Hydrocortisone Prevents Immunosuppression by Interleukin-10+ Natural Killer Cells After Trauma-Hemorrhage
Bibliographic record
Abstract
OBJECTIVE: Trauma induces a state of immunosuppression, which is responsible for the development of nosocomial infections. Hydrocortisone reduces the rate of pneumonia in patients with trauma. Because alterations of dendritic cells and natural killer cells play a central role in trauma-induced immunosuppression, we investigated whether hydrocortisone modulates the dendritic cell/natural killer cell cross talk in the context of posttraumatic pneumonia. DESIGN: Experimental study. SETTINGS: Research laboratory from an university hospital. SUBJECTS: Bagg Albino/cJ mice (weight, 20-24 g). INTERVENTIONS: First, in an a priori substudy of a multicenter, randomized, double-blind, placebo-controlled trial of hydrocortisone (200 mg/d for 7 d) in patients with severe trauma, we have measured the blood levels of five cytokines (tumor necrosis factor-α, interleukin-6, interleukin-10, interleukin-12, interleukin-17) at day 1 and day 8. In a second step, the effects of hydrocortisone on dendritic cell/natural killer cell cross talk were studied in a mouse model of posttraumatic pneumonia. Hydrocortisone (0.6 mg/mice i.p.) was administered immediately after hemorrhage. Twenty-four hours later, the mice were challenged with Staphylococcus aureus (7 × 10 colony-forming units). MEASUREMENTS AND MAIN RESULTS: Using sera collected during a multicenter study in patients with trauma, we found that hydrocortisone decreased the blood level of interleukin-10, a cytokine centrally involved in the regulation of dendritic cell/natural killer cell cluster. In a mouse model of trauma-hemorrhage-induced immunosuppression, splenic natural killer cells induced an interleukin-10-dependent elimination of splenic dendritic cell. Hydrocortisone treatment reduced this suppressive function of natural killer cells and increased survival of mice with posthemorrhage pneumonia. The reduction of the interleukin-10 level in natural killer cells by hydrocortisone was partially dependent on the up-regulation of glucocorticoid-induced tumor necrosis factor receptor-ligand (TNFsf18) on dendritic cell. CONCLUSIONS: These data demonstrate that trauma-induced immunosuppression is characterized by an interleukin-10-dependent elimination of dendritic cell by natural killer cells and that hydrocortisone improves outcome by limiting this immunosuppressive feedback loop.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".