Conversion of Tolerogenic CD4¯8¯ Dendritic Cells to Immunogenic Ones Inducing Efficient Antitumor Immunity
Bibliographic record
Abstract
Culturing conditions may affect dendritic cell (DC) maturation status and functional effects. We have previously demonstrated that different DC subsets play distinct roles in immune responses. The splenic CD4-8- DC subset that secretes transforming growth factor (TGF)-beta stimulates CD4+ regulatory T type 1 (Tr1) cell responses, and this leads to antitumor immune tolerance. In this study, we investigated the potential effect of culturing conditions, namely: (1) duration of culturing and (2) the dose of antigen ovalbumin (OVA) for DC pulsing, respectively, in the conversion of tolerogenic CD4-8- DC into immunogenic DCs. Our data showed that isolated CD4-8- DCs cultured for an additional 18 hours in medium containing 15-20 ng/mL granulocyte macrophage colony-stimulating factor (GM-CSF) became more mature compared to the freshly isolated CD4-8- DCs. When pulsed with OVA at the relatively high concentration of 1 mg/mL, but not at 0.1 mg/mL, the CD4-8- DCs could be converted into immunogenic CD4-8- DCs, which stimulated CD4+ T-cell differentiation into type 1 helper T (Th1) cells. Vaccination of mice with converted CD4-8- DCs induced strong OVA-specific cytotoxic T-lymphocyte (CTL) responses and protective immunity against OVA-expressing BL6-10OVA B16 melanoma. Taken together, our findings indicate that the conversion of DCs from a tolerogenic to an immunogenic state can be achieved by the elongation of DC culturing time in combination with a high-dose antigen for DC pulsing. Therefore, our results may have a significant impact in designing DC-based antitumor 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) | 0.000 | 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".