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Author: search.filters.author.Loreto CuitinoSubject: search.filters.subject.human

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    IL-10 and IL-6/IL-10 as predictive biomarkers for treatment response in non-infectious uveitis
    (Frontiers Media SA, 2025-05-13)
    Rodrigo A. Valenzuela
    ;
    Fabian Vega-Tapia
    ;
    Nathaly Elizalde
    ;
    Ivan Flores
    ;
    Felipe M. Rojas
    Uveitis, a group of heterogeneous diseases causing ocular inflammation, is a major contributor to vision loss globally. While systemic corticosteroids (CS) are the mainstay treatment, identifying CS-refractory patients remains a significant challenge. This study aimed to explore cytokine expression and Glucocorticoid Receptor (GR) levels as biomarkers for the early detection of CS-refractory cases in non-infectious uveitis. We assayed blood samples from 19 patients with non-infectious uveitis, for the expression of IL-6, IL-17A, TNF-α, IL-10 and GRα. The cohort included 11 refractory and 8 sensitive patients, categorized based on their clinical response to corticosteroids (prednisone 1 mg/kg/day). Blood draws were conducted at three time points (at baseline, day 7- and day 14 after CS initiation), and peripheral blood mononuclear cells (PBMCs) were isolated to measure cytokine and GRα transcript levels via real-time PCR. The expression levels of GRα and cytokines IL-6, IL-17A and TNF-α did not show significant changes between CS-sensitive and CS-refractory patients on the different days of treatment. However, IL-10 expression levels as the day14-to-day7 ratio were significantly higher in patients sensitive to CS therapy. A higher day14-to-day7 ratio was also found for the IL-6/IL-10, IL-17A/IL-10 and GRα/IL-10 ratios. ROC curve analysis demonstrated a robust predictive performance of IL-10 mRNA expression and the IL-6/IL-10 ratio for identifying CS-refractory patients. In conclusion, the expression of IL-10 and the IL-6/IL-10 ratio hold promise as early predictive biomarkers for CS treatment refractoriness in patients with non-infectious uveitis. These findings offer valuable insights into personalized treatment strategies, potentially leading to improved clinical outcomes.
    Scopus© Citations 6  1
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    Interpretable multimodal classification for age-related macular degeneration diagnosis
    (2024)
    Carla Vairetti
    ;
    Sebastián Maldonado
    ;
    Loreto Cuitino
    ;
    URZUA SALINAS, CRISTHIAN  
    ;
    Xu Yanwu
    <jats:p>Explainable Artificial Intelligence (XAI) is an emerging machine learning field that has been successful in medical image analysis. Interpretable approaches are able to “unbox” the black-box decisions made by AI systems, aiding medical doctors to justify their diagnostics better. In this paper, we analyze the performance of three different XAI strategies for medical image analysis in ophthalmology. We consider a multimodal deep learning model that combines optical coherence tomography (OCT) and infrared reflectance (IR) imaging for the diagnosis of age-related macular degeneration (AMD). The classification model is able to achieve an accuracy of 0.94, performing better than other unimodal alternatives. We analyze the XAI methods in terms of their ability to identify retinal damage and ease of interpretation, concluding that grad-CAM and guided grad-CAM can be combined to have both a coarse visual justification and a fine-grained analysis of the retinal layers. We provide important insights and recommendations for practitioners on how to design automated and explainable screening tests based on the combination of two image sources.</jats:p>
    Scopus© Citations 1
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    Association of retinal detachment with age 50 years or younger at onset in patients with acute retinal necrosis
    (2021)
    Cristhian A. Urzua
    ;
    Jared Knickelbein
    ;
    Loreto Cuitino
    ;
    Uriel Moreno
    ;
    Rodrigo Anguita
    Scopus© Citations 7  1
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    Definition of Uveitis Refractory to Treatment: A Systematic Review in the Absence of a Consensus
    (2020)
    Rodrigo A. Valenzuela
    ;
    Iván Flores
    ;
    Myriam Pujol
    ;
    Carolina Llanos
    ;
    Ester Carreño
      3Scopus© Citations 10