Percorrer por autor "Silva, Margarida"
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- Cyanobacteria for skin care and cosmeceutical formulationsPublication . Melo, Luísa; Morone, Janaína; Silva, Margarida; Ferraz, Ricardo; Martins, RosárioSkin acts as a barrier from external stimulus such as pollutants and ultraviolet radiation. Disturbs in the skin are involved in skin aging, which mostly results on a thinner epidermis, dryness, wrinkles, and loss of elasticity. Synthetic ingredients in cosmetics are known to be more toxic and to cause negative impact on the environment. In a society increasingly worried with human and environment health, natural skin care products gain special attention and boosted the search for natural sources. Due to the production of bioactive compounds, cyanobacteria emerged as an excellent source for cosmetic ingredients. Cyanobacteria strains of CIIMAR culture collection (LEGE-CC) have already proven to be interesting for cosmetic purposes [1,2]. In this project we aimed to evaluate the potential of four LEGE-CC cyanobacteria strains for skin care purposes. The cytotoxicity of acetonic and aqueous extracts was evaluated in keratinocytes (HaCAT), fibroblasts (3T3L1) and endothelial cells (hCMEC/D3) along with the total phenolic content and antioxidant properties. Cytotoxic assays revealed toxicity of acetonic extracts to keratinocytes in the higher concentrations tested. Aqueous extracts revealed low or non-cytotoxicity. Mainly in low concentrations of extracts, cellular proliferation was registered. Aqueous extracts from strain JM/RS021A and acetone extracts from strain JM/RS035B revealed higher values for total phenolic compounds. The acetone extracts of strain JM/RS021A showed the greatest scavenging activity values on the DPPH assay.
- 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.