Browsing by Author "Ferreira, Jorge"
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- ATILGP - Associação de Tradutores e Intérpretes de LGP - Uma História de Sucesso para a ComunicaçãoPublication . Barbosa, Susana; Macedo, Vera; Pereira, Guadalupe; Silva, Joana Filipa; Ferreira, Jorge; Loureiro, Rita; Borges, Francisca; Pedreira, Cláudia; Santos, Mónica; Fernandes, Paula Sofia; Barbosa, Sara; Branco, Susana; Oliveira, Ana; Almeida, Liliana; Sousa, Sara; Brito, Margarida de; Coelho, DinaUm dos propósitos deste livro é proporcionar uma viagem à origem da ATILGP - Associação de Tradutores e Intérpretes de Língua Gestual Portuguesa, contando e registando a sua história e a dos seus associados e, deste modo, contribuir para que as novas gerações de profissionais conheçam o passado e os feitos conquistados em prol da valorização e dignificação da profissão. A virtuosa contribuição dos trabalhos compilados neste livro representa, na sua essência, a forte comunidade de intérpretes que a ATILGP foi capaz de congregar ao longo destes 16 anos e cuja força associativa tem respaldo direto em todos os capítulos. Dá-se voz às questões profissionais, passando pelo processo de criação efetiva de uma associação, da construção de uma identidade coletiva e de uma imagem representativa dos seus valores e ambições, à luta incessante pela defesa por melhores condições de trabalho, por legislação atualizada e por uma sociedade cada vez mais inclusiva, destacando-se a importância de uma liderança sólida ao longo do tempo, que tem sido capaz de motivar os seus membros em torno de princípios e objetivos que unem a classe profissional, as boas práticas como fator determinante para o empoderamento e afirmação profissional, sem descurar o olhar sobre os valores éticos e deontológicos pelos quais a bússola associativa sempre se norteou.
- MicroRNA-375 plays a dual role in prostate carcinogenesisPublication . Costa-Pinheiro, Pedro; Ramalho-Carvalho, João; Quintela Vieira, Ana Filipa; Ferreira, Jorge; Oliveira, Jorge; Gonçalves, Céline S; Costa, Bruno M .; Henrique, Rui; Jeronimo, CarmenBackground: Prostate cancer (PCa), a highly incident and heterogeneous malignancy, mostly affects men from developed countries. Increased knowledge of the biological mechanisms underlying PCa onset and progression are critical for improved clinical management. MicroRNAs (miRNAs) deregulation is common in human cancers, and understanding how it impacts in PCa is of major importance. MiRNAs are mostly downregulated in cancer, although some are overexpressed, playing a critical role in tumor initiation and progression. We aimed to identify miRNAs overexpressed in PCa and subsequently determine its impact in tumorigenesis. Results: MicroRNA expression profiling in primary PCa and morphological normal prostate (MNPT) tissues identified 17 miRNAs significantly overexpressed in PCa. Expression of three miRNAs, not previously associated with PCa, was subsequently assessed in large independent sets of primary tumors, in which miR-182 and miR-375 were validated, but not miR-32. Significantly higher expression levels of miR-375 were depicted in patients with higher Gleason score and more advanced pathological stage, as well as with regional lymph nodes metastases. Forced expression of miR-375 in PC-3 cells, which display the lowest miR-375 levels among PCa cell lines, increased apoptosis and reduced invasion ability and cell viability. Intriguingly, in 22Rv1 cells, which displayed the highest miR-375 expression, knockdown experiments also attenuated the malignant phenotype. Gene ontology analysis implicated miR-375 in several key pathways deregulated in PCa, including cell cycle and cell differentiation. Moreover, CCND2 was identified as putative miR-375 target in PCa, confirmed by luciferase assay. Conclusions: A dual role for miR-375 in prostate cancer progression is suggested, highlighting the importance of cellular context on microRNA targeting.
- Modelling therapeutic response in asthmatic adults: a previous exploratory analysisPublication . Alves, Cristina; Faria, Brígida Mónica; Alves, Sandra Maria; Ferreira, Jorge; Faria, Brigida Monica; Alves, Sandra MariaAsthma is a respiratory disease characterized by chronic inflammation of the airways. Effective asthma management is essentially based on choosing the appropriate treatment for each individual (1). Data science and machine learning models offer valuable insights and enhance the outcomes achieved in asthma management (2). The main objective is to develop predictive models for therapy response in patients with asthma, and secondarily to identify clinical, functional and biological characteristics that influence this response. Data from fifty adults with asthma were analyzed, collecting information on anthropometric, clinical, functional, biological, therapeutic, occupational, and allergen exposure factors. The study followed the “Knowledge Discovery in Databases, KDD” methodology. The sample consisted of 50 asthmatic adult participants, aged between 21 and 81 years old mean age=54.02 (s=14.5), from which 20 (40%) were male and 30 (60%) were female. The analysis of the characteristic symptoms of asthma (dyspnea, cough, wheezing and chest tightness), reveals a statistically significant improvement (p<0.001) of all these symptoms after the treatment. The asthma control test, the life quality questionnaire and the asthma and allergic rhinitis control test evaluated before and after treatments, demonstrate a statistically significant difference (p=0.023, p <0.001 and p<0.001, respectively). On respiratory function, only FVC reveals a significant difference (p=0.409), after treatment. However, the average did not reach the minimal important difference (MID) of 200ml. The average number of exacerbations and SU recurrences difference was also significant in both cases (p<0.01), reaching MID (>50%). The majority of the individuals in this group had a positive, clinically important response to treatment. This result may be because they have severe atopic asthma, and Th2-High endotype, and for that reason they are undergoing more differentiated treatments, such as biological treatments.