A Mouse Model Suggests How an Industrialized Diet Alters Jaw Form
Bibliographic record
Abstract
Food texture affects jaw form by creating biomechanical stress during chewing. Chewing prompts remodelling via increased bone cell activities, including cell signalling, differentiation, proliferation, and matrix secretion. Vitamin D may influence jaw form via regulation of these bone cell activities. Modern, post‐Industrialized human diets are characterized by soft textured food with low levels of vitamin D, but how these two factors affect mandible shape and size is unclear. Via a mouse model and specialized diets, we tested the effects of dietary texture and vitamin D on mandibular form. We hypothesized that vitamin D intake is: 1) imperative for normal mandibular form, and; 2) a greater influence on mandibular form than diet texture. C57BL/6J mice weaned at 7 weeks were raised on a soft or hard diet with vitamin D (n=40; n=48), or on a soft diet without vitamin D (n=39). Post‐sacrifice, adult mice were scanned with a Skyscan1172 micro‐CT system and landmarked using Amira software. Landmark datasets underwent Generalized Procrustes Analysis followed by Principal Component Analysis (PCA) in Morphologika. Mice fed soft diets had shorter mandibles with diminutive alveolar bone, gentler masseteric crests, a wider angle between dentary bones, and larger bicondylar breadth. Diet groups occupied distinct but overlapping regions of shape space, with diet texture accounting for 30.8% (PC1) of total shape variance. Presence/absence of vitamin D accounted for 13.8% (PC2) of total shape variance between soft diet groups. Mice fed a soft diet with vitamin D had mandibles with more flaring angular processes, more robust masseteric crests, and elevated alveolar bone. Our results indicate that vitamin D influenced dentary bone shape less than diet texture did, and that diet had a greater role in mandibular bone remodelling, including increased variation at muscle attachment sites. Future work will explore whether vitamin D also influences muscle form and function.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".