Tendon-associated gene expression precedes osteogenesis in mid-palatal suture establishment
Bibliographic record
Abstract
Orthodontic maxillary expansion relies on intrinsic mid-palatal suture mechanobiology to induce guided osteogenesis, yet establishment of the mid-palatal suture within the continuous secondary palate and causes of maxillary insufficiency remain poorly understood. In contrast, advances in cranial suture research hold promise to improve surgical repair of prematurely fused cranial sutures in craniosynostosis to potentially restore the obliterated signaling environment and ensure continual success of the intervention. We hypothesized that mid-palatal suture establishment is governed by shared principles with calvarial sutures and involves functional linkage between expanding primary ossification centres with the midline mesenchyme. We characterized establishment of the mid-palatal suture from late embryonic to early postnatal timepoints. Suture establishment was visualized using histological techniques and multimodal transcriptomics. We identified that mid-palatal suture formation depends on a spatiotemporally controlled signalling milieu in which tendon-associated genes play a significant role. We mapped relationships between extracellular matrix-encoding gene expression, tenocyte markers, and novel suture patency candidate genes. We identified similar expression patterns in FaceBase-deposited scRNA-seq datasets from cranial sutures. These findings demonstrate shared biological principles for suture establishment, providing further avenues for future development and understanding of maxillofacial interventions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".