Role of Vascular Endothelium in Hypertension, Atherosclerosis and Peripheral Arterial Disease
Bibliographic record
Abstract
The vascular endothelium is a dynamic structure which lines the entire circulatory and lymphatic systems and interacts with local and systemic stimuli. It plays an important role in such processes as vasoconstriction/vasorelaxation, inflammation, cell proliferation, and hemostasis. Dysfunction of endothelial cells contributes to the development of different diseases including hypertension, atherosclerosis, and peripheral arterial disease, which are commonly seen in patients with chronic diabetes. Various risk factors including low density lipoprotein oxidation, inflammation, thrombosis as well as imbalance between NO and endothelin production are considered to induce the VE dysfunction and associated cardiovascular diseases. Although several interventions such as thrombolytic agents, anti-inflammatory agents, antioxidants, NO donors, endothelin inhibitors and stem cell therapy are used for the treatment of hypertension, atherosclerosis and peripheral artery disease, none of these have been found to exert satisfactory beneficial effects. Thus a great deal of research work needs to be carried out to define the exact molecular targets and develop newer therapies for the treatment of hypertension, atherosclerosis and peripheral vascular disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".