Bibliographic record
Abstract
Retrovirus vectors integrate into the genome, providing stable gene transfer, but integration contributes in part to transcriptional silencing that compromises long-term expression. In the case of gammaretrovirus vectors based on murine leukemia virus, many integration events are completely silenced in undifferentiated stem cells and in transgenic mice. Gammaretrovirus vectors are also subject to variegation in which sister cells bearing the same provirus differentially express, and cell differentiation can lead to extinction of vector expression. In contrast, lentivirus vectors based on human immunodeficiency virus type 1 appear to express more efficiently, although other reports indicate that lentivirus vectors can be silenced. This review summarizes the key features of gammaretrovirus vector silencing. The evidence for and against gene silencing of lentivirus vectors is described with special emphasis on the potential effects of vector design, provirus copy number, and integration site preferences on silencing. This analysis suggests that the difference between selfinactivating (SIN) lentivirus vectors and their modified SIN gammaretrovirus counterparts may be less dramatic than previously thought. It will therefore be important to further characterize the mechanisms of silencing, in order to create better gammaretrovirus and lentivirus vectors that consistently express at single copy for gene therapy.
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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) | 0.001 | 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".