Bibliographic record
Abstract
Atherosclerosis is a complex disease process in which genetic, lipid, cellular and immunological factors combine to determine the location, severity and timing of lesion development and clinical events. It has been demonstrated, however, that inflammation governs atherosclerosis during the course of development of atherosclerosis. It has also been demonstrated that regulation of the inflammatory reaction (e.g. statins) is effective in decreasing the cardiovascular events and improving the prognosis of atherosclerotic diseases. Other anti-atherosclerosis agents introduced in this study are adiponectin, testosterone, defibrase, angiotensin-converting enzyme inhibitors, dextromethorphan, paeonol, 15-lipoxygenase inhibitors, curcumin, interferon-beta, quercetin, AGI-1067, peroxisome proliferator-activated receptor gamma ligands and garlic. Some antiplatelet drugs described here are aspirin, clopidogrel and glycoprotein IIb/IIIa receptor antagonist. The mechanism of action of these agents is depicted. A new way of targeting anti-atherosclerosis and antiplatelet drugs to atherosclerosis areas is introduced. The author uses an antioxidized low-density lipoprotein antibody that is also conjugated to a mixture of anti-atherosclerosis agents or antiplatelet drugs to target these agents specifically to the atherosclerosis area. With this kind of targeting, we can use a much higher dose of anti-atherosclerosis agents or antiplatelet drugs and have much fewer side effects.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".