Extracellular <scp>L</scp>‐arginine is required for optimal NO synthesis by eNOS and iNOS in the rat mesenteric artery wall
Bibliographic record
Abstract
1. The formation of NO from endothelial nitric oxide synthase (eNOS) in rat superior mesenteric artery rings was dependent on extracellular L-arginine, and was optimal at a concentration of L-arginine close to the plasma level (carbachol-stimulated NO: control 15.7+/-0.9, L-arginine 100 micro M 22.8+/-1.3 nM). 2. Enhancement of NO output by L-arginine was stereospecific, required the cationic amino-acid transporter and was dependent on caveolin. 3. Induction of inducible nitric oxide synthase (iNOS) impaired the stimulated NO synthesis from eNOS (100 nM carbachol-stimulated NO: control 5.7+/-0.6, iNOS 0.3+/-0.3 nM). 4. The interaction between iNOS and eNOS was reversed by the superoxide scavenger MnTMPyP. Impairment of eNOS by iNOS was also prevented by L-arginine 100 micro M administered simultaneously with carbachol, but not by L-arginine administered during incubation with lipopolysaccharide. 5. These data provide functional evidence that supplementing L-arginine from the extracellular medium optimises the formation of NO from eNOS and suggests that the impairment of eNOS by iNOS is caused by excess formation of superoxide by NO synthase, which can be prevented by L-arginine. These results provide an explanation for the observations that extracellular L-arginine can enhance endothelium function only when the endothelium is impaired or when iNOS has been induced.
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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 | 0.001 |
| 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".