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Repositório Institucional da Universidade NOVA de Lisboa
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Consensus on Applications and Emerging Needs for Continuous Glucose Monitoring in Portugal
Publication . Neves, João Sérgio; Teixeira, Sofia; Neves, Ângela Santos; Sampaio, Lurdes; Reis, Mónica; Paiva, Sandra; Mirante, Alice; Freitas, Paula; Nortadas, Rita; Melo, Miguel; Raposo, João Filipe; NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM); Karger
Abstract – Continuous glucose monitoring (CGM) has revolutionised diabetes management, transforming both clinical practice and the self-management of people with diabetes. By providing a more detailed understanding of individual glycaemic patterns, CGM supports more informed therapeutic decisions and sustained behavioural changes that promote improved clinical outcomes and quality of life. In light of new evidence and accumulated experience, it has become necessary to update the clinical indications and public health strategy for the use of CGM in Portugal. The recommendations focused on three priority areas: (1) the clear definition of target populations for CGM use, namely, all individuals with diabetes treated with any insulin regimen (including basal therapy), as well as those not treated with insulin but at increased risk of hypoglycemia and its consequences, based on eligibility criteria grounded in scientific evidence and clinical needs; (2) the strengthening of therapeutic education and health literacy, promoted by multidisciplinary teams trained to support self-management and the appropriate use of technology; and (3) ensuring the quality and integrated functioning of CGM systems with other medical devices, including insulin pumps and digital monitoring platforms, in order to guarantee interoperability, reliability, and data management security.
Insights on genomic profiles of drug resistance and virulence in a cohort of Leishmania infantum isolates from the Mediterranean area
Publication . Carrasco-Martin, Marina; Martí-Carreras, Joan; Gómez-Ponce, Marcel; Alcover, Maria Magdalena; Roura, Xavier; Ferrer, Lluís; Baneth, Gad; Bruno, Federica; Chicharro, Carmen; Cordeiro da Silva, Anabela; Cristovão, José; Di Muccio, Trentina; Maia, Carla; Moreno, Javier; Priego, Anabel; Roca-Geronès, Xavier; Santarem, Nuno; Soriano, Anna Vila; Vitale, Fabrizio; Yasur-Landau, Daniel; Francino, Olga; Instituto de Higiene e Medicina Tropical (IHMT); Global Health and Tropical Medicine (GHTM); Laboratório Associado de Translacção e Inovação para a Saúde Global - LA Real (Pólo FCT); Laboratório Associado de Translacção e Inovação para a Saúde Global - LA Real (Pólo IHMT); Vector borne diseases and pathogens (VBD); BioMed Central (BMC)
BACKGROUND: Drug-resistant strains of Leishmania infantum challenge the effectiveness of treatments for clinical leishmaniosis and may lead to more frequent relapses. Copy number variation (CNV) at specific genetic loci is associated with drug resistance and virulence, but information about its prevalence in endemic regions is limited. This study examines the drug resistance and virulence status of Leishmania strains in human and canine isolates from the Mediterranean region. METHODS: Forty-eight Leishmania infantum isolates were whole-genome sequenced with nanopore long reads, followed by de novo assembly. We analyzed chromosomal aneuploidies and gene copy number variation in loci linked to drug resistance and virulence in Leishmania, alongside the genomic structure and rearrangements responsible for these variations. RESULTS: Complete genomes were de novo assembled for 35 L. infantum isolates (22 from dogs and 13 from humans), revealing significant chromosomal variability. We assessed copy number variation for 22 potential biomarkers: 15 genes related to drug resistance to first-line drugs (METK for allopurinol; LdSMT for amphotericin B; AQP1 and H-locus for antimonials; LdMT, LdRos3, and MSL for miltefosine; and PPM for paramomycin) and 7 genes related to virulence (lipophosphoglycan and proteophosphoglycan biosynthesis, and the Lack protein). Drug-resistance biomarkers were identified in 80% of the isolates. Canine strains primarily showed resistance to allopurinol and antimonials, while human isolates exhibited a broader resistance spectrum, especially to antimonials and paromomycin. The co-occurrence of resistance biomarkers was common, especially for allopurinol and antimonial resistance. Distinct mechanisms underlie the observed copy number variations. Virulence-associated genes were less variable among isolates. CONCLUSIONS: The prevalence of drug-resistance biomarkers in Leishmania infantum strains from the Mediterranean region, as revealed by this study, underscores the critical need for routine resistance surveillance in managing clinical leishmaniosis. These findings not only inform current clinical practice but also pave the way for more effective management strategies in the future.
