The role of damage- and pathogen-associated molecular patterns in inflammation-mediated vulnerability of atherosclerotic plaques
Bibliographic record
Abstract
Atherosclerosis is a chronic inflammatory disease resulting in the formation of the atherosclerotic plaque. Plaque formation starts with the inflammation in fatty streaks and progresses through atheroma, atheromatous plaque, and fibroatheroma leading to development of stable plaque. Hypercholesterolemia, dyslipidemia, and hyperglycemia are the risk factors for atherosclerosis. Inflammation, infection with viruses and bacteria, and dysregulation in the endothelial and vascular smooth muscle cells leads to advanced plaque formation. Death of the cells in the intima due to inflammation results in secretion of damage-associated molecular patterns (DAMPs) such as high mobility group box 1 (HMGB1), receptor for advanced glycation end products (RAGE), alarmins (S100A8, S100A9, S100A12, and oxidized low-density lipoproteins), and infection with pathogens leads to secretion of pathogen-associated molecular patterns (PAMPs) such as lipopolysaccharides, lipoteichoic acids, and peptidoglycans. DAMPs and PAMPs further activate the inflammatory surface receptors such as TREM-1 and toll-like receptors and downstream signaling kinases and transcription factors leading to increased secretion of pro-inflammatory cytokines such as tumor necrosis factor α, interleukin (IL)-1β, IL-6, and interferon-γ and matrix metalloproteinases (MMPs). These mediators and cytokines along with MMPs render the plaque vulnerable for rupture leading to ischemic events. In this review, we have discussed the role of DAMPs and PAMPs in association with inflammation-mediated plaque vulnerability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".