Peptide-Pulsed Dendritic Cells Have Superior Ability to Induce Immune-Mediated Tissue Destruction Compared to Peptide with Adjuvant
Bibliographic record
Abstract
Vaccines for cancer immunotherapy are of interest but in general have not yet achieved the desired therapeutic efficacy in clinical trials. We present here a novel model to evaluate vaccine strategies by following tissue destruction in a transgenic model, where a defined antigen is expressed on pancreatic islets. We found that the transfer of syngeneic antigen-pulsed dendritic cells (DCs) resulted in autoimmune cytotoxic T-lymphocyte activation that was not observed following vaccinations that were based on peptides and adjuvants. Importantly, the induction of diabetes by DC transfer is dependent upon the maturation of DCs prior to transfer. Furthermore, diabetes induction only occurred if DCs were pulsed with the immunodominant epitope in addition to at least one other peptide, suggesting greater cytolytic activity upon engagement of multiple T-cell specificities. While the tumor environment undoubtedly will be more complex than healthy tissue, the insights gained through this model provide useful information on variables that can affect CD8-mediated tissue cytolysis in vivo.
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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 | 0.001 |
| 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".