Liquid Crystal Monomers and Their Mixtures Alter Nuclear Receptor Signaling and Promote Adipogenesis In Vitro
Bibliographic record
Abstract
Liquid crystal monomers (LCMs) are ubiquitous environmental contaminants released from electronic devices' liquid crystal display (LCD) panels, which have led to the contamination of food, breast milk, and serum. As the toxicity of individual LCMs, not to mention their myriad mixtures, is currently very poorly characterized, there is a crucial need for investigations into the health hazards posed by exposure. In this study, 10 nonfluorinated (NF) and fluorinated (F) LCMs and 3 fluorination-based LCM mixtures were screened for metabolism and endocrine-disrupting potential in vitro at exposure-relevant concentrations using adipogenesis assays and luciferase reporter gene assays. Both NF-LCMs, F-LCMs, and their mixtures were found to alter the transcriptional activity of one or more nuclear receptors. Notably, 6 LCMs and all LCM mixtures were able to antagonize the progesterone receptor, with several displaying non-monotonic concentration-response curves. Multiple LCMs and their mixtures also increased triglyceride accumulation in murine preadipocytes and human mesenchymal stem cells in a concentration-dependent manner. The concentration addition principle underestimated the adipogenic potencies of LCM mixtures when compared with those derived from benchmark concentration analyses of empirical adipogenesis assay results, suggesting synergistic interactions. While no mechanistic pattern emerged between the bioactivities, results confirmed the metabolism and endocrine-disrupting potential of both NF-LCMs, F-LCMs, and their mixtures. This emphasizes the need to further investigate the metabolic and reproductive health impacts of LCM exposure in vivo, as well as the necessity of exploring alternative models to predict the toxicity of LCM mixtures.
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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".