Characterization of POP mixture redistribution and identification of their molecular signature in xenografted fat mice
Bibliographic record
Abstract
Persistent organic pollutants (POPs) are associated with many adverse health effects in humans, including cancers, immune, reproductive, neurological disorders and metabolic diseases. These chemicals are known to accumulate in fatty tissues, from which they can be released in other tissue compartments of living organisms, in particular, upon weight loss. This dynamic distribution of POPs remains, however poorly investigated. In this study, a xenografted POP-contaminated adipose tissue (AT) model was used to assess 1) their concentrations in the ATs, the liver and the brain and 2) their associated effects by transcriptomics, metabolomics and lipidomics approaches. In the ATs, the liver and the brain of mice grafted with POP-contaminated fat pad, most of POPs were detected 3 days and 21 days after the graft with the highest concentrations in the ATs and the lowest concentrations in the brain. Conversely, per- and polyfluoroalkyl substances presented a distinct profile as they persist in the liver but not in the ATs or in the brain. In the AT of POP-exposed mice, the most dysregulated pathways were related to mitochondrial functions, endobiotic (carbohydrate, lipid, amino acid) and xenobiotic metabolism and inflammatory response. In the liver of grafted mice, many pathways related to mitochondrial functions and metabolism were dysregulated. These results support that realistic mixture of POPs that accumulate in AT and liver induces a systemic metabolic dysfunction which may represent the mechanisms by which the POPs can promote metabolic diseases such as obesity, type 2 diabetes and cardiovascular diseases.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".