Implementing biomaterials, scaffolds, and stem cell therapy for neural tissue regeneration introduces a revolutionary strategy in regenerative healthcare. By embedding stem cells within intricately engineered scaffolds that replicate the natural extracellular matrix (ECM), remarkable advancements in patient well-being can be realized. These biomimetic scaffolds not only emulate native tissues; they also possess a dynamic, multidimensional structure. Their biocompatibility and capacity to influence cellular metabolism position them as exceptional platforms for bioengineering. The outstanding flexibility of this technique enables optimal selection of biomaterials, scaffold designs, cells, and inorganic materials. A formidable body of evidence is emerging to highlight the vast potential of biomimetic scaffolds in tissue engineering and personalized medicine. Recent scientific studies reveal a significant rise in in vivo testing of biomimetic scaffold-based products, highlighting the critical importance of this research domain and the pressing necessity for continued exploration to facilitate the safe advancement of human-compatible biomimetic tissues and organs. This comprehensive analysis illuminates the vital conditions, challenges, and exciting innovations in scaffold design, especially in the context of brain tissue engineering. Ultimately, this review formulates a comprehensive framework for generating scaffolds that incorporate biomimetic attributes and optimal structures. The innovation of brain-implantable scaffolds holds great promise for reducing the impact of neurological disorders (ND).
Somayeh Kakehbaraei, Cyrus Jalili, A. Bahreini et al.· Stem cell research & therape...· 0 citations
Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related deaths globally, with approximately 1,926,425 new cases and 904,019 deaths reported in 2022. Accurate histologic grading plays a critical role in prognosis and treatment planning for colorectal adenocarcinoma. In recent years, artificial intelligence and its subcategories, including machine learning and deep learning, have been increasingly employed for automated cancer detection and classification. An appropriate and well-organized dataset is the essential first step to achieve this goal. This paper introduces CRC-HGD, a histopathological microscopy image dataset of 1,914 images obtained from 214 colorectal adenocarcinoma patients (Grade I: 106, Grade II: 75, Grade III: 33). The specimens are H&E-stained colorectal tissue sections acquired at the Poursina Hakim Research Center of Isfahan University of Medical Sciences, Iran, diagnosed between 2014 and 2019, and graded according to the World Health Organization (WHO) criteria into three grades: well-differentiated (Grade I), moderately differentiated (Grade II), and poorly differentiated (Grade III). For each specimen, four magnification levels are provided: 4x, 10x, 20x, and 40x. The dataset is accessible via Mendeley Data (https://doi.org/10.17632/yfp5sfj47m.4) and at http://databiox.com, where the latest version is also available. The distinctive feature of this dataset is the provision of labeled specimens across all three differentiation grades at multiple magnification levels, enabling comprehensive computational analysis of colorectal cancer grading.
Elham Amjadi, A. Bahreini, Sayed Mohammad Hasan Emami et al.· 0 citations
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