Characteristics analysis of covid-19 observational and Interventional studies registered in portugal between 2020 and 2021

Tese de mestrado, Ciências Biofarmacêuticas, 2022, Universidade de Lisboa, Faculdade de Farmácia.A Doença por Coronavírus 2019 (COVID-19) é uma doença infeciosa causada pelo vírus SARS-CoV-2 que foi identificada pela primeira vez em Dezembro de 2019 na China, na cidade de Wuhan. Face à sua rápida disseminação e elevada escala de transmissão, em Março de 2020, foi declarada pandemia pela Organização Mundial da Saúde. Este vírus, nunca tinha sido identificado anteriormente e demonstrou poder causar pneumonia grave ou até morte. A partir deste momento, os estudos COVID-19 tornaram-se o principal foco dos investigadores para a descoberta de novas formas de diagnóstico, tratamento e prevenção a fim de regredir o mais rápido possível o avanço da pandemia. Mundialmente este vírus já causou mais de 6,04 milhões de mortes, totalizando 445 milhões de casos confirmados. Aparentemente, a facilidade de disseminação do vírus SARS-CoV-2 nos humanos, deve-se ao facto de este se ligar com alta eficácia a uma proteína chamada enzima conversora de angiotensina 2 (ECA2) localizada na superfície de diversas células. Uma vez que não existia um tratamento específico e eficaz contra a COVID-19, recorreu-se a medicação para o tratamento sintomático da doença. Já foi demonstrada a eficácia de medicamentos antivirais para tratar o COVID-19, uma vez que estes são capazes de prevenir a entrada do vírus na célula hospedeira e consecutivamente evitar a replicação viral. Por outro lado, a vacinação é considerada a opção mais preventiva para a COVID-19 uma vez que permite atingir a imunidade da população. A incrível pressão que a pandemia exerceu sobre investigadores, reguladores e decisores políticos, e reconhecendo o esforço coletivo de todos para conseguir desenvolver rapidamente mas em segurança numa época de tremenda incerteza opções terapêuticas eficazes numa escala mundial voltou a sublinhar a importância de investigação clínica, nomeadamente dos ensaios clínicos em grande escala estruturados de acordo com um protocolo metodologicamente bem desenhado, de forma coordenada e colaborativa para que os resultados obtidos sejam robustos, a importância de ter estruturas e incentivos para permitir uma partilha de dados mais rápida de conjuntos de dados anonimizados, ter mecanismos céleres de financiamento assim como a necessidade de proporcionar oportunidades semelhantes às dos países de elevado rendimento para a realização de ensaios clínicos em regiões de baixos recursos, com consideravelmente menos financiamento para a investigação clínica. Desde o aparecimento da doença têm sido propostos o repurposing de vários medicamentos já aprovados para outras indicações terapêuticas e surgiram em menores números algumas terapêuticas inovadoras. O benefício risco de todas estas opções terapêuticas (medicamentos, vacinas) têm vindo a ser demonstrado em ensaios clínicos de várias fases e com desenho adaptativo que permite acelerar o processo de desenvolvimento. Posto isto, o principal objetivo sempre passou pela realização de novos ensaios clínicos para o desenvolvimento de medicamentos com potenciais benefícios para tratamento e de vacinas para prevenção. Para uma melhor compreensão do envolvimento que Portugal teve nos estudos clínicos realizados para a COVID-19, dada a sua importância na saúde pública, seria relevante caracterizar e analisar os estudos registados em Portugal e por sua vez, identificar os centros de pesquisa que mais estiveram envolvidos. Assim, o principal objetivo deste trabalho é caracterizar o tipo de estudos (observacionais e de intervenção) registados nas bases de dados de registo que envolvem Portugal entre 2020 e 2021. O presente estudo tem como objetivo secundário analisar características como os tipos