Beneficial effects of rosiglitazone and losartan combination in diabetic rats
Bibliographic record
Abstract
Diabetes with vascular complication needs strict interventions to retard possible serious complications. This research estimated the possible interaction of rosiglitazone (RGN) with losartan (Los) in diabetic rats. Male Sprague-Dawley rats were randomly divided into nondiabetic rats, diabetic rats, and diabetic rats that received RGN, Los, or a combination of RGN and Los. Measurement of serum glucose, vascular adhesion molecule-1, interleukin-6, tumor necrosis factor-α, aortic lipid peroxide (malondialdehyde), glutathione, superoxide dismutase, and total nitrate/nitrite levels was done. Also, the effects of RGN on the relaxation created by acetylcholine and sodium nitroprusside, contraction of isolated aortic rings provoked by phenylephrine and angiotensin II were determined. Results revealed that RGN or Los had a vasodilating effect to variable degrees indicated by enhanced effects on both acetylcholine-induced relaxation and the antagonistic effect on angiotensin II and phenylephrine-stimulated contraction of diabetic aortas with significant amelioration in serum glucose, vascular adhesion molecule-1, interleukin-6, and tumor necrosis factor-α levels and aortic oxidant/antioxidant balance. Treatment of diabetic rats with a combination of RGN and Los produced a more pronounced effect on the measured parameters compared to the diabetic, RGN-, and Los-treated groups. These findings point out the beneficial effects of RGN and Los combination in diabetic 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 | 0.001 | 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".