Connexin 43 contributes to phenotypic robustness of the mouse skull
Bibliographic record
Abstract
BACKGROUND: We compared skull shape and variation among genetically modified mice that exhibit different levels of connexin43 (Cx43) channel function, to determine whether Cx43 contributes to craniofacial phenotypic robustness. Specifically, we used two heterozygous mutant mouse models (G60S/+ and I130T/+) that, when compared to their wildtype counterparts, have an ~80% and ~50% reduction in Cx43 function, respectively. RESULTS: Both mutant strains showed significant differences in skull shape compared to wildtype littermates and while these differences were more severe in the G60S/+ mouse, shape differences were localized to similar regions of the skull in both mutants. However, increased skull shape variation was observed in G60S/+ mutants only. Additionally, covariation of skull structures was disrupted in the G60S/+ mutants only, indicating that while a 50% reduction in Cx43 function is sufficient to cause a shift in mean skull shape, the threshold for Cx43 function for disrupting craniofacial phenotypic robustness is lower. CONCLUSIONS: Collectively, our results indicate Cx43 can contribute to phenotypic robustness of the skull through a nonlinear relationship between Cx43 gap junctional function and phenotypic outcomes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".