Interaction between silver nanoparticles of 20 nm (AgNP<sub>20</sub>) and human neutrophils: induction of apoptosis and inhibition of <i>de novo</i> protein synthesis by AgNP<sub>20</sub> aggregates
Bibliographic record
Abstract
Cytotoxic and proinflammatory properties of silver nanoparticles (AgNPs) have been reported in few studies but the direct interaction between AgNPs and neutrophils, which play a key role in inflammation, has never been documented. Here, we examined the role of AgNPs with a starting size of 20 nm (AgNP20 ) in human neutrophils. Using dynamic light scattering for the characterization of NPs suspended under identical conditions to those used for in vitro experiments, we found that, at 10 µg ml(-1) , 92% of AgNP20 possess a diameter of 17.1 nm but, at 100 µg ml(-1) , a tri-modal size distribution with large aggregates was observed (> 500 nm). Neutrophil cell size increased when treated with AgNP20 and transmission electronic microscopy experiments revealed that AgNP20 can rapidly interact with the cell membrane, penetrate neutrophils, localize in vacuole-like structures, and be randomly distributed in the cytosol after 24 h. Treatment with 100 µg ml(-1) AgNP20 for 24 h (but not 10 µg ml(-1) ) increased the neutrophil apoptotic rate and inhibited de novo protein synthesis. We conclude that AgNP20 induced apoptosis and can act as potent inhibitors of de novo protein synthesis at 100, but not 10 µg ml(-1) in human neutrophils.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".