Multiple Cytokine and Acute Phase Protein Gene Transcription in West Greenland Sledge Dogs (<em>Canis familiaris</em>) Dietary Exposed to Organic Environmental Pollutants
Bibliographic record
Abstract
Exposure levels of persistent organic pollutants - such as PCBs and DDTs - are high in Arctic apex predators and Inuit peoples, and are suspected to have negative impacts on their immune system. We conducted a controlled generational study on liver tissue and EDTA blood cytokine and acute phase protein (APP) mRNA expressions (RT-PCR) in West Greenland sledge dogs (Canis familiaris) using contaminated minke whale (Balaenoptera acutorostrata) blubber as a dietary pollutant source. Two of seven blood cytokine (IL-6, IL-12) and three of five APP (HP, HSP, FABP) expressions were lowest in the exposed group while the remaining five blood cytokine (IL-2, IL-10, IFN-ã, TNF-á, TGF-â) and two APP (MT1 and MT2) expressions were highest in the exposed group. In liver tissue, three cytokine (IL-10, IFN-ã, TNF-á) and two APP (MT1 and MT2) expressions were highest in the exposed group and the remaining cytokine and APP expressions lowest in the exposed group. Of these, the liver tissue expression of haptoglobin (HP) and fatty acid binding protein (FABP) was significantly lowest in the exposed group (both: p<0.05). As a consequence of our findings, we suggest that a daily intake of 50-200 g of polluted whale blubber is associated with a genotoxic decrease in HP and FABP gene expression in the liver of sledge dogs, and possibly of other top mammalian marine predators/consumers in the Arctic, indicating a restricted acute phase reaction and insufficient immune response. Finally, HP and FABP liver expression appear to be new and sensitive biomarkers of OHC exposure.
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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 | 0.000 | 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".