Improving the early diagnostic of prostate cancer by multiple biomarker detection with new biosensing devices

Prostate cancer (PCa) is the most common form of cancer in men, in Europe (World Health Organization data). The most recent statistics, in Portuguese territory, confirm this scenario, which states that about 50% of Portuguese men may suffer from prostate cancer and 15% of these will die from this condition. Its early detection is therefore fundamental. This is currently being done by Prostate Specific Antigen (PSA) screening in urine but false positive and negative results are quite often obtained and many patients are sent to unnecessary biopsy procedures. This early detection protocol may be improved, by the development of point-of-care cancer detection devices, not only to PSA but also to other biomarkers recently identified. Thus, the present work aims to screen several biomarkers in cultured human prostate cell lines, serum and urine samples, developing low cost sensors based on new synthetic biomaterials. Biomarkers considered in this study are the following: prostate specific antigen (PSA), annexin A3 (ANXA3), microseminoprotein-beta (MSMB) and sarcosine (SAR). The biomarker recognition may occurs by means of molecularly imprinted polymers (MIP), which are a kind of plastic antibodies, and enzymatic approaches. The growth of a rigid polymer, chemically stable, using the biomarker as a template allows the synthesis of the plastic antibody. MIPs show high sensitivity/selectivity and present much longer stability and much lower price than natural antibodies. This nanostructured material was prepared on a carbon solid. The interaction between the biomarker and the sensing-material produces electrical signals generating quantitative or semi-quantitative data. These devices allow inexpensive and portable detection in point-of-care testing.

Contribution to drug discovery and development for tauopathies using yeast as a model

This work aimed to contribute to drug discovery and development (DDD) for tauopathies, while expanding our knowledge on this group of neurodegenerative disorders, including Alzheimer’s disease (AD). Using yeast, a recognized model for neurodegeneration studies, useful models were produced for the study of tau interaction with beta-amyloid (Aβ), both AD hallmark proteins. The characterization of these models suggests that these proteins co-localize and that Aβ1-42, which is toxic to yeast, is involved in tau40 phosphorylation (Ser396/404) via the GSK-3β yeast orthologue, whereas tau seems to facilitate Aβ1-42 oligomerization. The mapping of tau’s interactome in yeast, achieved with a tau toxicity enhancer screen using the yeast deletion collection, provided a novel framework, composed of 31 genes, to identify new mechanisms associated with tau pathology, as well as to identify new drug targets or biomarkers. This genomic screen also allowed to select the yeast strain mir1Δ-tau40 for development of a new GPSD2TM drug discovery screening system. A library of unique 138 marine bacteria extracts, obtained from the Mid-Atlantic Ridge hydrothermal vents, was screened with mir1Δ-tau40. Three extracts were identified as suppressors of tau toxicity and constitute good starting points for DDD programs. mir1Δ strain was sensitive to tau toxicity, relating tau pathology with mitochondrial function. SLC25A3, the human homologue of MIR1, codes for the mitochondrial phosphate carrier protein (PiC). Resorting to iRNA, SLC25A3 expression was silenced in human neuroglioma cells, as a first step towards the engineering of a neural model for replicating the results obtained in yeast. This model is essential to understand the mechanisms of tau toxicity at the mitochondrial level and to validate PiC as a relevant drug target. The set of DDD tools here presented will foster the development of innovative and efficacious therapies, urgently needed to cope with tau-related disorders of high human and social-economic impact.

A utilização de indicadores biológicos em saúde ocupacional

RESUMO - A utilização de indicadores biológicos em programas de prevenção dos efeitos decorrentes da exposição profissional a agentes químicos vem, cada vez mais, a ser objecto da investigação científica, no sentido de proporcionar mais e melhores instrumentos de efectiva vigilância da saúde dos trabalhadores expostos. Tendo em conta as mais recentes reflexões a este propósito, os autores apresentam uma revisão conceptual no que diz respeito à monitorização biológica e às suas pertinência, vantagens e limitações, concluindo pela necessidade de tais programas preverem, sempre que disponíveis e de acordo com fundamentos científicos e técnicos validados, um mais frequente recurso aos indicadores biológicos designadamente de dose e de efeito.

