Advancements in adeno-associated viral gene therapy approaches: exploring a new horizon
Bibliographic record
Abstract
Gene therapy is a promising new therapeutic strategy that has been explored in a wide variety of diseases, ranging from cancer to hemophilia, and ocular disorders to autoimmune diseases, among others. Proof of concept of gene transfer approaches has been shown in over 100 studies of animal models of disease, although only a few are under development for clinical application. The US Food and Drug Administration and the European Medicines Agency have not approved any viral human gene therapy products for sale so far, but the amount of gene-related research and development occurring in the United States and Europe continues to grow at a fast rate. This review summarizes the current status of developments in the field of viral gene therapy using adeno-associated virus as a vector, with a special focus on arthritis. For rheumatoid arthritis, and to a lesser extent for other immune-related inflammatory disorders, several cell and gene transfer approaches have been investigated at the preclinical level and a few have been implemented in clinical trials. Finally, both the potential and the hurdles that are faced during development of a viral gene therapy through to its clinical application are discussed.
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".