Patient classification for intelligent wheelchair adaptation

A importância e preocupação dedicadas à autonomia e independência das pessoas idosas e dos pacientes que sofrem de algum tipo de deficiência tem vindo a aumentar significativamente ao longo das últimas décadas. As cadeiras de rodas inteligentes (CRI) são tecnologias que podem ajudar este tipo de população a aumentar a sua autonomia, sendo atualmente uma área de investigação bastante ativa. Contudo, a adaptação das CRIs a pacientes específicos e a realização de experiências com utilizadores reais são assuntos de estudo ainda muito pouco aprofundados. A cadeira de rodas inteligente, desenvolvida no âmbito do Projeto IntellWheels, é controlada a alto nível utilizando uma interface multimodal flexível, recorrendo a comandos de voz, expressões faciais, movimentos de cabeça e através de joystick. Este trabalho teve como finalidade a adaptação automática da CRI atendendo às características dos potenciais utilizadores. Foi desenvolvida uma metodologia capaz de criar um modelo do utilizador. A investigação foi baseada num sistema de recolha de dados que permite obter e armazenar dados de voz, expressões faciais, movimentos de cabeça e do corpo dos pacientes. A utilização da CRI pode ser efetuada em diferentes situações em ambiente real e simulado e um jogo sério foi desenvolvido permitindo especificar um conjunto de tarefas a ser realizado pelos utilizadores. Os dados foram analisados recorrendo a métodos de extração de conhecimento, de modo a obter o modelo dos utilizadores. Usando os resultados obtidos pelo sistema de classificação, foi criada uma metodologia que permite selecionar a melhor interface e linguagem de comando da cadeira para cada utilizador. A avaliação para validação da abordagem foi realizada no âmbito do Projeto FCT/RIPD/ADA/109636/2009 - "IntellWheels - Intelligent Wheelchair with Flexible Multimodal Interface". As experiências envolveram um vasto conjunto de indivíduos que sofrem de diversos níveis de deficiência, em estreita colaboração com a Escola Superior de Tecnologia de Saúde do Porto e a Associação do Porto de Paralisia Cerebral. Os dados recolhidos através das experiências de navegação na CRI foram acompanhados por questionários preenchidos pelos utilizadores. Estes dados foram analisados estatisticamente, a fim de provar a eficácia e usabilidade na adequação da interface da CRI ao utilizador. Os resultados mostraram, em ambiente simulado, um valor de usabilidade do sistema de 67, baseado na opinião de uma amostra de pacientes que apresentam os graus IV e V (os mais severos) de Paralisia Cerebral. Foi também demonstrado estatisticamente que a interface atribuída automaticamente pela ferramenta tem uma avaliação superior à sugerida pelos técnicos de Terapia Ocupacional, mostrando a possibilidade de atribuir automaticamente uma linguagem de comando adaptada a cada utilizador. Experiências realizadas com distintos modos de controlo revelaram a preferência dos utilizadores por um controlo compartilhado com um nível de ajuda associado ao nível de constrangimento do paciente. Em conclusão, este trabalho demonstra que é possível adaptar automaticamente uma CRI ao utilizador com claros benefícios a nível de usabilidade e segurança.

Metabolic signature of lung cancer: a metabolomic study of human tissues and biofluids

This thesis reports the application of metabolomics to human tissues and biofluids (blood plasma and urine) to unveil the metabolic signature of primary lung cancer. In Chapter 1, a brief introduction on lung cancer epidemiology and pathogenesis, together with a review of the main metabolic dysregulations known to be associated with cancer, is presented. The metabolomics approach is also described, addressing the analytical and statistical methods employed, as well as the current state of the art on its application to clinical lung cancer studies. Chapter 2 provides the experimental details of this work, in regard to the subjects enrolled, sample collection and analysis, and data processing. In Chapter 3, the metabolic characterization of intact lung tissues (from 56 patients) by proton High Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) spectroscopy is described. After careful assessment of acquisition conditions and thorough spectral assignment (over 50 metabolites identified), the metabolic profiles of tumour and adjacent control tissues were compared through multivariate analysis. The two tissue classes could be discriminated with 97% accuracy, with 13 metabolites significantly accounting for this discrimination: glucose and acetate (depleted in tumours), together with lactate, alanine, glutamate, GSH, taurine, creatine, phosphocholine, glycerophosphocholine, phosphoethanolamine, uracil nucleotides and peptides (increased in tumours). Some of these variations corroborated typical features of cancer metabolism (e.g., upregulated glycolysis and glutaminolysis), while others suggested less known pathways (e.g., antioxidant protection, protein degradation) to play important roles. Another major and novel finding described in this chapter was the dependence of this metabolic signature on tumour histological subtype. While main alterations in adenocarcinomas (AdC) related to phospholipid and protein metabolisms, squamous cell carcinomas (SqCC) were found to have stronger glycolytic and glutaminolytic profiles, making it possible to build a valid classification model to discriminate these two subtypes. Chapter 4 reports the NMR metabolomic study of blood plasma from over 100 patients and near 100 healthy controls, the multivariate model built having afforded a classification rate of 87%. The two groups were found to differ significantly in the levels of lactate, pyruvate, acetoacetate, LDL+VLDL lipoproteins and glycoproteins (increased in patients), together with glutamine, histidine, valine, methanol, HDL lipoproteins and two unassigned compounds (decreased in patients). Interestingly, these variations were detected from initial disease stages and the magnitude of some of them depended on the histological type, although not allowing AdC vs. SqCC discrimination. Moreover, it is shown in this chapter that age mismatch between control and cancer groups could not be ruled out as a possible confounding factor, and exploratory external validation afforded a classification rate of 85%. The NMR profiling of urine from lung cancer patients and healthy controls is presented in Chapter 5. Compared to plasma, the classification model built with urinary profiles resulted in a superior classification rate (97%). After careful assessment of possible bias from gender, age and smoking habits, a set of 19 metabolites was proposed to be cancer-related (out of which 3 were unknowns and 6 were partially identified as N-acetylated metabolites). As for plasma, these variations were detected regardless of disease stage and showed some dependency on histological subtype, the AdC vs. SqCC model built showing modest predictive power. In addition, preliminary external validation of the urine-based classification model afforded 100% sensitivity and 90% specificity, which are exciting results in terms of potential for future clinical application. Chapter 6 describes the analysis of urine from a subset of patients by a different profiling technique, namely, Ultra-Performance Liquid Chromatography coupled to Mass Spectrometry (UPLC-MS). Although the identification of discriminant metabolites was very limited, multivariate models showed high classification rate and predictive power, thus reinforcing the value of urine in the context of lung cancer diagnosis. Finally, the main conclusions of this thesis are presented in Chapter 7, highlighting the potential of integrated metabolomics of tissues and biofluids to improve current understanding of lung cancer altered metabolism and to reveal new marker profiles with diagnostic value.

