Adaptação cultural e contributo para a validação da escala petterns of activity measura - pain (POAM-P)

RESUMO: A dor lombar crónica (DLC) é uma das condições clínicas mais comuns e com elevados custos socioeconómicos no mundo ocidental. Estudos recentes indicam que os utentes com DLC apresentam diferentes padrões de atividade que influenciam os níveis de incapacidade funcional. Contudo, a evidência acerca destas associações é, ainda, limitada e inconclusiva. Em Portugal, não existe, do nosso conhecimento, nenhuma escala validada para a população portuguesa que meça estes padrões de atividade em utentes com DLC. Objetivos: Adaptar culturalmente a escala Patterns of Activity Measure – Pain (POAM-P) para a população portuguesa com dor lombar crónica inespecífica (DLCI) e contribuir para a sua validação. Metodologia: A versão original (inglesa) do POAM-P foi traduzida e adaptada para a língua portuguesa (POAM-P-VP) através de uma equipa multidisciplinar que incluiu tradutores, retrotradutores (cegos e independentes), peritos de diferentes áreas e utentes com DLCI, de acordo com as recomendações de linhas orientadoras atuais para este processo. A análise factorial e das propriedades psicométricas da POAM-P-VP contou com uma amostra de 132 utentes. A consistência interna foi analisada através do coeficiente alpha de Cronbach (α) e para a análise da fiabilidade teste-reteste recorreu-se ao coeficiente de correlação intraclasse (ICC:2,1). A análise da validade de construto convergente e discriminativa das componentes da POAM-P-VP foi conseguida através da aplicação da versão portuguesa da escala Tampa Scale of Kinesiophobia (TSK-13-VP), e recorrendo ao cálculo do coeficiente de Spearman. Todos os cálculos estatísticos foram realizados no software IBM SPSS Statistics (versão 20). Resultados: A análise factorial permitiu identificar três componentes da POAM-P-VP (evitamento, persistência excessiva e persistência consistente com a dor), sendo estruturalmente diferentes das subescalas do POAM-P original. Estas componentes apresentaram uma consistência interna boa a elevada. As componentes 1 e 2 apresentaram uma fiabilidade teste-reteste moderada a excelente, e a componente 3 uma fiabilidade teste-reteste pobre, limitando o seu poder de uso na prática clínica e em investigação. Relativamente à validade de construto, nenhuma das hipóteses estabelecidas no estudo apriori foram verificadas, não podendo aferir acerca da relação dos padrões de atividade com a cinesiofobia, medida pelo TSK-13-VP. Porém, a componente de evitamento da POAM-P-VP parece medir conteúdos partilhados com a TSK-13-VP (rs = 0.15, p<0.048). Conclusão: A adaptação e contributo para a validação da versão portuguesa da escala POAM-P constituiu um ponto de partida para a existência de um instrumento de medição de padrões de atividade de utentes portugueses com DLC, requerendo mais estudos para a sua validação. Apesar de algumas limitações, considera-se que este estudo é de grande importância para os fisioterapeutas e investigadores que buscam um maior conhecimento e efetividade das abordagens de intervenção em utentes com dor lombar crónica.-------------- ABSTRACT: Chronic low back pain (CLBP) is one of the most common clinical conditions as well as one with high economical costs within western countries. Recent studies have shown that patients with LBP present different patterns of activity which influence their levels of functional capacity. However, evidence on these associations is still limited and inconclusive. To our knowledge, there is in Portugal no valid scale for measuring these patterns of activity in CLBP patients. Purpose: Culturally adapt the Patterns of Activity Measure – Pain (POAM-P) scale to the Portuguese population with non-specific chronic low back pain (NSLBP) and contribute to its validation. Method: The original English version of POAM-P was blindly and independently translated, back translated and adapted to the Portuguese language (POAM-P-VP) by a multidisciplinary team of translators, experts from different fields, and patients with NSLBP, according to established guidelines for this process. Factorial and psychometric properties’ analysis of POAM-P-VP comprised a sample of 132 patients. The internal consistency was analyzed based on Cronbach's alpha-coefficient (α) and for test-retest reliability analysis the Intraclass Correlation Coefficient (ICC) was used. The analysis of convergent and discriminant construct validity of POAM-P-VP components was achieved through the use of the Portuguese version of the Tampa Scale of Kinesiophobia (TSK-13-VP), using the Spearman coefficient calculation. All statistical calculations were performed using IBM SPSS Statistics software (v.20). Results: The factor analysis allowed for the identification of three components of POAM-P-VP (avoidance, excessive persistence and pain-contingent persistence), structurally different from the original POAM-P subscales. These components demonstrated a good to high level of internal consistency. Components 1 and 2 demonstrated moderate to excellent test-retest reliability, whereas component 3 presented low test-retest reliability thus limiting its clinical and investigative use. With regard to construct validity, none of the previously established hypothesis was verified, therefore not making it possible to assess the relation between activity patterns and kinesiophobia, measured by TSK-13-VP. However, the avoidance component of POAM-P-VP seems to share measurable contents with TSK-13-VP (rs = 0.15, p<0.048). Conclusion: The adaptation and contribution to the validation of the Portuguese version of POAM-P scale, sets a starting point to the existence of a useful instrument for measuring activity patterns in Portuguese CLBP patients, requiring further studies towards its validation. Despite some limitations, this study is considered of high importance to physiotherapists as well as investigators in search of deeper knowledge and effective practical approaches on chronic low back pain patients.

