Persistent organic pollutants meet adipose tissue hypoxia: does cross‐talk contribute to inflammation during obesity?
Bibliographic record
Abstract
Lipophilic persistent organic pollutants (POPs) accumulate in lipid-rich tissues such as human adipose tissue. This is particularly problematic in individuals with excess adiposity, a physiological state that may be additionally characterized by local adipose tissue hypoxia. Hypoxic patches occur when oxygen diffusion is insufficient to reach all hypertrophic adipocytes. POPs and hypoxia independently contribute to the development of adipose tissue-specific and systemic inflammation often associated with obesity. Inflammation is induced by increased proinflammatory mediators such as tumour necrosis factor-alpha, interleukin-6, and monocyte chemotactic protein-1, as well as reduced adiponectin release, an anti-inflammatory and insulin-sensitizing adipokine. The aryl hydrocarbon receptor (AhR) mediates the cellular response to some pollutants, while hypoxia responses occur through the oxygen-sensitive transcription factor hypoxia-inducible factor (HIF)-1. There is some overlap between the two signalling pathways since both require a common subunit called the AhR nuclear translocator. As such, it is unclear how adipocytes respond to simultaneous POP and hypoxia exposure. This brief review explores the independent contribution of POPs and adipose tissue hypoxia as factors underlying the inflammatory response from adipocytes during obesity. It also highlights that the combined effect of POPs and hypoxia through the AhR and HIF-1 signalling pathways remains to be tested.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".