Biomarkers of exposure to nanosilver and silver accumulation in yellow perch (<i>Perca flavescens</i>)
Bibliographic record
Abstract
Abstract There is a risk of exposure of aquatic organisms to silver nanoparticles (AgNPs) from discharges of municipal and industrial wastewater. In the present study, yellow perch (Perca flavescens) were exposed to environmentally relevant concentrations (1 mg/L and 100 mg/L) of AgNPs and silver ions (Ag+) in static-renewal experiments conducted over 96 h and 10 d. The greatest accumulation of total Ag occurred in the liver of P. flavescens, and there was >10-fold more accumulation in the treatments with Ag+ relative to the AgNP treatments. Residues of total Ag increased with concentration and duration of exposure in liver, gill, and muscle. Both exposures caused a 2-fold induction of gene expression for metallothionein (mt) in liver tissue after 96 h of exposure and reductions in levels of oxidized glutathione (GSSG) in liver after 10 d of exposure. Both AgNPs and Ag+ decreased the expression of heat-shock proteins (hsp70). Exposure to the high concentration of AgNPs for 10 d significantly increased lipid peroxidation in gill tissue, as indicated by the thiobarbituric acid reactive substances (TBARS) assay. There was a negative correlation between mean levels of GSSG and TBARS for both gill and liver tissue when data for all treatments were combined. It is significant that these biological responses were observed in P. flavescens exposed to AgNPs, even though accumulation of total Ag was at least 10-fold lower relative to the treatments with Ag+. Environ Toxicol Chem 2017;36:1211–1220. © 2016 SETAC
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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) | 0.000 | 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".