Interferon-γ increases monocyte HLA-DR expression without effects on glucose and fat metabolism in postoperative patients
Bibliographic record
Abstract
Tissue injury is associated with decreased cellular immunity and enhanced metabolism. Immunodepression is thought to be counteracted by interferon (IFN)-gamma, which increases human leukocyte antigen (HLA)-DR expression. Hypermetabolism could be enhanced by IFN-gamma because cytokines induce a hypermetabolic response to stress. In healthy humans, IFN-gamma enhanced HLA-DR expression without effects on glucose and fat metabolism. In the present study, we evaluated whether IFN-gamma lacks potential harmful side effects on metabolic and endocrine pathways while maintaining its beneficial effects on the immune system under conditions in which the inflammatory response system is activated. In 13 patients scheduled for major surgery, we studied HLA-DR expression on peripheral blood monocytes before surgery and postoperatively randomized the patients into an intervention and a placebo group. Subsequently, we evaluated the effects of a single dose of IFN-gamma vs. saline on short-term monocyte activation, glucose and lipid metabolism, and glucose and lipid regulatory hormones. HLA-DR expression on monocytes was restored from postoperative levels of 54% (42-60%; median and interquartiles) to 92% (91-96%) 24 h after IFN-gamma administration but stayed low in the placebo-treated patients. IFN-gamma did not affect glucose metabolism (plasma glucose, rate of appearance and disappearance of glucose) and lipid metabolism (plasma glycerol, plasma free fatty acids, and rates of appearance and disappearance of glycerol). IFN-gamma had no effect on plasma cortisol, adrenocorticotropic hormone, growth hormone, insulin, C-peptide, glucagon, epinephrine, and norepinephrine concentrations. We conclude that IFN-gamma exerts a favorable effect on cell-mediated immunity in patients after major surgery without effects on glucose and lipid metabolism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".