Mice selectively bred for increased tibia length exhibit accelerated fracture repair
Bibliographic record
Abstract
Bone fracture repair is a unique form of scarless tissue regeneration in mammals that recapitulates many aspects of endochondral ossification seen in developing long bones. For example, transgenic mouse studies have shown that many development-related genes involved in endochondral ossification (EO), which involves transformation of transient cartilaginous tissue into bone, are also redeployed during the bone repair process. While there is an expanding appreciation for the mechanistic overlap between bone development and repair, little is known about the relationship between rates of bone growth and bone repair in natural populations. To examine whether bones that grow faster also heal faster, we employed the Longshanks mouse, which produces 15-20% longer tibiae at skeletal maturity than random-bred Control mice, as a result of increased postnatal EO rates. We generated experimental unstabilized tibial fractures in sex-balanced and age-matched Longshanks and Control mice and monitored their recovery over 6 weeks using longitudinal in vivo micro-computed tomography (µCT) imaging at key milestones in fracture repair. In parallel, we analyzed callus tissue composition and gene expression in a cross-sectional cohort of Longshanks mouse fractures during repair. In this study, we showed that Longshanks mice produce larger fracture calluses at faster rates than Control mice during EO, without compromising callus bone quality. Moreover, we demonstrated that differences in µCT fracture mineralization correlated with an accelerated program of EO in Longshanks mouse calluses, favoring earlier cartilage maturation. These findings highlight a deep evolutionary conservation of EO in both development and repair, and provide evidence for correlated selection responses between organism morphology and repair physiology.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.007 | 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".