Corticosteroidogenesis and StAR Protein of Rainbow Trout Disrupted by Human-Use Pharmaceuticals: Data for Use in Risk Assessment
Bibliographic record
Abstract
Recent reports of presence of pharmaceutical drugs in surface waters (Kolpin et al., 2002; Metcalfe et al., 2004; Miao et al., 2002) raised concerns about the potential effects of these chemicals in nontarget species, especially those in the aquatic environment (Trudeau et al., 2005). The study highlighted in this issue, “Salicylate disrupts interrenal steroidogenesis and brain glucocorticoid receptor expression in rainbow trout” by Gravel and Vijayan (2006), demonstrated, using state-of-the-art molecular tools in a well-characterized physiological model relevant for environmental toxicology, the disruption of corticosteroidogenesis by acetaminophen, ibuprofen, and salicylic acid, three human-use pharmaceuticals often detected in surface waters. Pharmaceuticals, substances designed to exert specific physiological effects to prevent, cure, or alleviate symptoms of disease, include drugs, antibiotics, hormones, and veterinary feed additives. This new class of environmental pollutants differs from other pollutants such as endocrine-disrupting chemicals, which only incidentally interfere with normal function of nontarget species. Pharmaceuticals usually have a high therapeutic index, eliciting their desired effects in the target species (humans, livestock, or pets) at very low concentrations, with low or no toxicity. The target species are vertebrates sharing many of the basic biochemical and cellular structures with the numerous nontarget species, other vertebrates including fish (Mommsen and Moon, 2005; Norris and Carr, 2006). Fate and sources of some pharmaceuticals are already known: many are released from sewage treatment plants, landfills, or agricultural lands amended with manure and biosolids into lakes, rivers, and streams, where they are detected with high-precision analytical methods (Boxall et al., 2004; Metcalfe et al., 2004). As our analytical capabilities improve and the loading of surface waters potentially augments with changing demographics of the human population and as livestock industries expand to supply our needs, risk assessments for these new pollutants are required (Sanderson et al., 2004). We will have to determine if pharmaceuticals detected in receiving waters do pose a health risk to nontarget aquatic species and by extension to other organisms, including humans, that may be subjected to uncontrolled exposures through drinking water. There is an urgent need for studies designed to elucidate the mode of action of pharmaceuticals in nontarget species and to set safe exposure guidelines. The article by Gravel and Vijayan (2006) is an excellent example of a study that provides data regarding the mechanism of action and effects of pharmaceuticals in an environmentally relevant model species, the rainbow trout.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".