Prednisolone Dose-Dependently Influences Inflammation and Coagulation during Human Endotoxemia
Bibliographic record
Abstract
The effects of steroids on the outcome of sepsis are dose dependent. Low doses appear to be beneficial, but high doses do not improve outcome for reasons that are insufficiently understood. The effects of steroids on systemic inflammation as a function of dose have not previously been studied in humans. To determine the effects of increasing doses of prednisolone on inflammation and coagulation in humans exposed to LPS, 32 healthy males received prednisolone orally at doses of 0, 3, 10, or 30 mg (n = 8 per group) at 2 h before i.v. injection of Escherichia coli LPS (4 ng/kg). Prednisolone dose-dependently inhibited the LPS-induced release of cytokines (TNF-alpha and IL-6) and chemokines (IL-8 and MCP-1), while enhancing the release of the anti-inflammatory cytokine IL-10. Prednisolone attenuated neutrophil activation (plasma elastase levels) and endothelial cell activation (von Willebrand factor). Most remarkably, prednisolone did not inhibit LPS-induced coagulation activation, measured by plasma concentrations of thrombin-antithrombin complexes, prothrombin fragment F1+2, and soluble tissue factor. In addition, activation of the fibrinolytic pathway (tissue-type plasminogen activator and plasmin-alpha(2)-antiplasmin complexes) was dose-dependently enhanced by prednisolone. These data indicate that prednisolone dose-dependently and differentially influences the systemic activation of different host response pathways during human endotoxemia.
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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) | 0.000 | 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 | 0.001 |
| 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".