Mechanical signals promote osteogenic fate through a primary cilia‐mediated mechanism
Bibliographic record
Abstract
It has long been suspected, but never directly shown, that bone formed to accommodate an increase in mechanical loading is related to the creation of osteoblasts from skeletal stem cells. Indeed, biophysical stimuli potently regulate osteogenic lineage commitmentin vitro In this study, we transplanted bone marrow cells expressing green fluorescent protein, to enable lineage tracing, and subjected mice to a biophysical stimulus, to elicit a bone-forming response. We detected cells derived from transplanted progenitors embedded within the bone matrix near active bone-forming surfaces in response to loading, demonstrating for the first time, that mechanical signals enhance the homing and attachment of bone marrow cells to bone surfaces and the commitment to an osteogenic lineage of these cellsin vivo Furthermore, we used an inducible Cre/Lox recombination system to delete kinesin family member 3A (Kif3a), a gene that is essential for primary cilia formation, at will in transplanted cells and their progeny, regardless of which tissue may have incorporated them. Disruption of the mechanosensing organelle, the primary cilium in a progenitor population, significantly decreased the amount of bone formed in response to mechanical stimulation. The collective results of our study directly demonstrate that, in a novel experimental stem cell mechanobiology model, mechanical signals enhance osteogenic lineage commitmentin vivoand that the primary cilium contributes to this process.-Chen, J. C., Hoey, D. A., Chua, M., Bellon, R., Jacobs, C. R. Mechanical signals promote osteogenic fate through a primary cilia-mediated mechanism.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".