Proteomic Profiles of White Sucker (<i>Catostomus commersonii</i>) Sampled from within the Thunder Bay Area of Concern Reveal Up-Regulation of Proteins Associated with Tumor Formation and Exposure to Environmental Estrogens
Bibliographic record
Abstract
White sucker (Catostomus commersonii) sampled from the Thunder Bay Area of Concern were assessed for health using a shotgun approach to compile proteomic profiles. Plasma proteins were sampled from male and female fish from a reference location, an area in recovery within Thunder Bay Harbour, and a site at the mouth of the Kaministiquia River where water and sediment quality has been degraded by industrial activities. The proteins were characterized using reverse-phase liquid chromatography tandem to a quadrupole-time-of-flight (LC-Q-TOF) mass spectrometer and were identified by searching in peptide databases. In total, 1086 unique proteins were identified. The identified proteins were then examined by means of a bioinformatics pathway analysis to gain insight into the biological functions and disease pathways that were represented and to assess whether there were any significant changes in protein expression due to sampling location. Female white sucker exhibited significant (p = 0.00183) site-specific changes in the number of plasma proteins that were related to tumor formation, reproductive system disease, and neurological disease. Male fish plasma had a significantly different (p < 0.0001) number of proteins related to neurological disease and tumor formation. Plasma concentrations of vitellogenin were significantly elevated in females from the Kaministiquia River compared to the Thunder Bay Harbour and reference sites. The protein expression profiles indicate that white sucker health has benefited from the remediation of the Thunder Bay Harbour site, whereas white sucker from the Kaministiquia River site are impacted by ongoing contaminant discharges.
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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 | 0.000 |
| 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) | 0.001 | 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".