Predição precoce de risco de Pré-eclâmpsia na doença hipertensiva e diabetes na gravidez: Papel de fatores angiogénicos

RESUMO: A pré-eclâmpsia tem elevada morbi-mortalidade materna e perinatal. A sua etiologia multi-fatorial tem sido objeto de investigação, não sendo ainda totalmente conhecida. Não se conhece também a razão da diferente suscetibilidade individual e das diferentes expressões da doença. A hipertensão crónica e a diabetes são fatores de risco reconhecidos, e o adiamento da maternidade contribui para que estas duas patologias sejam atualmente mais prevalentes entre as mulheres grávidas. Uma vez que o seu quadro fisiopatológico precede em meses o quadro clínico, tem-se investigado a possibilidade de serem encontrados marcadores precoces e indicadores de risco. Em Portugal, os estudos relativos à hipertensão na gravidez são escassos, bem como a investigação sobre fatores de risco e marcadores para a mesma. No sentido de avaliar possíveis marcadores de risco para o desenvolvimento de préeclâmpsia ou complicações hipertensivas foi colhida, para esta dissertação, uma amostra de 1215 mulheres que frequentaram a consulta de Hipertensão ou de Diabetes na gravidez de um centro terciário, entre 2004 e 2013. Optou-se pela realização de três estudos independentes, abrangendo os dois primeiros um leque temporal de 9 e de 2 anos respetivamente. O primeiro, centrado na hipertensão, pesquisou, em 521 mulheres com hipertensão na presente ou em anterior gravidez, fatores de risco capazes de influenciar a progressão para pré-eclâmpsia. O segundo, direcionado para a diabetes gestacional, considerou uma amostra de 334 grávidas, parte das quais tinha também hipertensão crónica e procurou identificar fatores que contribuíram para o aparecimento de complicações hipertensivas. O terceiro estudo, realizado em 2012 e 2013, em três coortes de grávidas com hipertensão crónica, com diabetes gestacional, e sem estas patologias - procurou avaliar no 1º trimestre o comportamento de dois marcadores placentares obtidos no 1º trimestre - proteína plasmática A associada à gravidez (PAPP-A) e o fator de crescimento placentar (PlGF) - e o seu papel, quer como bio-marcadores isolados, quer em associação aos fatores de risco encontrados nos anteriores estudos, na construção de um modelo preditivo de préeclâmpsia. No primeiro estudo, a nuliparidade, a hipertensão gestacional, a fluxometria das artérias uterinas com IP superiores ao P95 entre as 20-22 semanas e a existência de restrição de crescimento fetal, foram os fatores que contribuíram para a construção de um modelo preditivo de pré-eclâmpsia. No segundo estudo, a coexistência de diabetes e hipertensão crónica agravou o prognóstico, associando-se as complicações hipertensivas à multiparidade, obesidade, idade materna e etnia negra. No terceiro estudo verificou-se uma redução da PlGf e da PAPP-A no 1º trimestre nas duas primeiras coortes, comparativamente à coorte sem patologia; na análise separada de cada coorte, quando se verificaram complicações hipertensivas ou pré-eclâmpsia, as concentrações de PlGf e PAPP-A também foram inferiores. Contudo, na elaboração de um modelo preditivo de pré-eclâmpsia, em conjunto com marcadores encontrados, apenas a PlGf pode ser integrada no modelo preditivo, o que se verificou na coorte com hipertensão crónica. Os marcadores bioquímicos em estudo tiveram valores inferiores nas coortes com patologia hipertensiva, demonstrando uma deficiente produção destas proteínas placentares nestas situações, podendo ser importante a sua pesquisa. Contudo, neste estudo, apenas na coorte de hipertensão crónica a PlGf teve participação como fator de risco, na construção de um modelo preditivo de pré-eclâmpsia.--------------------------------------------------------------------------------------------------ABSTRACT: Preeclampsia is associated with a great maternal and perinatal morbimortality. Its multifactorial etiology has been under investigation and is still insufficiently understood. The reason why there are differences in individual susceptibility and differences in expressions of the disease is still unknown. Chronic hypertension and diabetes are known risk factors for preeclampsia and maternity delay contributes to the great prevalence of these pathologies among pregnant women. As the physiopathological signs antedate by months the clinical course of the disease, early risk factors and biological markers are object of clinical research. In Portugal, scarce clinical studies were devoted to hypertension in pregnancy and to risk factors and markers of this pathology. This dissertation inquires 1215 pregnant women who were treated for hypertension or diabetes in a tertiary care center between 2004 and 2013, in order to find risk markers for hypertensive complications or preeclampsia. We conducted three independent studies for this purpose. In the first one we investigated which risk factors could influence the progression to preeclampsia in 521 pregnant women with present or past history of hypertension. The second one was conducted to find what factors were associated to hypertensive complications, with a sample of 334 pregnant women with gestational diabetes, some also with chronic hypertension, addressing the identification of the factors contributing to hypertensive complications. The third study was conducted between 2012 and 2013 with three cohorts of pregnant women, with chronic hypertension, gestational diabetes, and in the third one, pregnant women had a low risk pregnancy. The objective of the study was to evaluate the behavior of two placental markers – PAPP-A and PlGf – obtained in the first trimester, and the role of these markers as isolated biomarkers or in association with other risk factors, in order to define a predictive model of early preeclampsia. In the first study, nuliparity, gestational hypertension, uterine arteries doppler with PI above P95 between 20-22 weeks of gestation and the presence of fetal growth restriction were the markers involved in a predictive model for preeclampsia. In the second study the cohort with the coexistence of diabetes and hypertension had registered worse result and hypertensive complications were associated to multiparity, obesity, maternal age and black ethnicity. In the third study there was a reduction of the PlGf and a PAPP-A concentration for the first trimester in the two first cohorts comparatively to the low risk cohort; the separate analysis of each cohort showed that plGf and PAPP-A concentrations were reduced when hypertensive complications appeared. However, when trying to find a preeclampsia predictive model, only plGf gave significant results for being considered in the model and this was only possible in the chronic hypertension cohort. The biochemical markers investigated in this study were reduced in the cohorts when high blood pressure complications occurred, showing a defective production of these placenta proteins, and suggesting that they should be investigated as first trimester biomarkers. Nevertheless, for this research, in the cohort of chronic hypertension only PlGf had a significant result, when multivariate analysis of all the risk factors was considered for the construction of a preeclampsia predictive model.