Concentration of tumor biomarkers using aqueous biphasic systems

According to the World Health Organization, around 8.2 million people die each year with cancer. Most patients do not perform routine diagnoses and the symptoms, in most situations, occur when the patient is already at an advanced stage of the disease, consequently resulting in a high cancer mortality. Currently, prostate cancer is the second leading cause of death among males worldwide. In Portugal, this is the most diagnosed type of cancer and the third that causes more deaths. Taking into account that there is no cure for advanced stages of prostate cancer, the main strategy comprises an early diagnosis to increase the successful rate of the treatment. The prostate specific antigen (PSA) is an important biomarker of prostate cancer that can be detected in biological fluids, including blood, urine and semen. However, the commercial kits available are addressed for blood samples and the commonly used analytical methods for their detection and quantification requires specialized staff, specific equipment and extensive sample processing, resulting in an expensive process. Thus, the aim of this MSc thesis consisted on the development of a simple, efficient and less expensive method for the extraction and concentration of PSA from urine samples using aqueous biphasic systems (ABS) composed of ionic liquids. Initially, the phase diagrams of a set of aqueous biphasic systems composed of an organic salt and ionic liquids were determined. Then, their ability to extract PSA was ascertained. The obtained results reveal that in the tested systems the prostate specific antigen is completely extracted to the ionic-liquid-rich phase in a single step. Subsequently, the applicability of the investigated ABS for the concentration of PSA was addressed, either from aqueous solutions or urine samples. The low concentration of this biomarker in urine (clinically significant below 150 ng/mL) usually hinders its detection by conventional analytical techniques. The obtained results showed that it is possible to extract and concentrate PSA, up to 250 times in a single-step, so that it can be identified and quantified using less expensive techniques.

Concentration of tumor biomarkers using aqueous biphasic systems

According to the World Health Organization, around 8.2 million people die each year with cancer. Most patients do not perform routine diagnoses and the symptoms, in most situations, occur when the patient is already at an advanced stage of the disease, consequently resulting in a high cancer mortality. Currently, prostate cancer is the second leading cause of death among males worldwide. In Portugal, this is the most diagnosed type of cancer and the third that causes more deaths. Taking into account that there is no cure for advanced stages of prostate cancer, the main strategy comprises an early diagnosis to increase the successful rate of the treatment. The prostate specific antigen (PSA) is an important biomarker of prostate cancer that can be detected in biological fluids, including blood, urine and semen. However, the commercial kits available are addressed for blood samples and the commonly used analytical methods for their detection and quantification requires specialized staff, specific equipment and extensive sample processing, resulting in an expensive process. Thus, the aim of this MSc thesis consisted on the development of a simple, efficient and less expensive method for the extraction and concentration of PSA from urine samples using aqueous biphasic systems (ABS) composed of ionic liquids. Initially, the phase diagrams of a set of aqueous biphasic systems composed of an organic salt and ionic liquids were determined. Then, their ability to extract PSA was ascertained. The obtained results reveal that in the tested systems the prostate specific antigen is completely extracted to the ionic-liquid-rich phase in a single step. Subsequently, the applicability of the investigated ABS for the concentration of PSA was addressed, either from aqueous solutions or urine samples. The low concentration of this biomarker in urine (clinically significant below 150 ng/mL) usually hinders its detection by conventional analytical techniques. The obtained results showed that it is possible to extract and concentrate PSA, up to 250 times in a single-step, so that it can be identified and quantified using less expensive techniques.