Adenovirus-mediated Transgene-engineered Dendritic Cell Vaccine of Cancer
Bibliographic record
Abstract
Dendritic cells (DCs) are the most effective antigen presenting cells (APCs) to elicit both primary and secondary T-cell response that is critical for antitumor immunity and elimination of intracellular pathogens. Therefore, DCs pulsed ex vivo with antigens have the potential used as cell-based vaccines against tumors. Viral vectors derived from adenoviruses have been extensively used to pulse DCs ex vivo by delivering genes encoding immunomodulatory molecules and tumor antigens to DCs since these vectors are relatively safe, effective in inducing the maturation of DCs, and can accommodate large expression cassettes encoding antigens. One of the hurdles for gene delivery to DCs by adenovirus (Ad) vectors, however, is low transfection efficiency of DCs due to the paucity of Ad receptor on DCs. To overcome this obstacle, targeted Ad vectors have been made by modifying viral capsid proteins. These targeted Ad vectors not only enhance the gene delivery to DCs, but also allow in vivo gene delivery to DCs, thus avoiding ex vivo manipulation of DCs.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".