The Effects of Selected Metals and Rare Earth Elements On the Peroxidase Toxicity Assay
Bibliographic record
Abstract
The search for quick and sensitive biosensors to detect changes in water quality and identify potentially toxic compounds, as recommendations to reduce fish testing increase. The peroxidase toxicity (Perotox) screen was recently proposed as a quick and sensitive biosensor for toxicity investigation. The purpose of this study was to examine the potential toxicity of environmentally relevant heavy metals cadmium (Cd) and copper (Cu), arsenic (As) and rare earth elements cerium (Ce), gadolinium (Gd), lanthanum (La) and samarium (Sm) using the Perotox screening test. Horseradish peroxidase (Per) without/with exogenously added DNA (DNA protection index) was exposed to increasing concentrations of the above elements for 5 min and its activity assessed. The data revealed that most elements were able to reduce Per activity at different potencies based on the calculated concentration that inhibit Per activity by 20% (IC20): Cd~Cu>La~Gd>Sm>Ce>As. Inhibitions in Per activity led to oxidation of the monounsaturated detergent Tween 80 in the incubation media. The addition of exogenous DNA prevented Per inhibitions where the following elements show strongest interaction with DNA:Ce, Sm and As. However, the DNA protection index did not always lead to the formation of DNA strand breaks. The Perotox IC20 values were significantly correlated (r=0.6) with the reported 96 h acute lethality in Oncorhynchus mykiss juveniles making it a potential alternative for fish toxicity screening for compliance investigations. In conclusion, a simple, quick and inexpensive enzyme biosensor based on Per inhibitions is presented as a pre-screening methodology for the toxicity of various chemicals to fish.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".