Lipid oxidation and metabolism in relation to contaminants in polar bears from the Canadian high arctic and Hudson Bay
Bibliographic record
Abstract
Environmental contaminants, particularly persistent organic pollutants (POPs) and mercury, pose significant threats to wildlife health, with complex interactions between contaminants and biological processes that are challenging to assess in field studies. This research investigates the oxylipin metabolome in liver samples from polar bears ( Ursus maritimus ) in two geographically distinct subpopulations from Western Hudson Bay (WHB) and Baffin Bay (BB), Canada, with the aim of elucidating the impact of environmental contaminants on metabolic and inflammatory pathways. Oxylipins, bioactive lipid metabolites derived from polyunsaturated fatty acids, regulate critical biological processes such as inflammation and vascular tone. We identified significant differences in oxylipin levels between the two subpopulations, with WHB bears showing higher concentrations of several key metabolites, including Prostaglandin E 2 (PGE2) and 5-iPF2a-VI, an isoprostane, both associated with inflammation and oxidative stress. Contaminant analysis revealed elevated levels of specific POPs, including polybrominated diphenyl ether (PBDEs), in WHB polar bears. These results suggest a potential link between contaminant exposure and altered oxylipin metabolism, which may contribute to liver dysfunction and inflammation. Multivariate analysis also revealed correlations between contaminants and metabolic pathways related to liver disease, including arginine biosynthesis and alanine, aspartate and glutamate metabolism. Our findings underscore the importance of considering the interplay between environmental contaminants and lipid signaling in wildlife health, particularly in the context of Arctic ecosystems, and highlight the need for further research to explore the long-term impacts of these exposures on polar bear populations and other wildlife species in the Arctic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".