de promotores do estudo, os financiadores, ensaios nacionais ou internacionais), número de participantes recrutados, tipos de intervenção, publicação e centros nacionais envolvidos. Para melhor compreender as adaptações que tiveram que ser realizada na implementação e condução de estudos clínicos em contexto pandémico, tanto a nível de autoridades reguladoras, como promotores, centros de ensaio, equipas de investigação e participantes começou-se por recolher os dados das publicações relativas às orientações nacionais e internacionais. Estas medidas excecionais foram sendo emitidas pela Agência Europeia de Medicamentos (EMA), e a nível nacional pela Autoridade Nacional de Medicamentos e Produtos de Saúde (INFARMED) e o Comité Nacional de Ética para Investigação Clínica (CEIC). Para atingir o objetivo principal do estudo, foi realizada uma pesquisa sistemática dos registos entre 1 de janeiro de 2020 e 31 de dezembro de 2021 utilizando quatro plataformas de registo de ensaios clínicos - ClinicalTrials.gov, EUCTR ANZCTR e RNEC. A pesquisa nestas quatro plataformas de registo de ensaios clínicos, permitiu a identificação e caracterização de estudos observacionais (ClinicalTrials.gov) e com intervenção a decorrer em Portugal para a COVID-19. Após a identificação de todos os estudos registados em Portugal no período mencionado anteriormente, foi feita a análise e caracterização de todas as informações recolhidas. Para isso, os dados foram organizados e registados numa tabela do Microsoft Office Excel, divididos por diversos parâmetros (de acordo com as informações dos estudos mais relevantes para o trabalho. Através desta análise, no presente estudo foram identificados em Portugal 29 estudos clínicos para a COVID-19 dos quais 14 são estudos observacionais e 15 são estudos de intervenção (ensaios clínicos). Durante o ano de 2020, Portugal esteve envolvido em mais estudos observacionais (n=11) do que estudos de intervenção (n=6). Em contraste, em 2021 Portugal registou mais estudos de intervenção para COVID-19 (n=9) do que estudos observacionais (n=3). Estes ensaios têm sido promovidos maioritariamente por universidades e companhias farmacêuticas. Os estudos observacionais foram promovidos maioritariamente por universidades, em que se destacaram a Universidade de Lisboa, a Universidade do Porto e a Universidade do Minho, e os estudos de intervenção por companhias farmacêuticas. Nos estudos com intervenção, em oposição aos estudos observacionais, verificou-se uma grande percentagem de estudos multinacionais, provavelmente por 60% por serem ensaios comerciais e possuírem maior capacidade de financiamento. Os tipos de financiadores vêm de encontro aos promotores, isto é, os estudos observacionais são maioritariamente financiados por organizações públicas e em estudos com intervenção por organizações privadas, principalmente empresas farmacêuticas com o objetivo da comercialização do produto. Todos os estudos de intervenção feitos no âmbito da COVID-19, foram ensaios clínicos randomizados já que permitem entender e avaliar o efeito de cada intervenção realizada, uma vez que cada grupo recebe uma intervenção diferente. A análise de dados destes estudos revelou que 80% dos estudos registados em Portugal eram para estudar medicamentos capazes de tratar e regredir o avanço da doença. A identificação dos centros de investigação nem sempre era possível, pois nem sempre eram mencionados. No entanto, quando se trata de estudos de intervenção, utilizando o RNEC foi possível identificar a maior parte. As áreas com maior número de centros de investigação concentram-se nas grandes áreas metropolitanas de Lisboa, Porto e Braga, coincidindo com as áreas com mais estudos de COVID-19 registados em Portugal. No distrito de Lisboa, destacaram-se a Nova Medical School, da Universidade NOVA de Lisboa, o Centro Hospitalar Universitário Lisboa