IL-4 is more effective than IL-13 for in vitro differentiation of dendritic cells from peripheral blood mononuclear cells
Bibliographic record
Abstract
Dendritic cells (DCs) are the most potent professional antigen-presenting cells which can activate T cells to induce the primary immune response. For clinical studies, DCs are often differentiated in vitro from peripheral blood mononuclear cells (PBMCs) through treatment with granulocyte macrophage colony-stimulating factor (GM-CSF) and IL-4. However, IL-13, a cytokine closely related to IL-4, has also been reported to induce differentiation equally or more efficiently when used with GM-CSF. For the present study, we compared the DC characteristics exhibited by iDCs and LPS-matured DCs differentiated from PBMCs using GM-CSF and IL-4 or IL-13. Physical characteristics examined include cellular morphology and surface phenotype. Functional traits investigated include FITC-dextran uptake, IL-10 and IL-12 production, allostimulation and cytokine production by stimulated T cells and antigen-specific T cell stimulation. Compared with IL-13-derived DCs, IL-4 treatment yielded more differentiated DCs, with extensive dendrites and higher expression of DC-SIGN, DEC-205, CD86 and HLA-DR. In addition, IL-4 DCs were more efficient at inducing allogeneic T cell proliferation and immature IL-4 DCs had higher endocytic activity at low FITC-dextran concentrations (1 microg ml(-1)). Although IL-13 was capable of generating DCs from PBMCs, it was not as effective as IL-4 in generating DC phenotype and functionality. Thus, the use of GM-CSF and IL-4 is the more efficient treatment for inducing DC differentiation from PBMCs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".