Identification of IDO-Positive and IDO-Negative Human Dendritic Cells after Activation by Various Proinflammatory Stimuli
Bibliographic record
Abstract
Dendritic cells (DCs) can induce tolerance or immunity. We identified and characterized an IDO-expressing and an IDO-negative human DC population after stimulation by various proinflammatory stimuli. IDO expression was strongly dependent on the maturation status of the cells (CD83-positive cells only). The two DC subpopulations remained IDO positive and IDO negative, respectively, over a time period of at least 48 h. IDO enzyme activity of human DCs was highest during stimulation by strongly maturation-inducing TLR ligands such as highly purified LPS (TLR4 ligand) or polyriboinosinic-polyribocytidilic acid (TLR3 ligand); factors of the adaptive immune system such as IFN-γ, a mixture of cytokines, and IFN-α had lesser stimulatory capacity for IDO induction and activity. After stimulation with CD40L, IDO-positive DCs expressed significantly increased levels of B7 family molecules such as CD40, CD80, CD86, ICOS ligand, as well as PD-L1 (B7-H1) and PD-L2 (B7-DC) compared with the IDO-negative DC subset. At the same time, the inhibitory receptors Ig-like transcripts 3 and 4 were significantly downregulated on IDO-positive cells. Functionally, IDO-positive DCs produced significantly more IL-1β and IL-15 and less IL-10 and IL-6 than the IDO-negative subset after CD40L stimulation. These results show that IDO expression is associated with a distinctive phenotype and functional capacity in mature DCs. It seems likely that the IDO-positive DC subset possesses a regulatory function and might skew a T cell response toward tolerance.
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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".