Disorders of high‐density lipoprotein biogenesis
Bibliographic record
Abstract
The characterization of the atheroprotective role of high‐density lipoproteins (HDL) made in the past two decades has rekindled interest in modulating HDL for therapeutic purposes. Rare deficiencies of HDL have allowed the identification of specific proteins acting as structural moieties, enzymes, lipid transfer proteins, cellular lipid transporters, and ligands for cellular receptors; these, in turn, represent potential drug targets. The study of several of these HDL deficiency states has shown the importance of cellular cholesterol transport in HDL metabolism. Based on a better understanding of the physiology of HDL formation, many cases of severe HDL deficiency in man can now be explained at the cellular level. Disorders of HDL biogenesis in man—apolipoprotein (apo) AI defects, mutations at the adenosine triphosphate (ATP) binding cassette AI (ABCA1), defects of specific lipases that modulate HDL, such as sphingomyelinase, the HDL deficiency seen in Niemann‐Pick disease type C (NPC)—can be linked to abnormal formation of nascent HDL particles via the ABCA1 transporter. Deficiency in lecithin:cholesterol acyl transferase impedes the formation of mature HDL particles, once nascent HDL particles are formed. As a consequence, modulating cellular cholesterol efflux and apo AI secretion—and thereby nascent HDL particles—may be an appealing strategy to raise HDL for therapeutic purposes.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".