Browsing by Subject "Optical imaging"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Publication
    Open Access
    Activation of alternative pathways of angiogenesis and involvement of stem cells following anti-angiogenesis treatment in glioma
    (F. Hernández y Juan F. Madrid. Universidad de Murcia: Departamento de Biología Celular e Histología, 2012) Arbab, Ali S.
    Malignant gliomas are hypervascular tumors that are highly resistant to all the currently available multimodal treatments. Therefore, anti-angiogenic therapies targeting VEGF or VEGF receptors (VEGFRs) were designed and thought to be an effective tool for controlling the growth of malignant gliomas. However, recent results of early clinical trials using humanized monoclonal antibodies against VEGF (Bevacizumab), as well as small-molecule tyrosine kinase inhibitors that target different VEGF receptors (VEGFRs) (Vatalanib, Vandetanib, Sunitinib, Sorafenib, etc) alone or in combination with other therapeutic agents demonstrated differing outcomes, with the majority of reports indicating that glioma developed resistance to the employed anti-angiogenic treatments. It has been noted that continued anti-angiogenic therapy targeting only the VEGF-VEGFR system might affect pro-angiogenic factors other than VEGF, such as basic fibroblast growth factor (bFGF), stromal derived factor 1 (SDF-1) and Tie-2. These factors may in turn stimulate angiogenesis by mobilizing bone marrow derived precursor cells, such as endothelial progenitor cells (EPCs), which are known to promote angiogenesis and vasculogenesis. In this short review, the current antiangiogenic treatments, possible mechanisms of activation of alternative pathways of angiogenesis, and possible involvement of bone marrow derived progenitor cells in the failure of anti-angiogenic treatments are discussed
  • Thumbnail Image
    Publication
    Restricted
    Automated Detection of Corneal Edema With Deep Learning-Assisted Second Harmonic Generation Microscopy
    (Institute of Electrical and Electronics Engineers, 2023-11-06) Anton, Stefan; Martínez Ojeda, Rosa M.; Hristu, Radu; Stanciu, George A.; Toma, Antonela; Banica, Cosmin K.; Fernández, Enrique J.; Huttunen, Mikko J.; Bueno, Juan M.; Stanciu, Stefan G.; Stanciu, Stefan G.; Bueno, Juan M.; Física
    Second Harmonic Generation Microscopy (SHG) is widely acknowledged as a valuable non-linear optical imaging tool, its contrast mechanism providing the premises to non-invasively identify, characterize, and monitor changes in the collagen architecture of tissues.However, the interpretation ofSHGdata can pose difficulties even for experts histopathologists, which represents a bottleneck for the translation of SHG-based diagnostic frameworks to clinical settings. The use of artificial intelligence methods for automated SHG analysis is still in an early stage, with only few studies having been reported to date, none addressing ocular tissues yet. In this work we explore the use of three Deep Learning models, the highly popular InceptionV3 and ResNet50, alongside FLIMBA, a custom developed architecture, requiring no pre-training, to automatically detect corneal edema in SHG images of porcine cornea. We observe that Deep Learning models building on different architectures provide complementary results for the classification ofcornea SHG images and demonstrate an AU-ROC=0.98 for their joint use. These results have potential to be extrapolated to other diagnostics scenarios, such as automated extraction of hydration level of cornea, or identification of corneal edema causes, and thus pave the way for novel methods for precision diagnostics of the cornea with Deep-Learning assisted SHG imaging.