ArticleCells2023
Evaluating Differentiation Status of Mesenchymal Stem Cells by Label-Free Microscopy System and Machine Learning.
Article in Cells, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Deep learning-based in silico labeling for analyzing morphological features of MSCs to predict immunomodulatory capacity.Communications biology · 2026Article
- AI-Driven Innovations for Quality Control and Standardization: Future Strategies in Adipose-Derived Stem Cell Manufacturing.International journal of molecular sciences · 2026Review
- Review
- Efficient derivation of hiPSC-derived photoreceptor precursor cells and their neuroprotective effects in retinal degeneration.iScience · 2025Article
- Current Trends and Future Opportunities of AI-Based Analysis in Mesenchymal Stem Cell Imaging: A Scoping Review.Journal of imaging · 2025Review
- Application of Machine Learning in Predicting Osteogenic Differentiation of Mesenchymal Stem Cells.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial intelligence and systems biology analysis in stem cell research and therapeutics development.Stem cells translational medicine · 2025Review
- Integrating Graphene Oxide and Mesenchymal Stem Cells in 3D-Printed Systems for Drug Delivery and Tissue Regeneration.Pharmaceutics · 2025Review
- Comparative Study of Deep Transfer Learning Models for Semantic Segmentation of Human Mesenchymal Stem Cell Micrographs.International journal of molecular sciences · 2025Article
- Review
- Fluorescent marker prediction for non-invasive optical imaging in bovine satellite cells using deep learning.Frontiers in artificial intelligence · 2025Article
- Mesenchymal Stromal Cell Therapy for Thoracic Surgeons: An Update.Journal of personalized medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.