Differential immunomodulation of T-cells by immunoglobulin replacement therapy in primary and secondary antibody deficiency
Bibliographic record
Abstract
Patients with primary or secondary antibody deficiency (PAD or SAD) are at increased risk of recurrent infections that can be alleviated by immunoglobulin replacement therapy (IRT). In addition to replenishing antibody levels, IRT has been suggested to modulate immune response in patients with antibody deficiency. Although both commonly treated with IRT, the underlying causes of PAD and SAD vary greatly, suggesting differential modulation of T-cell function that may lead to different responses to IRT. To explore this, peripheral blood mononuclear cells (PBMCs) were sampled from 17 PAD and 14 SAD patients before and 2-10 months after initiation of IRT, and analyzed for changes in T-cell phenotype and function. Proportions of CD4, CD8, Treg, or memory T-cells did not significantly change post-IRT compared to pre-IRT. However, we report distinct modulation in T-cell function between PAD and SAD patients post-IRT. Upon α-CD3/CD28 stimulation, proportion of IFN-γ+ CD4 and CD8 T-cells increased in SAD (p = 0.005) but not PAD patients post-IRT compared to baseline. Interestingly, total T-cell proliferation was reduced post-IRT in both PAD and SAD patients, although the reduction in proliferation was primarily due to reduced CD4 T-cell proliferation in PAD (p = 0.025) in contrast to CD8 T-cells in SAD (p = 0.042). In summary, even though IRT provides patients with passive humoral immunity-mediated protection in PAD and SAD, our findings suggest that IRT immunomodulation of T-cells is different in T-cell subsets depending on underlying immunodeficiency.
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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".