Upper jaw development in the absence of teeth: New insights for craniodental evo‐devo integration
Bibliographic record
Abstract
In p63‐null mice (p63−/−), teeth fail to form but the mandible forms normally; conversely, the upper jaw skeleton is malformed. Here we explored whether lack of dental tissues contributed to midfacial dysmorphologies in p63−/− mice by testing if facial prominence defects appeared before odontogenesis failed. We also investigated gene dose effects by testing if one wild type (WT) p63 allele (p63+/−) was sufficient for normal upper jaw skeleton formation. We micro‐CT scanned PFA‐fixed p63−/−, p63+/−, and WT (p63+/+) adult and embryonic mice aged E10–E14. Next, we landmarked mandibular (MdP), maxillary (MxP) and nasal prominences (NPs), and facial bones. 3D landmark data were assessed using Principal Component, Canonical Variate, Partial Least Squares, and other statistical analyses. The p63−/− embryos showed MxP and NP malformations by E12, despite the presence of dental tissues. MdP shape was comparable among p63−/−, p63+/−, and p63+/+ embryos. Upper jaw shape was comparable between p63+/+ and p63+/− adults. The upper jaw and its dentition both require p63 signaling, but not each other's presence, to form properly. One WT p63 allele enables normal midfacial morphogenesis; gene dose may be a target for jaw macroevolution. Jaw‐specific genetic mechanisms likely integrate the evo‐devo of dentitions with upper versus lower jaws.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".