AI Article Synopsis
- Mesenchymal stem cells (MSCs) are vital for tissue engineering as their differentiation affects the quality of cultured tissue, crucial for successful transplantation.
- Precise control of MSC differentiation is necessary to prevent issues like tumor formation in stem cell therapy.
- Researchers used advanced imaging techniques and a K-means machine learning model to automate the evaluation of MSC differentiation, enhancing analysis and potential applications in stem cell research.
Article Abstract
Mesenchymal stem cells (MSCs) play a crucial role in tissue engineering, as their differentiation status directly affects the quality of the final cultured tissue, which is critical to the success of transplantation therapy. Furthermore, the precise control of MSC differentiation is essential for stem cell therapy in clinical settings, as low-purity stem cells can lead to tumorigenic problems. Therefore, to address the heterogeneity of MSCs during their differentiation into adipogenic or osteogenic lineages, numerous label-free microscopic images were acquired using fluorescence lifetime imaging microscopy (FLIM) and stimulated Raman scattering (SRS), and an automated evaluation model for the differentiation status of MSCs was built based on the K-means machine learning algorithm. The model is capable of highly sensitive analysis of individual cell differentiation status, so it has great potential for stem cell differentiation research.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10252613 | PMC |
| http://dx.doi.org/10.3390/cells12111524 | DOI Listing |
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