Reprogramming in vivo th17 into th17/th2 by Sirp-α dendritic cells in the lungs
Bibliographic record
Abstract
Dendritic cells (DCs) play a crucial role in the development of the adaptive immune response. Unbalance DC response can cause Th1, Th17 or Th2-mediated diseases. By in vitro manipulation, Th2 and Th17 cell lines can be reprogrammed into Th1. This highlights the notion of the plasticity of different populations of CD4 T helper cells. So far, the conversion of Th17 memory cells into Th2 cells has not been demonstrated in the tissues. Mice were immunized by repetitive administration of inflammatory DCs loaded with OVA protein antigen (OVA-DC), locally (intra-tracheal) or systematically (intravenous). Mice were sacrificed 24h after the last challenge and lymph nodes, serum, lungs and bronchoalveolar lavage were collected to evaluate the immune response. We showed here, that administration of OVA-DCs generated antigen-specific CD4 T cells that produced IL-17, IL-13 and IL-4 (Th17/Th2) and expressed GATA-3 in the lungs and the lymph nodes. The immunized mice developed an IgE-independent lung inflammation that displayed resistance to treatment with corticosteroids. This inflammation was characterized by a mixed infiltration of neutrophils and eosinophils in the bronchoalveolar lavage. We demonstrated that airway inflammatory SIRP-a DCs converted in vitro -generated Th17 but not Th2 cell lines into Th17/Th2. Finally, passive transfer of Th17/Th2 cells was sufficient to drive airway inflammation in naïve mice. We propose that immunization with inflammatory DCs, regardless of the route of immunization, induces chronic inflammation of the airways, which is associated with a Th2/Th17 response.
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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 | 0.001 |
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".