Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (2)
  • Open Access

    REVIEW

    Unraveling the RAGE axis in pulmonary disorders: Mechanisms and therapeutical potential

    SHUOCHEN PANG1, TAO JIA1,*, ZIFENG YANG2,*

    BIOCELL, Vol.48, No.12, pp. 1721-1734, 2024, DOI:10.32604/biocell.2024.055753 - 30 December 2024

    Abstract The Receptor for Advanced Glycation End Products (RAGE) is a multiligand receptor of the immunoglobulin superfamily, notably highly expressed in the lungs. Its interaction with a variety of ligands, including advanced glycation end products (AGEs), S100 proteins, and high mobility group box 1 (HMGB1), activates multiple signaling pathways that are pivotal in the pathogenesis of numerous pulmonary diseases and comorbidities. However, comprehensive reviews on the role of ligands-RAGE signaling in specific lung diseases are rare. This review aims to elucidate the mechanisms by which RAGE-mediated signaling pathways either provide protective or pathogenic effects in pulmonary More >

  • Open Access

    ARTICLE

    Pulmonary Diseases Decision Support System Using Deep Learning Approach

    Yazan Al-Issa1, Ali Mohammad Alqudah2,*, Hiam Alquran3,2, Ahmed Al Issa4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 311-326, 2022, DOI:10.32604/cmc.2022.025750 - 18 May 2022

    Abstract Pulmonary diseases are common throughout the world, especially in developing countries. These diseases include chronic obstructive pulmonary diseases, pneumonia, asthma, tuberculosis, fibrosis, and recently COVID-19. In general, pulmonary diseases have a similar footprint on chest radiographs which makes them difficult to discriminate even for expert radiologists. In recent years, many image processing techniques and artificial intelligence models have been developed to quickly and accurately diagnose lung diseases. In this paper, the performance of four popular pretrained models (namely VGG16, DenseNet201, DarkNet19, and XceptionNet) in distinguishing between different pulmonary diseases was analyzed. To the best of… More >

Displaying 1-10 on page 1 of 2. Per Page