Inhibition of immune responses and related proteins in <i>Rhamdia quelen</i> exposed to diclofenac
Bibliographic record
Abstract
Nonsteroidal anti-inflammatory drugs are among the most widely detected pharmaceuticals in surface water worldwide. The nonsteroidal anti-inflammatory drug diclofenac is used to treat many types of pain and inflammation. Diclofenac's potential to cause adverse effects in exposed wildlife is a growing concern. To evaluate the effects of waterborne diclofenac on the immune response in Rhamdia quelen (South American catfish), fish were exposed to 3 concentrations of diclofenac (0.2, 2.0, and 20.0 μg/L) for 14 d. Some of the exposed fish were also given an intraperitoneal injection on day 14 of 1 mg/kg of carrageenan to evaluate cell migration to the peritoneum. Total blood leukocyte count and carrageenan-induced leukocyte migration to the peritoneal cavity, particularly of polymorphonuclear cells, were significantly affected for all diclofenac exposure groups. Nitric oxide production was significantly reduced in the diclofenac-treated fish. Plasma and kidney proteins were analyzed by means of liquid chromatography-tandem mass spectrometry in a shotgun proteomic approach. In both plasma and kidney of diclofenac-exposed R. quelen, the expression of 20 proteins related to the inflammatory process, nitric oxide production, leukocyte migration, and the complement cascade was significantly altered. In addition, class I major histocompatibility complex was significantly decreased in plasma of diclofenac-treated fish. Thus, waterborne exposure to diclofenac could lead to suppression of the innate immune system in R. quelen. Environ Toxicol Chem 2017;36:2092-2107. © 2017 SETAC.
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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 | 0.000 | 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 | 0.000 |
| 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".