Aplicação de células estaminais dentárias na Medicina e na Medicina Dentária

A temática relativa às células estaminais inicia-se na década de 60 com a descoberta da primeira fonte viável destas células: a medula óssea. Diversos estudos permitiram definir a sua função de renovação tecidular e regeneração pós-dano, assim como a sua caraterização num grupo heterogéneo de células indiferenciadas, clonogénicas, definidas pela capacidade de auto-renovação e diferenciação em células maduras. Nos últimos anos, estas células ganharam popularidade face à alternativa terapêutica que representam para muitas doenças, tais como: diabetes, anomalias congénitas, danos do tecido nervoso, Parkinson, Alzheimer e outras alterações degenerativas, exposições pulpares, defeitos periodontais e perda do órgão dentário. Apesar do seu potencial terapêutico, apresentam vários efeitos adversos, especialmente em relação ao seu envolvimento direto (via transformação maligna das MSCs) e indireto (via efeito modulatório das MSCs) no desenvolvimento do cancro. Preconiza-se o seu uso no âmbito da Engenharia Tecidular, introduzindo o processo de regeneração tecidular através da utilização combinada de biomateriais e mediadores biológicos, a fim de proporcionar novas ferramentas para a medicina regenerativa. Mais tarde, tornou-se possível identificar cinco populações de células estaminais de origem dentária (DPSCs, SHEDs, DFPCs, SCAPs e PDLCs) que, para além da sua multipotência e capacidade de diferenciação, constituem fontes acessíveis para recolha. O isolamento destas células constitui ainda uma prática relativamente recente, na qual se torna preponderante isolar células com fenótipo pré-determinado e cultivá-las em meios de cultura adequados. Estudos comprovam que o método de isolamento e as condições de cultura utilizados podem dar origem a diferentes linhas celulares. A conservação é uma prática baseada na convicção de que a medicina regenerativa é o caminho mais promissor para o desenvolvimento da medicina personalizada. Informação adicional relativa à terapia com células estaminais é ainda necessária. Esta utiliza princípios de biomimética altamente desejáveis, pelo que os resultados obtidos têm vindo a despoletar grandes expetativas e a sua implementação na Engenharia Tecidular apresenta-se promissora.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.

NK Cells of Kidney Transplant Recipients Display an Activated Phenotype that Is Influenced by Immunosuppression and Pathological Staging

To explore phenotype and function of NK cells in kidney transplant recipients, we investigated the peripheral NK cell repertoire, capacity to respond to various stimuli and impact of immunosuppressive drugs on NK cell activity in kidney transplant recipients. CD56(dim) NK cells of kidney transplanted patients displayed an activated phenotype characterized by significantly decreased surface expression of CD16 (p=0.0003), CD226 (p<0.0001), CD161 (p=0.0139) and simultaneously increased expression of activation markers like HLA-DR (p=0.0011) and CD25 (p=0.0015). Upon in vitro stimulation via Ca++-dependent signals, down-modulation of CD16 was associated with induction of interferon (IFN)-gamma expression. CD16 modulation and secretion of NFAT-dependent cytokines such as IFN-gamma, TNF-alpha, IL-10 and IL-31 were significantly suppressed by treatment of isolated NK cells with calcineurin inhibitors but not with mTOR inhibitors. In kidney transplant recipients, IFN-gamma production was retained in response to HLA class I-negative target cells and to non-specific stimuli, respectively. However, secretion of other cytokines like IL-13, IL-17, IL-22 and IL-31 was significantly reduced compared to healthy donors. In contrast to suppression of cytokine expression at the transcriptional level, cytotoxin release, i.e. perforin, granzyme A/B, was not affected by immunosuppression in vitro and in vivo in patients as well as in healthy donors. Thus, immunosuppressive treatment affects NK cell function at the level of NFAT-dependent gene expression whereby calcineurin inhibitors primarily impair cytokine secretion while mTOR inhibitors have only marginal effects. Taken together, NK cells may serve as indicators for immunosuppression and may facilitate a personalized adjustment of immunosuppressive medication in kidney transplant recipients.

