Modulation of nitric oxide affects myocardial perfusion-contraction matching in anaesthetized dogs with recurrent no-flow ischaemia
Bibliographic record
Abstract
Myocardial perfusion and contraction are closely coupled; however, the effect of recurrent no-flow ischaemia on perfusion-contraction matching remains to be established. In the present studies, we examined the influence of modulating nitric oxide availability on perfusion-contraction matching after recurrent no-flow ischaemia in acute open-chest, anaesthetized dogs. The following three groups were studied: (1) saline; (2) L-NAME (10 mg kg(-1) I.V.); and (3) enalaprilat (1.5 mg kg(-1) I.V.). Regional myocardial blood flow was measured with microspheres and contractile function with piezoelectric crystals to determine systolic wall thickening. Dogs underwent four cycles of 5 min acute ischaemia and 5 min coronary reperfusion; area at risk was similar for all groups. In all dogs, ischaemic zone contractile function was depressed after recurrent no-flow ischaemia despite increased myocardial blood flow during reperfusion; contractile function was further depressed during L-NAME and was partly restored with enalaprilat. Within the ischaemic region, blood flow in subendocardial and subepicardial layers increased significantly compared with baseline during each reperfusion period independently of treatment. Our findings suggest that reduced NO availability can significantly impair myocardial perfusion-contraction matching, which is partly restored by administration of an NO donor.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".