Wild yellow perch (Perca flavescens) oxidative stress induced by cadmium and low selenium exposures.
Bibliographic record
Abstract
44% of the yellow perch collected suffer lipid peroxidation (high [MDA]).High proportions of GSSG relative to GSH indicate the presence of free radicals.Proportion of GSSG is related to the [Se] in the HSP fraction, which suggests that higher [Se] reduced oxidative stress Fish suffering oxidative stress have higher concentrations of Cd and Zn in sensitive cell fractions (HDP+Organelles) compared to those in the detoxified (HSP) fractionWe used this equation to quantify Cd and Zn spillover:[Se] in the liver is negatively correlated with lipid peroxidationOur study is the first to observe an antagonist effect in nature between selenium and a metal other than mercury[MDA] (µmol g -1 )[MDA] (µmol g -1 ) GSH/GSSG % GSSG AbstractYellow perch (Perca flavescens) collected from lakes in the mining regions of Sudbury (ON) and Rouyn-Noranda (QC) display wide ranges in their concentrations of trace elements (Cd, Cu, Ni, Se, Tl and Zn).To determine if these fish are suffering oxidative stress, we measured the concentrations of glutathione (GSH), its disulfide (GSSG) and malondialdehyde (MDA).We conclude that 44% of the individuals collected from eight lakes were at risk of cellular oxidative stress and lipid peroxidation.However, selenium appears to act as an antioxidant because higher fish selenium concentrations were coincident with lower proportions of GSSG as opposed to GSH and lower concentrations of MDA.Furthermore, fish suffering oxidative stress had higher proportions of some trace metals (Cd and Zn) in sensitive subcellular fractions (organelles and heat-denatured proteins), which suggests that oxidative stress causes the release of these metals from metal-binding proteins and that Cd further exacerbates the negative effects of the low Se exposure.
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) | 0.003 | 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".