Mechanisms of Vascular Inflammation and Potential Therapeutic Targets: A Position Paper From the ESH Working Group on Small Arteries
Bibliographic record
Abstract
Inflammatory responses in small vessels play an important role in the development of cardiovascular diseases, including hypertension, stroke, and small vessel disease. This involves various complex molecular processes including oxidative stress, inflammasome activation, immune-mediated responses, and protein misfolding, which together contribute to microvascular damage. In addition, epigenetic factors, including DNA methylation, histone modifications, and microRNAs influence vascular inflammation and injury. These phenomena may be acquired during the aging process or due to environmental factors. Activation of proinflammatory signaling pathways and molecular events induce low-grade and chronic inflammation with consequent cardiovascular damage. Identifying mechanism-specific targets might provide opportunities in the development of novel therapeutic approaches. Monoclonal antibodies targeting inflammatory cytokines and epigenetic drugs, show promise in reducing microvascular inflammation and associated cardiovascular diseases. In this article, we provide a comprehensive discussion of the complex mechanisms underlying microvascular inflammation and offer insights into innovative therapeutic strategies that may ameliorate vascular injury in cardiovascular 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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".