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- Book of Abstracts of the 8th Meeting on Medicinal BiotechnologyPublication . Pereira, CassildaO 8º Encontro sobre Biotecnologia Medicinal, é um evento obrigatório para quem se interessa por inovação em saúde e biotecnologia. O encontro reúne pesquisadores, estudantes e profissionais, destacando avanços de última geração e apoiando a colaboração, o networking e o intercâmbio de ideias. Apresentando palestras plenárias inspiradoras de Cláudia Serra (CIIMAR) e Miguel Garrido Costa (Colorifix), ao lado de apresentações orais e de pôsteres, este evento dinâmico da tarde oferece uma oportunidade de se conectar com especialistas e se envolver com os mais recentes avanços científicos em biotecnologia medicinal.
- Biotechnological potential of Macroalgae associated ActinomycetotaPublication . Moreira, Gabriela Alves; Girão, Mariana; Eusébio, Nádia; Mendes, Marta V.; Carvalho, Maria de Fátima; Alves Moreira, GabrielaActinomycetota are Gram-positive bacteria widely distributed across diverse environments, from ter-restrial to marine ecosystems, where they may occur as free-living organisms or in association with other organisms. This phylum is well-known for its remark-able biotechnological potential, with several members producing bioactive compounds such as antimicrobial and anticancer agents. However, macroalgae-associ-ated Actinomycetota remain underexplored despite having a metabolic potential comparable to free-living organisms.The main objective of this study was to explore the biosynthetic potential of 15 Actinomycetota isolates obtained from two marine macroalgae from the Por-tuguese coast, Codium tomentosum and Chondrus cris-pus, through the analysis of their genomes.Total DNA was sequenced using Illumina technology and 17 de novo genomes were assembled. Phyloge-nomic analysis identified 5 different genera, Actinoal-loteichus, Cellulosimicrobium, Kocuria, Nocardia and Streptomyces, with 3 putative novel species.Genome mining, using manually curated antiSMASH data, identified 452 biosynthetic gene clusters (BGCs) across 21 metabolite classes. Among the BGCs iden-tified are antimicrobial and anticancer compounds, siderophores and conserved metabolites. More than 60% of these BGCs have none or low similarity to pre-viously known compounds, which can be an indicator of novelty. Compared with Streptomyces, one of the most studied genera, non-Streptomyces genera exhib-ited a higher novelty percentage (ca. 80%).Additionally, BiG-SCAPE and clinker were used to visualize the data and assess the similarity between BGCs for the same compound in different strains.Overall, these results confirm that macroalgae-as-sociated Actinomycetota represent a valuable and underexplored source of new species and biosynthet-ic diversity, supporting further study of this bacterial group.
- Bioactive contact lens incorporating umbilical cord-derived Mesenchymal Stem Cell Secretome for corneal regenerationPublication . Coutinho, Ana; Sousa, Ana Catarina; Maurício, Ana ColetteCorneal injuries and degenerative corneal disorders remain a significant clinical challenge, as impaired tis-sue regeneration can lead to corneal opacity, neovas-cularization, and progressive vision loss [1,2]. Current therapeutic approaches, including topical eye drops and corneal transplantation, are limited by poor bio-availability, insufficient regenerative potential, and the risk of immune rejection [3]. Therefore, there is a pressing need for innovative, non-invasive strategies capable of restoring corneal function. In this context, conditioned medium derived from umbilical cord mes-enchymal stem cells (UC-MSCs), rich in bioactive fac-tors (secretome), has shown promising regenerative and immunomodulatory effects in corneal repair. This study aims to develop a bioactive contact lens as a non-invasive therapeutic platform for corneal regener-ation. The system is based on a hydrogel composed of polyvinyl alcohol (PVA), hyaluronic acid (HA), and UC-MSC-derived conditioned medium. Prior to incorpo-ration, the secretome will be characterized to identify key biomarkers associated with immunomodulation and tissue regeneration and validated in vitro through cytocompatibility and cell migration (scratch) assays. The bioactive contact lenses will be fabricated using mold-based techniques to ensure reproducible geom-etry and structural consistency. Within the hydrogel matrix, PVA provides mechanical stability and flexi-bility, while HA enhances hydration, biocompatibility, and mimics native corneal properties, contributing to comfort and biological performance. This platform enables sustained and localized delivery of bioactive factors directly to the injured corneal surface. The re-sulting hydrogel will undergo physicochemical charac-terization using Fourier Transform Infrared Spectros-copy (FTIR) and Scanning Electron Microscopy (SEM). Biological performance will be evaluated in vitro using corneal epithelial cells under conditions that mimic the ocular environment. Overall, this work proposes a promising cell-free regenerative strategy for corneal wound healing, with the potential to enhance epitheli-al repair, reduce fibrosis, and improve visual outcomes.
