Development of Sample Pretreatment and Liquid Chromatographic Techniques for Antioxidative Compounds

In this study, novel methodologies for the determination of antioxidative compounds in herbs and beverages were developed. Antioxidants are compounds that can reduce, delay or inhibit oxidative events. They are a part of the human defense system and are obtained through the diet. Antioxidants are naturally present in several types of foods, e.g. in fruits, beverages, vegetables and herbs. Antioxidants can also be added to foods during manufacturing to suppress lipid oxidation and formation of free radicals under conditions of cooking or storage and to reduce the concentration of free radicals in vivo after food ingestion. There is growing interest in natural antioxidants, and effective compounds have already been identified from antioxidant classes such as carotenoids, essential oils, flavonoids and phenolic acids. The wide variety of sample matrices and analytes presents quite a challenge for the development of analytical techniques. Growing demands have been placed on sample pretreatment. In this study, three novel extraction techniques, namely supercritical fluid extraction (SFE), pressurised hot water extraction (PHWE) and dynamic sonication-assisted extraction (DSAE) were studied. SFE was used for the extraction of lycopene from tomato skins and PHWE was used in the extraction of phenolic compounds from sage. DSAE was applied to the extraction of phenolic acids from Lamiaceae herbs. In the development of extraction methodologies, the main parameters of the extraction were studied and the recoveries were compared to those achieved by conventional extraction techniques. In addition, the stability of lycopene was also followed under different storage conditions. For the separation of the antioxidative compounds in the extracts, liquid chromatographic methods (LC) were utilised. Two novel LC techniques, namely ultra performance liquid chromatography (UPLC) and comprehensive two-dimensional liquid chromatography (LCxLC) were studied and compared with conventional high performance liquid chromatography (HPLC) for the separation of antioxidants in beverages and Lamiaceae herbs. In LCxLC, the selection of LC mode, column dimensions and flow rates were studied and optimised to obtain efficient separation of the target compounds. In addition, the separation powers of HPLC, UPLC, HPLCxHPLC and HPLCxUPLC were compared. To exploit the benefits of an integrated system, in which sample preparation and final separation are performed in a closed unit, dynamic sonication-assisted extraction was coupled on-line to a liquid chromatograph via a solid-phase trap. The increased sensitivity was utilised in the extraction of phenolic acids from Lamiaceae herbs. The results were compared to those of achieved by the LCxLC system.

Bayesian statistical analysis of bacterial diversity

Bacteria play an important role in many ecological systems. The molecular characterization of bacteria using either cultivation-dependent or cultivation-independent methods reveals the large scale of bacterial diversity in natural communities, and the vastness of subpopulations within a species or genus. Understanding how bacterial diversity varies across different environments and also within populations should provide insights into many important questions of bacterial evolution and population dynamics. This thesis presents novel statistical methods for analyzing bacterial diversity using widely employed molecular fingerprinting techniques. The first objective of this thesis was to develop Bayesian clustering models to identify bacterial population structures. Bacterial isolates were identified using multilous sequence typing (MLST), and Bayesian clustering models were used to explore the evolutionary relationships among isolates. Our method involves the inference of genetic population structures via an unsupervised clustering framework where the dependence between loci is represented using graphical models. The population dynamics that generate such a population stratification were investigated using a stochastic model, in which homologous recombination between subpopulations can be quantified within a gene flow network. The second part of the thesis focuses on cluster analysis of community compositional data produced by two different cultivation-independent analyses: terminal restriction fragment length polymorphism (T-RFLP) analysis, and fatty acid methyl ester (FAME) analysis. The cluster analysis aims to group bacterial communities that are similar in composition, which is an important step for understanding the overall influences of environmental and ecological perturbations on bacterial diversity. A common feature of T-RFLP and FAME data is zero-inflation, which indicates that the observation of a zero value is much more frequent than would be expected, for example, from a Poisson distribution in the discrete case, or a Gaussian distribution in the continuous case. We provided two strategies for modeling zero-inflation in the clustering framework, which were validated by both synthetic and empirical complex data sets. We show in the thesis that our model that takes into account dependencies between loci in MLST data can produce better clustering results than those methods which assume independent loci. Furthermore, computer algorithms that are efficient in analyzing large scale data were adopted for meeting the increasing computational need. Our method that detects homologous recombination in subpopulations may provide a theoretical criterion for defining bacterial species. The clustering of bacterial community data include T-RFLP and FAME provides an initial effort for discovering the evolutionary dynamics that structure and maintain bacterial diversity in the natural environment.

