Bibliographic record
Abstract
PURPOSE OF REVIEW: Gene-based therapies are designed to modulate gene expression in specific tissues by introducing into cells transgenes, antisense oligonucleotides, RNA interference, microRNAs, or a variety of other oligonucleotide-based compounds and their delivery systems. Several types of gene-based therapies are already available or in clinical development to treat severe lipid-related disorders and associated risk. The review briefly presents the current status and future challenges of these therapies in clinical lipidology, focusing on most advanced and promising agents or mechanisms. RECENT FINDINGS: Gene-based agents address several unmet medical needs in lipidology such as homozygous familial hypercholesterolemia, familial or multifactorial chylomicronemia, severe hypertriglyceridemia, elevated lipoprotein (a), familial partial lipodystrophy, nonalcoholic fatty liver disease, and hypoalphalipoproteinemia. Most advanced antisense oligonucleotide drugs target apolipoprotein C-III, apolipoprotein (a), angiopoietin-like 3, and diacylglycerol o-acyltransferase-2. Long-term efficacy and safety data are now available for two gene-based agents, mipomersen, approved in the USA for homozygous familial hypercholesterolemia and Glybera, AAV1-LPLS447X gene therapy, conditionally approved in Europe for lipoprotein lipase deficiency. SUMMARY: Although positive to date, the overall benefit-risk ratio of gene-based therapies is yet to be documented long term across additional patients and conditions. The next generation of such therapies might improve their therapeutic index.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".