Role of oxygen as a regulator of stem cell fate during the spontaneous repair of osteochondral defects
Bibliographic record
Abstract
The complexity of the in vivo environment makes it is difficult to isolate the effects of specific cues on regulating cell fate during regenerative events such as osteochondral defect repair. The objective of this study was to develop a computational model to explore how joint specific environmental factors regulate mesenchymal stem cell (MSC) fate during osteochondral defect repair. To this end, the spontaneous repair process within an osteochondral defect was simulated using a tissue differentiation algorithm which assumed that MSC fate was regulated by local oxygen levels and substrate stiffness. The developed model was able to predict the main stages of tissue formation observed by a number of in vivo studies. Following this, a parametric study was conducted to better understand why interventions that modulate angiogenesis dramatically impact the outcome of osteochondral defect healing. In the simulations where angiogenesis was reduced, by week 12, the subchondral plate was predicted to remain below the native tidemark, although the chondral region was composed entirely of cartilage and fibrous tissue. In the simulations where angiogenesis was increased, more robust cell proliferation and cartilage formation were observed during the first 4 weeks, however, by week 12 the subchondral plate had advanced above the native tidemark although any remaining tissue was either hypertrophic cartilage or fibrous tissue. These results suggest that osteochondral defect repair could be enhanced by interventions where angiogenesis is promoted but confined to within the subchondral region of the defect. © 2015 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 34:1026-1036, 2016.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".