Preventive cancer vaccination with P5 HER-2/neo-derived peptide‐pulsed peripheral blood mononuclear cells in a mouse model of breast cancer
Bibliographic record
Abstract
This study compared the prophylactic effects from vaccines based on dendritic cells (DCs) and peripheral blood mononuclear cells (PBMCs) by pulsing the cells in-vitro with p5 peptide. The different test groups of mice were injected with free peptide or with peptide pulsed with DCs or PBMCs. Two weeks after the last booster dose, immunological tests were performed on splenocyte suspensions from three mice in each group and the remaining mice (5/each group) were evaluated for tumor growth and survival time. The levels of IFN-γ, granzyme B, and IL-10 were detected in T cells. Additionally, IFN-γ and perforin as well as mRNA levels of some genes associated with immune responses were assessed after challenging the splenocytes with TUBO cells. A significant increase was observed in frequency of CD4+ IFN-γ+, CD8+ IFN-γ+, and CD8+ granzyme B+ T cells, and the perforin of supernatants from mice in the DC and PBMC treatment groups. Significant expression levels of Fas ligand (FasL) and forkhead box P3 (Foxp3) were observed in the DC and PBMC groups. These responses led to smaller tumors and longer survival time in our mouse model of breast cancer. The efficacy of the PBMC-based vaccine in improving the protective immune response makes it a simpler and less expensive candidate vaccine compared with DC-based vaccines.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".