Enhanced antitumor immunity derived from a novel vaccine of fusion hybrid between dendritic and engineered myeloma cells.
Bibliographic record
Abstract
AIM: Dendritic cell-tumor cell fusion hybrid vaccines which facilitate antigen presentation represent a new powerful strategy in cancer immunotherapy. The clinical frequency of objective responses to the conventional fusion hybrid vaccines is still quite low, indicating that the current conventional protocol of simply fusing dendritic cells (DCs) and tumor cells needs further improvement to enhance its antitumor efficiency. METHODS: In the present study, we generated a novel fusion hybrid DC/J558(CD40L) by fusing DCs and an engineered J558(CD40L) myeloma cells expressing CD40 ligand (CD40L) molecule using polyethylene glycol (PEG). The fusion efficiency was approximately 20%. We investigated the antitumor immunity derived from vaccination of the fusion hybrid DC/J558(CD40L). RESULTS: Our results showed that vaccination of mice with DC/J558(CD40L) hybrids induced more efficient cytotoxic T lymphocyte (CTL) responses and protective immunity against J558 tumor cells, than that of the conventional fusion hybrid DC/J558 from the fusion of DCs and J558 tumor cells. The antitumor immunity derived from vaccination of DC/J558(CD40L) was mainly mediated by CD4(+) and CD(8+)cT cells, but not natural killer (NK) cells. CONCLUSION: Therefore, this novel fusion hybrid vaccine which combines gene-modified tumor and DC vaccines may be an attractive strategy for cancer immunotherapy.
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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".