Bibliographic record
Abstract
One of the biggest challenges in optimizing viral vectors for gene therapy relates to the immune response of the host. Adeno-associated virus (AAV) vectors are associated with low immunogenicity and toxicity, resulting in vector persistence and long-term transgene expression. The inability of AAV vectors to efficiently transduce or activate antigen presenting cells (APCs) may account for their decreased immunogenicity. AAV mediated gene therapy however, leads to the development of antibodies against the vector capsid. Anti-AAV antibodies have neutralizing effects that decrease the efficiency of in vivo gene therapy and can prevent vector re-administration. Furthermore, recent studies have shown that AAV vectors can elicit both cellular and humoral immune responses against the transgene product. Both cell-mediated response and humoral response to the delivered gene depend on a number of variables; including the nature of the transgene, the promoter used, the route and site of administration, vector dose and host factors. The response of the host to the vector, in terms of antigen-specific immunity, will play a substantial role in clinical outcome. It is therefore important to understand both, why AAV vectors are able to escape immunity and the circumstances and mechanisms that lead to the induction of immune responses. This review will summarize innate and adaptive immune responses to AAV vectors, discuss possible mechanisms and outline strategies, such as capsid modifications, use of alternative serotypes, or immunosuppression, which have been used to circumvent them.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".