Human neutrophil peptides induce inflammatory atherosclerotic events (P5115)
Bibliographic record
Abstract
Abstract Atherosclerosis includes complex inflammatory events that leafs to leukocyte recruitment, adhesion and migration, foam cell formation, and platelet aggregation. Human neutrophil peptides (HNP), the major protein content in the azurophilic granules, are released upon PMN activation, and have been found in the lesion area of coronary artery diseases. We hypothesized that HNP play an important role in PMN-mediated inflammatory cardiovascular responses in atherosclerosis. Human coronary artery endothelial cells were stimulated with HNP at a clinical relevant concentration for 4 h and 8 h, followed by cocultured with human peripheral monocyters for 2 h. The monocyte adhesion to the endothelial cells and transmigration increased in the HNP-treated conditions than in the controls. Stimulation of human monocyte-derived macrophages with HNP resulted in oxidative stress that accelerated foam cell formation, which was attenuated by the administration of superoxide dismutase. Stimulation of platelets with HNP resulted in platelet aggregation and deletion of LDL receptor-related protein8 (LRP8), the only LRP phenotype expressed in platelets had no response to HNP. The HNP-induced production of reelin, a LRP8 ligand, by the arterial endothelial cells was required for platelet aggregation. In conclusion, HNP exert pro-atherosclerotic properties mediated through reelin-LRP8 signaling pathways.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".