The NO-cGMP axis in endothelial ischemia and ischemic preconditioning
Bibliographic record
Abstract
The biology of cardiac and peripheral ischemia and reperfusion (IR) injury is extremely complex, and the vascular endothelium plays a central role both in protecting from ischemic damage as well as in mediating this damage. Due to its strategic location and its intense biosynthetic activity, the vascular endothelium is particularly sensitive to IR: the endothelium is the first tissue damaged by IR, and human in vivo models of isolated endothelial IR injury have been developed that allow investigating the mechanisms of this phenomenon. Particularly during reperfusion, the rapid formation of superoxide anion and other reactive oxygen species causes endothelial damage, leading to the so-called no-reflow phenomenon. In this way, the endothelium determines permanent impairment to tissue reperfusion, extending the ischemic damage. At the same time, the endothelium, like any other tissue, can be preconditioned against IR damage, i.e., it is able to develop a protective phenotype that defends itself and the tissues from IR. We will discuss how the endothelium is the first casualty in the setting of IR, and at the same time how this tissue can be protected by physical and pharmacological stimuli, which opens new therapeutic possibilities. from 3rd International Conference on cGMP Generators, Effectors and Therapeutic Implications Dresden, Germany. 15–17 June 2007
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".