Fusion Hybrid of Dendritic Cells and Engineered Tumor Cells Expressing Interleukin-12 Induces Type 1 Immune Responses against Tumor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIMS AND BACKGROUND: Dendritic cell (DC)-tumor fusion hybrid vaccinees that facilitate antigen presentation represent a novel powerful strategy in cancer immunotherapy. Preclinical studies have demonstrated that IL-12 promotes specific antitumor immunity mediated by T cells in several types of tumors. In the present study, we investigated the antitumor immunity derived from vaccination of fusion hybrids between DCs and engineered J558/IL-12 myeloma cells secreting Th1 cytokine IL-12. METHODS: The expression vector pcDNA-IL-12 was generated and transfected into J558 myeloma cells and then bone marrow-derived DCs were fused with engineered J558/IL-12 cells. The antitumor immunity derived from vaccination of the fusion hybrid DC/J558/IL-12 was evaluated in vitro and in vivo. RESULTS: DC/J558/IL-12 cells secreted recombinant IL-12 (1.6 ng/mL), and inoculation of BALB/c mice with DC/J558/IL-12 hybrid induced a Th1 dominant immune response and resulted in tumor regression. Immunization of mice with engineered DC/J558/IL-12 hybrid elicited stronger J558 tumor-specific cytotoxic T lymphocyte (CTL) responses in vitro as well as more potent protective immunity against J558 tumor challenge in vivo than immunization with the mixture of DCs and J558/IL-12, J558/IL-12 and J558, respectively. Furthermore, the anti-tumor immunity mediated by DC/J558/IL-12 tumor cell vaccination in vivo appeared to be dependent on CD8+ CTL. CONCLUSIONS: These results demonstrate that the engineered fusion hybrid vaccines that combine Th1 cytokine gene-modified tumor cells with DCs may be an attractive strategy for cancer immunotherapy.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 it