Naringin prevents bone damage in the experimental metabolic syndrome induced by a fructose-rich diet
Bibliographic record
Abstract
We analyzed the effect of naringin (NAR), a flavonoid from citric fruits, on bone quality and biomechanical properties, as well as the redox state of bone marrow in rats fed a fructose-rich diet (FRD), an experimental model to mimic human metabolic syndrome. NAR blocked the increase in the number of osteoclasts and adipocytes and the decrease in the number of osteocytes and osteocalcin (+) cells caused by FRD. Trabecular number was significantly higher in the FRD+NAR group. FRD induced a decrease in the femoral trabecular and cortical bone mineral density, which was blocked by NAR. The fracture and ultimate loads were also decreased in the FRD and FRD+NAR groups. NAR increased the number of nodes to terminal trabecula, the number of nodes to node trabecula, the number of nodes, and the number of nodes with 2 terminals and decreased the Dist (mean size of branches) value. FRD decreased bone marrow catalase activity, an effect that was prevented by NAR. In conclusion, FRD has detrimental effects on the long bones, which are associated with oxidative stress in the bone marrow. Most of these changes are prevented by NAR through its antioxidant properties and promotion of bone formation. Novelty: Fructose-rich diets have detrimental effects on long bones, which are associated with oxidative stress in the bone marrow. Most of these changes are prevented by naringin through its antioxidant properties and promotion of bone formation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".