Tumor necrosis factor α is involved in the myogenic response of resistance arteries
Bibliographic record
Abstract
The increase in vascular resistance observed in heart failure primarily results from an enhanced myogenic response (MR), the intrinsic property of resistance arteries (RA) to adapt their diameter and hence, resistance, to changes in pressure. We have recently shown that the cytokine tumor necrosis factor α (TNFα) augments the MR by activating sphingosine kinase‐1 (Sk1). We hypothesized that the TNFα‐Sk1 axis also operates under physiological conditions and that TNFα is an integral signaling element underlying the MR. Isolated hamster RA (n=6) were cultured in a pressure myograph for 20hrs prior to functional tests. Elevation of transmural pressure induced a robust MR (80±23% reversal of pressure‐induced distension). The soluble TNFα receptor etanercept (ETAN; 10μg/mL), which sequesters TNFα, abrogated the MR. This result was confirmed in mouse cremaster RA (n=5). Constriction to norepinephrine (NE) and dilation to acetylcholine (ACh) remained unaffected by ETAN. Heat‐inactivated ETAN (n=6) did not affect the MR or NE‐/ACh‐stimulated responses. TNFα increased Erk1/2 phosphorylation, which is tightly linked to Sk1 activation in resistance arteries. Our data suggest that: (i) TNFα is readily available in the artery wall, (ii) its activation is sensitive to changes in transmural pressure and (iii) TNFα is a key element of the signaling pathway that regulates the MR. Funding: NSERC/Heart and Stoke Foundation
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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".