Bibliographic record
Abstract
Endothelium, the inner-most layers of blood vessels is an active monolayer of cells which has been evolved to become specialized barrier between blood and other structures of the vessel wall. Endothelial cell layer is known to communicate between blood and extravascular tissues and is actively involved in cardiovascular homeostasis. Intact endothelium regulates vascular tone, permeability and maintains non-inflammatory, anti-thrombotic surface. Through its ability to express procoagulants, anticoagulants, vasoconstrictors, vasodilators, cell adhesion molecules and cytokines, the endothelium has emerged as one of the pivotal regulators of haemostasis. Under normal conditions, endothelial cell sustains a vasodilatory, anticoagulant and fibrinolytic state in which coagulation, platelet adhesion as well as leukocyte activation and inflammation are suppressed by continuous release of nitric oxide (NO). Endothelium plays a major role in development of cardiovascular diseases (CVDs). Endothelial cell injuries and resultant endothelial dysfunction (ED) plays a key role in the deployment of CVDs. Therefore, the imbalance of endothelial function due to suppression of anticoagulant molecules like nitric oxide (NO), tissue factor pathway inhibition (TFPI), thrombomodulin etc, and over expression of procoagulant molecules like tissue actor (TF), endothelin-1, von Willebrand factor (vWF), plasminogen activator inhibitor (PAI)-1 secreted by endothelial surface isseen during stress. Several factors like infection, hyperglycaemia, hyperlipidaemia, malignancy, oxidative stress, and aging can interfere with endothelial function. It is widely believed, that ED plays a crucial role in the development of cardiovascular diseases. Also it has been reported to be involved in atherosclerosis, thrombosis, hypertension, diabetes and other vascular conditions. In this article we will specifically highlight andreview the role ofED in different vascular conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".