Priming of Hepatocytes Enhances <i>In Vivo</i> Liver Transduction with Lentiviral Vectors in Adult Mice
Bibliographic record
Abstract
Lentiviral vectors are promising tools for liver disease gene therapy, because they can achieve protracted expression of transgenes in hepatocytes. However, the question as to whether cell division is required for optimal hepatocyte transduction has still not been completely answered. Liver gene-transfer efficiency after in vivo administration of recombinant lentiviral vectors carrying a green fluorescent protein reporter gene under the control of a liver-specific promoter in mice that were either hepatectomized or treated with cholic acid or phenobarbital was compared. Phenobarbital is known as a weak inducer of hepatocyte proliferation, whereas cholic acid has no direct effect on the cell cycle. This study shows that cholic acid is able to prime hepatocytes without mitosis induction. Both phenobarbital and cholic acid significantly increased hepatocyte transduction six- to ninefold, although cholic acid did not modify the mitotic index or cell-cycle entry. However, the effect of either compound was weaker than that observed after partial hepatectomy. In no cases was there a correlation between the expression of cell-cycle marker and transduction efficiency. We conclude that priming of hepatocytes should be considered a clinically applicable strategy to enhance in vivo liver gene therapy with lentiviral vectors. Pichard and colleagues investigate whether cell division is required for optimal liver gene transfer efficiency of recombinant lentiviral vectors in mice. Mice were either hepatectomized or treated with proliferation-inducing cholic acid or phenobarbital before vector administration. All treatments resulted in significantly higher transduction, with hepatectomized mice presenting the highest level of transduction.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".