Diet composition but not rat source affects bone quantity and strength in rats with subclinical inflammation
Bibliographic record
Abstract
Previous studies have shown differences in sensitivity to colon carcinogens among similar strains of rats from different suppliers, suggesting differences in colonic inflammation and epithelial cell proliferation. It is unclear if these differences may affect bone health differently. We determined if rats from different suppliers fed either control or experimental diet would have different bone mineral content (BMC), bone mineral density (BMD), and bone strength. Male Wistar rats (supplier was Harlan or Charles River, n = 12/group) were randomized to control diet (AIN‐76A, carbohydrate as glucose) or an experimental diet (AIN‐76A, carbohydrate as fructose as well as high iron, low calcium). Left femur (LF) and lumbar vertebrae (LV) were collected after 11 weeks of feeding. BMC and BMD of LF and LV were measured using DEXA. Peak load (PL), the maximum force a bone can withstand before fracture, was measured by 3‐point bending (LF) and compression (LF neck, LV3). The experimental diet resulted in lower (P <0.001) BMC and BMD of LF and LV, and lower (P <0.001) PL at all three sites, regardless of rat source. There was an interaction of rat source and diet in which Harlan rats fed control diet had higher (P < 0.05) PL at the LF neck than Charles River rats fed control diet. These results suggest that rat source and differences in subclinical inflammation are lesser determinants of bone quantity and strength than diet composition.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".