Granulocytic myeloid-derived suppressor cells inversely correlate with plasma arginine and overall survival in critically ill patients
Bibliographic record
Abstract
Critically ill patients display a state of immunosuppression that has been attributed in part to decreased plasma arginine concentrations. However, we and other authors have failed to demonstrate a clinical benefit of L-arginine supplementation. We hypothesize that, in these critically ill patients, these low plasma arginine levels may be secondary to the presence of granulocytic myeloid-derived suppressor cells (gMDSC), which express arginase known to convert arginine into nitric oxide (NO) and citrulline. Indeed, in a series of 28 non-surgical critically ill patients, we showed a dramatic increase in gMDSC compared to healthy subjects (P = 0·0002). A significant inverse correlation was observed between arginine levels and gMDSC (P = 0·01). As expected, gMDSC expressed arginase preferentially in these patients. Patients with high gMDSC levels on admission to the medical intensive care unit (MICU) presented an increased risk of death at day 7 after admission (P = 0·02). In contrast, neither plasma arginine levels, monocytic MDSC levels nor neutrophil levels were associated with overall survival at day 7. No relationship was found between body mass index (BMI) or simplified acute physiology score (SAPS) score, sequential organ failure assessment (SOFA) score or gMDSC levels, eliminating a possible bias concerning the direct prognostic role of these cells. As gMDSC exert their immunosuppressive activity via multiple mechanisms [production of prostaglandin E2 (PGE2 ), interleukin (IL)-10, arginase, etc.], it may be more relevant to target these cells, rather than simply supplementing with L-arginine to improve immunosuppression and its clinical consequences observed in critically ill patients.
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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 | 0.002 |
| 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".