Contrasting effects of myeloid dendritic cells transduced with an adenoviral vector encoding interleukin‐10 on organ allograft and tumour rejection
Bibliographic record
Abstract
Mouse bone marrow-derived myeloid dendritic cells (DC) propagated in granulocyte-macrophage colony-stimulating factor and transforming growth factor-beta1 (TGF-beta1) (so-called 'TGF-beta DC') are phenotypically immature, and prolong allograft survival. Interleukin-10 (IL-10) has been shown to inhibit the maturation of DC by down-regulating surface major histocompatibility complex (MHC) class II, co-stimulatory and adhesion molecule expression. Genetic engineering of TGF-beta DC to overexpress IL-10 might enhance their tolerogenic potential. In this study, adenoviral (Ad) vectors encoding the mouse IL-10 gene were transduced into B10 (H2b) TGF-beta DC. Transduction with Ad-IL-10 at a multiplicity of infection (MOI) of 50-100 resulted in a modest reduction in the incidence of DC expressing surface MHC class II, CD40, CD80 and CD86. Paradoxically, Ad-IL-10 transduction enhanced the allostimulatory activity of DC in mixed leucocyte reactions and cytotoxic T lymphocyte assays, and increased their natural killer cell stimulatory activity. Systemic injection of normal C3H recipients with Ad-IL-10-transduced B10-DC 7 days before organ transplantation, exacerbated heart graft rejection and augmented circulating anti-donor alloantibody titres. Contrasting effects were observed in relation to tumour growth. All mice preimmunized with Ad-IL-10-transduced, tumour antigen (B16F10)-pulsed DC developed palpable tumours, associated with significant inhibition of splenic anti-tumour cytotoxic T lymphocyte generation. Animals pretreated with control Ad-LacZ-transduced, B16F10-pulsed DC however, remained tumour free. These findings are consistent with the multifunctional immunomodulatory properties of mammalian IL-10.
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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 | 0.001 |
| 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".