Leptin Deficiency and Its Effects on Tibial and Vertebral Bone Mechanical Properties in Mature Genetically Lean and Obese JCR:LA-Corpulent Rats
Bibliographic record
Abstract
Leptin signaling deficient rodents have emerged as models of obesity/insulin resistance syndrome. Altered leptin signaling, however, can affect axial and appendicular bone geometrical properties differently, and, thus, we hypothesized that leptin-deficiency would differentially influence mechanical properties of vertebrae and tibiae compared to lean rats. Mature (9 mo) leptin receptor deficient obese (cp/cp; n = 8) and lean (+/?; n = 7) male JCR:LA-corpulent rats were used to test that hypothesis. Tibiae and the sixth lumbar vertebrae (L(6)) were scanned with micro-CT and were broken in three point-bending (tibiae) or axial loading (L(6)). Supporting the hypothesis, vertebrae and tibiae were differentially affected by leptin signaling deficiency. Tibiae, but not vertebrae, were significantly shorter in obese rats and achieved a significantly greater load (>18%), displacement (>15%), and stress (>18%) at the proportional limit, relative to the lean rats. Conversely, L(6) in obese rats had significantly reduced displacement (>25%) and strain (>32%) at proportional limit, relative to the lean rats. Those combined results suggest that the etiology and duration of obesity may be important determinants of bone mechanical properties, and axial and appendicular bones may be affected differently.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".