none of the lotions had any objective affects whatsoever, neither good nor bad, on the development of erythema. This does not exclude that the patients experienced some positive subjective improvements from the use of lotions.

T

Conclusion and final thoughts

38

Finally, a general problem when working with clinical studies is that they often are time-consuming and the number of available patients is limited. Future work should be based on the promising findings of this thesis and in order to obtain trustworthy results it is necessary to ensure that the studied groups are sufficiently large. Much work still lies ahead where existing and new potential techniques need to be evaluated. A key to success will be the use of multivariate data analysis.

Appendix I

39

55.. AAppppeennddiixx II -- Overview of the studies

Diabetics References Paper # # of males # of females # of males # of females

I 8 7 14 14 II 34 23

Paper # # of electron

treated patients # of photon treated patients

III 12 16 IV and V 50

40

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Acknowledgements

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77.. AAcckknnoowwlleeddggeemmeennttss Först så vill jag tacka mina båda handledare Britta Sethson och Paul Geladi för att ni gav mig möjligheten att doktorera. Jag vill också tacka er för att ni alltid ställt upp och tagit er tid att hjälpa mig. David för att du alltid finns där för mig och för att jag du haft tålamod med mig fast än jag arbetat hela sommaren. Tack till mamma Eva, pappa Agne och mina syskon Johanna, Julia och Hjalmar för att ni finns där för mig. Tack till Lars Franzén, Ann-Christine Svensk och Johan Larsson för att ni möjliggjort strålbehandlingsstudierna. Tack till Ida Bodén, Peter Naredi och Anders Nilsson för ett bra samarbete i melanomprojektet. Tack till Folke Lithner för finansieringen och samarbetet under diabetesstudierna. Även tack till Jan Eriksson för att du möjliggjorde den andra diabetesstudien. Tack till Stig Ollmar för din tidiga handledning. Tack till Anders Nilsson och Eigil Dåbakk för lån av NIR-instrument. Tack till Jenny, Jenny, Linn och Margareta för att ni hjälpt mig med mina projekt. Tack till Johan och Michael och Berit för att ni läst igenom min avhandling.

Acknowledgements

46

Tack till Esbjörn för att du att tålamodigt lagt ner timme efter timme på att fixa min cykel som transporterat mig till och från mina mätningar på lasarettet. Tack William för att du hjälpt mig med mina headers och footers. Tack till Anna för alla du varit min följeslagare på otaliga försök att lära oss frisim samt att du alltid haft tid över för ett fika. Slutligen vill jag tacka alla som jag jobbat tillsammans med på Analytisk Kemi!