Central e o Centro Hospitalar Universitário de Lisboa Norte. No Porto, o Centro Hospitalar Vila Nova de Gaia e o Centro Hospitalar de São João foram os que mais participaram. Relativamente à publicação dos estudos, conclui-se que dos 29 estudos clínicos realizados para COVID-19 registados em Portugal entre 2020 e 2021, 66.67% dos que estão concluídos nos registos já deram origem a uma publicação. Apesar de todo o esforço feito por muitas entidades reguladoras nacionais, Portugal ainda apresenta várias lacunas e falta de harmonização que atrasam a implementação de ensaios clínicos internacionais. Por conseguinte, o desenvolvimento da investigação clínica deve basear-se numa estratégia nacional que reúna as autoridades de saúde para promover um conjunto estimulante de políticas públicas e financiamento.Coronavirus Disease 2019 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus that was first identified in December 2019 in China, in the city of Wuhan. In view of its rapid spread and high scale of transmission, in March 2020, it was declared a pandemic by the World Health Organization. This virus has never been identified before and has been shown to cause severe pneumonia or even death. Therefore, COVID-19 studies have become the main focus of researchers for the discovery of new ways of diagnosis, treatment and prevention in order to reverse the advance of the pandemic as quickly as possible. Worldwide, this virus has already caused more than 6.04 million deaths, totaling 445 million confirmed cases. The effectiveness of antiviral drugs to treat COVID-19 has already been demonstrated, since they are able to prevent the entry of the virus into the host cell and consecutively prevent viral replication. On the other hand, vaccination is considered the most preventive option for COVID-19 as it allows the population to be immunized. That said, the main objective has always been to carry out new clinical trials for the development of drugs with potential benefits for treatment and vaccines for prevention. For a better understanding of the involvement that Portugal had in the clinical studies carried out for COVID-19, the main objective of this work is to characterize the type of studies (observational and interventional) registered in the registration databases involving Portugal between 2020 and 2021. To achieve the primary objective of the study, a systematic search of registries was performed between 1 January 2020 and 31 December 2021 using four clinical trial registry platforms - ClinicalTrials.gov, EUCTR ANZCTR and RNEC. Research on these four clinical trial registration platforms allowed the identification and characterization of observational studies (ClinicalTrials.gov) and interventional studies taking place in Portugal for COVID-19. After the identification and characterization of all studies registered in Portugal in the aforementioned period, all data were registered and analyzed in Microsoft Office Excel. Through this analysis, in the present study, 29 clinical studies for COVID-19 were identified in Portugal, of which 14 are observational studies and 15 are interventional studies (clinical trials). These trials have mostly been sponsored by universities and pharmaceutical companies. The areas with the highest number of research centers are concentrated in the large metropolitan areas of Lisbon, Porto and Braga. Regarding the publication of the studies, it is concluded that of the 29 clinical studies carried out for COVID-19 registered in Portugal between 2020 and 2021, 66.67% of those that are completed in the records have already given a publication. Despite all the efforts made by many national regulatory entities to facilitate the approval processes for clinical trials, Portugal still has several gaps and lack of harmonization that delay the implementation of international clinical trials.PTCRIN