Prognostic modelling of breast cancer patients: a benchmark of predictive models with external validation

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores – Sistemas Digitais e Percepcionais pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Validation of NASA rotor 67 with openFOAM's transonic density-based solver

Dissertação para obtenção do Grau de Mestre em Engenharia Mecânica

Constraint reasoning for differential models

The basic motivation of this work was the integration of biophysical models within the interval constraints framework for decision support. Comparing the major features of biophysical models with the expressive power of the existing interval constraints framework, it was clear that the most important inadequacy was related with the representation of differential equations. System dynamics is often modelled through differential equations but there was no way of expressing a differential equation as a constraint and integrate it within the constraints framework. Consequently, the goal of this work is focussed on the integration of ordinary differential equations within the interval constraints framework, which for this purpose is extended with the new formalism of Constraint Satisfaction Differential Problems. Such framework allows the specification of ordinary differential equations, together with related information, by means of constraints, and provides efficient propagation techniques for pruning the domains of their variables. This enabled the integration of all such information in a single constraint whose variables may subsequently be used in other constraints of the model. The specific method used for pruning its variable domains can then be combined with the pruning methods associated with the other constraints in an overall propagation algorithm for reducing the bounds of all model variables. The application of the constraint propagation algorithm for pruning the variable domains, that is, the enforcement of local-consistency, turned out to be insufficient to support decision in practical problems that include differential equations. The domain pruning achieved is not, in general, sufficient to allow safe decisions and the main reason derives from the non-linearity of the differential equations. Consequently, a complementary goal of this work proposes a new strong consistency criterion, Global Hull-consistency, particularly suited to decision support with differential models, by presenting an adequate trade-of between domain pruning and computational effort. Several alternative algorithms are proposed for enforcing Global Hull-consistency and, due to their complexity, an effort was made to provide implementations able to supply any-time pruning results. Since the consistency criterion is dependent on the existence of canonical solutions, it is proposed a local search approach that can be integrated with constraint propagation in continuous domains and, in particular, with the enforcing algorithms for anticipating the finding of canonical solutions. The last goal of this work is the validation of the approach as an important contribution for the integration of biophysical models within decision support. Consequently, a prototype application that integrated all the proposed extensions to the interval constraints framework is developed and used for solving problems in different biophysical domains.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.