Implication of microRNA deregulation in the response of vertebrates to endocrine disrupting chemicals
Bibliographic record
Abstract
Micro ribonucleic acids (miRNAs) are recently discovered small regulatory molecules that control messenger RNA (mRNA) translation in plants and animals and have been implicated in a variety of hormone-related physiological pathways. Estrogens, thyroid hormones, and gonadotropins are all known to act on miRNA abundance to cause major shifts in cellular activity, physiology, and homeostatic control mechanisms. Research on cancer biology has also recently considered miRNA as therapeutic targets, because the deregulation of specific miRNAs in various tissues has been correlated with tumorigenesis and other carcinogenic responses. Because many pharmaceuticals are considered to be endocrine-disrupting chemicals (EDCs), their effects on miRNAs may be important to our understanding of basic physiological control and phenotypic outcomes of wildlife exposed to EDCs. Presented is a brief overview of the synthesis, control, and action of miRNAs, focusing on endocrine systems. The antidepressant fluoxetine will be used as an example for miRNA studies in aquatic species, one of the few examples in ecotoxicology. Given the mounting evidence that miRNAs are regulated by hormones, a clear need exists to investigate the potential for environmental EDCs to deregulate miRNA expression and action.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".