Effects of Polar Bear and Killer Whale Derived Contaminant Cocktails on Marine Mammal Immunity
Bibliographic record
Abstract
Most controlled toxicity studies use single chemical exposures that do not represent the real world situation of complex mixtures of known and unknown natural and anthropogenic substances. In the present study, complex contaminant cocktails derived from the blubber of polar bears (PB; Ursus maritimus ) and killer whales (KW; Orcinus orca ) were used for in vitro concentration–response experiments with PB, cetacean and seal spp. immune cells to evaluate the effect of realistic contaminant mixtures on various immune functions. Cytotoxic effects of the PB cocktail occurred at lower concentrations than the KW cocktail (1 vs 16 μg/mL), likely due to differences in contaminant profiles in the mixtures derived from the adipose of each species. Similarly, significant reduction of lymphocyte proliferation occurred at much lower exposures in the PB cocktail (EC 50: 0.94 vs 6.06 μg/mL; P < 0.01), whereas the KW cocktail caused a much faster decline in proliferation (slope: 2.9 vs 1.7; P = 0.04). Only the KW cocktail modulated natural killer (NK) cell activity and neutrophil and monocyte phagocytosis in a concentration- and species-dependent manner. No clear sensitivity differences emerged when comparing cetaceans, seals and PB. Our results showing lower effect levels for complex mixtures relative to single compounds suggest that previous risk assessments underestimate the effects of real world contaminant exposure on immunity. Our results using blubber-derived contaminant cocktails add realism to in vitro exposure experiments and confirm the immunotoxic risk marine mammals face from exposure to complex mixtures of environmental contaminants.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".