Murine renal cell carcinoma: evaluation of a dendritic‐cell tumour vaccine
Bibliographic record
Abstract
OBJECTIVE: To use a murine model of renal cell carcinoma (RCC), Renca, to aid in developing a dendritic cell (DC)-mediated tumour vaccine for RCC; as conventional therapy has been unsuccessful for RCC and therapy using immune modulators has had limited success, novel therapies enhancing further the immune system must be developed. MATERIALS AND METHODS: DCs were obtained from mouse bone marrow enriched for the haematopoietic progenitors, and cultured in the presence of interleukin-4 and granulocyte macrophage-colony stimulating factor. In vivo vaccines and in vitro proliferation assays were used to assess ability of the DCs to present tumour antigen. RESULTS: The presence of DCs was confirmed in the cultures by fluorescent-activated cell sorting analysis. In vivo, tumour-bearing animals receiving tumour extract-pulsed DCs as a vaccine showed a two to threefold reduction in tumour growth at day 12 and day 16 but no significant difference at day 28. In vitro, tumour extract-pulsed DCs stimulated significant proliferation of splenocytes from naive animals but not tumour-bearing animals. In addition, splenocytes from tumour-bearing animals had an attenuated immune response in vitro. CONCLUSION: These results show that it is possible to use the DC vaccine to modulate the immune response to achieve an antitumour effect, but further manipulation of the DC vaccine may be needed to overcome the tumour-induced immune suppression.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".