Discordant growth of nasal cartilage and bone contributes to phenotypic variability of the skull in a mouse model
Bibliographic record
Abstract
BACKGROUND: We previously determined a nonlinear relationship between connexin 43 (Cx43) function and craniofacial phenotypic variation in the mutant mouse model G60S/+, and that this variation was driven by nasal bone deviation. While nonlinearities in the genotype-phenotype map appear to be common, few studies have looked at the developmental processes that underlie this nonlinearity. Here, we investigated the potential tissue-level developmental determinants of the variation in nasal bone phenotype in G60S/+ mice through postnatal development. RESULTS: The deviated nasal bone phenotype arises by postnatal day 21 and becomes more severe by 3 months in G60S/+ mice. Measures of nasal bone remodeling including the number of osteoclasts, mineralizing surface, mineral apposition rate, and bone formation rate are significantly greater in G60S/+ mice compared to wild-type mice at 2 months, but these differences do not correspond with nasal bone deviation. The degree of nasal bone deviation does significantly and negatively correlate with the ratio between nasal bone and cartilaginous nasal septum length. CONCLUSIONS: Our findings indicate that the mean phenotypic changes observed between G60S/+ and wild-type mice are due to reduced bone growth, but the increased phenotypic variation found within mutant mice is due to discordant growth between nasal cartilage and bone.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".