Biomedical signal analysis in automatic classification problems

A lo largo de la última década hemos asistido a un desarrollo sin precedentes de las tecnologías de la salud. Los avances en la informatización, la creación de redes, las técnicas de imagen, la robótica, las micro/nano tecnologías, y la genómica, han contribuido a aumentar significativamente la cantidad y diversidad de información al alcance del personal clínico para el diagnóstico, pronóstico, tratamiento y seguimiento de los pacientes. Este aumento en la cantidad y diversidad de datos clínicos requiere del continuo desarrollo de técnicas y metodologías capaces de integrar estos datos, procesarlos, y dar soporte en su interpretación de una forma robusta y eficiente. En este contexto, esta Tesis se focaliza en el análisis y procesado de señales biomédicas y su uso en problemas de clasificación automática. Es decir, se focaliza en: el diseño e integración de algoritmos para el procesado automático de señales biomédicas, el desarrollo de nuevos métodos de extracción de características para señales, la evaluación de compatibilidad entre señales biomédicas, y el diseño de modelos de clasificación para problemas clínicos específicos. En la mayoría de casos contenidos en esta Tesis, estos problemas se sitúan en el ámbito de los sistemas de apoyo a la decisión clínica, es decir, de sistemas computacionales que proporcionan conocimiento experto para la decisión en el diagnóstico, pronóstico y tratamiento de los pacientes. Una de las principales contribuciones de esta tesis consiste en la evaluación de la compatibilidad entre espectros de resonancia magnética (ERM) obtenidos mediante dos tecnologías de escáneres de resonancia magnética coexistentes en la actualidad (escáneres de 1.5T y de 3T). Esta compatibilidad se evalúa en el contexto de clasificación automática de tumores cerebrales. Los resultados obtenidos en este trabajo sugieren que los clasificadores existentes basados en datos de ERM de 1.5T pueden ser aplicables a casos obtenidos con la nueva tecnologFuster García, E. (2012). Biomedical signal analysis in automatic classification problems [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17176Palanci

Use of multiple platform “omics” datasets to define new biomarkers in oral cancer and to determine biological processes underpinning heterogeneity of the disease