Page Composition using PPML as a Link-editing Script

The advantages of a COG (Component Object Graphic) approach to the composition of PDF pages have been set out in a previous paper [1]. However, if pages are to be composed in this way then the individual graphic objects must have known bounding boxes and must be correctly placed on the page in a process that resembles the link editing of a multi-module computer program. Ideally the linker should be able to utilize all declared resource information attached to each COG. We have investigated the use of an XML application called Personalized Print Markup Language (PPML) to control the link editing process for PDF COGs. Our experiments, though successful, have shown up the shortcomings of PPML's resource handling capabilities which are currently active at the document and page levels but which cannot be elegantly applied to individual graphic objects at a sub-page level. Proposals are put forward for modifications to PPML that would make easier any COG-based approach to page composition.

Vulcanus 2.0: A Recommender System for Accessibility

Even though the use of recommender systems is already widely spread in several application areas, there is still a lack of studies for accessibility research field. One of these attempts to use recommender system benefits for accessibility needs is Vulcanus. The Vulcanus recommender system uses similarity analysis to compare user’s trails. In this way, it is possible to take advantage of the user’s past behavior and distribute personalized content and services. The Vulcanus combined concepts from ubiquitous computing, such as user profiles, context awareness, trails management, and similarity analysis. It uses two different approaches for trails similarity analysis: resources patterns and categories patterns. In this work we performed an asymptotic analysis, identifying Vulcanus’ algorithm complexity. Furthermore we also propose improvements achieved by dynamic programming technique, so the ordinary case is improved by using a bottom-up approach. With that approach, many unnecessary comparisons can be skipped and now Vulcanus 2.0 is presented with improvements in its average case scenario.

Aplicação de técnicas de classificação em séries temporais

Hoje, há um interesse crescente pela aprendizagem de uma segunda língua, quer seja por razões profissionais ou pessoais. Esta é uma tendência que se vai afirmando num mundo cada vez mais interconectado. Por outro lado, a democratização das tecnologias computacionais torna possível pensar em desenvolver novas técnicas de ensino de línguas mais automatizadas e personalizadas. Esta dissertação teve como objetivo estudar e implementar um conjunto de técnicas de processamento de sinal e de classificação de séries temporais úteis para o desenvolvimento de metodologias do ensino oral com feedback automático. São apresentados resultados preliminares sobre a prestação destas técnicas, e avaliada a viabilidade deste tipo de abordagem.

Novel pharmacogenomic markers of irinotecan-induced severe toxicity in metastatic colorectal cancer patients

