VEGA, a new platform combining QSAR and read across for the prediction of chemical properties
Bibliographic record
Abstract
concentrations of PW, QUATs, Imidazoline and phosphate ester used in the present study caused greater DNA damage in haemocytes than gill cells, suggesting that the gills may not be as susceptible.SOD activities increased significantly in gills of mussels exposed to QUATs (>0.01 mg/l; P<0.05), PW (≥ 0.001 mg/l; P≤0.001) imidazoline (≥ 0.001 mg/l; P≤0.001) and phosphate ester (≥ 0.01 mg/l; P≤0.001).The levels of lipid proxidation in mussel gills were significantly elevated compared to the control at concentrations > 0.5 mg/l (QUATs; P<0.05), ≥ 0.3 mg/l (imadazoline; P<0.05), ≥ 0.01 mg/l (Phosphate ester; P<0.05), ≥ 0.001 mg/l (PW; P<0.05).Previous studies have shown similar levels of lipid peroxidation in the digestive glands of mussels following exposure to 45 µg/l HgCl 2 [14][15].A significant increase in lysosomal membrane damage haemocytes occurred in mussels exposed to QUATs (≥ 0.5 mg/l; P<0.05), imidazoline (≥ 0.3 mg/l; P≤0.001).Whilst the bioaccumulation of C 16 QUATs in tissues of mussels was observed, this work for Phosphate esters and imidazoline are ongoing.Conclusions The findings of the present study suggest that environmentally relevant concentrations of PW, QUATs, Imidazoline and phosphate ester caused oxidative stress, and inflicted significant DNA damage in marine mussels under laboratory conditions, which could therefore have consequences for disposal of produced water at sea.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".