Application of a nonradioactive assay for high throughput screening for inhibition of thyroid hormone uptake via the transmembrane transporter MCT8
Bibliographic record
Abstract
Thyroid hormones (THs) play important roles in almost all physiological processes. High-throughput screening (HTS) assays are needed to screen the vast numbers of chemicals for their potential to disrupt TH signalling. The current work has confirmed the ability of a rapid assay to identify substances inhibiting TH uptake through monocarboxylate transporter (MCT) 8. Perturbation of MCT8 function results in significant developmental impairments, suggesting substances inhibiting MCT8 may be important developmental toxicants. We examined the accuracy and consistency of a recently described method to identify TH inhibitors via MCT8, using MDCK cells overexpressing human MCT8 gene. We confirmed the method detected T3 uptake in a concentration/time-dependent manner, and this effect was blocked by substances previous reported to block TH uptake via MCT8. Assay performance was assessed extensively and the system was found to have high signal dynamic range and Z' factor. The assay was also validated with a diverse set of training chemicals. This assay was then used to screen chemicals suspected to disrupt TH signalling. Other than bisphenol A (BPA), all substances tested were negative. Our results suggest that this assay could be part of a battery of screening assays to predict the potential thyroid disrupting activity of chemicals.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".