Hepatic proteome network data in zebrafish (Danio rerio) liver following dieldrin exposure
Bibliographic record
Abstract
Dieldrin is an environmental contaminant that adversely affects aquatic organisms. The data presented in this study are proteomic data collected in liver of zebrafish that were exposed to the pesticide in a dietary exposure. For label free proteomics, data were collected with a quadrupole Time-of-Flight mass spectrometer and for iTRAQ proteomics, data were acquired using a hybrid quadrupole Orbitrap (Q Exactive) MS system. Using formic acid digestion and label free proteomics, 2,061 proteins were identified, and among those, 103 were differentially abundant (p < 0.05 in at least one dose). In addition, iTRAQ proteomics identified 722 proteins in the liver of zebrafish following dieldrin treatment. The label-free approach identified 21 proteins that followed a dose dependent response. Of the differentially abundant proteins identified by iTRAQ, there were 26 unique expression patterns for proteins based on the three doses of dieldrin. Proteins were queried for disease networks to learn more about adverse effects in the liver following dieldrin exposure. Differentially abundant proteins were related to metabolic disease, steatohepatitis and lipid metabolism disorders, drug-induced liver injury, neoplasms, tissue degeneration and liver metastasis. The proteomics data described here is associated with a research article, “Label-free and iTRAQ proteomics analysis in the liver of zebrafish ( Danio rerio ) following a dietary exposure to the organochlorine pesticide dieldrin” (Simmons et al. 2019). This investigation reveals new biomarkers of toxicity and will be of interest to those studying aquatic toxicology and pesticides.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".