Therapeutically Targeting Tumor Necrosis Factor-α/Sphingosine-1-Phosphate Signaling Corrects Myogenic Reactivity in Subarachnoid Hemorrhage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Subarachnoid hemorrhage (SAH) is a complex stroke subtype characterized by an initial brain injury, followed by delayed cerebrovascular constriction and ischemia. Current therapeutic strategies nonselectively curtail exacerbated cerebrovascular constriction, which necessarily disrupts the essential and protective process of cerebral blood flow autoregulation. This study identifies a smooth muscle cell autocrine/paracrine signaling network that augments myogenic tone in a murine model of experimental SAH: it links tumor necrosis factor-α (TNFα), the cystic fibrosis transmembrane conductance regulator, and sphingosine-1-phosphate signaling. METHODS: Mouse olfactory cerebral resistance arteries were isolated, cannulated, and pressurized for in vitro vascular reactivity assessments. Cerebral blood flow was measured by speckle flowmetry and magnetic resonance imaging. Standard Western blot, immunohistochemical techniques, and neurobehavioral assessments were also used. RESULTS: We demonstrate that targeting TNFα and sphingosine-1-phosphate signaling in vivo has potential therapeutic application in SAH. Both interventions (1) eliminate the SAH-induced myogenic tone enhancement, but otherwise leave vascular reactivity intact; (2) ameliorate SAH-induced neuronal degeneration and apoptosis; and (3) improve neurobehavioral performance in mice with SAH. Furthermore, TNFα sequestration with etanercept normalizes cerebral perfusion in SAH. CONCLUSIONS: Vascular smooth muscle cell TNFα and sphingosine-1-phosphate signaling significantly enhance cerebral artery tone in SAH; anti-TNFα and anti-sphingosine-1-phosphate treatment may significantly improve clinical outcome.
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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) | 0.001 | 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".