PubMed İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397

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  • Article
    Citation - WoS: 5
    Citation - Scopus: 6
    Genetic Variants in Genes Correlated to the PI3K/AKT Pathway: The Role of ARAP3, CDH5, KIF and RELN Primary Lymphedema
    (International Society of Lymphology, 2024-08-28) Dundar, Mehmet Sait; Belanová, I.; Bonetti, Gabriele; Gelanová, V.; Kozáčiková, R.; Vešelényiová, Dominika; Donato, Kevin; Michelini, S.
    Genetic anomalies affecting lymphatic development and function can lead to lymphatic dysfunction, which could manifest as lymphedema- Understanding the signaling pathways governing lymphatics function is crucial for developing targeted diagnostic and therapeutic interventions. This study aims to characterize genetic variants in genes involved in the PUKIAKT signaling pathway, which plays a critical role in lymphangiogenesis. 408 patients diagnosed with primary lymphedema were sequenced usinga next-generation sequencing (NGS) gene panel composed of 28 diagnostic genes and 71 candidate genes. The analysis revealed six variants in genes RFLN, ARAP3,CDHS and K1F11. Five of these variants have never been reported in the literature. All these genes have been correlated to lymphatic activity and are involved in the P13K/AKT pathway. As the P13K/AKT signaling pathway plays an essential role in lymphangiogenesis and lymphatic function, genetic variants in genes correlated to this pathway could lead to lymphedema. Our findings underscore the potential of the P13K/AKT pathway in lymphedema pathogenesis, supporting the role of RELN,ARAP3,CDH5,and KIF11 as diagnostic and therapeutic targets. © 2024 Elsevier B.V., All rights reserved.
  • Article
    Citation - Scopus: 15
    An Effective Colorectal Polyp Classification for Histopathological Images Based on Supervised Contrastive Learning
    (Elsevier Ltd, 2024-04) Yengec-Tasdemir, Sena Busra; Aydin, Zafer; Akay, Ebru; Doǧan, Serkan; Yilmaz, Bulent
    Early detection of colon adenomatous polyps is pivotal in reducing colon cancer risk. In this context, accurately distinguishing between adenomatous polyp subtypes, especially tubular and tubulovillous, from hyperplastic variants is crucial. This study introduces a cutting-edge computer-aided diagnosis system optimized for this task. Our system employs advanced Supervised Contrastive learning to ensure precise classification of colon histopathology images. Significantly, we have integrated the Big Transfer model, which has gained prominence for its exemplary adaptability to visual tasks in medical imaging. Our novel approach discerns between in-class and out-of-class images, thereby elevating its discriminatory power for polyp subtypes. We validated our system using two datasets: a specially curated one and the publicly accessible UniToPatho dataset. The results reveal that our model markedly surpasses traditional deep convolutional neural networks, registering classification accuracies of 87.1% and 70.3% for the custom and UniToPatho datasets, respectively. Such results emphasize the transformative potential of our model in polyp classification endeavors. © 2024 Elsevier B.V., All rights reserved.