Oral cancer in early stages (I and II) may be curable by surgery or radiation therapy alone but advanced stage disease (III and IV) has a relatively low survival rate. The pathogenic pathways that contribute to Oral Squamous Cell Carcinoma (OSCC) remain poorly characterised and the critical factor in the lack of prognostic improvement is that a significant proportion of cancers initially are asymptomatic lesions and are not diagnosed or treated until they reach an advanced stage. Hence, a clinically applicable gene expression signature is in high demand and improved characterization of the OSCC gene expression profile would constitute substantial progress. For OSCC, possible themes that might be addressed using microarray data include distinguishing the disease from normal at the molecular level; determining whether specific biomarkers or profiles are predictive for tumour behaviour; and identifying biologic pathways necessarily altered in tumourigenesis, potentially illuminating novel therapeutic targets. However, OSCC is a heterogeneous disease, making diagnostic biomarker development difficult. Although this phenotypic variation is striking when one compares OSCC from different geographic locales, little is known about the underpinning biological mechanisms. Cancer may be accompanied by the production and release of a substantial number of proteins, metabolites and/or hormones into the blood, saliva, and other body fluids that could also serve as useful markers for assessing prognosis, metastasis, monitoring treatment, and detecting malignant disease at an early stage. The primary aim of this thesis is to investigate metabolomic and transcriptomic profiles using multiple bioinformatics approaches and biological annotation tools in an attempt to identify specific biomarkers and prediction models for OSCC from each profile as well as from the interface outcomes of integrating the two platforms. Additional aims of the thesis go further to identify the mechanisms underlying the biological changes during tumorigenic transformation of OSCC, as well as to determine biological processes underpinning the heterogeneity of the disease among populations. Two review studies were carried out in this thesis. The review study of published transcriptomic profiles of OSCC specified several genes and pathways exhibiting substantially altered expression in cancerous versus noncancerous states across studies. However, the result of the review suggests not relying on the final set of genes published by the individual studies, but to access the raw data of each study and start subsequent analysis from that stage using unified bioinformatics approaches to acquire useful and complete understanding of the systems biology. The other review study focused on the metabolic profiles of OSCC and revealed a systemic metabolic response to cancer, which bears great potential for biomarker development and diagnosis of oral cancer. However, the metabolic signature still needs to improve specificity for OSCC from other types of cancer. In an attempt to detect a robust gene signature of OSCC overcoming the limitation of the transcriptomic review in accessing the raw data from the previous works, four public microarray raw datasets (comprising 365 tumour and normal samples) of OSCC were successfully integrated using ComBat data integration method in R software, determining the common set of genes, biomarkers, and the relative regulatory pathways possibly accountable for tumour transformation and growth in OSCC. Examination of the meta-analysis datasets showed several discriminating gene expression signatures for OSCC relative to normal oral mucosa; with a signature of 8 genes (MMP1, LAMC2, PTHLH, TPBG, GPD1L, MAL, TMPRSS11B, and SLC27A6) exhibiting the best discriminating performance and show potential as a diagnostic biomarker set. In addition, 32 biomarkers specific to OSCC and HNSCC were identified with the majority involved in extracellular matrix (ECM), interleukins, and peptidase activity where around 2/3 of them are located in the extracellular space and plasma membrane. Additionally, investigation of the interactive network created by merging metabolic and transcriptomic profiles highlighted the significant molecular and cellular biofunctions, pathways, and biomarkers distinguishing OSCC from normal oral mucosa. The results highlighted interactions of significantly altered expression of Dglucose, ethanol, glutathione, GABA, taurine, choline, creatinine, and pyruvate metabolites with the expressed PTGS2, IL1B, IL8, IL6, MMP1, MMP3, MMP9, SERPINE1, COL1A1, COL4A1, LAMC2, POSTN, ADAM12, CDKN2A, PDPN, TGM3, SPINK5, TIMP4, KRT19, and CRYAB biomarkers of OSCC. Such a pattern may represent a clinically useful surrogate for the presence of OSCC which might help in deciphering some of the obscure multifaceted mechanisms underlying carcinogenesis of OSCC which emerged from dysregulated genetic and metabolic system of the body. In an attempt to define pathways of importance in two phenotypically different forms of OSCC, transcriptomic analysis of OSCC from UK and Sri Lankan patients was undertaken. The development of OSCCs in UK and Sri Lankan populations appears largely mediated by similar biological pathways despite the differences related to race, ethnicity, lifestyle, and/or exposure to environmental carcinogens. However, results revealed a highly activated “Cell-mediated Immune Response” in Sri Lankan tumour and normal samples relative to UK cohorts which may reflects a role in resistance of patients to invasiveness, metastasis, and mortality observed in Sri Lankan relative to UK patients. In conclusion, multiple molecular profiles were able to identify a unique transcriptomic profile for OSCC and could further discriminate the tumour from normal oral mucosa on the basis of 8 genes. Altered expression of several metabolic and transcriptomic biomarkers specific for OSCC were identified, along with several dysregulated pathways and molecular processes found common in patient with oral cancer. Integrating both metabolomic and transcriptomic signatures revealed a promising strategy in analysing the concurrent perturbation in both genetic and metabolic systems of the body. Additional results revealed possible impact of specific supplementary dietary components in boosting the immune system of the body against invasion, progression, and metastasis of the disease. Further clinical studies are required to confirm and validate the current results

Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry

Metabolomics entails the comprehensive characterization of the ensemble of endogenous and exogenous metabolites present in a biological specimen. Metabolites represent, at the same time, the downstream output of the genome and the upstream input from various external factors, such as the environment, lifestyle, and diet. Therefore, in the last few years, metabolomic phenotyping has provided unique insights into the fundamental and molecular causes of several physiological and pathophysiological conditions. In parallel, metabolomics has been demonstrating an emerging role in monitoring the influence of different manufacturing procedures on food quality and food safety. In light of the above, this collection includes the latest research from various fields of NMR-based metabolomics applications ranging from biomedicine to data mining and food chemistry

Common Metabolites, Distinct Pathways: The Use of High-field NMR Spectroscopy Metabolomics in Neurology and Immunology