- Evaluation of deep learning models for Aptamer Modelling in SilicoPublication . Dias, Félix; Martins, Fábio; Sousa, Sérgio F.Aptamers are single-stranded DNA or RNA oligo-nucleotides that fold into complex three-dimensional structures to bind molecular targets with high affinity and specificity (1). Often termed “chemical antibodies,” these biomaterials are vital for diverse applications in therapeutics, diagnostics, bioassays, and in vitro and in vivo imaging (2). Historically, aptamer discovery has relied on the Systematic Evolution of Ligands by EXponential Enrichment (SELEX), an in vitro process involving iterative selection, amplification and enrich-ment rounds. However, SELEX is time-consuming and often results in low success rates (3). Consequently, an in silico approach is essential to streamline the discov-ery process, reduce experimental costs, and provide insights into aptamer-target interactions. Recently, various deep learning models capable of predicting the 3d structure of aptamer-target complexes have become available. In this work, we evaluated gener-al biomolecular prediction models, like Alphafold-3, Protenix-v2, Chai-1, Boltz-2, Openfold-3, RoseTTA-Fold2NA, and two specialized aptamer modelling tools, AptaTrans and AptaBLE. Among the evaluated models, AlphaFold-3 stands as the current state-of-the-art general model for aptamer modelling, with Protenix-v2, Chai-1, Boltz-2 and RoseTTAFold2NA, accuracies falling short of AlphaFold-3. The only mod-el that rivals Alphafold-3 in aptamer-target bench-marks is OpenFold-3. On the other hand, specialized tools such as AptaTrans and AptaBLE, significantly outperform AlphaFold3 on validated DNA and RNA aptamers, which can be attributed to their optimiza-tion for sequence-based patterns. Overall, this work highlights the importance of an in silico approach for aptamer discovery and evaluates deep learning mod-els capable of predicting the 3D structure of aptam-er-target complexes. While general models provide a robust baseline for aptamer-target complex structure prediction, specialized tools offer enhanced perfor-mance for aptamer discovery. Future work will include a systematic comparison of the evaluated models.
- Blood group influence in umbilical cord blood immune cell profilePublication . Pina, Joana; Oliveira, Melissa; Ferraz, Ricardo; Gomes, Andreia; Ferraz, RicardoStem cells, characterised by their remarkable re-generative potential and ability to differentiate into different cell types, hold immense promise in thera-peutic applications. The umbilical cord is one source of stem cells which is easy, safe, and non-invasive for the mother and infant. BebéVida, a cryopreservation laboratory in Porto, served as the pivotal setting for this study. Beyond the therapeutic potential of stem cells, this study focuses on the intricate relationship between blood groups and the immune system. Vari-ations in immune cell numbers across different blood groups suggest a potential influence of ABO antigens on early immune cell composition. The human immune system undergoes a remarkable journey from embry-onic stages to infancy, marked by a gradual transition from reliance on innate immunity, which is genetically encoded, to the development of adaptive responses. ABO blood group antigens, determined by genetic in-heritance, further shape immune characteristics, with antibodies passed from the mother providing tempo-rary protection until the infant’s immune system ma-tures. This intricate interplay between genetic factors, maternal influence, and blood group genetics lays the foundation for lifelong immune health and disease susceptibility. As it has been demonstrated in differ-ent studies, there are potential links between ABO blood groups and cancer, cardiovascular, infectious (bacteria, viruses, and parasites), metabolic, and aller-gic diseases, and that is why this study was conducted, to understand the influence of blood groups on the umbilical cord blood immune cell profile. The parame-ters analysed were the nucleated cells, WBC, neutro-phils, monocytes, lymphocytes, NRBC, and platelets. In fact, this study underscores the dual significance of umbilical cord stem cells in therapeutics and the intri-cate interplay between blood groups and immune sys-tem dynamics. By elucidating these relationships, we can advance personalized medicine approaches and improve our understanding of disease predisposition across diverse population.
