Nonviral Gene Therapy Approaches to Hemophilia
Bibliographic record
Abstract
The goal of hemophilia gene therapy is to obtain long-term therapeutic levels of factor VIII (FVIII) or factor IX (FIX) without stimulating an immune response against the transgene product or the vector. The success of gene therapy is largely dependent on the development of appropriate gene delivery vectors. Both viral vectors and nonviral vectors have been considered for the development of hemophilia gene therapy. In general, viral vectors are far more efficient than nonviral gene delivery approaches and resulted in long-term therapeutic levels of FVIII or FIX in preclinical animal models. However, there are several reasons why a nonviral treatment would still be desirable, particularly because some viral vectors are associated with inflammatory reactions, that render transgene expression transient, or with an increased risk of insertional oncogenesis when random integrating vectors are used. Nonviral vectors may obviate some of these concerns. Since nonviral vectors are typically assembled in cell-free systems from well-defined components, they have significant manufacturing advantages over viral vectors. The continued development of improved nonviral gene delivery approaches offers new perspectives for gene therapy of chronic diseases including hemophilia.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.005 | 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".