TNFα is central to the augmented myogenic response of skeletal muscle resistance arteries in heart failure
Bibliographic record
Abstract
A hallmark of heart failure (HF) is elevated peripheral resistance, which primarily results from an augmented myogenic response (MR, the intrinsic property of resistance arteries (RA) to adapt diameter to changes in pressure). In cerebral arteries from HF mice, tumor necrosis factor α (TNFα) plays a central role for the enhancement of MRs (through activation of sphingosine kinase‐1). We hypothesized that TNFα is equally important for the augmentation of MRs in skeletal muscle RA and hence, increased peripheral resistance in HF. HF was induced in Male C57/BL6 mice through surgical ligation of the left anterior descending coronary artery. 6–8wks post‐ligation, cremaster muscle RA were isolated and cannulated on a pressure myograph. Elevations in transmural pressure (20–100mmHg in 20mmHg steps) induced MRs that were increased in RA from HF compared to sham‐operated mice (n=7). MRs were not enhanced in RA from HF TNFα −/− mice (n=5). However, treatment with the TNFα scavenger etanercept (1mg/kg 2x/wk for 6wks post‐ligation) did not block the HF‐induced increase in MRs. Our data suggest that: (i) the augmented MR in skeletal muscle arteries contributes to increased vascular resistance in HF, (ii) TNFα is a mandatory component of this response, and (iii) chronic scavenging of TNFα might be compensated by redundant organization of MR signaling pathways. Funding: NSERC, HSFO, and CIHR
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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) | 0.001 | 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 | 0.001 |
| 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".