Preparation of Helper-Dependent Adenoviral Vectors
Bibliographic record
Abstract
Adenoviruses (Ads) are excellent mammalian gene transfer vectors because of their ability to infect efficiently a wide variety of quiescent and proliferating cell types from various species to direct high-level gene expression. Consequently, Ad vectors are extensively used as potential recombinant viral vaccines, for high-level protein production in cultured cells and for gene therapy ( 1 – 4 ). First-generation Ad vectors typically have foreign DNA inserted in place of early region 1 (E1). E1-deleted vectors are replication deficient and are propagated in E1-complementing cells such as 293 ( 5 ). Although these vectors remain very useful for many applications, it has become clear that transgene expression in vivo is only transient. Several factors contribute to this, including strong innate and inflammatory responses to the vector ( 6 , 7 ), acute and chronic toxicity caused by low-level viral gene expression from the vector backbone ( 8 ), and generation of anti-Ad cytotoxic T-lymphocytes caused by de novo viral gene expression ( 9 – 12 ) or processing of virion proteins ( 13 ). Although high-level transient transgene expression afforded by first-generation Ad vectors may be adequate, or even desirable, for many gene transfer and gene therapy applications, the transient nature of expression kinetics renders these vectors unsuitable when prolonged, stable expression is required. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.013 |
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".