Analyse des signaux stabilométriques et de la stabilité chez l’Homme : application à la biométrie

Biometrics refers to automatic recognition of individuals. It is based on their physiological and / or behavioral. The postural control, despite that is a human behavioral characteristic, has not been well developed in the field of biometrics. The work performed in this thesis is based on the stabilometric signals analysis ant biometric application. Firstly, a study of the postural information especially the stabilometric signal is carried out through traditional analysis namely temporal, frequency and stochastic analysis and two decomposition methods named principle components analysis (ACP) decomposition and wavelet decomposition. The ACP method, based on the additive model, allows decomposing the signal into three components: a trend signal, a rambling signal and a trembling signal. The wavelet decomposition method allows decomposing the signal into three levels of detail signals and three signal levels of approximation. Through the study of postural stability, spectral analysis and phase analysis of the different components from the ACP and the wavelet decomposition, the comparison of these two methods concludes that the ACP method is more appropriate than the wavelet decomposition to analyze the stabilogram. From the decomposition methods and classical methods of analysis, several parameters are extracted to study the effect of different factors on postural stability and the center of mass displacement. These factors are named vision, direction, proprioception, age, gender, height and weight. A second aspect of this work is devoted to the application of biometrics, from the extracted parameters and through ANOVA statistic analysis, those that are most discriminative are used to identify subjects and classify them according to age, gender, weight and size. This biometric application is performed by three classification methods namely, K-NN, LDA and SVM. Biometric applications result in respectable recognition rate exceeding 80%. Therefore, it is inferred that the analysis of postural control is promising in the field of biometricsLa biométrie se réfère à la reconnaissance automatique des individus. Elle est basée sur leurs caractéristiques physiologiques et/ou comportementales. Le contrôle postural, bien que soit une caractéristique comportementale de l'Homme, n'a pas été bien développée dans le domaine de la biométrie. Le travail mené dans cette thèse repose sur l'analyse des signaux stabilométriques et l'application à la biométrie. Dans un premier volet, une étude de l'information posturale, en particulier le signal stabilométrique, est effectuée à travers des méthodes d'analyses classiques à savoir et l'analyse spatio-temporelle, spectrale et stochastique et à travers aussi deux méthodes de décomposition : la décomposition appelée analyse en composantes principales (ACP) et la décomposition en ondelettes. La méthode ACP, basée sur le modèle additif, permet de décomposer le signal en trois composantes: un signal de tendance, un signal d'excursion et un signal de tremblements. La méthode de décomposition en ondelettes permet de décomposer le signal en trois niveaux de signaux de détail et trois niveaux de signaux d'approximation. Suite à l'étude de la stabilité posturale, l'analyse spectrale et l'analyse de la phase des différentes composantes issues de la ACP et de la décomposition en ondelettes, la comparaison de ces deux méthodes conclut que la méthode ACP est plus appropriée que la décomposition en ondelettes pour analyser le stabilogramme. A partir des méthodes de décomposition et des méthodes d'analyses classiques, des paramètres sont extraits afin d'étudier l'effet de différents facteurs sur la stabilité posturale et sur le déplacement du centre de masse. Ces facteurs sont la vision, la direction, la proprioception, l'âge, le genre, la taille et le poids. Un deuxième volet de ce travail est consacré à l'application biométrique, à partir des paramètres extraits et suite à une analyse statistique ANOVA, ceux qui sont les plus discriminatifs sont utilisés pour identifier des sujets et les classer selon leur âge, genre, poids et taille. Cette application biométrique est effectuée par trois méthodes de classification à savoir, K-ppv, ADL et SVM. Les applications biométriques aboutissent à des taux de reconnaissance respectables dépassant 80%. De ce fait, il est à déduire que l'analyse du contrôle postural est prometteuse dans le domaine de la biométri

