The role of selectins and integrins in adenovirus vector-induced neutrophil recruitment to the liver
Bibliographic record
Abstract
Adenovirus vectors for human gene therapy induce early host inflammatory responses in transduced tissues that limit gene transfer efficiency and can result in significant morbidity. The present study aimed to elucidate the cellular mechanisms underlying the acute inflammation induced by adenovirus vectors in the liver. Leukocyte rolling and adhesion in response to an intravenously administered adenovirus vector was examined by intravital microscopy in mouse liver. Adenovirus vectors significantly increased leukocyte rolling and adhesion in the postsinusoidal venules within minutes of transduction. Unlike other inflammatory states in the liver, no leukocyte retention was seen in the sinusoids in response to adenovirus vector administration. Inhibition of P-selectin, alpha(4)-integrin, and E-selectin was necessary to completely block leukocyte rolling and subsequent adhesion. The administration of an anti-alpha(4)-integrin antibody alone significantly reduced leukocyte adhesion. In contrast, adenovirus vector-induced leukocyte adhesion was unchanged in CD18-knockout mice. Depletion of circulating neutrophils eliminated leukocyte rolling and adhesion in response to adenovirus vector transduction in the liver. In conclusion, adenovirus vectors induce rapid neutrophil-mediated inflammation in the post-sinusoidal venules by selectins and alpha(4)-integrin but surprisingly not by CD18.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".