High Dose Intravenous IgG Therapy Modulates Multiple NK Cell and T Cell Functions in Patients With Immune Dysregulation
Bibliographic record
Abstract
Intravenous immunoglobulin (IVIG) is an effective immunomodulatory treatment for immune dysregulation diseases. However, the mechanisms by which it reduces systemic inflammation are not well understood. NK cell cytotoxicity is decreased by IVIG in women with reduced fertility, but IVIG effects on NK cells in immune dysregulation are less clear. We hypothesized that IVIG modulation of lymphocyte function, especially in NK cells, is important for resolution of inflammation. Our aim was to identify IVIG-induced changes in a cohort of patients with Kawasaki disease (KD) and those that occur broadly in pediatric patients with various immune dysregulatory diseases. Peripheral blood mononuclear cells (PBMCs) of patients with KD or autoimmune/inflammatory diseases were phenotyped pre and post high dose IVIG treatment by flow cytometry. In KD patients, after IVIG infusion T reg cell frequency and the proportion of activated CD25 + immunoregulatory CD56 bright NK cells was increased, and multiple lymphocyte subsets showed increased expression of the lymphoid tissue homing receptor CD62L. Importantly, IVIG treatment decreased the frequency of cells expressing the degranulation marker CD107a among cytotoxic CD56 dim NK cells, which was reflected in a significant reduction in target cell killing and in decreased production of multiple pro-inflammatory mediators. Interestingly, the activating receptor CD336 was expressed on a higher proportion of CD56 bright NK cells after IVIG in both KD and autoimmune/inflammatory patients while other NK receptors were increased differentially in each cohort. In autoimmune/inflammatory patients IVIG induced the proliferation marker CD71 on a higher percentage of CD56 dim NK cells, and in contrast to KD patients, CD107a + cells were increased in this subset. Furthermore, when PBMCs were stimulated ex vivo with IL-2 or Candida antigen in autologous plasma, more of the CD4 + T cells of KD patients expressed CD25 after IVIG therapy but fewer cytotoxic T cells were degranulated based on CD107a expression. In summary, IVIG treatment in patients with immune dysregulation has multiple effects, especially on NK cell subsets and CD4 + T cells, which are compatible with promoting resolution of inflammation. These novel findings provide insight into the immunomodulatory actions of IVIG in autoimmune and inflammatory conditions.
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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 | 0.000 |
| 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 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".