Adenoviral-mediated Gene Transfer to Mouse Salivary Glands
Bibliographic record
Abstract
Adenoviral vectors effectively transfer genes to rat salivary glands. However, potent immune responses limit their use in vivo. Mice offer more opportunities than rats for the study of these immune processes. We first established conditions for infection of mouse salivary glands, with an adenoviral vector. The effects of time, viral dose, viral diluent buffer volume, and dexamethasone on expression of a transgene, luciferase, were determined by means of the recombinant vector AdCMVluc. Optimal luciferase expression was observed when the vector was suspended in 50 microL of buffer. This volume completely filled the gland parenchyma and slightly distended the capsule. Dexamethasone increased immediate transgene expression and reduced the acute inflammation one day following viral administration, but did not alter subsequent mononuclear inflammation or transgene expression 14 or 28 days later. An adenoviral vector encoding either anti-inflammatory cytokine IL-4 or IL-10 was co-administered with AdCMVluc to increase transgene expression at 14 and 28 days. While this strategy did not extend the duration of luciferase expression, co-administration of AdCMVIL-10 with AdCMVluc almost completely eliminated the chronic inflammatory infiltrate in the glands after 28 days. This study demonstrates that adenoviral-mediated gene transfer to mouse submandibular glands is possible by intraductal cannulation and that reduction of either the acute or chronic inflammatory infiltrates was insufficient to increase long-term transgene expression in this tissue.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".