The endothelium: influencing vascular smooth muscle in many ways
Bibliographic record
Abstract
The endothelium, although only a single layer of cells lining the vascular and lymphatic systems, contributes in multiple ways to vascular homeostasis. Subsequent to the 1980 report by Robert Furchgott and John Zawadzki, there has been a phenomenal increase in our knowledge concerning the signalling molecules and pathways that regulate endothelial - vascular smooth muscle communication. It is now recognised that the endothelium is not only an important source of nitric oxide (NO), but also numerous other signalling molecules, including the putative endothelium-derived hyperpolarizing factor (EDHF), prostacyclin (PGI(2)), and hydrogen peroxide (H(2)O(2)), which have both vasodilator and vasoconstrictor properties. In addition, the endothelium, either via transferred chemical mediators, such as NO and PGI(2), and (or) low-resistance electrical coupling through myoendothelial gap junctions, modulates flow-mediated vasodilatation as well as influencing mitogenic activity, platelet aggregation, and neutrophil adhesion. Disruption of endothelial function is an early indicator of the development of vascular disease, and thus an important area for further research and identification of potentially new therapeutic targets. This review focuses on the signalling pathways that regulate endothelial - vascular smooth muscle communication and the mechanisms that initiate endothelial dysfunction, particularly with respect to diabetic vascular disease.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".