Browsing by Issue Date, starting with "2025-04-21"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- Endothelial dysfunction in cardiovascular diseases: mechanisms and in vitro modelsPublication . Grego, Ana; Fernandes, Cristiana; Fonseca, Ivo; Dias-Neto, Marina; Costa, Raquel; Leite-Moreira, Adelino; Oliveira, Sandra Marisa; Trindade, Fábio; Nogueira-Ferreira, RitaEndothelial cells (ECs) are arranged side-by-side to create a semi-permeable monolayer, forming the inner lining of every blood vessel (micro and macrocirculation). Serving as the first barrier for circulating molecules and cells, ECs represent the main regulators of vascular homeostasis being able to respond to environmental changes, either physical or chemical signals, by producing several factors that regulate vascular tone and cellular adhesion. Healthy endothelium has anticoagulant properties that prevent the adhesion of leukocytes and platelets to the vessel walls, contributing to resistance to thrombus formation, and regulating inflammation, and vascular smooth muscle cell proliferation. Many risk factors of cardiovascular diseases (CVDs) promote the endothelial expression of chemokines, cytokines, and adhesion molecules. The resultant endothelial activation can lead to endothelial cell dysfunction (ECD). In vitro models of ECD allow the study of cellular and molecular mechanisms of disease and provide a research platform for screening potential therapeutic agents. Even though alternative models are available, such as animal models or ex vivo models, in vitro models offer higher experimental flexibility and reproducibility, making them a valuable tool for the understanding of pathophysiological mechanisms of several diseases, such as CVDs. Therefore, this review aims to synthesize the currently available in vitro models regarding ECD, emphasizing CVDs. This work will focus on 2D cell culture models (endothelial cell lines and primary ECs), 3D cell culture systems (scaffold-free and scaffold-based), and 3D cell culture models (such as organ-on-a-chip). We will dissect the role of external stimuli—chemical and mechanical—in triggering ECD.
- Near-infrared spectroscopy machine-learning spectral analysis tool for blueberries (vaccinium corymbosum) cultivar discriminationPublication . Ribeiro, Pedro; Barbosa, Maria Inês; Sousa, Clara; Rodrigues, Pedro MiguelVaccinium corymbosum is one of the main sources of commercialized blueberries across the world. This species has a large number of distinct cultivars, leading to significantly different berries characteristics such as size, sweetness, production rate, and growing season. In this context, accurate cultivar discrimination is of significant relevance, and currently, it is mostly performed through berries examination. In this work, we developed a method to discriminate 19 cultivars from the V. corymbosum species through their leaves’ near-infrared spectra. Spectra were acquired from fresh blueberry leaves collected from two geographic regions and across three seasons. Machine-learning-based models, selected from a pool of 10 classifiers based on their discrimination power under a twofold stratified cross-validation process, were trained/tested with 1 to 20 components obtained by the application of data dimensionality reduction (DDR) techniques (dictionary learning, factor analysis, fast individual component analysis, and principal component analysis) to different near-infrared (NIR) spectra regions’ data, to either analyze a single spectral region and season or combine spectral regions and/or seasons for each side of the blueberry leaf. The percentage of correct cultivar discrimination ranged from 52.27 to 100%, with the best spectral results obtained with the adaxial side of the leaves in the fall (100% Accuracy); the fast ICA DDR technique was present in 83.33% of the best analyses (five out of six); and the LinearSVC was present in 66.67% (four out six best analyses). The results obtained in this work denote that near-infrared spectroscopy is a suitable and accurate technique for V. corymbosum cultivar discrimination.