Intravenous immunoglobulin G (IVIg) concurrently promotes Th2 and T regulatory cell development while abrogating Th17 cells
Bibliographic record
Abstract
Abstract We aimed to further elucidate the mechanisms by which IVIg modulates the balance between Th17, Th2, and Treg. Using an OVA-driven murine model of allergic airway disease exhibiting a mixed Th17/Th2 response, we examined lung inflammation by H & E staining and histology, and performed cellular phenotyping of lung homogenates using flow cytometry. In addition, we assessed cytokine production by intracellular staining and ELISA, and performed co-culture studies. IVIg treatment reduced lung inflammation and diminished the frequency and absolute number of lung Th17 cells, IL-17A production, and neutrophilia. In contrast, the frequency and absolute number of eosinophils and basophils was increased, while IgE production was abrogated in the IVIg group. This was accompanied by a significant increase in IL-13+ and IL-5+ T cells and in IL-5 concentration in the BAL. The frequency of Foxp3+IL10+ + cells within CD4+ T cells (1.43 % ± .061 vs 2.57 % ± .30, p < .01) and IL-10 secreting dendritic cells (0.28 % ± 0.060 vs 1.20 % ± 0.27, p < .05) was augmented. We also observed an increase in the frequency of CD206 expressing macrophages, or M2 macrophages, in the IVIg group (10.81% ± 2.21 vs 19.80 % ± 1.81, p < .01). Using an in vitro DC: T cell co-culture, IVIg-treated DC inhibited IL-17A production by T cells and increased IL-13 production and Treg differentiation. In conclusion, IVIg modulates Th2/Th17/Treg balance by favoring Treg and Th2 cells while inhibiting Th17 cells. Furthermore, IVIg renders both dendritic cells and macrophages tolerogenic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".