Craniofacial sutures: Signaling centres integrating mechanosensation, cell signaling, and cell differentiation
Bibliographic record
Abstract
Cranial sutures are dynamic structures in which stem cell biology, bone formation, and mechanical forces interface, influencing the shape of the skull throughout development and beyond. Over the past decade, there has been significant progress in understanding mesenchymal stromal cell (MSC) differentiation in the context of suture development and genetic control of suture pathologies, such as craniosynostosis. More recently, the mechanosensory function of sutures and the influence of mechanical signals on craniofacial development have come to the forefront. There is currently a gap in understanding of how mechanical signals integrate with MSC differentiation and ossification to ensure appropriate bone development and mediate postnatal growth surrounding sutures. In this review, we discuss the role of mechanosensation in the context of cranial sutures, and how mechanical stimuli are converted to biochemical signals influencing bone growth, suture patency, and fusion through mediation of cell differentiation. We integrate key knowledge from other paradigms where mechanosensation forms a critical component, such as bone remodeling and orthodontic tooth movement. The current state of the field regarding genetic, cellular, and physiological mechanisms of mechanotransduction will be contextualized within suture biology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".