Effects of Triclocarban, Triclosan, and Methyl Triclosan on Thyroid Hormone Action and Stress in Frog and Mammalian Culture Systems
Bibliographic record
Abstract
Triclosan (TCS) and triclocarban (TCC) are widely used broad spectrum bactericides that are common pollutants of waterways and soils. Methyl triclosan (mTCS) is the predominant bacterial TCS metabolite. Previous studies have shown that TCS disrupts thyroid hormone (TH) action; however, the effects of mTCS or TCC are not known. The present study uses the cultured frog tadpole tail fin biopsy (C-fin) assay and the TH-responsive rat pituitary GH3 cell line to assess the effects of these three chemicals (1-1000 nM) on TH signaling and cellular stress within 48 h. mRNA abundance of TH receptor β, Rana larval keratin type I (TH-response), heat shock protein 30, and catalase (stress-response) was measured using quantitative real-time polymerase chain reaction in the C-fin assay. The TH-responsive gene transcripts encoding growth hormone, deiodinase I, and prolactin were measured in GH3 cells with the heat shock protein 70 transcript acting as a cellular stress indicator. We found alteration of stress indicators at a wide range of concentrations of TCS, mTCS, and TCC in both test systems. mTCS and TCC affected TH-responsive gene transcripts at the highest concentration in mammalian cells, whereas a modest effect included lower concentrations in the C-fin assay. In contrast, TCS did not affect TH-responsive transcripts. These results identify nontarget biological effects of these bacteriocides on amphibian and mammalian cells and suggest the TH-disrupting effects observed for TCS could be mediated through its metabolite.
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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".