Bibliographic record
Abstract
Pre-existing immunity against human adenovirus (HAd) serotype 5 derived vector in the human population is widespread, thus hampering its clinical use. Various components of the immune system, including neutralizing antibodies (nAbs), Ad specific T cells and type I IFN activated NK cells, contribute to dampening the efficacy of Ad vectors in individuals with pre-existing Ad immunity. In order to circumvent pre-existing immunity to adenovirus, numerous strategies, such as developing alternative Ad serotypes, varying immunization routes and utilizing prime-boost regimens, are under pre-clinical or clinical phases of development. However, these strategies mainly focus on one arm of pre-existing immunity. Selection of alternative serotypes has been largely driven by the absence in the human population of nAbs against them with little attention paid to cross-reactive Ad specific T cells. Conversely, varying the route of immunization appears to mainly rely on avoiding Ad specific tissue-resident T cells. Finally, prime-boost regimens do not actually circumvent pre-existing immunity but instead generate immune responses of sufficient magnitude to confer protection despite pre-existing immunity. Combining the above strategies and thus taking into account all components regulating pre-existing Ad immunity will help further improve the development of Ad vectors for animal and human use.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".