L’irinotécan est un agent de chimiothérapie largement utilisé pour le traitement de tumeurs solides, particulièrement pour le cancer colorectal métastatique (mCRC). Fréquemment, le traitement par l’irinotécan conduit à la neutropénie et la diarrhée, des effets secondaires sévères qui peuvent limiter la poursuite du traitement et la qualité de vie des patients. Plusieurs études pharmacogénomiques ont évalué les risques associés à la chimiothérapie à base d’irinotécan, en particulier en lien avec le gène UGT1A, alors que peu d’études ont examiné l’impact des gènes codant pour des transporteurs. Par exemple, le marqueur UGT1A1*28 a été associé à une augmentation de 2 fois du risque de neutropénie, mais ce marqueur ne permet pas de prédire la toxicité gastrointestinale ou l’issue clinique. L’objectif de cette étude était de découvrir de nouveaux marqueurs génétiques associés au risque de toxicité induite par l’irinotécan, en utilisant une stratégie d’haplotype/SNP-étiquette permettant de maximiser la couverture des loci génétiques ciblés. Nous avons analysé les associations génétiques des loci UGT1 et sept gènes codants pour des transporteurs ABC impliqués dans la pharmacocinétique de l’irinotécan, soient ABCB1, ABCC1, ABCC2, ABCC5, ABCG1, ABCG2 ainsi que SLCO1B1. Les profils de 167 patients canadiens atteints de mCRC sous traitement FOLFIRI (à base d’irinotécan) ont été examinés et les marqueurs significatifs ont par la suite été validés dans une cohorte indépendante de 250 patients italiens. Nous avons découvert dans la région intergénique en aval du gène UGT1, un nouveau marqueur (rs11563250G) associé à un moindre risque de neutropénie sévère (rapport des cotes (RC)=0.21; p=0.043 chez les canadiens, RC=0.27; p=0.036 chez les italiens, et RC=0.31 p=0.001 pour les deux cohortes combinées). De plus, le RC est demeuré significatif après correction pour multiples comparaisons (p=0.041). Par ailleurs, pour l’haplotype défini par les marqueurs rs11563250G et UGT1A1*1 (rs8175347 TA6), le RC était de 0.17 (p=0.0004). Un test génétique évaluant ces marqueurs permettrait d’identifier les patients susceptibles de bénéficier d’une augmentation de dose d’irinotécan. En revanche, une autre combinaison de marqueurs, ABCC5 rs3749438 et rs10937158 (T–C), a prédit un risque plus faible de diarrhée sévère dans les deux cohortes (RC = 0.43; p=0.001). La coexistence des marqueurs ABCG1 rs225440T et ABCC5 rs2292997A a prédit un risque accru de neutropénie (RC=5.93; p=0.0002), alors qu’une prédiction encore plus significative a été obtenue lorsque ces marqueurs sont combinés au marqueur de risque bien établi UGT1A1*28 rs8175347 (RC=7.68; p<0.0001). Enfin, les porteurs de l’allèle de protection UGT1 rs11563250G en absence d’allèles de risque, ont montré une incidence réduite de neutropénie sévère (8.2% vs. 34.0%; p<0.0001). Nous concluons que ces nouveaux marqueurs génétiques prédictifs pourraient permettre d’améliorer l’évaluation du risque de toxicité et personnaliser le traitement à base d’irinotécan pour les patients atteints du cancer colorectal métastatique.

Development of computer application for determination of chemical diffusion properties in biological tissues

The main objective of this work was to develop an application capable of determining the diffusion times and diffusion coefficients of optical clearing agents and water inside a known type of muscle. Different types of chemical agents can also be used with the method implemented, such as medications or metabolic products. Since the diffusion times can be calculated, it is possible to describe the dehydration mechanism that occurs in the muscle. The calculation of the diffusion time of an optical clearing agent allows to characterize the refractive index matching mechanism of optical clearing. By using both the diffusion times and diffusion of water and clearing agents not only the optical clearing mechanisms are characterized, but also information about optical clearing effect duration and magnitude is obtained. Such information is crucial to plan a clinical intervention in cooperation with optical clearing. The experimental method and equations implemented in the developed application are described in throughout this document, demonstrating its effectiveness. The application was developed in MATLAB code, but the method was personalized so it better fits the application needs. This process significantly improved the processing efficiency, reduced the time to obtain he results, multiple validations prevents common errors and some extra functionalities were added such as saving application progress or export information in different formats. Tests were made using glucose measurements in muscle. Some of the data, for testing purposes, was also intentionally changed in order to obtain different simulations and results from the application. The entire project was validated by comparing the calculated results with the ones found in literature, which are also described in this document.

Heart Transplantation in Patients Older than 65 Years: Worthwhile or Wastage of Organs?

BACKGROUND: Patients older than 65 years have traditionally not been considered candidates for heart transplantation. However, recent studies have shown similar survival. We evaluated immediate and medium-term results in patients older than 65 years compared with younger patients. METHODS: From November 2003 to December 2013, 258 patients underwent transplantation. Children and patients with other organ transplantations were excluded from this study. Recipients were divided into two groups: 45 patients (18%) aged 65 years and older (Group A) and 203 patients (81%) younger than 65 years (Group B). RESULTS: Patients differed in age (67.0 ± 2.2 vs. 51.5 ± 9.7 years), but gender (male 77.8 vs. 77.3%; p = 0.949) was similar. Patients in Group A had more cardiovascular risk factors and ischemic cardiomyopathy (60 vs. 33.5%; p < 0.001). Donors to Group A were older (38.5 ± 11.3 vs. 34.0 ± 11.0 years; p = 0.014). Hospital mortality was 0 vs. 5.9% (p = 0.095) and 1- and 5-year survival were 88.8 ± 4.7 versus 86.8 ± 2.4% and 81.5 ± 5.9 versus 77.2 ± 3.2%, respectively. Mean follow-up was 3.8 ± 2.7 versus 4.5 ± 3.1 years. Incidence of cellular/humoral rejection was similar, but incidence of cardiac allograft vasculopathy was higher (15.6 vs. 7.4%; p = 0.081). Incidence of diabetes de novo was similar (p = 0.632), but older patients had more serious infections in the 1st year (p = 0.018). CONCLUSION: Heart transplantation in selected older patients can be performed with survival similar to younger patients, hence should not be restricted arbitrarily. Incidence of infections, graft vascular disease, and malignancies can be reduced with a more personalized approach to immunosuppression. Allocation of donors to these patients does not appear to reduce the possibility of transplanting younger patients.

