Identification of Hydrocarbon Sulfonates as Previously Overlooked Transthyretin Ligands in the Environment
Bibliographic record
Abstract
Incidences of thyroid disease, which has long been hypothesized to be partially caused by exposure to thyroid hormone disrupting chemicals (TDCs), have rapidly increased in recent years. However, known TDCs can only explain a small portion (∼1%) of in vitro human transthyretin (hTTR) binding activities in environmental samples, indicating the existence of unknown hTTR ligands. In this study, we aimed to identify the major environmental hTTR ligands by employing protein A ffinity P urification with N ontargeted A nalysis (APNA). hTTR binding activities were detected in all 11 indoor dust and 9 out of 10 sewage sludge samples by the FITC-T 4 displacement assay. By using APNA, 31 putative hTTR ligands were detected including perfluorooctanesulfonate (PFOS). Two of the most abundant ligands were identified as hydrocarbon surfactants ( e.g., dodecyl benzenesulfonate). Moreover, another abundant ligand was surprisingly identified as a disulfonate fluorescent brightener, 4,4′-bis(2-sulfostyryl)biphenyl sodium (CBS). CBS was validated as a nM-affinity hTTR ligand with an IC 50 of 345 nM. In total, hydrocarbon surfactants and fluorescent brighteners explain 1.92–17.0 and 5.74–54.3% of hTTR binding activities in dust and sludge samples, respectively, whereas PFOS only contributed <0.0001%. Our study revealed for the first time that hydrocarbon sulfonates are previously overlooked hTTR ligands in the environment.
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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 | 0.001 | 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".