CFTR Therapeutics Normalize Cerebral Perfusion Deficits in Mouse Models of Heart Failure and Subarachnoid Hemorrhage
Bibliographic record
Abstract
The cystic fibrosis transmembrane conductance regulator (CFTR) is a significant modulator of cerebrovascular reactivity; the loss of CFTR function enhances myogenic vasoconstriction.Heart failure and subarachnoid hemorrhage downregulate cerebrovascular CFTR protein expression; this leads to enhanced cerebral artery vasoconstriction, reduced cerebral perfusion, neuronal injury, and ultimately, neurologic deficits.CFTR therapeutics that maintain CFTR expression normalize the perfusion deficits, reduce neuronal injury, and improve neurologic function in these pathological settings. SUMMARYHeart failure (HF) and subarachnoid hemorrhage (SAH) chronically reduce cerebral perfusion, which negatively affects clinical outcome.This work demonstrates a strong relationship between cerebral artery cystic fibrosis transmembrane conductance regulator (CFTR) expression and altered cerebrovascular reactivity in HF and SAH.In HF and SAH, CFTR corrector compounds (C18 or lumacaftor) normalize pathological alterations in cerebral artery CFTR expression, vascular reactivity, and cerebral perfusion, without affecting systemic hemodynamic parameters.This normalization correlates with reduced neuronal injury.Therefore, CFTR therapeutics have emerged as valuable clinical tools to manage cerebrovascular dysfunction, impaired cerebral perfusion, and neuronal injury.(
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".