Phenotypic Analysis of Long Bones in Pannexin 3 Knockout Mice Using a Geometric Morphometric Approach
Bibliographic record
Abstract
Pannexin 3 (Panx3) is a channel‐forming glycoprotein that is expressed in skin, mammary gland and skeletal tissues. In vitro studies have shown that Panx3 is induced at the growth plate where it is thought to promote cell differentiation, a key role in bone formation. However, whether or not the ablation of Panx3 in mice causes phenotypic anomalies is unknown. The study objective is to analyze the phenotype of long bones in a new Panx3 knockout (KO) mouse to assess the role of Panx3 in bone formation. Ten KO and 10 wild‐type (WT) adult mice were scanned using high resolution in vivo micro‐CT. Each long bone studied was digitized using homologous landmarks. Data analysis using geometric morphometrics (multivariate statistical methods) allowed for a quantitative comparison of shape, size and variation of long bones between KO and WT mice. KO mice demonstrated distinct long bone shape differences that were greatest at the joint surfaces. As well, long bones in KO mice were 5% smaller. A significant portion of the shape difference (20%) can be attributed to the size component of shape. KO mice demonstrated greater shape variation that also appeared to be driven by different patterns of growth. Our findings suggest that Panx3 affects long bone shape through its effect on size. Importantly, KO mice appear to have altered and less predictable patterns of long bone growth. Thus, Panx3 may have a role in maintaining optimum bone growth.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".