Interplay between the kinin B<sub>1</sub>receptor and inducible nitric oxide synthase in insulin resistance
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Kinins are vasoactive and pro-inflammatory peptides whose biological effects are mediated by two GPCRs, named B1 and B2 receptors. While the B2 receptor plays a protective role in the cardiovascular system via the activation of endothelial NOS, the B1 receptor is associated with vascular inflammation, insulin resistance and diabetic complications. Because the B1 receptor is a potent activator of the inducible form of NOS (iNOS), this study has addressed the role of iNOS in the deleterious effects of B1 receptors in insulin resistance. EXPERIMENTAL APPROACH: Male Sprague-Dawley rats (50-75 g) had free access to a drinking solution containing 10% d-glucose or tap water (control) for 9 weeks. During the last week, a selective iNOS inhibitor (1400W, 1 mg·kg(-1) twice daily) or its vehicle was administered s.c. KEY RESULTS: Prolonged glucose treatment caused insulin resistance and several hallmarks of type 2 diabetes. Whereas the treatment with 1400W had no impact on the elevated systolic blood pressure and leptin levels in glucose-fed rats, it significantly reversed or attenuated hyperglycaemia, hyperinsulinaemia, insulin resistance (HOMA index), body weight gain, peroxynitrite formation (nitrotyrosine expression) and the up-regulation of biomarkers of inflammation (B1 receptor, carboxypeptidase M, iNOS and IL-1β) in renal cortex and aorta and to some extent in the liver. CONCLUSIONS AND IMPLICATIONS: Pharmacological blockade of iNOS prevents the formation of peroxynitrite, which amplifies the pro-inflammatory effects of B1 receptors through a positive feedback mechanism. Hence, targeting iNOS can prevent the deleterious effects of B1 receptors in insulin resistance and peripheral inflammation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".