Stabilometric signals analysis and biometric application

La biométrie se réfère à la reconnaissance automatique des individus. Elle est basée sur leurs caractéristiques physiologiques et/ou comportementales. Le contrôle postural, bien que soit une caractéristique comportementale de l'Homme, n'a pas été bien développée dans le domaine de la biométrie. Le travail mené dans cette thèse repose sur l'analyse des signaux stabilométriques et l'application à la biométrie. Dans un premier volet, une étude de l'information posturale, en particulier le signal stabilométrique, est effectuée à travers des méthodes d'analyses classiques à savoir et l'analyse spatio-temporelle, spectrale et stochastique et à travers aussi deux méthodes de décomposition : la décomposition appelée analyse en composantes principales (ACP) et la décomposition en ondelettes. La méthode ACP, basée sur le modèle additif, permet de décomposer le signal en trois composantes: un signal de tendance, un signal d'excursion et un signal de tremblements. La méthode de décomposition en ondelettes permet de décomposer le signal en trois niveaux de signaux de détail et trois niveaux de signaux d'approximation. Suite à l'étude de la stabilité posturale, l'analyse spectrale et l'analyse de la phase des différentes composantes issues de la ACP et de la décomposition en ondelettes, la comparaison de ces deux méthodes conclut que la méthode ACP est plus appropriée que la décomposition en ondelettes pour analyser le stabilogramme. A partir des méthodes de décomposition et des méthodes d'analyses classiques, des paramètres sont extraits afin d'étudier l'effet de différents facteurs sur la stabilité posturale et sur le déplacement du centre de masse. Ces facteurs sont la vision, la direction, la proprioception, l'âge, le genre, la taille et le poids. Un deuxième volet de ce travail est consacré à l'application biométrique, à partir des paramètres extraits et suite à une analyse statistique ANOVA, ceux qui sont les plus discriminatifs sont utilisés pour identifier des sujets et les classer selon leur âge, genre, poids et taille. Cette application biométrique est effectuée par trois méthodes de classification à savoir, K-ppv, ADL et SVM. Les applications biométriques aboutissent à des taux de reconnaissance respectables dépassant 80%. De ce fait, il est à déduire que l'analyse du contrôle postural est prometteuse dans le domaine de la biométrieBiometrics refers to automatic recognition of individuals. It is based on their physiological and / or behavioral. The postural control, despite that is a human behavioral characteristic, has not been well developed in the field of biometrics. The work performed in this thesis is based on the stabilometric signals analysis ant biometric application. Firstly, a study of the postural information especially the stabilometric signal is carried out through traditional analysis namely temporal, frequency and stochastic analysis and two decomposition methods named principle components analysis (ACP) decomposition and wavelet decomposition. The ACP method, based on the additive model, allows decomposing the signal into three components: a trend signal, a rambling signal and a trembling signal. The wavelet decomposition method allows decomposing the signal into three levels of detail signals and three signal levels of approximation. Through the study of postural stability, spectral analysis and phase analysis of the different components from the ACP and the wavelet decomposition, the comparison of these two methods concludes that the ACP method is more appropriate than the wavelet decomposition to analyze the stabilogram. From the decomposition methods and classical methods of analysis, several parameters are extracted to study the effect of different factors on postural stability and the center of mass displacement. These factors are named vision, direction, proprioception, age, gender, height and weight. A second aspect of this work is devoted to the application of biometrics, from the extracted parameters and through ANOVA statistic analysis, those that are most discriminative are used to identify subjects and classify them according to age, gender, weight and size. This biometric application is performed by three classification methods namely, K-NN, LDA and SVM. Biometric applications result in respectable recognition rate exceeding 80%. Therefore, it is inferred that the analysis of postural control is promising in the field of biometric

Journal off Mechanical Design 1& l|i>««'M sJBM l^^A J Contributed by the Power Transmission and Gearing Committee for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received

This paper presents an analytical expression of the time-varying contact length between perfect involute spur and helical gears. It is shown that contact lengths can be expressed as where AQ (r) and Bo ( T ) are 2 periodic piecewise linear functions represented in AO(T) and BO(T) can be easily decomposed in Fourier series as: AO(T) = -+ X «n • cos (InnT BO(T) * ao contact zone with: sin (2nn(ea + tp)IP)'\ Ea: transverse contact ratio eg: overlap contact ratio The length of a contact line starting at r = (' for either spur or helical gears can be deduced from (1) by: li(T) = lo(T -i) and the total contact length at T becomes: Lir) = S Zo(r -0 1=0 (5) 58

Experimental and numerical analysis of the effect of gear center distance variation and misalignment error on the dynamic behavior of narrow-faced spur gear drives

In this paper, we present an experimental and numerical analysis of the influence of gear center distance variation and misalignment amplitude on dynamic response of single stage spur gear transmission systems. The experimental part is conducted on a specific test bench conceived and constructed at LASEM and composed essentially of a motor, a test gearbox and a receptor. It allows the measurement of the acceleration on the motor and receptor bearing shafts for different values of gear center distances and misalignment amplitudes. The numerical study is based on a 3D non-linear model with 36 degrees of freedom including torsional, flexural and axial displacements of the gear-shaft-bearing system. The equations of motion and the contact problem are solved simultaneously using an original procedure by coupling a normal contact algorithm and a numerical Hilbert-Hughes-Taylor schema. Agreement between measurements and calculations is satisfactory and illustrates the influence of these mounting errors on the dynamic behavior of Narrow-Faced Spur Gear

Kinematic Optimization of Energy Extraction Efficiency for Flapping Airfoil by using Response Surface Method and Genetic Algorithm

