Prospects for atherosclerosis regression through increase in high-density lipoprotein and other emerging therapeutic targets
Bibliographic record
Abstract
In a process often seen as progressive and irreversible, deposition and retention of lipoproteins and the consequent inflammatory reaction result in the accumulation of atherosclerotic plaques from an early age. However, striking effects observed in experimental models support the concept that atherosclerosis can regress. This is often accompanied by changes in plaque composition favouring stability and decreased likelihood of rupture. Large clinical trials have established the value of low-density lipoprotein cholesterol reduction with statin treatment, although this may prevent no more than 30% of all cardiovascular events, and the magnitude of effect on plaque regression seems relatively modest. High-density lipoprotein cholesterol (HDL-C) is well recognised as an important and independent protective factor, although treatment options to increase HDL-C have until now been limited. The recent emergence of new treatments will probably establish increased HDL-C as another important strategy in antiatherosclerosis treatment. Beyond HDL-C increases, further appreciation of mechanisms of cellular lipid homoeostasis and regulation of gene transcription have revealed new targets for atherosclerosis treatment. This review considers emerging approaches to plaque regression together with some of the parallel developments in imaging technology that will improve our appreciation of response to treatment.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".