Understanding the role of host innate immune responses in viral vector-mediated gene delivery and strategies to achieve sustained transgene expression (P6357)
Bibliographic record
Abstract
Abstract Stunning progress in genomic research has changed the way we address diseases. With the identification of disease causing genetic modifications, genomic research has helped us to decipher the patho-physiology of diseases and given us an extra tool to address the basic cause by correcting those genes. Only few drugs target the cause of disase, but for most of them we still need to deliver proper genes. Although many viral vectors are being used to deliver genes to target cells, most have problems related to safety and sustained expression. In our previous studies we have used HD-AD to successfully deliver cystic fibrosis correcting human-CFTR gene to mice achieving sustained expression and disease correction in CFTR KO mice. Where as in larger animals, there is an acute phase of reduction in transgene expression. Our preliminary studies suggest that, this difference may be due to difference in NK cell activity. To address this problem we have designed human cell based in-vitro evaluation assay to find out the role of NK cells and to find out effective small molecule to block NK cells during the window period of their activity. Our results show that NK cells are killing viral vector- transduced cells. We also demonstrate that this gene transduced cell killing can be inhibited by JAK and NF-kB inhibitors. These findings will help in designing better viral vector-based gene therapy to achieve sustained expression in large animals and in human studies.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".