Morphological and biomechanical characterization of long bones and peri-implant bone repair in type 2 diabetic rats treated with resveratrol
Bibliographic record
Abstract
Type 2 diabetes interferes with bone remodeling mechanisms, requiring studies to reverse this damage, and resveratrol is a polyphenol with rich properties. This study aimed to characterize the long bone morphology and peri-implant biomechanics of normoglycemic and type 2 diabetic animals treated with resveratrol. Thirty-two male Wistar rats were used and divided into normoglycemic and diabetic with or without treatment. They had the installation of implants in the tibia and treatment with oral resveratrol within 45 days. Resveratrol was responsible for weight homeostasis and decreased glycemic levels in rats with type 2 diabetes. The three-point bending testing, resveratrol showed positive effects on the biomechanics of long bones, corroborating a more resistant bone in comparison to untreated diabetics. Micro-ct revealed how bone metabolism is affected by systemic disease, decreasing bone quality. The counter-torque of normoglycemic animals showed superior osseointegration to diabetes, with no differences in the administration of the polyphenol, showing the sovereignty of the deleterious effects of the disease when there is a tissue lesion and an inflammatory picture installed. Overall, resveratrol acted positively in the etiopathogenesis of type 2 diabetes and revealed positive effects on the strength of long bones.
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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 | 0.000 | 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".