In vivo biodistribution analysis of transmission competent and defective RNA virus-based episomal vector
Bibliographic record
Abstract
RNA virus-based episomal vector (REVec) is an emerging viral vector system that mediates long-term stable gene expression in variety of cell types in vitro. However, little is known about its tissue tropism and persistence of gene expression in vivo. Here, to evaluate the feasibility of REVec for in vivo gene delivery, we conducted biodistribution analysis of transmission competent REVec and transmission defective ΔG-REVec in Lewis rats. Following intracranial administration of REVec, transgene expression was detected in various tissues. In contrast, transgene expression was only observed in the brain after ΔG-REVec administration. Low levels of vector shedding in the feces and blood and of neutralizing antibody in the serum were detected after REVec injection. In the brain, microglia, astrocytes and neurons were susceptible to REVec-mediated transduction. However, the animals administered with REVec, but not with ΔG-REVec showed a significant decrease in body weight compared to mock treated animals. Additionally, CD8 T cell infiltration was observed in the brain of these animals. In summary, we demonstrated that REVec promotes long-term transgene expression in vivo without causing high vector shedding or neutralizing antibody production; however, suggests the need to attenuate vector associated pathogenicity in the future.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".