IL-4 is more effective than IL-13 for in vitro differentiation of dendritic cells from peripheral blood mononuclear cells
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 it