While current metabolic imaging approaches, such as positron emission tomography, hyperpolarized magnetic resonance (MR) imaging and localized MR spectroscopy, provide information on localization and visualization of metabolically-active tissues and metabolites in vivo, additional ex vivo validation and investigations are used for a deeper molecular elucidation of biological events. Metabolomics provides an insight into ongoing cellular processes in a living organism by analyzing and investigating alterations of small molecule polar compounds and lipids. Many of these alterations are directly affected by environmental and genetic factors, such as RNA expression. In recent years, besides mass spectrometry-based approaches, nuclear magnetic resonance (NMR) spectroscopy has been successfully established as an important analytical technique in metabolomics due to its non-destructive nature, high reproducibility and generation of absolute concentration values. NMR, furthermore, is one of the closest approaches to established in vivo MR technologies allowing a direct link from in vivo MR spectra to ex vivo NMR. NMR spectroscopy is, therefore, a perfect tool to quantitatively analyze small polar molecules, such as amino acids, short-chain fatty acids, energy, growth and redox metabolism-related compounds as well as metabolites of gut microbiota and lipids. The broad variety of NMR applications has led to the development of increased sensitivity probes and fully-automated commercial assays. Inventions, such as the ultrasensitive 1.7 mm microprobe, have made it possible to analyze smaller tissue or biofluid amounts of precious samples, including small preclinical organs and tissues or patient tumor biopsies, with appropriate precision and minimal tissue metabolite dilution. In this thesis project, we aimed to investigate and characterize specific metabolic alterations to clinically-relevant immunological and neurological conditions by employing NMR spectroscopy-based metabolomics. In line with the previous preclinical imaging work, two preclinical models – i) acute and chronic inflammation progression and ii) neurological gut-brain axis – were selected as core examples for a comprehensive ex vivo metabolome characterization. In the first project i), the delayed-type hypersensitivity reaction (DTHR) mouse model was subjected to a dynamic immunometabolism and inflammation characterization with the hypothesizing that the inflammation progression is related to dynamic systemic alterations for which metabolomics readout can elucidate the ongoing bio-molecular events. The inflammation was induced by repeated contact tissue challenges on mouse ear tissue. Different metabolic patterns were identified arising from either acute or chronic DTHR that correlated with the resident immune cell response and further active cell infiltration to the inflamed location. Distinct metabolic events, including switches between the scavenging of reactive oxygen and nitrogen species, facilitated the detailed characterization of the detrimental effect of prolonged inflammation and the emergency state of the system. Continuous inflammation led to limited access to substrates for energy metabolism. Chronic DTHR further required alternative anabolic pathways to sustain the cellular growth and repair process. In the second project ii), a gastric bypass surgery rat model was used for the metabolomic characterization of gut microbiota metabolites and potential gut-brain axis communication. We hypothesized that gut-brain axis communication could be responsible for the beneficial lasting effects after surgical intervention. Rats were fed a liquid sucrose diet to induce obesity since high-sugar beverages and liquid caloric consumption have become a pandemic in the adolescent population hindered by the general concept of the Western diet. Plasma and feces were studied as gut metabolism readout and further analyzed in the context of fecal microbiome, hepatic lipid profiles, and brain activity imaging to obtain a holistic overview of the systemic effects of the Roux-En-Y gastric bypass (RYGB) surgery. The gut metabolite γ-aminobutyrate (GABA) was increased in surgery animal feces together with GABA-producing microbiota species abundance compared to sham controls. RYGB surgery animals showed greater neuronal activation in midbrain regions that are known to be rich in GABAergic cells, pointing towards an activated gut-brain communication resulting from the surgery. Two main projects demonstrate how the usage of optimized preanalytical procedures, together with harmonized analytical NMR workflows provide reproducible data with comprehensive insight into the metabolism of an organism ex vivo. Further outlook includes metabolomics result integration in the context of in vivo imaging data, as the combined result evaluation can facilitate the understanding of health and disease progression, and help to streamline diagnostic, such as novel radiotracer development, and therapeutic approaches

Exploring the Multifaceted Roles of Glycosaminoglycans (GAGs) - New Advances and Further Challenges

Glycosaminoglycans are linear, anionic polysaccharides (GAGs) consisting of repeating disaccharides. GAGs are ubiquitously localized throughout the extracellular matrix (ECM) and to the cell membranes of cells in all tissues. They are either conjugated to protein cores in the form of proteoglycans, e.g., chondroitin/dermatan sulfate (CS/DS), heparin/heparan sulfate (Hep/HS) and keratan sulfate (KS), as well as non-sulfated hyaluronan (HA). By modulating biological signaling GAGs participate in the regulation of homeostasis and also participate in disease progression. The book, entitled “Exploring the multifaceted roles of glycosaminoglycans (GAGs)—new advances and further challenges”, features original research and review articles. These articles cover several GAG-related timely topics in structural biology and imaging; morphogenesis, cancer, and other disease therapy and drug developments; tissue engineering; and metabolic engineering. This book also includes an article illustrating how metabolic engineering can be used to create the novel chondroitin-like polysaccharide.A prerequisite for communicating in any discipline and across disciplines is familiarity with the appropriate terminology. Several nomenclature rules exist in the field of biochemistry. The historical description of GAGs follows IUPAC and IUB nomenclature. New structural depictions such as the structural nomenclature for glycan and their translation into machine-readable formats have opened the route for cross-references with popular bioinformatics resources and further connections with other exciting “omics” fields

Magnetic Hybrid-Materials

Externally tunable properties allow for new applications of suspensions of micro- and nanoparticles in sensors and actuators in technical and medical applications. By means of easy to generate and control magnetic fields, fluids inside of matrices are studied. This monnograph delivers the latest insigths into multi-scale modelling, manufacturing and application of those magnetic hybrid materials

Applications and Experiences of Quality Control

The rich palette of topics set out in this book provides a sufficiently broad overview of the developments in the field of quality control. By providing detailed information on various aspects of quality control, this book can serve as a basis for starting interdisciplinary cooperation, which has increasingly become an integral part of scientific and applied research