- Mobile application for cardiovascular risk assessmentPublication . Teixeira, Pedro; Miranda, Leonor; Pinto, Mário; Miranda, LeonorCardiovascular risk assessment plays a crucial role in the primary prevention of cardiovascular diseases, which are one of the leading causes of global morbid-ity and mortality. Modern society has benefited from portable and accessible means of assessing cardiovas-cular risk through contemporary technological trends. This effort is aligned with the scientific community’s mission to reduce disability and mortality associated with cardiovascular pathologies, such as myocardi-al infarction or stroke, widely recognized in scientif-ic literature. The development and incorporation of cardiovascular risk assessment tools can be consid-ered accessible and effective strategies, both in the primary and secondary prevention of these diseases in the context of today’s highly technological society, and can also contribute to a significant advance, sup-ported by scientific evidence. This paper describes all phases of project development, covering the entire life cycle of a Web product development: project plan-ning, state-of-the-art study, technical design process, prototyping and application development (methods and techniques, software implementation). This appli-cation may contribute to reducing cardiovascular risk and to the promotion of cardiovascular health literacy.
- Optimization of SPE-HPLC-FLD methodologies for the extraction and detection of steroid hormones in plasmaPublication . Bracchi, Isabella; Paíga, Paula; Delerue-Matos, Cristina; Pestana, Diogo; Negrão, Rita; Keating, Elisa; Fernandes, VirgíniaThe quantification of steroid hormones, such as es-tradiol and ethinylestradiol, in plasma requires sensi-tive and reliable analytical methodologies1. This study presents the optimization of a bioanalytical method based on Solid-Phase Extraction (SPE) coupled with High-Performance Liquid Chromatography with Flu-orescence Detection (HPLC-FLD). For sample ex-traction, Strata-X (polymeric reversed-phase) cartridg-es were used, where critical SPE parameters, including cartridge conditioning, matrix washing, and analyte elution, were evaluated. Chromatographic separation was achieved on a C18 stationary phase using a mobile phase of acetonitrile and 0.1% formic acid in ultrapure water. Both isocratic and gradient elution programs were tested to maximize resolution and peak shape. The method was validated for linearity, recovery, and precision (intra- and inter-day), meeting all established criteria for bioanalytical applications. This combined optimization resulted in improved selectivity and sen-sitivity for the determination of estradiol and ethinyl-estradiol in plasma (Figure 1), demonstrating a robust bioanalytical tool for monitoring these compounds in complex biological matrices.
- Optimizing melanoma screening: AI-based risk stratification for efficient patient prioritizationPublication . Silva, Margarida; Prudêncio, Cristina; Coelho, Pedro; Coelho, PedroMelanoma is a malignant neoplasm derived from melanocytes that is highly curable when detected early. However the 5-year survival rate drops from 99% to 35% when diagnosed at advanced stages [1,2]. In Portugal, constraints in the public health care system (SNS) waiting lists may compromise this critical treatment window [3]. This word presents the development of an artificial intel-ligence (AI)-powered clinical decision support system designed to assist dermatologists by pre-classifying melanoma risk in dermoscopic images obtained in primary healthcare settings, while optimizing patient prioritization. The proposed image classification methodology employs a two-stage deep learning computer vision pipeline implemented in Python using TensorFlow and trained on the publicly available ISIC 2020 and HAM10000 skin lesion datasets. First, skin lesions are identified, isolated, and segmented using a U-Net architecture. Subsequently, the seg-mented lesion masks are classified using an Efficient-Net-based model to estimate malignancy probability. The trained models were integrated into a real-time web application developed using the Reflex frame-work. Preliminary results demonstrate that the U-Net architecture effectively segments lesions, reducing the influence of irrelevant artifacts during classification. Following segmentation, the Efficient-Net classifier achieved an accuracy of 98.67% and an area under the receiver operating characteristic curve (AUC) of 99.8% on a test set comprising 4,500 der-moscopic images. Importantly for clinical screening applications, the model achieved a sensitivity (recall) of 0.99 for malignant lesions, corresponding to only 17 false negatives among 2,250 malignant cases. The proposed two-stage deep-learning pipeline shows potential as a clinical support tool for dermatologists. By providing automated and interpre-table AI-based risk assessments, the system may as-sist physicians in prioritizing urgent cases with greater confidence. As a proof-of-concept, this tool has the potential to augment clinical workflows and help re-duce diagnostic delays, particularly within the public healthcare system.
