Dendritic Cell Differentiation Signals Induce Anti-Inflammatory Properties in Human Adult Microglia
Bibliographic record
Abstract
Microglia are resident cells of the CNS that belong to the myeloid cell lineage. In experimental models of neuroinflammation, they have limited capacity to function as APCs when compared with dendritic cells (DCs). Human peripheral blood monocytes have the plasticity to differentiate into mature DCs when exposed to GM-CSF and IL-4 followed by LPS. In this study we addressed the potential of human microglia to acquire phenotypic and functional properties of mature DCs under similar inducing conditions. Treated adult and fetal microglia became CD14(low) and acquired limited expression of CD209 (DC-SIGN); they remained CD1a(-) and CD83(-), and decreased MHCII expression, suggesting that they had not achieved a complete DC phenotype. The monocyte-derived DCs efficiently promoted CD4 T cell proliferation in an allogeneic MLR, whereas differentiated adult microglia had a decreased ability to stimulate CD4 T cell proliferation compared with their untreated counterparts. Differentiated fetal microglia did support CD4 T cell proliferation, whereas untreated cells could not. Fetal and adult microglia produced significant amounts of IL-10 following differentiation but no detectable IL-12 p70, in contrast to differentiated monocytes that produced IL-12 p70. Our data indicate that neither adult nor fetal microglia acquired the full characteristic phenotype of mature stimulatory DCs when treated with DC-inducing cytokines in vitro. Moreover, such treatment, especially of adult microglia, induces functional responses that could promote an antiinflammatory environment in the CNS.
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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) | 0.002 | 0.001 |
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".