Vascular inflammation: a role in vascular disease in hypertension?
Bibliographic record
Abstract
PURPOSE OF REVIEW: In this review, we summarize the more recent clinical evidence highlighting the importance of vascular inflammation in terms of clinical risk prediction, and the mechanisms mediating the upregulation of inflammatory mediators in cardiovascular disease and hypertension. RECENT FINDINGS: Markers of inflammation have been shown to be upregulated in different forms of cardiovascular disease, and to correlate with vascular risk. Atherosclerosis is characterized by chronic inflammation of the vascular wall. The I-kappaB/nuclear factor-kappaB system is considered a major intracellular inflammatory pathway, mediating most of the vascular inflammatory responses. Increasing evidence indicates that hypertension, through the vasoactive peptides angiotensin and endothelin-1, promotes and accelerates the atherosclerotic process via inflammatory mechanisms. In animal and human studies proinflammatory properties of angiotensin II have been demonstrated in large conduit and small arteries, in the kidney as well as in the heart. The angiotensin II receptors involved in the inflammatory process and the interaction between angiotensin II and nitric oxide in mediating vascular inflammation have been identified. In addition, recent advances concerning the role of endothelin-1 as another important mediator of chronic inflammation in the vascular wall has been documented, and the relationship between endothelin-1 and angiotensin II on vascular inflammation demonstrated. SUMMARY: Inflammatory mechanisms are important participants in the pathophysiology of hypertension and cardiovascular disease. The identification of useful markers of inflammation, of new therapeutic targets to interfere with these mechanisms, and the evaluation of the efficacy of antiinflammatory treatments will allow progress in our ability to combat cardiovascular disease and the complications of hypertension.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".