Proteomics in the assessment of the therapeutic response of antineoplastic drugs: strategies and practical applications

Uncovering mechanisms of unknown pathological mechanisms and body response to applied medication are the drive forces toward personalized medicine. In this post-genomic era, all eyes are tuned to proteomic field, searching for the answers and explanations by investigating the final physiological functional units – proteins and their proteoforms. Development of cutting-edge mass spectrometric technologies and powerful bioinformatics tools, allowed life-science community mining of disease-specific proteins as biomarkers, which are often hidden by high complexity of the samples and/or small abundance. Nowadays, there are several proteomics-based approaches to study the proteome. This chapter focuses on gold standard proteomics strategies and related issues towards candidate biomarker discovery, which may have diagnostic/prognostic as well as mechanistic utility.

DATA DRIVEN APPROACHES TO IDENTIFY DETERMINANTS OF HEART DISEASES AND CANCER RESISTANCE

Cancer and cardio-vascular diseases are the leading causes of death world-wide. Caused by systemic genetic and molecular disruptions in cells, these disorders are the manifestation of profound disturbance of normal cellular homeostasis. People suffering or at high risk for these disorders need early diagnosis and personalized therapeutic intervention. Successful implementation of such clinical measures can significantly improve global health. However, development of effective therapies is hindered by the challenges in identifying genetic and molecular determinants of the onset of diseases; and in cases where therapies already exist, the main challenge is to identify molecular determinants that drive resistance to the therapies. Due to the progress in sequencing technologies, the access to a large genome-wide biological data is now extended far beyond few experimental labs to the global research community. The unprecedented availability of the data has revolutionized the capabilities of computational researchers, enabling them to collaboratively address the long standing problems from many different perspectives. Likewise, this thesis tackles the two main public health related challenges using data driven approaches. Numerous association studies have been proposed to identify genomic variants that determine disease. However, their clinical utility remains limited due to their inability to distinguish causal variants from associated variants. In the presented thesis, we first propose a simple scheme that improves association studies in supervised fashion and has shown its applicability in identifying genomic regulatory variants associated with hypertension. Next, we propose a coupled Bayesian regression approach -- eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combinations of regulatory genomic variants that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance in samples, but also predicts gene expression more accurately than other methods. We demonstrate that eQTeL accurately detects causal regulatory SNPs by simulation, particularly those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal. The challenge of identifying molecular determinants of cancer resistance so far could only be dealt with labor intensive and costly experimental studies, and in case of experimental drugs such studies are infeasible. Here we take a fundamentally different data driven approach to understand the evolving landscape of emerging resistance. We introduce a novel class of genetic interactions termed synthetic rescues (SR) in cancer, which denotes a functional interaction between two genes where a change in the activity of one vulnerable gene (which may be a target of a cancer drug) is lethal, but subsequently altered activity of its partner rescuer gene restores cell viability. Next we describe a comprehensive computational framework --termed INCISOR-- for identifying SR underlying cancer resistance. Applying INCISOR to mine The Cancer Genome Atlas (TCGA), a large collection of cancer patient data, we identified the first pan-cancer SR networks, composed of interactions common to many cancer types. We experimentally test and validate a subset of these interactions involving the master regulator gene mTOR. We find that rescuer genes become increasingly activated as breast cancer progresses, testifying to pervasive ongoing rescue processes. We show that SRs can be utilized to successfully predict patients' survival and response to the majority of current cancer drugs, and importantly, for predicting the emergence of drug resistance from the initial tumor biopsy. Our analysis suggests a potential new strategy for enhancing the effectiveness of existing cancer therapies by targeting their rescuer genes to counteract resistance. The thesis provides statistical frameworks that can harness ever increasing high throughput genomic data to address challenges in determining the molecular underpinnings of hypertension, cardiovascular disease and cancer resistance. We discover novel molecular mechanistic insights that will advance the progress in early disease prevention and personalized therapeutics. Our analyses sheds light on the fundamental biological understanding of gene regulation and interaction, and opens up exciting avenues of translational applications in risk prediction and therapeutics.

