Optimization of an in vitro assay methodology for competitive binding of thyroidogenic xenobiotics with thyroxine on human transthyretin and albumin
Bibliographic record
Abstract
Thyroid hormones (THs) are involved in the regulation of many physiological processes in vertebrates. Competition for TH binding sites on serum transport proteins can interfere with delivery of THs to target tissues, and this is a potential mechanism of action of exogenous thyroidogenic substances. To date, detailed accounts of in vitro methods for competitive binding with THs on TH transport proteins (human or wildlife) are sparse. In the limited number of published studies on in vitro radio-labelled TH-TH transport protein interactions, method descriptions were brief and with insufficient details for successful replication. Furthermore, upon review of these methodologies, we identified several opportunities for optimization. The present study addresses the methodological deficiencies and describes, in detail, a fully optimized and validated competitive T4 radio-ligand binding assay with human transthyretin (TTR) and albumin (ALB). •Significant improvements were made over previous methods, including better maintenance of protein stability and enhanced measurement of competition between different ligands. •Sample size was reduced to allow use of small pre-packed size exclusion chromatography columns, which eliminates the rinsing step during the separation procedure. •The assay was parameterized for use with T4 and human TTR and ALB.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".