ArticlePLoS computational biology2020
Machine learning to predict mesenchymal stem cell efficacy for cartilage repair.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 29 citations in OpenAlex.
- Mitochondrial Communication Networks in the Bone Microenvironment: From the Maintenance of Homeostasis to Translational Interventions for Bone Diseases.Stem cell reviews and reports · 2026Review
- Application and prospect of zinc oxide nanoparticle-enhanced biomimetic hydrogels in articular cartilage repair.Biochemistry and biophysics reports · 2026Review
- Chondrogenic Differentiation of Stem Cells for Cartilage Regeneration: Advances and Future Perspectives.Tissue engineering and regenerative medicine · 2026Review
- Global clinical trial landscape of stem cell-based therapies for osteoarthritis: trends and translational implications.Frontiers in cell and developmental biology · 2026Article
- Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026Review
- Chondrogenic Differentiation of Stem Cells for Cartilage Regeneration: Advances and Future Perspectives.Tissue engineering and regenerative medicine · 2026Review
- 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
- Precision Medicine in Orthobiologics: A Paradigm Shift in Regenerative Therapies.Bioengineering (Basel, Switzerland) · 2025Review
- Optimizing bone marrow harvesting sites for enhanced mesenchymal stem cell yield and efficacy in knee osteoarthritis treatment.World journal of methodology · 2025Review
- Acceptability of Allogeneic Mesenchymal Stromal Cell-Based Tissue Engineering for the Treatment of Periodontitis: A Qualitative Study in France.International dental journal · 2025Article
- Combining transcriptomic and metabolomic insights to guide the clinical application of adipose- and bone marrow-derived mesenchymal stem cells.Medical review (2021) · 2025Article
- How artificial intelligence can enable personalized mesenchymal stem cell-based therapeutic strategies in systemic lupus erythematosus.Frontiers in immunology · 2025Review
- Artificial intelligence powers regenerative medicine into predictive realm.Regenerative medicine · 2024Review
- Artificial Intelligence (AI): A Potential Game Changer in Regenerative Orthopedics-A Scoping Review.Indian journal of orthopaedics · 2024Article
- Mechano-responsive hydrogel for direct stem cell manufacturing to therapy.Bioactive materials · 2023Article
- Recent trends in stem cell-based therapies and applications of artificial intelligence in regenerative medicine.World journal of stem cells · 2021Review
- Update on Novel Non-Operative Treatment for Osteoarthritis: Current Status and Future Trends.Frontiers in pharmacology · 2021Review
- Proteomic Analysis of Hypoxia-Induced Senescence of Human Bone Marrow Mesenchymal Stem Cells.Stem cells international · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inconsistent therapeutic efficacy of mesenchymal stem cells (MSCs) in regenerative medicine has been documented in many clinical trials. Precise prediction on the therapeutic outcome of a MSC therapy based on the patient's conditions would provide valuable references for clinicians to decide the treatment strategies. In this article, we performed a meta-analysis on MSC therapies for cartilage repair using machine learning. A small database was generated from published in vivo and clinical studies. The unique features of our neural network model in handling missing data and calculating prediction uncertainty enabled precise prediction of post-treatment cartilage repair scores with coefficient of determination of 0.637 ± 0.005. From this model, we identified defect area percentage, defect depth percentage, implantation cell number, body weight, tissue source, and the type of cartilage damage as critical properties that significant impact cartilage repair. A dosage of 17 - 25 million MSCs was found to achieve optimal cartilage repair. Further, critical thresholds at 6% and 64% of cartilage damage in area, and 22% and 56% in depth were predicted to significantly compromise on the efficacy of MSC therapy. This study, for the first time, demonstrated machine learning of patient-specific cartilage repair post MSC therapy. This approach can be applied to identify and investigate more critical properties involved in MSC-induced cartilage repair, and adapted for other clinical indications.
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.