IVIS: Search and visualization on heterogeneous information networks

We build a system to support search and visualization on heterogeneous information networks. We first build our system on a specialized heterogeneous information network: DBLP. The system aims to facilitate people, especially computer science researchers, toward a better understanding and user experience about academic information networks. Then we extend our system to the Web. Our results are much more intuitive and knowledgeable than the simple top-k blue links from traditional search engines, and bring more meaningful structural results with correlated entities. We also investigate the ranking algorithm, and we show that the personalized PageRank and proposed Hetero-personalized PageRank outperform the TF-IDF ranking or mixture of TF-IDF and authority ranking. Our work opens several directions for future research.

Principais fatores motivacionais na compra de livros de ficção

O presente projeto de Mestrado assenta no estudo dos principais fatores motivacionais na compra de livros de ficção, aprofundando a dicotomia uso próprio versus oferta, englobando duas perspetivas complementares: a sustentação bibliográfica e uma pesquisa quantitativa que foi concretizada através da distribuição de um inquérito por questionário, em formato digital, que permitiu obter 487 respostas válidas e completas. Os dados obtidos foram tratados no SPSS (Statistical Package for the Social Sciences), utilizando análises descritivas, que foram complementadas com técnicas de inferência estatística. Os resultados permitiram concluir que, apesar de homens e mulheres oferecerem um número idêntico de livros, as mulheres compram e leem mais. Além disso, cerca de um terço dos livros comprados destina-se a oferta. Ficou ainda provado que a interação com os autores nas redes sociais apresenta uma associação positiva com a importância atribuida a esse fator na hora de comprar o(s) livro(s). As mulheres são mais sensíveis às “Políticas de Marketing” (Atendimento Personalizado; Facilidade de Pagamento; Local com Cartão de Fidelização; Preços e Descontos; Elevada quantidade de livros), enquanto critérios de seleção do local de compra. Importa referir que a compra é concretizada de forma menos impulsiva quando o livro é para oferta. Os atributos da capa mais relevantes no processo de decisão de compra são o “Título” e a “Sinopse”. Contudo, os diferentes fatores da capa estudados não apresentam diferenças significativas na compra para uso próprio comparativamente à oferta, no que respeita à sua importância. “Autores com prémios literários” e “Nobel” e “Ser uma novidade” (livro) são outros fatores (não da capa) que apresentam diferenças significativas favoráveis à compra para oferta. Por outro lado, “Recomendação de Amigos e Familiares” e “Presença/Ligação/Interação com o Autor nas redes sociais” são mais favoráveis à compra para uso próprio. As conclusões são relevantes para enquadrar as preferências dos consumidores, possibilitando aos profissionais do marketing algumas referências para definir estratégias que respondam às suas necessidades e ambições. Além disso, ao explorar a dimensão dos submercados (Uso próprio versus Oferta), será possível ir ao encontro das exigências dos compradores, tornando a subjetividade da aquisição/utilização numa ferramenta eficaz da própria idealização e personalização dos livros, permitindo conceber e implementar ideias empreendedoras que visem estimular a compra e, consequentemente, melhorar os hábitos de leitura de todos os portugueses.

Formulation and characterization of a 0.1% rapamycin cream for the treatment of Tuberous Sclerosis Complex-related angiofibromas

International audience

An ecosystem of products and systems for ambient intelligence – the AAL4ALL users perspective

Texto completo em atas de encontros científicos internacionais com arbitragem