Peroxynitrite and nitric oxide differ in their effects on pig coronary artery smooth muscle
Bibliographic record
Abstract
Peroxynitrite generated in arteries from superoxide and nitric oxide (NO) may damage their function. Here, we compare the effects of peroxynitrite and peroxynitrite/NO-generating agents SIN-1 (3-morpholinosydnonimine hydrochloride), SNAP (S-nitroso-N-acetyl-penicillamine), SNP (sodium nitroprusside), and NONOate (spermine NONOate) on pig coronary artery. Deendothelialized artery rings were pretreated with these agents and then washed before examining their contractility. Pretreatment with all agents (200 microM) results in a decrease in the force of contraction in response to the sarco(endo)plasmic Ca(2+) (SERCA) pump inhibitor cyclopiazonic acid (CPA): SNAP > NONOate > or = peroxynitrite > or = SIN-1 > SNP. Pretreatment with SNAP, NONOate, or SIN-1 also inhibits the force of contraction produced with 30 mM KCl, with SNAP being the most potent. Including catalase plus superoxide dismutase (SOD) during the preincubation has no effect. Including an NO scavenger [2-(4-carboxyphenyl)-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide] or a guanylate cyclase inhibitor (1H-[1,2,4]oxadiazolo[4,3-a]quinoxalin-1-one) partially protects against SNAP. Pretreatment of cultured cells with peroxynitrite, but not with SNAP, inhibits the Ca(2+) transients produced in response to CPA. Pretreating isolated membrane vesicles with peroxynitrite inhibits the Ca(2+) uptake due to the SERCA pump, with all the other agents being less effective. Thus peroxynitrite and NO both inhibit the CPA-induced contractions in deendothelialized artery rings, peroxynitrite by damage to the SERCA pump and NO possibly by a step downstream from the increase in cytosolic Ca(2+).
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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 | 0.001 | 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".