In this paper, numerical simulations have been performed to study the performance of a single fully activated flapping wing serving as energy harvester. The aims of the paper are predicting and maximizing the energy extraction efficiency by using optimization methodology. The metamodeling and the genetic algorithms are applied in order to find the optimal configuration improving the efficiency. A response surface method (RSM) based on Box–Behnken experimental design and genetic algorithm has been chosen to solve this problem. Three optimization factors have been manipulated, i.e. the dimensionless heaving amplitude h0, the pitching amplitude θ0 and the flapping frequency f. The ANSYS FLUENT 14 commercial software has been used to compute the governing flow equations at a Reynolds number of 1100, while the flapping movement combined from heaving and pitching of the NACA0015 foil has been carried out by using an in house user-defined function (UDF). A maximum predicted efficiency of 34.02% has been obtained with high accuracy of optimal kinematic factors of dimensionless heaving amplitude around the chord, high pitching amplitude and low flapping frequency of 0.304 hertz. Results have also showed that the interaction effect between optimization factors is important and the quadratic effect of the frequency is strong confirming the great potential of the applied optimization methodology

Hydrothermal synthesis of chiral inorganic-organic CoII complex: Structural, thermal and catalytic evaluation

By heating the cobalt chloride hexahydrate (CoCl·6HO) with the R form of the organic amine α-methylbenzylamine (CHN) under hydro(solvo)thermal conditions, we have successfully generated the corresponding non-centrosymmetric homochiral hybrid tris (α-methylbenzylammonium tetrachloridocobaltate chloride [R-(CHN)][CoCl]Cl abbreviated R(MBA)Co. We present the growth conditions together with a characterization of the single crystals by means of X-ray single-crystal diffraction, Fourier-transform infrared, TG/TDA thermal decomposition and catalytic properties. This inorganic–organic hybrid compound crystallizes in the chiral space group P2 and exhibits a supramolecular-layered organization wherein a double layer of (R)-methylbenzylammonium cations and the uncoordinated chloride anions are sandwiched between anionic layers, formed by isolated tetrachloridocobaltate tetrahedra. The crystal packing is governed by a three-dimensional network of N/C–H⋯Cl hydrogen bonds between the inorganic and organic moieties and C–H···π interactions between the aromatic rings of the organic moieties themselves. Thermal analysis discloses a phase transition at the temperature 130 °C. The Co complex was also employed as suitable catalyst activating the acetal formation reaction of aldehydes using MeOH as solvent and as the unique source of acetalization.R.P.H. thanks Ministerio de Economía, Industria y Competitividad MINECO-FEDER CTQ2017-88091-P and Diputación General de Aragón (DGA) (Research Group E07-17R) for financial support of her research.Peer Reviewe

Effect analysis of age and gender on postural stability using PCA decomposition

International audienceThis paper presents an analysis of stabilogram using the Principal Component Analysis (PCA) decomposition. It shows also the effects of different aspects on the human postural stability. The stabilogram measures either lateral displacement or forward-backward displacement of a subject. These measurements are taken to quantify posture while standing in one of four controlled positions. By using Principal Component Analysis (PCA), the stabilogram is decomposed into three components with biological meaning. The components are trend, rambling and trembling. This paper proposes to create analytic signals for rambling (deterministic) and trembling (random) and use the resulting complex trajectories to identify the effect of age and gender on postural stability. The proposed method employs a signal analysis front end (PCA analysis) and a signal interpretation backend (clustering of complex trajectories). Experimental results show the efficiency of the PCA analysis to identify the effect of age and gender on the postural stability. These results are able to discriminate between control and young groups and indicate a less well-controlled posture for control subjects (34.5 ± 7.5yrs) relatively to young subjects (22.5 ± 2.5yrs). Results are also able to discriminate between female subjects and male subjects and indicate a less well-controlled posture for female subjects relatively to males

Décomposition et extraction de phase du signal stabilométrique. Etude de l’effet de différentes entrées sur la stabilité

International audienceno abstrac