Measuring Biotic Ligand Model (BLM) Parameters in Vitro: Copper and Silver Binding to Rainbow Trout Gill Cells as Cultured Epithelia or in Suspension
Bibliographic record
Abstract
Biotic ligand models (BLMs) for metals are useful for risk assessment. The modeling of metal complexation by the biotic ligand has received little attention relative to the modeling of organic and inorganic complexation of metals in solution. We used ion selective electrodes (ISEs) to directly characterize copper and silver binding to rainbow trout gill cells, either as cultured reconstructed epithelia, or dispersed in suspension. Preparations were composed of pavement cells (PVCs) alone or mixtures of PVCs (≈85%) and mitochondria-rich cells (MRCs, ≈15%). Mixed cells showed up to an order of magnitude greater binding for both metals, indicating that MRCs were much more important than PVCs. Also, cell orientation had a dramatic effect; cells cultured as epithelia exhibited much greater binding than cells in suspension. Silver and copper demonstrated generally similar binding behavior, with stronger (logK ≈ 10 or greater) and weaker binding sites (logK ≈ 8). Comparisons to existing BLM calibrations show good agreement, but reveal that selection of analytical window can impact which binding sites are titrated. We conclude that cultured gill epithelia in vitro provide a powerful approach to studying metal complexation directly at the biotic ligand.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".