Impact of Cytomegalovirus Infection and Genetic Background on the Frequencies of Peripheral Blood Suppressor Cells in Human Twins
Bibliographic record
Abstract
Frequencies and proportions of regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs) in peripheral blood may be informative biomarkers for certain disease states. The influence of genetics and lifetime pathogen exposures on Treg and MDSC frequencies is largely unexplored. Cytomegalovirus (CMV) establishes a latent infection and causes an accumulation of late-differentiated CD8+ memory T cells, commonly associated with a lower frequency of naive cells. Here, analyzing peripheral blood mononuclear cells by multicolor flow cytometry, we found a tendency towards lower frequencies of CD4+CD25+FoxP3+ Tregs in CMV-seropositive than -seronegative middle-aged individuals (p = 0.054), whereas frequencies of lineage-negative CD14+HLA-DR-MDSCs were significantly lower in CMV-seropositive participants (p = 0.005). Assessing associations with the presence of antibodies against different CMV structural proteins, rather than merely assigning seropositivity or seronegativity, failed to yield any closer associations. Examining Treg subsets revealed at most a minor role of the individual’s genetic background, based on an analysis of monozygotic (MZ, n = 42) versus dizygotic (DZ, n = 39) twin pairs from the Danish Twin Registry. The same was true for MDSCs. These initial results suggest that an immunological history of exposures is more important than genetics in determining overall human suppressor cell levels.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".