Binding of trace elements (Ag, Cd, Co, Cu, Ni, and Tl) to cytosolic biomolecules in livers of juvenile yellow perch (<i>Perca flavescens</i>) collected from lakes representing metal contamination gradients
Bibliographic record
Abstract
Abstract Biomolecules involved in handling cytosolic metals in the liver of the yellow perch (Perca flavescens) were characterized in juvenile fish collected from 4 lakes constituting metal contamination gradients. Using size-exclusion liquid chromatography coupled to an inductively coupled mass spectrometer, we determined metal distributions among ligands of different molecular weights in the cytosol, before and after a heat denaturation step designed to isolate metallothionein-like peptides and proteins. Silver, Cd, and Cu found in the heat-stable protein supernatants were indeed largely present as metallothionein-like peptide complexes; but Co, Ni, and Tl, also present in the heat-stable protein supernatants, did not coelute with metallothionein-like peptides and proteins. This difference in metal partitioning is consistent with the known preference of “soft” metals such as Ag, Cd, and Cu(I) for thiolated ligands and the contrasting tendency of Co and Ni to bind to ligands with oxygen and nitrogen as donor atoms. Metal handling in the whole cytosol also reflected these differences in metal-binding behavior. For Cd and Cu, the importance of the molecular weight pool that includes metallothionein-like peptides and proteins increased relative to the other pools as the total cytosolic metal concentration ([M]cytosol) increased, consistent with a concentration-dependent detoxification response. In contrast, for Ni and Tl the increase in [M]cytosol was accompanied by a marked increase in the high–molecular weight (670–33 kDa) pool, suggesting that hepatic Ni and Tl are not effectively detoxified. Overall, the results suggest that metal detoxification is less effective for Ni, Tl, and Co than for Ag, Cd, and Cu. Environ Toxicol Chem 2018;37:576–586. © 2017 SETAC Abstract
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".