Volume 16, Issue 3 (Autumn 2024)                   3 2024, 16(3): 71-80 | Back to browse issues page

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Rezazadehshirazi A, Mehboudi S, Tarahomi M. Deep Learning-Based Analysis of Axolotl Limb Regeneration. 3 2024; 16 (3) :71-80
URL: http://jmb.ahvaz.iau.ir/article-1-1018-en.html
Aquatic Animal Disease Health Group, Faculty of Veterinary Medicine, Islamic Azad University, Kazerun Branch, Fars, Iran
Abstract:   (371 Views)
Regeneration of complex organs in adult vertebrates is one of the most challenging biomedical issues. Among them, the axolotl (Ambystoma mexicanum) as a unique model with the ability to completely regenerate motor organs, spinal cord, heart, and brain has provided a valuable platform for studying the biological mechanisms of regeneration. This study was designed to comprehensively and multi-layeredly analyze the process of organ regeneration in axolotl and focused on investigating cellular dynamics, signaling pathway activity, and genetic and epigenetic regulation. Sampling was performed at different phases of regeneration (days 0, 3, 7, 14, 21, 28). Histological, immunohistochemical, qRT-PCR, and RNA-seq studies were used to analyze the expression of key genes such as fgf8, msx1, wnt5a, and notch1. The results showed that the FGF, Wnt, and Notch pathways play a pivotal role in blastema formation and activation of stem and progenitor cells. Also, transcriptomic analysis revealed that the regulation of gene expression is influenced by the interaction between endogenous factors, environmental signals, and epigenetic mechanisms. The findings indicate a complex coordination between the extracellular matrix, immune responses, and signaling pathways in successful organ regeneration. By drawing a detailed view of the biological mechanisms of regeneration in axolotl, this study provides a new perspective for the development of regenerative therapies in humans and is an effective step towards translational applications of regenerative biology.
 
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Type of Study: Research | Subject: Fisheries
Received: 2024/09/20 | Accepted: 2024/11/30 | Published: 2024/11/30

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