Biochemical Markers of Neurotoxicity in Wildlife and Human Populations: Considerations for Method Development
Bibliographic record
Abstract
Disruption of neurochemical parameters in blood and brain tissues can be used as early biomarkers of neurotoxicity in human and wildlife epidemiological studies. To investigate the feasibility of biomarker measurements in field samples obtained from remote locations, tissue storage limits were determined with human blood and mink cortex tissue using efficient and cost-effective microplate assays. Results show that isolated blood platelets and plasma can be stored at 4°C for 4 wk before measurement of monoamine oxidase (MAO) and cholinesterase (ChE) activities, while human lymphocytes can be stored at 4°C for up to 2 d before muscarinic acetylcholine (mACh) receptor binding analysis. Blood cells stored frozen resulted in decreased MAO activity and mACh receptor function. These data suggest that mink brain tissue obtained from field samples can be stored at various temperatures without affecting dopamine (D2) and mACh receptor densities; however, MAO and ChE activities were most stable in samples stored in a −20°C domestic freezer or at 4°C. Multiple freeze/thaw cycles alter mACh and D2 receptors and MAO activity in mink cortex samples and should therefore be minimized. In conclusion, these neurochemical biomarkers can efficiently be measured in large human and wildlife neurotoxicity studies, provided proper storage conditions are maintained. This study was financially supported by the Collaborative Mercury Research Network (COMERN) and the Natural Science and Engineering Research Council (NSERC) of Canada. C. J. Stamler and N. Basu are both recipients of NSERC postgraduate fellowships. We also thank Mary Gamberg (Gamberg Consulting, Whitehorse, Yukon Territory) and Dr. Steven J. Bursian (Michigan State University, East Lansing) for kind donation of mink tissues. Assistance by Donna Leggee, Leah Tivoli, Kovana M. Loua, and Stephanie Bailey is greatly appreciated.
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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 | 0.027 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".