TLR ligands efficiently convert tolerogenic CD4‐8‐ DC into immunogenic ones stimulating long‐lasting antitumor immune response
Bibliographic record
Abstract
Different dendritic cell (DC) subsets play distinct roles in immune responses. CD4 − 8 − DC secreting transforming growth factor (TGF)‐β stimulate CD4 + regulatory T type 1 (Tr1) cell responses leading to antitumor immune tolerance. CD4 − 8 − DC could be converted into inducing Th1 rather Tr1 by being cultured for another 16–24 hrs and pulsed with 1 mg/ml OVA protein. In this study we investigate whether TLR ligands, agonist anti‐CD40 and their combinations could more efficiently convert CD4 − 8 − DC to induce Th1 response. We found that CpG, LPS and anti‐CD40 alone or CpG+anti‐CD40, but not LPS+anti‐CD40 stimulate those DCs to express similar higher level of CD40, CD54, CD80, CD86 and I‐Ab after 8hrs culture than newly isolated DCs; Among the above groups, CpG group showed the strongest capacity of stimulating the proliferation of OT1 CD8 and OTII CD4 T cells and induced 100% animal protection from tumor challenge with highest OVAI specific CTL ratio and in vivo cytotoxicity; LPS+anti‐CD40 group was the weakest in every above aspect and could not provide any animal protection. Taken together, our findings indicate that CpG 8 hrs treatment could convert tolerogenic DC into immunogenic ones provoking strong antitumor immunity; and TLR ligands plus anti‐CD40 combination should be carefully tested before using as adjuvant for DC maturation. Thus our results might have great impact on designing the future tumor vaccine.
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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 | 0.000 |
| 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".