Computational Techniques for Haplotype Inference and for Local Alignment Significance

This thesis which consists of an introduction and four peer-reviewed original publications studies the problems of haplotype inference (haplotyping) and local alignment significance. The problems studied here belong to the broad area of bioinformatics and computational biology. The presented solutions are computationally fast and accurate, which makes them practical in high-throughput sequence data analysis. Haplotype inference is a computational problem where the goal is to estimate haplotypes from a sample of genotypes as accurately as possible. This problem is important as the direct measurement of haplotypes is difficult, whereas the genotypes are easier to quantify. Haplotypes are the key-players when studying for example the genetic causes of diseases. In this thesis, three methods are presented for the haplotype inference problem referred to as HaploParser, HIT, and BACH. HaploParser is based on a combinatorial mosaic model and hierarchical parsing that together mimic recombinations and point-mutations in a biologically plausible way. In this mosaic model, the current population is assumed to be evolved from a small founder population. Thus, the haplotypes of the current population are recombinations of the (implicit) founder haplotypes with some point--mutations. HIT (Haplotype Inference Technique) uses a hidden Markov model for haplotypes and efficient algorithms are presented to learn this model from genotype data. The model structure of HIT is analogous to the mosaic model of HaploParser with founder haplotypes. Therefore, it can be seen as a probabilistic model of recombinations and point-mutations. BACH (Bayesian Context-based Haplotyping) utilizes a context tree weighting algorithm to efficiently sum over all variable-length Markov chains to evaluate the posterior probability of a haplotype configuration. Algorithms are presented that find haplotype configurations with high posterior probability. BACH is the most accurate method presented in this thesis and has comparable performance to the best available software for haplotype inference. Local alignment significance is a computational problem where one is interested in whether the local similarities in two sequences are due to the fact that the sequences are related or just by chance. Similarity of sequences is measured by their best local alignment score and from that, a p-value is computed. This p-value is the probability of picking two sequences from the null model that have as good or better best local alignment score. Local alignment significance is used routinely for example in homology searches. In this thesis, a general framework is sketched that allows one to compute a tight upper bound for the p-value of a local pairwise alignment score. Unlike the previous methods, the presented framework is not affeced by so-called edge-effects and can handle gaps (deletions and insertions) without troublesome sampling and curve fitting.

Clinical Studies on Epidural and Spinal Postoperative Analgesia : With Special Reference to Continuous Techniques of Administration and Adjuvant Drugs

