Silver nanoparticles rapidly induce atypical human neutrophil cell death by a process involving inflammatory caspases and reactive oxygen species and induce neutrophil extracellular traps release upon cell adhesion. Author links open the author workspace.
Bibliographic record
Abstract
Inflammation is one of the major toxic effects reported in response to in vitro or in vivo nanoparticle (NP) exposure. Among engineered NPs, silver nanoparticles (AgNPs) are very attractive for the development of therapeutic strategies, especially because of their antimicrobial properties. In humans, neutrophils, key players in inflammation, are the most abundant blood leukocytes that spontaneously undergo apoptosis, a central cell death mechanism regulating inflammation. The aim of this study was to evaluate the effect of AgNPs on neutrophil apoptosis. Transmission electronic microscopy reveals that AgNPs rapidly penetrate inside neutrophils. AgNPs induced atypical cell death where the cell volume increased and the cell surface expression of CD16 remained unaltered unlike apoptotic neutrophils where cell shrinkage and loss of CD16 are typically observed. The AgNP-induced atypical cell death is distinct from necrosis and reversed by a pancaspase inhibitor or by inhibitors of the inflammatory caspase-1 and caspase-4. In addition, AgNPs induced IL-1β production inhibited by caspase-1 and caspase-4 inhibitors and also induced caspase-1 activity. Reactive oxygen species (ROS) production was increased by AgNPs and the atypical cell death was inhibited by the antioxidant n-acetylcysteine. Under similar experimental conditions, adhesion of neutrophils leads to neutrophil extracellular trap (NET) release induced by AgNPs. However, this process was not reversed by caspase inhibitors. We conclude that AgNPs rapidly induced an atypical cell death in neutrophils by a mechanism involving caspase-1, -4 and ROS. However, in adherent neutrophils, AgNPs induced NET release and, therefore, are novel agents able to trigger NET release.
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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".