Cytosolic Distribution of Metals (Cd, Cu) and Metalloids (As, Se) in Livers and Gonads of Field-Collected Fish Exposed to an Environmental Contamination Gradient: An SEC-ICP-MS Analysis
Bibliographic record
Abstract
The distribution of As, Cd, Cu and Se among biomolecules of different molecular weight (MW) in the heat-treated cytosolic fraction of livers and gonads of white suckers (WS; Catostomus commersonii) collected in a reference lake and in a lake subject to multi-metal contamination was investigated. Distribution profiles were obtained by separation of the heat-stable protein and peptide (HSP) fractions using size-exclusion high performance-liquid chromatography, coupled online to an inductively coupled plasma mass spectrometer, to quantify the associated metals. Metal-handling strategies did not vary between the reference and exposed fish, with the exception of As. Cadmium and Cu appeared associated with the heat-stable peptides metallothioneins (MTs), indicating their reasonable detoxification and regulation in WS. In contrast, Se and As were not bound to MTs, but rather, to biomolecules of lower MW (<2 kDa). Arsenic was found associated with the same biomolecules in fish from both lakes, but their proportions changed between reference and exposed fish. For future work, the identification of the Se and As binding biomolecules would be of great interest to determine if these metalloids are detoxified or if, conversely, the biomolecules are metal-sensitive and their binding to Se or As represents a threat for the health of these 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 | 0.000 | 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) | 0.001 | 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".