Adeno-associated viruses for gene therapy – clinical implications and liver-related complications, a guide for hepatologists
Bibliographic record
Abstract
Gene therapy has garnered increasing interest over recent decades. Several therapies employing gene transfer mechanisms have been developed, and, of these, adeno-associated virus (AAV) vectors have demonstrated viability for use with in vivo gene therapy. Several AAV-based therapeutics have received regulatory approval in the last few years including those for retinal disease, spinal muscular atrophy or aromatic L-amino acid decarboxylase deficiency. Lately, with the introduction of novel liver-directed AAV vector-based therapeutics for the treatment of haemophilia A and B, gene therapy has attracted significant attention in the hepatology community, with the liver increasingly recognised as a target for gene therapy. However, the introduction of foreign DNA into hepatocytes is associated with a risk of hepatic reactions, with raised ALT (alanine aminotransferase) and AST (aspartate aminotransferase) being - so far - the most commonly reported side effects. The complete mechanisms underlying the ALT flairs remain to be determined and the long-term risks associated with these new treatments is not yet known. The liver community is increasingly being asked to support liver-directed gene therapy to mitigate potential liver associated harm. In this review, we focus on AAV vector-based gene therapy, shedding light on this promising technique and its remarkable success in haemophilia, with a special focus on hepatic complications and their management in daily clinical practice.
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 | 0.002 | 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) | 0.005 | 0.005 |
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".