Proximal Cerebral Arteries Develop Myogenic Responsiveness in Heart Failure via Tumor Necrosis Factor-α–Dependent Activation of Sphingosine-1-Phosphate Signaling
Bibliographic record
Abstract
BACKGROUND: Heart failure is associated with neurological deficits, including cognitive dysfunction. However, the molecular mechanisms underlying reduced cerebral blood flow in the early stages of heart failure, particularly when blood pressure is minimally affected, are not known. METHODS AND RESULTS: Using a myocardial infarction model in mice, we demonstrate a tumor necrosis factor-α (TNFα)-dependent enhancement of posterior cerebral artery tone that reduces cerebral blood flow before any overt changes in brain structure and function. TNFα expression is increased in mouse posterior cerebral artery smooth muscle cells at 6 weeks after myocardial infarction. Coordinately, isolated posterior cerebral arteries display augmented myogenic tone, which can be fully reversed in vitro by the competitive TNFα antagonist etanercept. TNFα mediates its effect via a sphingosine-1-phosphate (S1P)-dependent mechanism, requiring sphingosine kinase 1 and the S1P(2) receptor. In vivo, sphingosine kinase 1 deletion prevents and etanercept (2-week treatment initiated 6 weeks after myocardial infarction) reverses the reduction of cerebral blood flow, without improving cardiac function. CONCLUSIONS: Cerebral artery vasoconstriction and decreased cerebral blood flow occur early in an animal model of heart failure; these perturbations are reversed by interrupting TNFα/S1P signaling. This signaling pathway may represent a potential therapeutic target to improve cognitive function in heart failure.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".