Optimization of Acetylcholinesterase and Metabolic Enzyme Activity in Multiple Fish Species
Bibliographic record
Abstract
Abstract An examination of acetylcholinesterase (AChE), citrate synthase (CS) and lactate dehydrogenase (LDH) activities in the brains of a range of fish species, occupying different trophic levels, is useful to investigate the full extent of the effects of sublethal or pulse exposures to pesticides. This investigation explores the optimization of microplate procedures for AChE, CS and LDH measurements in the brain tissue of species commonly used in laboratory investigations and those common to Canadian watersheds. A microplate assay was optimized for the measurement of AChE in the brain homogenates of seven fish species. The critical aspects of this assay requiring optimization were pH, substrate concentration and tissue dilution. Incubation with specific cholinesterase inhibitors indicated that enzymatic activity in the brain homogenates of each species was attributed to AChE only. Microplate assays were also optimized for the measurement of the metabolic enzymes, CS and LDH, in the brain homogenates of six fish species. For these assays, low interspecies variability was exhibited between optimized factors including pH, substrate, chromogen and cofactor concentrations. For each assay optimized, enzyme activities in the brain homogenates were stable for 2 to 3 hours post-thaw. Results from the present study will aid future ecotoxicological investigations of the potential impacts of AChE inhibition on neuronal glucose metabolism.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".