Ca2+-dependent nitric oxide release in the injured endothelium of excised rat aorta: a promising mechanism applying in vascular prosthetic devices in aging patients
Bibliographic record
Abstract
BACKGROUND: Nitric oxide is key to endothelial regeneration, but it is still unknown whether endothelial cell (EC) loss results in an increase in NO levels at the wound edge. We have already shown that endothelial damage induces a long-lasting Ca²⁺ entry into surviving cells though connexin hemichannels (CxHcs) uncoupled from their counterparts on ruptured cells. The physiological outcome of injury-induced Ca²⁺ inflow is, however, unknown. METHODS: In this study, we sought to determine whether and how endothelial scraping induces NO production (NOP) in the endothelium of excised rat aorta by exploiting the NO-sensitive fluorochrome, DAF-FM diacetate and the Ca²⁺-sensitive fluorescent dye, Fura-2/AM. RESULTS: We demonstrated that injury-induced NOP at the lesion site is prevented in presence of the endothelial NO synthase inhibitor, L-NAME, and in absence of extracellular Ca²⁺. Unlike ATP-dependent NO liberation, the NO response to injury is insensitive to BTP-2, which selectively blocks store-operated Ca²⁺ inflow. However, injury-induced NOP is significantly reduced by classic gap junction blockers, and by connexin mimetic peptides specifically targeting Cx37Hcs, Cx40HCs, and Cx43Hcs. Moreover, disruption of caveolar integrity prevents injury-elicited NO signaling, but not the accompanying Ca²⁺ response. CONCLUSIONS: The data presented provide the first evidence that endothelial scraping stimulates NO synthesis at the wound edge, which might both exert an immediate anti-thrombotic and anti-inflammatory action and promote the subsequent re-endothelialization.
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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 | 0.001 | 0.000 |
| 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".