The pesticide dieldrin disrupts proteins related to oxidative respiration and mitochondrial stress in the central nervous system
Bibliographic record
Abstract
Quantitative proteins analysis was carried out in the hypothalamus of zebrafish following dietary exposure to the legacy pesticide dieldrin. Data were collected using iTRAQ labeling methodology and data were acquired using a hybrid quadrupole Orbitrap (Q Exactive) MS system (Thermo Fisher Scientific, Bremen, Germany). There were 3941 proteins identified in the hypothalamus of zebrafish, and these proteins comprised 23 unique expression patterns for proteins based on the three doses of dieldrin. There were 226 proteins that were regulated in one or more doses of dieldrin and 3715 proteins that were not affected. Thus, 5.7% of the proteins detected responded to the treatment. Many proteins that were differentially expressed were those found in, or associated with, the mitochondria. The proteomics data described in this article is associated with a research article, “Transcriptomic and proteomic analysis implicates the immune system and mitochondria as molecular targets of dieldrin in the zebrafish ( Danio rerio ) central nervous system” (A.M. Cowie, K.I. Sarty, A. Mercer, J. Koh, K.A. Kidd, C.J. Martyniuk, submitted) [1] , and serves as a resource for researchers working in the field of pesticide exposures and protein biomarkers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".