Chronic treatment with angiotensin AT<sub>1</sub>receptor antagonists reduced serum but not bone TGF-β<sub>1</sub>levels in ovariectomized rats
Bibliographic record
Abstract
Approximately 50% of hypertensive patients are postmenopausal women; therefore, any antihypertensive therapy must not adversely affect bone loss in this population. Recently, however, concern has been raised that use of angiotensin AT1 receptor antagonists may increase the tendency to develop postmenopausal osteoporosis by decreasing transforming growth factor-beta1 (TGF-beta1), which has been implicated in bone mass maintenance. In the present study, we selected telmisartan and valsartan as representatives of angiotensin AT1 receptor antagonists and used ovariectomized (OVX) rats as a model of human postmenopausal osteoporosis. After 3 months treatment with telmisartan (5 mg/kg daily) or valsartan (10 mg/kg daily), OVX rats showed no signs of adverse effects on bone mineral density of the lumbar vertebrae (L1-L5) or the total femur, nor did treatment affect serum levels of osteocalcin and osteoclast-derived tartrate-resistant acid phosphatase (TRACP-5b). Bone TGF-beta1 content remained unchanged, although treatment with telmisartan and valsartan significantly reduced serum TGF-beta1 levels (p < 0.05). In conclusion, chronic treatment with angiotensin AT1 receptor antagonists reduced serum but not bone TGF-beta1 levels and did not accelerate ovariectomy-induced bone loss in rats.
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".