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https://www.selleckchem.com/screening-libraries.html Regenerative medicine (RM) is an interdisciplinary field that uses different approaches to accelerate the repair and regeneration or replace damaged or diseased human cells or tissues to achieve normal tissue function. These approaches include the stimulation of the body's own repair processes, transplantation of progenitor cells, stem cells, or tissues, as well as the use of cells and exosomes as delivery-vehicles for cytokines, genes, or other therapeutic agents. COVID-19 pneumonia is a specific disease consistent with diffuse alveolar damage resulting in severe hypoxemia. Therefore, the most serious cause of death from COVID-19 is lung dysfunction. Here, we consider RM approaches to cure COVID-19 pneumonia based on what RM has so far used to treat lung diseases, injuries, or pneumonia induced by other pathogens. These approaches include stem and progenitor cell transplantation, stem cell-derived exosomes, and microRNAs therapy.Aims and Scope Computed tomography (CT) is one of the most efficient clinical diagnostic tools. The main goal of CT is to reproduce an acceptable reconstructed image of an object (either anatomical or functional behaviour) with the help of a limited set of projections at different angles. To achieve this goal, one of the most commonly iterative reconstruction algorithm called Maximum Likelihood Expectation Maximization (MLEM) is used. The conventional Maximum Likelihood (ML) algorithm can achieve quality images in CT. However, it still suffers from optimal smoothing as the number of iterations increases. For solving this problem, this paper presents a novel statistical image reconstruction algorithm for CT, which utilizes a nonlocal means of fuzzy complex diffusion as a regularization term for noise reduction and edge preservation. The proposed model was evaluated on four test cases phantoms. Qualitative and quantitative analyses indicate that the proposed technique has higher efficiency

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