Mercury but not Organochlorines Inhibits Muscarinic Cholinergic Receptor Binding in the Cerebrum of Ringed Seals (<i>Phoca hispida</i>)
Bibliographic record
Abstract
Elevated concentrations of organochlorines and mercury (Hg) have been reported in marine mammals on a global scale. While risk assessments are generally based on quantifying body burdens of toxicants, much less is known about associated adverse health effects and their underlying mechanisms. The purpose of this study was to characterize the inhibitory effects of methylmercury (MeHg+), mercuric chloride (Hg2+), p,p'-DDT, Arochlor 1254, chlordane,dieldrin, lindane, and toxaphene on [3H]quinuclidinyl benzilate ([3H]-QNB) binding to the muscarinic cholinergic (mACh) receptor in cellular membranes isolated from the cerebrum of ringed seals (Phoca hispida). [3H]-QNB binding to the mACh receptor was saturable with a mean receptor density (B(max)) of 826.9 +/- 68.4 fmol/mg and ligand affinity (K(d)) of 0.31 +/- 0.04 nM. MeHg+ and Hg2+ were the only neurotoxicants that inhibited radioligand binding by greater than 50%. Hg2+ was significantly more potent at inhibiting mACh receptor binding than MeHg+ when the IC50 data were compared (IC50 = 1.92 +/- 0.06 microM versus 2.75 +/- 0.22 microM), but when the data were normalized to derive inhibition constants (K(i)) there was no statistical difference in inhibition (Hg2+ = 1.38 +/- 0.07 mM; MeHg+ = 1.26 +/- 0.12 microM). Toxaphene also inhibited mACh receptor binding by 22.4%, but this was only significant at the highest concentration tested (320 microM). Overall, these data suggest that Hg, and not organochlorines,inhibits ligand binding to the mACh receptor. These mechanistic findings may be used to support and develop specific biomarkers of Hg exposure and neurotoxicity in sensitive ecological species.
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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 | 0.001 | 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".