A soft computing approach to kidney diseases evaluation

Kidney renal failure means that one’s kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis.The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

An assessment of chronic kidney diseases

Kidney renal failure means that one’s kidney have unexpectedlystoppedfunctioning,i.e.,oncechronicdiseaseis exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapiddeteriorationoftherenalfunction,butisoftenreversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis. The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow onetoconsiderincomplete,unknown,and evencontradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

Biochemical and physical surface functionalization of polycaprolactone as a key mediator of osteogenic differentiation and vascularized tissue morphogenesis

Tese de Doutoramento em Engenharia de Tecidos, Medicina Regenerativa e Células Estaminais.

Nonparametric estimation of time-dependent ROC curves conditional on a continuous covariate

The receiver-operating characteristic (ROC) curve is the most widely used measure for evaluating the performance of a diagnostic biomarker when predicting a binary disease outcome. The ROC curve displays the true positive rate (or sensitivity) and the false positive rate (or 1-specificity) for different cut-off values used to classify an individual as healthy or diseased. In time-to-event studies, however, the disease status (e.g. death or alive) of an individual is not a fixed characteristic, and it varies along the study. In such cases, when evaluating the performance of the biomarker, several issues should be taken into account: first, the time-dependent nature of the disease status; and second, the presence of incomplete data (e.g. censored data typically present in survival studies). Accordingly, to assess the discrimination power of continuous biomarkers for time-dependent disease outcomes, time-dependent extensions of true positive rate, false positive rate, and ROC curve have been recently proposed. In this work, we present new nonparametric estimators of the cumulative/dynamic time-dependent ROC curve that allow accounting for the possible modifying effect of current or past covariate measures on the discriminatory power of the biomarker. The proposed estimators can accommodate right-censored data, as well as covariate-dependent censoring. The behavior of the estimators proposed in this study will be explored through simulations and illustrated using data from a cohort of patients who suffered from acute coronary syndrome.

