Lung Endothelial Dysfunction in Congestive Heart Failure
Bibliographic record
Abstract
RATIONALE: Congestive heart failure (CHF) frequently results in remodeling and increased tone of pulmonary resistance vessels. This adaptive response, which aggravates pulmonary hypertension and thus, promotes right ventricular failure, has been attributed to lung endothelial dysfunction. OBJECTIVE: We applied real-time fluorescence imaging to identify endothelial dysfunction and underlying molecular mechanisms in an experimental model of CHF induced by supracoronary aortic banding in rats. METHODS AND RESULTS: Endothelial dysfunction was evident in lungs of CHF rats as impaired endothelium-dependent vasodilation and lack of endothelial NO synthesis in response to mechanical stress, acetylcholine, or histamine. This effect was not attributable to downregulation of endothelial NO synthase. Imaging of the cytosolic Ca(2+) concentration ([Ca(2+)](i)) revealed a singular impairment of endothelial [Ca(2+)](i) homeostasis and signaling characterized by a lack of [Ca(2+)](i) oscillations and deficient or attenuated [Ca(2+)](i) responses to mechanical stress, histamine, acetylcholine, or thapsigargin. Reconstitution of a [Ca(2+)](i) signal by ionophore treatment restored endothelial NO production, but lack of endothelial responsiveness was not primarily attributable to downregulation of Ca(2+) influx channels in CHF. Rather, we identified a massive remodeling of the endothelial cytoskeleton in the form of an increased expression of beta-actin and F-actin formation which contributed critically to endothelial dysfunction in CHF because cytoskeletal disruption by cytochalasin D largely reconstituted endothelial [Ca(2+)](i) signaling and NO production. CONCLUSIONS: Our findings characterize a unique scenario of endothelial dysfunction in CHF that is caused by a singular impairment of [Ca(2+)](i) signaling, and identify cytoskeletal reorganization as a major regulator of endothelial signaling and function.
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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".