Toxicological interactions of silver nanoparticles and organochlorine pesticides in mouse peritoneal macrophages
Bibliographic record
Abstract
Nanotechnology occupies a prominent space in economy and science due to the beneficial properties of nanomaterials. However, nanoparticles may pose risks to living organisms due to their adsorption and pro-oxidative properties. The aim of the current study was to investigate the effects of polymer-coated silver nanoparticles (AgNPs) and organochlorine pesticides (OCPs), as well as their combined effects on mouse peritoneal macrophages. Macrophages were isolated and exposed to three concentrations of AgNPs (groups: N1 = 30, N2 = 300 and N3 = 3000 ng.ml(-1)), two concentrations of OCPs (groups: P1 = 30 and P2 = 300 ng.ml(-1)) and the six possible combinations of these two contaminants for 24 h. AgNPs had irregular shape, Feret diameter of 8.7 ± 7.5 nm and zeta potential of -28.7 ± 3.9 mV in water and -10.7 ± 1.04 mV in culture medium. OCP mixtures and the lower concentrations of AgNPs had no detectable effects on cell parameters, but the highest AgNPs concentration showed high toxicity (trypan blue and MTT assays) resulting in morphological changes, increase of nitric oxide levels and phagocytic index. Foremost, the association of N3 and P2 led to distinct effects from those observed under single exposure.
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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 | 0.001 | 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".