ArticleJournal, genetic engineering & biotechnology2023
Optimizing the generation of mature bone marrow-derived dendritic cells in vitro: a factorial study design.
Article in Journal, genetic engineering & biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed, 4 citations in OpenAlex.
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5 authors at 2 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundFactorial design is a simple, yet elegant method to investigate the effect of multiple factors and their interaction on a specific response simultaneously. Hence, this type of study design reaches the best optimization conditions of a process. Although the interaction between the variables is widely prevalent in cell culture procedures, factorial design per se is infrequently utilized in improving cell culture output. Therefore, we aim to optimize the experimental conditions for generating mature bone marrow-derived dendritic cells (BMDCs). Two different variables were investigated, including the concentrations of the inducing factors and the starting density of the bone marrow mononuclear cells. In the current study, we utilized the design of experiments (DoE), a statistical approach, to systematically assess the impact of factors with varying levels on cell culture outcomes. Herein, we apply a two-factor, two-level (2
resultsThe current study showed that the highest total viable cells and cells yield obtained were in cell group seeded at 2 × 10
conclusionThe analysis of this study indicates a substantial interaction between cytokines combinations and cell densities on BMDC maturation. However, higher cell density is not associated with optimizing DC maturation. Notably, applying DoE in bioprocess designs increases experimental efficacy and reliability while minimizing experiments, time, and process costs.
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