Progesterone, IL-10 and TGF-β act in synergy to impair plasmacytoid dendritic cell functions in cord blood (57.19)
Bibliographic record
Abstract
Abstract Plasmacytoid dendritic cells (pDC) are specialized dendritic cells, also known as IFN-α producing cells. pDC are key inducers of immune responses upon viral infections; the environment although modulate their phenotype and functions. We have shown that cord blood (CB) pDC are unable to produce IFN-α after stimulation while they retain the capacity to maturate in antigen presenting cells. To get insights into the mechanisms of pDC regulation in CB, we first investigated the levels of immune regulators secreted by the placenta in CB. We show that progesterone (PG), IL-10 and TGF-β are present at high levels in CB as compared to adult blood. Using in vitro differentiated pDCs we analysed the effect of these molecules, separately or in combination, on pDC differentiation and activation. We reveal that physiological concentrations of these three factors individually have low impact on pDCs while in combination they inhibit pDC differentiation and IFN-α production. Higher concentrations of PG, IL-10 or TGF-β are also able to inhibit IFN-α production, but these conditions are not relevant to physiological levels in CB. Our results therefore shed new light on the synergy of immune regulators secreted by the placenta that have an impact on foetal and neonatal immune responses.
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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) | 0.003 | 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".