CYP1A expression in freshwater fish of western New York as an indicator of pollution levels
Bibliographic record
Abstract
Various species of freshwater fish regulate the expression of certain proteins in response to environmental contamination. Previous research has shown that CYP1A expression increases in response to contaminant levels, and can result in increased tumor formation. Fish in contaminated environments would thus benefit by downregulating the expression of CYP1A to reduce tumor prevalence as an adaptive strategy. Alternatively, regulation of the CYP1A protein in fish can serve as a bioindicator of the pollution level of an environment. This study evaluated CYP1A expression in twelve different species of freshwater fish from seven bodies of water throughout western NY including Cuba Lake, Genesee River, Hanging Bog, Love Canal, Moss Lake, Rushford Lake and Tifft Nature Preserve. Western blot analysis was used to measure CYP1A expression as a marker of site pollution and potential fish population adaptation. It was hypothesized that low CYP1A expression at a site with known contamination would suggest signs of adaptation to pollution levels present. Furthermore, if at least one sample from a species showed CYP1A expression, then the CYP1A antibody (Caymen Chemical, USA; 173132) had compatibility with that species, eliminating falsely suspected adaptation. The results from this study suggest possible adaptation of fish may be occurring in the polluted Tifft Nature Preserve and Genesee River. In contrast, CYP1A expression in fish from Cuba Lake, Hanging Bog, Love Canal, Moss Lake, and Rushford Lake appear to represent known pollution levels and adaptation is not likely occurring. Results from this study are preliminary and next steps include collection and analysis of sediment to provide a stronger correlation between pollution at sites and CYP1A expression.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".