Silver nanoparticles of 70 nm and 20 nm affect differently the biology of human neutrophils
Bibliographic record
Abstract
The influence of size of nanoparticles (NP), especially in regard to pulmonary toxicity, has been widely investigated. In general, NP with smaller diameters are more pro-inflammatory in vivo, at least in terms of neutrophil influx. Nevertheless, the influence of size of NP on polymorphonuclear neutrophil (PMN) cell biology is poorly documented. In the study here, it was decided to determine if AgNP with a diameter of 70 nm (AgNP70) will alter the biology of human PMN similarly to AgNP20 previously reported to induce apoptosis and inhibit de novo protein synthesis. The results here indicated that, in contrast to AgNP20, AgNP70 delayed PMN apoptosis. However, both AgNP20 and AgNP70 inhibited de novo protein synthesis. Both forms of AgNP did not significantly increase reactive oxygen species (ROS) production, but AgNP20 significantly increased the cell production of the CXCL8 chemokine (IL-8). In addition, AgNP20, but not AgNP70, induced the release of albumin and matrix metalloproteinase-9 (MMP-9/gelatinase B) into culture supernatants. Consistent with this latter observation, gelatinase activity was increased by AgNP20, as assessed by zymography. From these outcomes, it is concluded that two NP with different initial diameters can possess similar - as well as distinct - biological properties in modulating human PMN functions. These outcomes are testimony to the complexity of the modes of action of NP at the cellular level.
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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".