Myeloid-derived suppressor cells and their association with vaccine immunogenicity in South African infants
Bibliographic record
Abstract
Abstract The role of Myeloid-Derived Suppressor Cells (MDSC) in infant immune ontogeny is unknown. Here, we evaluated MDSC frequency and relationship with infant vaccine responses throughout the first year of life in a prospective cohort study. Ninety-one South African infant-mother pairs were enrolled at delivery, and blood samples were collected at 0, 6, 10, and 14 weeks, 6 months, 9 months, and 1 year. MDSC frequencies were quantified, and immune responses to the childhood vaccines Bacillus Calmette-Guérin (BCG), hepatitis B (HepB), and combination diphtheria, tetanus, and pertussis (dTaP) were measured by Ag-specific CD4+ T cell proliferation and interferon gamma (IFN-γ) production. Vaccine-specific Ab responses to HepB, dTaP, and Haemophilus influenzae type b (Hib) were quantified via Enzyme-Linked Immunosorbent assay (ELISA). MDSC frequency in mother-infant pairs was strongly correlated; the frequency of MDSC decreased in both mothers and infants during the months after delivery/birth; and by 1 year, infant MDSC frequencies rebounded to birth levels. Higher MDSC frequency at vaccination was associated with a lack of subsequent IFN-γ release in response to vaccine Ags, with the exception of BCG. With the exception of a weak, positive correlation between MDSC frequency at 6 weeks (time of initial vaccination) and peak Hepatitis B surface antigen Ab titer, Polymorphonuclear Myeloid-Derived Suppressor Cells (PMN-MDSC) was not correlated with T cell proliferation or Ab responses in this study. The potential for MDSC-mediated suppression of vaccine Ag-specific IFN-γ responses should be explored further, and considered when evaluating candidate infant vaccines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".