The Role of Myeloid-Derived Suppressor Cells in Immune Ontogeny
Bibliographic record
Abstract
Myeloid-derived suppressor cells (MDSC) are a heterogeneous population of granulocytic or monocytic cells that suppress innate as well as adaptive immune responses. In healthy adults, immature myeloid cells differentiate into macrophages, dendritic cells, and granulocytes in the bone marrow and MDSC are rarely detected in peripheral blood. However, in certain pathologies, in particular malignancies and chronic infection, differentiation of these cells is altered resulting in accumulation of circulating suppressive myeloid cells. MDSC express suppressive factors such as arginase-1, reactive oxygen species, and inducible nitric oxide synthase, which have the ability to inhibit T cell proliferation and cytoxicity, induce the expansion of regulatory T cells, and block natural killer cell activation. It is increasingly recognized that MDSC alter the immune response to several cancers, and perhaps chronic viral infections, in clinically important ways. In this review, we outline the potential contribution of MDSC to the generation of feto-maternal tolerance and to the ineffective immune responses to many infections and vaccines observed in early post-natal life. Granulocytic MDSC are present in large numbers in pregnant women and in cord blood, and wane rapidly during infancy. Furthermore, cord blood MDSC suppress in vitro T cell and NK responses, suggesting that they may play a significant role in human immune ontogeny. However, there are currently no data that demonstrate in vivo effects of MDSC on feto-maternal tolerance or immune ontogeny. Studies are ongoing to evaluate the functional importance of MDSC, including their effects on control of infection and response to vaccination in infancy. Importantly, several pharmacologic interventions have the potential to reverse MDSC function. Understanding the role of MDSC in infant ontogeny and their mechanisms of action could lead to interventions that reduce mortality due to early-life infections.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".