Effects of streptozotocin-induced diabetes mellitus on some bone turnover markers in the vertebrae of ovary-intact and ovariectomized adult rats
Bibliographic record
Abstract
Diabetes mellitus and estrogen deficit are known causes of osteopenia in animal models as well as in humans. In the present work, the combined effect of ovariectomy and diabetes was investigated. Diabetes was induced in ovary-intact and ovariectomized female Wistar rats with a single injection (50 mg/kg body weight, i.p.) of streptozotocin. The rats were administered insulin (I) daily or 17-beta estradiol (E2) on alternate days for a period of 35 days and sacrificed. Serum calcium (Ca2+), phosphorus (P), alkaline phosphatase (ALP), tartrate-resistant acid phosphatase (TRAP), vertebral ALP, collagen, and glycosaminoglycans were estimated. The levels of serum Ca2+ and P increased in diabetic rats, but decreased after I or E2 treatments. Serum ALP and TRAP activity increased in the ovary-intact and ovariectomized diabetic rats. Vertebral ALP activity increased in ovariectomized diabetic rats, but decreased in diabetic rats, which were treated with I or E2. In the vertebrae, TRAP activity was elevated as a result of diabetes, but this was prevented by insulin or estradiol. Diabetes induced a decrease in total collagen in the vertebrae, while I or E2 treatment induced an increase. The levels of chondroitin sulphate and heparan sulphate decreased significantly in the vertebrae of both ovary-intact and ovariectomized diabetic rats, while hyaluronic acid increased. In conclusion, diabetes and ovariectomy each seem to affect the process of matrix formation and mineralization in the bone, and this is aggravated by the combination of diabetes and ovariectomy. The effects of I and E2 were similar, and both hormones reversed the changes brought about by diabetes.
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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) | 0.000 | 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) | 0.001 | 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".