Skin Allografting Activates Anti-tumor Immunity and Suppresses Growth of Colon Cancer in Mice
Bibliographic record
Abstract
INTRODUCTION: The tumor cells could escape from the immune elimination through the immunoediting mechanisms including the generation of immunosuppressive or immunoregulative cells. By contrast, allograft transplantation could activate the immune system and induce a strong allogenic response. The aim of this study was to investigate the efficacy of allogenic skin transplantation in the inhibition of tumor growth through the activation of allogenic immune response. METHODS: Full-thickness skin transplantation was performed from C57BL/6 (H-2b) donors to BALB/c (H-2d) recipients that were receiving subcutaneous injection of isogenic CT26 colon cancer cells (2 × 106 cells) at the same time. The tumor size and pathological changes, cell populations and cytokine profiles were evaluated at day 14 post-transplantation. RESULTS: The results showed that as compared to non-transplant group, the allogenic immune response in the skin-grafting group inhibited the growth of tumors, which was significantly associated with increased numbers of intra-tumor infiltrating lymphocytes, increased populations of CD11c+MHC-classII+CD86+ DCs, CD3+CD4+ T cells, CD3+CD8+ T cells, and CD19+ B cells, as well as decreased percentage of CD4+CD25+Foxp3+ T cells in the spleens. In addition, the levels of serum IgM and IgG, tumor necrosis factor (TNF)-α and interferon (IFN)-γ were significantly higher within the tumor in skin transplant groups than that in non-transplant group. CONCLUSIONS: Allogenic skin transplantation suppresses the tumor growth through activating the allogenic immune response, and it may provide a new immunotherapy option for the clinical refractory tumor treatment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".