Sickle cell disease (SCD) is a monogenic disease, resulting from a single-point mutation, that presents a complex pathophysiology and high clinical heterogeneity. Inflammation stands as a prominent characteristic of SCD. Over the past few decades, the role of different cells and molecules in the regulation of the inflammatory process has been elucidated.
View Article and Find Full Text PDFDeep learning applied to raw data has demonstrated outstanding image classification performance, mainly when abundant data is available. However, performance significantly degrades when a substantial volume of data is unavailable. Furthermore, deep architectures struggle to achieve satisfactory performance levels when distinguishing between distinct classes, such as fine-grained image classification, is challenging.
View Article and Find Full Text PDFImage watermarking often involves the use of handheld devices under non-structured conditions for authentication purposes, particularly in the print-cam process where smartphone cameras are used to capture watermarked printed images. However, these images frequently suffer from perspective distortions, making them unsuitable for automated information detection. To address this issue, Cam-Unet, an end-to-end neural network architecture, is presented to predict the mapping from distorted images to rectified ones, specifically tailored for print-cam challenges applied to ID images.
View Article and Find Full Text PDFIntroduction: Adipose tissue mesenchymal stem/stromal cells (ASC) can be used as advanced therapy medicinal product in regenerative and cancer medicine. We previously demonstrated Supernatant Rich in Growth Factors (SRGF) can replace fetal bovine serum (FBS) to expand ASC by a clinical grade compliant protocol. The therapeutic potential of ASC is based also on their homing capacity toward inflammatory/cancer sites: oriented cell migration is a fundamental process in this scenario.
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