Effect of everolimus on the immunomodulation of the human neutrophil inflammatory response and activation
Bibliographic record
Abstract
The primary cause of mortality at 5 years following a cardiac transplantation is the development of atherosclerosis, termed coronary allograft vasculopathy (CAV). This pathology is characterized by diffused intimal hyperplasia and emanates from coronary arterial injuries caused by immune inflammatory cells. Neutrophils play an important role in this inflammatory process; however, their potential participation in the pathogenesis of CAV is poorly understood. Despite their essential contribution to the prevention of graft rejection, immunosuppressive drugs could have detrimental effects owing to their pro-inflammatory activities. Thus, we investigated the impact of different immunosuppressive drugs on the inflammatory response of neutrophils isolated from the blood of healthy volunteers. Under basal conditions, mammalian target of rapamycin (mTOR) inhibitors (sirolimus and everolimus) had the most potent anti-inflammatory effect, decreasing both IL-8 release (≈-80%) and vascular endothelial growth factor (VEGF) release (≈-65%) and preserving the release of the anti-inflammatory cytokine interleukin-1 receptor antagonist (IL-1RA). In TNF-α-treated neutrophils, pre-incubation with everolimus provided the most potent effect, simultaneously reducing the release of both VEGF and IL-8 while doubling the release of IL-1RA. This latter effect of everolimus was maintained even when administered in combination with other immunosuppressive drugs. Sirolimus and everolimus decreased the tumor necrosis factor (TNF)-α-induced adhesion of neutrophils to human endothelial cells and human extracellular matrix. This effect was largely dependent on the ability of these compounds to alter β2-integrin/CD18 activation. Our results suggest a potential mechanism for the beneficial effect of everolimus in the prevention of CAV in heart transplant recipients.
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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 | 0.000 |
| 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".