Effect of Telmisartan on Olanzapine Induced Metabolic Syndrome in Ovariectomized Female Sprague-Dawley Rats
Bibliographic record
Abstract
Background: Menopause increases the prevalance of metabolic syndrome in women. Olanzapine is an atypical antipsychotic drug which is used in the treatment of psychiatric disorders, include schizophrenia and bipolar disorder. But it is associated with serious metabolic side-effects, include weight gain, hypertension, hyperlipidemia, hyperglycemia, glucose intolerance and insulin resistance which results in metabolic syndrome. The prevalence of metabolic syndrome is more in patients with schizophrenia compared to the general population. Telmisartan an antihypertensive agent, is an angiotensin II type-I receptor blocker (ARB) also activates peroxisome proliferator-activated receptor gamma (PPARy) and provide beneficial effects for glucose and lipid metabolism. Thus the objective of the present study was to evaluate the effect of telmisartan and additional influence of menopause status on olanzapine induced metabolic syndrome in female Sprague-Dawley rats. Methods: After four weeks of ovariectomy, olanzapine (5 mg/kg) was administered by oral route for 28 days to induce metabolic syndrome in female Sprague-Dawley rats. Thirty female Sparague-Dawley rats were randomly divided into five groups as normal control; ovariectomy control (OVX); ovariectomy + olanzapine control (OVX + OLZ); OVX + Telmisartan (5 mg/kg); OVX + OLZ + Telmisartan (5 mg/kg). After 28 days of treatment, the blood samples were collected and analyzed for blood glucose, plasma insulin, lipid profiles, SGOT, SGPT and body weights of all groups were recorded. Results: OVX control and OVX + OLZ control groups showed significant (P Conclusion: Telmisartan attenuate the development of metabolic syndrome induced by olanzapine in ovariectomized female Sprague-Dawley rats. doi:10.4021/jem100w
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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 | 0.001 | 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) | 0.003 | 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".