Two distinct pathways account for EDHF‐dependent dilatation in the <i>gracilis</i> artery of dyslipidaemic hApoB<sup>+/+</sup> mice
Bibliographic record
Abstract
1 A universal endothelium-derived hyperpolarising factor (EDHF--non-NO/non-PGI(2)) has not been identified. EDHF, however, is essential for the physiological control of resistance artery tone. The impact of dyslipidaemia (DL), a risk factor for cardiovascular diseases, on the nature and the efficacy of EDHF has not been evaluated yet. 2 Pressurised (80 mmHg) gracilis arterial segments isolated from mice expressing the human apoB-100 and C57Bl/6 wild-type (WT) mice were used. EDHF-dependent dilatations to acetylcholine (ACh) were measured in the presence of L-NNA (100 microM, NOS inhibitor) and indomethacin (10 microM, COX inhibitor). 3 Maximal EDHF-induced dilatations were increased in DL when compared to WT (95+/-2 versus 86+/-4% in WT; P<0.05). Combination of apamin and charybdotoxin strongly reduced (P<0.05) ACh-induced dilatation in WT (22+/-4%) and DL (25+/-5%). 4 Combined addition of barium (Ba(2+)) and ouabain abolished EDHF-induced dilatations in WT arteries (13+/-3%; P<0.05). In vessels isolated from DL mice, however, only the addition of 14,15-EEZE (a 14,15-EET antagonist) to Ba(2+) and ouabain prevented EDHF-induced dilatations (5+/-3% compared to 54+/-11% in the presence of combined Ba(2+) and ouabain; P<0.05). 5 Our data suggest that EDHF-mediated dilatation depends on the opening of endothelial SK(Ca) and IK(Ca) channels. This is associated with the opening of K(ir) channels and activation of the Na(+)/K(+)-ATPase pump on smooth muscle cells leading to dilatation. In arteries from DL mice, a cytochrome P450 metabolite likely to be 14,15-EET equally contributes to the dilatory action of ACh. The early increased efficacy of EDHF in arteries isolated from DL mice may originate from the duplication of the EDHF pathways.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".