Building Research Competence Across a Nursing Program
Publication . Nunes, Lucília; Cerqueira, Andreia Ferreri; Poeira, Ana; NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM); Comprehensive Health Research Centre (CHRC) - pólo NMS; MDPI - Multidisciplinary Digital Publishing Institute
The organized integration of research competencies into nursing curricula is still a global challenge and is key for preparing professionals to respond to complex clinical contexts, technological advancements, and contemporary societal demands. At the School of Health of the Polytechnic Institute of Setúbal, a longitudinal research axis was implemented across the four years of the undergraduate nursing program, involving epistemological foundations, the research process, evidence-based practice, and applied practice. Objective: The objective of this study was to describe the design and implementation of the longitudinal axis of research, analyzing institutional indicators of academic success and the progressive development of students’ scientific competencies. Methods: A descriptive documentary study based on institutional data analysis (the number of enrolled students, pass rates, and mean grades in the four research-related curricular units) was conducted, complemented by a review of pedagogical materials produced (two published course booklets: “Research I—From the origin to the dissemination of knowledge” and “Research II—(De)Constructing the Research Process: A Critical and Practical Analysis”) and evidence of scientific dissemination (conference presentations and published articles). Results: A continuous progression in academic performance was observed across the research curricular units, accompanied by increased complexity of student work and enhanced scientific literacy. The sequential structure proved essential: the articulation of epistemology, methodology, critical appraisal, and scientific production demonstrated strong coherence and pedagogical efficiency. Conclusions: The longitudinal research axis constitutes a curricular innovation that strengthens essential scientific competencies in undergraduate nursing education. Longitudinal models that reflect both conceptual and practical progression can significantly contribute to the development of nurses who are critical thinkers, reflective practitioners, and capable of integrating evidence into clinical decision-making.
Protocol to isolate endosomal and small extracellular vesicles from cultured cells through ultracentrifugation
Publication . Domingues, Maria Nolasco; Palhinhas, Luís; Pereira, Paulo; Ferreira, João Vasco; NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM); iNOVA4Health - pólo NMS; Cell Press
Here, we present a protocol to isolate endosomal fractions using sucrose-density gradient ultracentrifugation and to recover small extracellular vesicles (sEVs), enriched in exosomes, using sequential ultracentrifugation from mammalian cell lines. This combined approach enables the separation and analysis of early endosome (EE), late endosome (LE), and sEV fractions. We provide detailed procedures for cell culture preparation, conditioned media collection, differential centrifugation, gradient layering, and fraction purification, facilitating downstream characterization and functional assays. For complete details on the use and execution of this protocol, please refer to Ferreira et al.
Autoencoder/RandomForest–TabPFN for cross-cancer metabolomics
Publication . Hauns, Sven; Pinto, Frederico G.; Khyriem, Costerwell; Singh, Ankita; Al-Sadi, Azzat; Yazeedi, Talal Al; Mohammad, Rasheed; Cisse, Babacar; Garrett, Timothy J.; Uddin, Mohammed; Soares, Nelson C.; Backofen, Rolf; Alkhnbashi, Omer S.; NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM); Comprehensive Health Research Centre (CHRC) - pólo NMS; Oxford University Press
Accurate and rapid disease diagnosis, particularly in prostate cancer (PC) and breast cancer (BC), is critical for early intervention and improved patient outcomes. Metabolomic signatures represent a robust molecular framework for elucidating cancer-associated biochemical reprogramming. The use of artificial intelligence (AI) in biology in recent years has become widespread and promising. This study introduces a novel predictive method that integrates an Autoencoder, random forest-based feature selection and Tabular Prior-data Fitted Network (TabPFN) to achieve high diagnostic accuracy from metabolomics data of prostate and BC patients. The datasets were acquired using paper spray ionization mass spectrometry and flow injection-traveling-wave ion mobility-mass spectrometry of individuals diagnosed with PC and BC. When leveraging metabolomic profiling data from two distinct sources, PC urine and serum samples, the proposed model achieved an accuracy up to 98.75% in distinguishing diseased from healthy conditions. Additionally, we employed a BC dataset containing metabolic and lipidomic signatures acquired from core needle biopsies using a miniature MS platform coupled with PSI to assess the fidelity of our implementation across distinct cancer types. Our results on a well-characterized targeted dataset show that we can effectively reduce high-dimensional data into latent feature representations. At the same time, TabPFN captures tumor progression-related changes and feature interaction, thereby enhancing the possibility that the model will be a highly potent and effective tool for stage-specific diagnostic precision. Most existing machine learning approaches for disease diagnosis primarily rely on imaging, genomics, or clinical parameters, often overlooking the critical role of metabolites in identifying disease-specific biochemical signatures. By integrating metabolite-specific data with a robust deep-learning approach, this study demonstrates the transformative potential of AI in metabolomics-based diagnostics. The proposed model offers scalability and versatility, with applications extending beyond oncology to a much broader disease profiling aspect. These findings emphasize the value of combining multi-source metabolomic data with deep learning to advance personalized medicine and enhance diagnostic efficiency in clinical practice.