Craniofacial sutures: Signaling centres integrating mechanosensation, cell signaling, and cell differentiation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it