The combined treatment of insulin and naringin improves bone properties in rats with type 1 diabetes mellitus
Bibliographic record
Abstract
We have studied the effects of individual and combined treatment of insulin (I) and naringin (NAR) on the bone structure and biomechanical properties of femurs from streptozotocin (STZ)-induced diabetic rats. Male Wistar rats were divided into five groups: (1) controls, (2) STZ-induced diabetic rats, (3) STZ-induced diabetic rats treated with I, (4) STZ-induced diabetic rats treated with NAR, and (5) STZ-induced diabetic rats treated with I + NAR. Bone mineral density (BMD), bone histomorphometry, biomechanical testing, and bone biomarker expressions were accomplished in femur of all animals, as well as serum biochemical analyses. The combined treatment of I + NAR increased the body weight and the femur BMD from STZ-induced diabetic rats. The bone biomechanical properties and the bone morphology of the femurs from STZ-induced diabetic rats were also improved by the combined treatment. The increased number of osteoclasts in STZ-induced diabetic rats was partially prevented by I, NAR, or I + NAR. NAR or I + NAR completely blocked the decrease in the number of osteocalcin (+) cells in the femur from STZ-induced diabetic rats. RUNX family transcription factor 2 immunostaining was much lower in STZ-induced diabetic rats than in control animals; the combination of I + NAR totally blocked this effect. The combined treatment not only ameliorated bone quality and function, but also normalized the variables related to glucose metabolism. Therefore, the combination of I + NAR might be a better therapeutic strategy than the individual I or NAR administration to reduce bone complications in diabetic patients.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".