Bibliographic record
Abstract
Gene therapy is a clinical strategy that may potentially treat an array of genetic and nongenetic diseases, as well as a novel method for drug delivery and vaccination. To these ends, adenovirus vectors are a promising means to deliver specific genes of interest into the patient. A major limitation of the use of adenovirus vectors is the host immune response. Adenovirus vectors induce the innate arm of the immune system that results in inflammation of transduced tissues and efficient clearance of administered vectors. Unlike adaptive immunity, the innate response is mediated by the adenovirus particle and does not require viral transcription. In vivo, the innate immune response involves the induction of cytokines and activation of effector leukocytes that comprise the host response to these agents. A number of interactions with leukocytes and with epithelial and endothelial cells are essential in triggering the host response to adenovirus vectors. Signal transduction via MAP kinases and NF-kappaB-mediated gene transcription are triggered during early virus-cell interactions and are key events in the innate recognition of adenovirus vector transduction. This review aims to describe data examining cellular and molecular mechanisms involved in the adenovirus-mediated innate immune response.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".