The role of damage- and pathogen-associated molecular patterns in inflammation-mediated vulnerability of atherosclerotic plaques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it