Continuous epidural analgesia (CEA) and continuous spinal postoperative analgesia (CSPA) provided by a mixture of local anaesthetic and opioid are widely used for postoperative pain relief. E.g., with the introduction of so-called microcatheters, CSPA found its way particularly in orthopaedic surgery. These techniques, however, may be associated with dose-dependent side-effects as hypotension, weakness in the legs, and nausea and vomiting. At times, they may fail to offer sufficient analgesia, e.g., because of a misplaced catheter. The correct position of an epidural catheter might be confirmed by the supposedly easy and reliable epidural stimulation test (EST). The aims of this thesis were to determine a) whether the efficacy, tolerability, and reliability of CEA might be improved by adding the α2-adrenergic agonists adrenaline and clonidine to CEA, and by the repeated use of EST during CEA; and, b) the feasibility of CSPA given through a microcatheter after vascular surgery. Studies I IV were double-blinded, randomized, and controlled trials; Study V was of a diagnostic, prospective nature. Patients underwent arterial bypass surgery of the legs (I, n=50; IV, n=46), total knee arthroplasty (II, n=70; III, n=72), and abdominal surgery or thoracotomy (V, n=30). Postoperative lumbar CEA consisted of regular mixtures of ropivacaine and fentanyl either without or with adrenaline (2 µg/ml (I) and 4 µg/ml (II)) and clonidine (2 µg/ml (III)). CSPA (IV) was given through a microcatheter (28G) and contained either ropivacaine (max. 2 mg/h) or a mixture of ropivacaine (max. 1 mg/h) and morphine (max. 8 µg/h). Epidural catheter tip position (V) was evaluated both by EST at the moment of catheter placement and several times during CEA, and by epidurography as reference diagnostic test. CEA and CSPA were administered for 24 or 48 h. Study parameters included pain scores assessed with a visual analogue scale, requirements of rescue pain medication, vital signs, and side-effects. Adrenaline (I and II) had no beneficial influence as regards the efficacy or tolerability of CEA. The total amounts of epidurally-infused drugs were even increased in the adrenaline group in Study II (p=0.02, RM ANOVA). Clonidine (III) augmented pain relief with lowered amounts of epidurally infused drugs (p=0.01, RM ANOVA) and reduced need for rescue oxycodone given i.m. (p=0.027, MW-U; median difference 3 mg (95% CI 0 7 mg)). Clonidine did not contribute to sedation and its influence on haemodynamics was minimal. CSPA (IV) provided satisfactory pain relief with only limited blockade of the legs (no inter-group differences). EST (V) was often related to technical problems and difficulties of interpretation, e.g., it failed to identify the four patients whose catheters were outside the spinal canal already at the time of catheter placement. As adjuvants to lumbar CEA, clonidine only slightly improved pain relief, while adrenaline did not provide any benefit. The role of EST applied at the time of epidural catheter placement or repeatedly during CEA remains open. The microcatheter CSPA technique appeared effective and reliable, but needs to be compared to routine CEA after peripheral arterial bypass surgery.

Modern computed tomography techniques in stroke management

Technological development of fast multi-sectional, helical computed tomography (CT) scanners has allowed computed tomography perfusion (CTp) and angiography (CTA) in evaluating acute ischemic stroke. This study focuses on new multidetector computed tomography techniques, namely whole-brain and first-pass CT perfusion plus CTA of carotid arteries. Whole-brain CTp data is acquired during slow infusion of contrast material to achieve constant contrast concentration in the cerebral vasculature. From these data quantitative maps are constructed of perfused cerebral blood volume (pCBV). The probability curve of cerebral infarction as a function of normalized pCBV was determined in patients with acute ischemic stroke. Normalized pCBV, expressed as a percentage of contralateral normal brain pCBV, was determined in the infarction core and in regions just inside and outside the boundary between infarcted and noninfarcted brain. Corresponding probabilities of infarction were 0.99, 0.96, and 0.11, R² was 0.73, and differences in perfusion between core and inner and outer bands were highly significant. Thus a probability of infarction curve can help predict the likelihood of infarction as a function of percentage normalized pCBV. First-pass CT perfusion is based on continuous cine imaging over a selected brain area during a bolus injection of contrast. During its first passage, contrast material compartmentalizes in the intravascular space, resulting in transient tissue enhancement. Functional maps such as cerebral blood flow (CBF), and volume (CBV), and mean transit time (MTT) are then constructed. We compared the effects of three different iodine concentrations (300, 350, or 400 mg/mL) on peak enhancement of normal brain tissue and artery and vein, stratified by region-of-interest (ROI) location, in 102 patients within 3 hours of stroke onset. A monotonic increasing peak opacification was evident at all ROI locations, suggesting that CTp evaluation of patients with acute stroke is best performed with the highest available concentration of contrast agent. In another study we investigated whether lesion volumes on CBV, CBF, and MTT maps within 3 hours of stroke onset predict final infarct volume, and whether all these parameters are needed for triage to intravenous recombinant tissue plasminogen activator (IV-rtPA). The effect of IV-rtPA on the affected brain by measuring salvaged tissue volume in patients receiving IV-rtPA and in controls was investigated also. CBV lesion volume did not necessarily represent dead tissue. MTT lesion volume alone can serve to identify the upper size limit of the abnormally perfused brain, and those with IV-rtPA salvaged more brain than did controls. Carotid CTA was compared with carotid DSA in grading of stenosis in patients with stroke symptoms. In CTA, the grade of stenosis was determined by means of axial source and maximum intensity projection (MIP) images as well as a semiautomatic vessel analysis. CTA provides an adequate, less invasive alternative to conventional DSA, although tending to underestimate clinically relevant grades of stenosis.

