Lipid Metabolic Disorders Induced by Organophosphate Esters in Silver Carp from the Middle Reaches of the Yangtze River
Bibliographic record
Abstract
), has been linked to organophosphate esters (OPEs) contaminant exposure. In this study, the adverse effect of OPEs on lipid metabolism in silver carp captured from the Yangtze River was examined, and our results indicated that muscle concentrations of the OPEs were positively associated with serum cholesterol and total lipid levels. In vivo laboratory results revealed that exposure to environmental concentrations of OPEs significantly increased the concentrations of triglyceride, cholesterol, and total lipid levels. Lipidome analysis further confirmed the lipid metabolism dysfunction induced by OPEs, and glycerophospholipids and sphingolipids were the most affected lipids. Hepatic transcriptomic analysis found that OPEs caused significant alterations in the transcription of genes involved in lipid metabolism. Pathways associated with lipid homeostasis, including the peroxisome proliferator-activated receptor (PPAR) signal pathway, cholesterol metabolism, fatty acid biosynthesis, and steroid biosynthesis, were significantly changed. Furthermore, the affinities of OPEs were different, but the 11 OPEs tested could bind with PPARγ, suggesting that OPEs could disrupt lipid metabolism by interacting with PPARγ. Overall, this study highlighted the harmful effects of OPEs on wild fish and provided mechanistic insights into OPE-induced metabolic disorders.
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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 | 0.001 | 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".