Bibliographic record
Abstract
INTRODUCTION Genetically modified adenoviruses (Ads) make attractive vectors for the delivery of exogenous DNA to mammalian cells for basic science and gene therapy applications. Ad vector production consists of (1) cloning a transgene into an infectious plasmid by in vivo recombination in bacteria, (2) rescuing and propagating the vector in complementing cells, and (3) purifying the vector. All of this can be accomplished using commercially available reagents, plasmids, and cell lines. First-generation Ads have a large cloning capacity (5-14 kbp) and efficiently transduce a wide range of both quiescent and proliferating cell types. They are readily propagated to produce high-titer stocks (1011-1013 vector particles from a 3-L culture). Furthermore, Ads rarely integrate into the host genome and are relatively safe. However, Ad vector production typically takes 4-6 wk, and promiscuous host-cell transduction can occur in vivo. Furthermore, immune responses against viral proteins encoded by the vector backbone can occur, which limits the duration of transgene expression in vivo. Regardless of these limitations, Ad remains one of the more versatile and efficient gene delivery systems. Here, we discuss methods for the generation, propagation, purification, and characterization of first-generation Ad vectors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".