A VEP study in sleeping and awake one-month-old infants and its relation with social behavior

With the present study we aimed to analyze the relationship between infants' behavior and their visual evoked-potential (VEPs) response. Specifically, we want to verify differences regarding the VEP response in sleeping and awake infants and if an association between VEP components, in both groups, with neurobehavioral outcome could be identified. To do so, thirty-two full-term and healthy infants, approximately 1-month of age, were assessed through a VEP unpatterned flashlight stimuli paradigm, offered in two different intensities, and were assessed using a neurobehavioral scale. However, only 18 infants have both assessments, and therefore, these is the total included in both analysis. Infants displayed a mature neurobehavioral outcome, expected for their age. We observed that P2 and N3 components were present in both sleeping and awake infants. Differences between intensities were found regarding the P2 amplitude, but only in awake infants. Regression analysis showed that N3 amplitude predicted an adequate social interactive and internal regulatory behavior in infants who were awake during the stimuli presentation. Taking into account that social orientation and regulatory behaviors are fundamental keys for social-like behavior in 1-month-old infants, this study provides an important approach for assessing physiological biomarkers (VEPs) and its relation with social behavior, very early in postnatal development. Moreover, we evidence the importance of the infant's state when studying differences regarding visual threshold processing and its association with behavioral outcome.

Natural gaps associated with oxidative stress in Willisornis poecilinotus (Aves: Thamnophilidae) in a tropical forest

Natural disturbances in tropical forests modify the availability and quality of resources and alter the patterns of bird distribution. These environmental changes increase the metabolic rate and disrupt the redox balance promoting oxidative stress. This study aimed to compare the abundance of Willisornis poecilinotus between gaps and the understory of a forest with undisturbed canopy at Caxiuanã National Forest. The abundance was correlated with vegetation heights. The oxidative stress and the stress promoting factors were determined in both sites of sampling. We captured 81 specimens of W. poecilinotus. The number of captures was high in gaps. The specimens sampled at gaps showed high levels of oxidative stress. The biomarkers of oxidative stress were significantly correlated in gaps. The variability of oxidative stress and oxidative damage were explained only by site of sampling. These results suggest that gaps are stressors sites to W. poecilinotus, which probably can be due to an increase of metabolic rate to deal with new flight strategies of foraging and avoid predation

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Urothelial bladder cancer progression: lessons learned from the bench

Urothelial bladder carcinoma (UBC) is an intricate malignancy with a variable natural history and clinical behavior. Despite developments in diagnosis/prognosis refinement and treatment modalities, the recurrence rate is high, and progression from non-muscle to muscle invasive UBC commonly leads to metastasis. Moreover, patients with muscle-invasive or extra-vesical disease often fail the standard chemotherapy treatment, and overall survival rates are poor. Thus, UBC remains a challenge in the oncology field, representing an ideal candidate for research on biomarkers that could identify patients at increased risk of recurrence, progression, and chemo-refractoriness. However, progress toward personalized medicine has been hampered by the unique genetic complexity of UBC. Recent genome-wide expression and sequencing studies have brought new insights into its molecular features, pathogenesis and clinical diversity, revealing a landscape where classical pathology is intersected by the novel and heterogeneous molecular groups. Hence, it seems plausible to postulate that only an integrated signature of prognostic/predictive biomarkers inherent in different cancer hallmarks will reach clinical validation. In this review, we have summarized ours and others' research into novel putative biomarkers of progression and chemoresistance that encompass several hallmarks of cancer: tumor neovascularization, invasion and metastasis, and energy metabolism reprogramming of the tumor microenvironment.

O efeito da insulina na placentação: estudos in vitro com células HTR8-SVneo

Dissertação de mestrado em Bioquímica Aplicada

MMP-9/RECK imbalance: a mechanism associated with high-grade cervical lesions and genital infection by Human Papillomavirus (HPV) and Chlamydia trachomatis

"Manuscript"

Impact of mutated KRAS signalling on autophagy regulation in colorectal carcinoma

Programa Doutoral em Biologia Molecular e Ambiental