Biotransformation of PCBs in Relation to Phase I and II Xenobiotic-Metabolizing Enzyme Activities in Ringed Seals (<i>Phoca hispida</i>) from Svalbard and the Baltic Sea
Bibliographic record
Abstract
Polychlorinated biphenyls (PCBs) may induce activity of hepatic enzymes, mainly Phase I monooxygenases and conjugating Phase II enzymes, that catalyze the metabolism of PCBs leading to formation of metabolites and to potential adverse health effects. The present study investigates the concentration and pattern of PCBs, the induction of hepatic phase I and II enzymes, and the formation of hydroxy (OH) and methylsulfonyl (CH3SO2=MeSO2) PCB metabolites in two ringed seal (Phoca hispida) populations, which are contrasted by the degree of contamination exposure, that is, highly contaminated Baltic Sea (n=31) and less contaminated Svalbard (n=21). Phase I enzymes were measured as ethoxyresorufin-O-deethylation (EROD), benzyloxyresorufin-O-dealkylation (BROD), methoxyresorufin-O-demethylation (MROD), and pentoxyresorufin-O-dealkylation (PROD) activities, and phase II enzymes were measured as uridine diphosphophate glucuronosyl transferase (UDPGT) and glutathione-S-transferase (GST). Geographical comparison, multivariate, and correlation analysis indicated that sigma-PCB had a positive impact on Phase I enzyme and GST activities leading to biotransformation of group III (vicinal ortho-meta-H atoms and < or =1 ortho-chlorine (Cl)) and IV PCBs (vicinal meta-para-H atoms and < or =2 ortho-Cl). The potential precursors for the main OH-PCBs detected in plasma in the Baltic seals were group III PCBs. MeSO2-PCBs detected in liver were mainly products of group IV PCB metabolism. Both CYP1A- and CYP2B-like enzymes are suggested to be involved in the PCB biotransformation in ringed seals.
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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) | 0.000 | 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 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".