Noninvasive techniques in assessing coronary artery disease

Conventional invasive coronary angiography is the clinical gold standard for detecting of coronary artery stenoses. Noninvasive multidetector computed tomography (MDCT) in combination with retrospective ECG gating has recently been shown to permit visualization of the coronary artery lumen and detection of coronary artery stenoses. Single photon emission tomography (SPECT) perfusion imaging has been considered the reference method for evaluation of nonviable myocardium, but magnetic resonance imaging (MRI) can accurately depict structure, function, effusion, and myocardial viability, with an overall capacity unmatched by any other single imaging modality. Magnetocardiography (MCG) provides noninvasively information about myocardial excitation propagation and repolarization without the use of electrodes. This evolving technique may be considered the magnetic equivalent to electrocardiography. The aim of the present series of studies was to evaluate changes in the myocardium assessed with SPECT and MRI caused by coronary artery disease, examine the capability of multidetector computed tomography coronary angiography (MDCT-CA) to detect significant stenoses in the coronary arteries, and MCG to assess remote myocardial infarctions. Our study showed that in severe, progressing coronary artery disease laser treatment does not improve global left ventricular function or myocardial perfusion, but it does preserve systolic wall thickening in fixed defects (scar). It also prevents changes from ischemic myocardial regions to scar. The MCG repolarization variables are informative in remote myocardial infarction, and may perform as well as the conventional QRS criteria in detection of healed myocardial infarction. These STT abnormalities are more pronounced in patients with Q-wave infarction than in patients with non-Q-wave infarctions. MDCT-CA had a sensitivity of 82%, a specificity of 94%, a positive predictive value of 79%, and a negative predictive value of 95% for stenoses over 50% in the main coronary arteries as compared with conventional coronary angiography in patients with known coronary artery disease. Left ventricular wall dysfunction, perfusion defects, and infarctions were detected in 50-78% of sectors assigned to calcifications or stenoses, but also in sectors supplied by normally perfused coronary arteries. Our study showed a low sensitivity (sensitivity 63%) in detecting obstructive coronary artery disease assessed by MDCT in patients with severe aortic stenosis. Massive calcifications complicated correct assessment of the lumen of coronary arteries.

Multivariate Techniques for Identifying Diffractive Interactions at the LHC

Close to one half of the LHC events are expected to be due to elastic or inelastic diffractive scattering. Still, predictions based on extrapolations of experimental data at lower energies differ by large factors in estimating the relative rate of diffractive event categories at the LHC energies. By identifying diffractive events, detailed studies on proton structure can be carried out. The combined forward physics objects: rapidity gaps, forward multiplicity and transverse energy flows can be used to efficiently classify proton-proton collisions. Data samples recorded by the forward detectors, with a simple extension, will allow first estimates of the single diffractive (SD), double diffractive (DD), central diffractive (CD), and non-diffractive (ND) cross sections. The approach, which uses the measurement of inelastic activity in forward and central detector systems, is complementary to the detection and measurement of leading beam-like protons. In this investigation, three different multivariate analysis approaches are assessed in classifying forward physics processes at the LHC. It is shown that with gene expression programming, neural networks and support vector machines, diffraction can be efficiently identified within a large sample of simulated proton-proton scattering events. The event characteristics are visualized by using the self-organizing map algorithm.