- Pharmacogenomics and substance use disorders in Portugal: bridging the gap between scientific evidence and drug labellingPublication . Torres, Ana; Ribeiro, Ana; António, Jéssica; Santos, Marlene; Santos, MarleneSubstance use represent a major global public health burden requiring pharmacological interventions grounded in robust scientific evidence (1). Pharmacogenomics offers a critical framework for personalised medicine by tailoring therapies to indi-vidual genetic profiles an approach particularly rele-vant in addiction treatment (2). To evaluate the degree of alignment between current pharmacogenomic evidence and regulatory documents, namely Summaries of Product Characteristics (SPCs) and Patient Information Leaflets (PILs), for drugs classified under the WHO N07B group and bupropion (N06AX). A structured review was conducted for drugs spanning ATC subgroups: N07BA (varenicline, cytisinicline, nicotine); N07BB (disulfiram, naltrexone, nalmefene); N07BC (methadone, buprenorphine, buprenorphine/naloxone); and bupropion (N06AX) (3). SPCs and PILs were retrieved from Infomed (4). Pharmacogenomic evidence was systematically sourced from CPIC guidelines and PharmGKB annotations (5). For each drug, the presence, accuracy, and completeness of pharmacogenomic information in regulatory documents were assessed against these reference databases, with particular focus on clinically actionable genedrug interactions involving variants such as CYP2D6, ADH1B, ALDH2, and CHRNA5. Genetic variants including CYP2D6, ADH1B, ALDH2, and CHRNA5 are established modulators of drug metabolism and dependence susceptibility across this therapeutic class. Nevertheless, SPCs and PILs for the ma-jority of reviewed drugs contain absent or outdated pharmacogenomic information, with poor alignment to current CPIC and PharmGKB recommendations. Better incorporation of validated genetic data into SPCs and PILs would strengthen clinical decision making, and support treatment personalisation, in the management of substance use disorders.
- Fungal-mediated biodegradation of microplastics: Evaluation of degradative potential and pathogenic traitsPublication . Barbosa, Joel; Fernandes, Virgínia Cruz; Rufo, João; Vieira, Mónica; Prudêncio, Cristina; Pereira, Clara; Fernandes, Virgínia; Cavaleiro Rufo, João; Almeida Vieira, Mónica Andreia; Prudêncio, CristinaThe increasing accumulation of plastics and micro-plastics in the environment represents a major global concern due to their persistence, widespread distribu-tion, and potential impact on ecosystems and human health1. Microbial biodegradation has emerged as a promising and sustainable strategy for plastic reme-diation, particularly through the use of fungi with en-zymatic degradative capacity. This project aims to test the biodegradative potential of different fungal spe-cies against the widely used microplastics Polypropyl-ene, Polystyrene and Poly(ehylene-co-cinyl acetate), under optimized growth conditions. The fungi, Mucor spp, Rhizopus spp, Scedosporium spp. and Nigrospora spp. were specifically selected because they have nev-er previously been investigated as plastic degraders, thereby offering the opportunity to identify novel bio-degradation capabilities. Aspergillus flavus will also be included as a control, since this species has recognized degradative capacity against some types of microplas-tics2.To identify growth conditions that maximize bio-degradation, preliminary studies using Scedosporiumspp. were performed under different glucose concen-trations and medium replacement intervals. Low-glu-cose media combined with fortnightly medium renew-al promoted the highest degradative activity and were selected for the subsequent biodegradation assays. Biodegradation will be assessed through microplastic mass loss determination, Fourier-transform infrared spectroscopy (FTIR), and Raman spectroscopy, allow-ing the identification of structural and chemical mod-ifications on microplastic surfaces. Fungal adherence to microplastics will be evaluated by microscopic ob-servation and surface colonization analysis, providing insight into biofilm formation and the role of fungal activity in the degradation process. Additionally, fun-gal pathogenicity-related traits, including biofilm for-mation and antifungal susceptibility, will be evaluated to determine whether long-term exposure to micro-plastics influences fungal virulence. This study may support the development of sustainable plastic bio-degradation strategies and improve understanding of fungal adaptation to plastic-rich environments.
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