Food texture and vitamin <scp>D</scp> influence mouse mandible form and molar roots
Bibliographic record
Abstract
Industrialization influenced several facets of lifestyle, including softer nutrient-poor diets that contributed to vitamin D deficiency in post-industrzialized populations, with concomitantly increased dental problems. Here we simulated a post-industrialized diet in a mouse model to test the effects of diet texture and vitamin D level on mandible and third molar (M3) forms. Mice were raised on a soft diet with vitamin D (VitD) or without it (NoD), or on a hard diet with vitamin D. We hypothesized that a VitD/hard diet is optimal for normal mandible and tooth root form, as well as for timely M3 initiation. Subsets of adult NoD/soft and VitD/soft groups were bred to produce embryos that were micro-computed tomography (μCT) scanned to stage M3 development. M3 stage did not differ between embryos from mothers fed VitD and NoD diets, indicating that vitamin D does not affect timing of M3 onset. Sacrificed adult mice were μCT-scanned, their mandibles 3D-landmarked and M3 roots were measured. Principal component (PC) analysis described the largest proportion of mandible shape variance (PC1, 30.1%) related to diet texture, and nominal shape variance (PC2, 13.8%) related to vitamin D. Mice fed a soft diet had shorter, relatively narrower, and somewhat differently shaped mandibles that recapitulated findings in human populations. ANOVA and other multivariate tests found significantly wider M3 roots and larger root canals in mice fed a soft diet, with vitamin D having little effect. Altogether our experiments using a mouse model contribute new insights about how a post-industrial diet may influence human craniodental variation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".