Blood CD33(+)HLA-DR(−) myeloid-derived suppressor cells are increased with age and a history of cancer
Bibliographic record
Abstract
As we age, the composition of our peripheral leukocytes changes dramatically. Many of these alterations contribute to the general immune dysfunction that burdens the elderly, which in turn, contributes to increased susceptibility to disease. MDSCs represent a heterogeneous population of immunosuppressive leukocytes that are elevated in the peripheral blood of cancer patients. Given the relation between cancer incidence and age, this study examined the frequency of peripheral blood CD33(+)HLA-DR(-) MDSCs across three cohorts: healthy adults (19-59 years old), community-dwelling seniors (61-76 years old), and frail elderly (67-99 years old). This analysis is the first to demonstrate that MDSCs and specifically the CD11b(+)CD15(+) MDSC subset are increased with age. Proinflammatory cytokines that are required for the differentiation of MDSCs (e.g., TNF-α, IL-6, and IL-1β) were similarly found to be increased in the serum of the frail elderly. Furthermore, the proportion of MDSCs and the CD11b(+)CD15(+) subset were found to be elevated significantly in elderly donors with a history of cancer. This age-related elevation in the frequency of MDSCs may contribute to the increased cancer incidence that occurs with age. Further investigation into the functional consequences of elevated MDSCs will provide valuable insight into the progression of age-related pathologies.
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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) | 0.002 | 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".