Unvaporized e-liquid toxicity elevates CD44-dependent hyaluronan catabolic gene expression and triggers inflammation in human vocal fold fibroblasts
Bibliographic record
Abstract
Electronic (e)-cigarette and e-liquid exposure have been linked to vocal fold inflammation and dysphonia, yet no targeted non-surgical therapies currently exist. Hyaluronan, a key extracellular matrix component essential for vocal fold structure, repair, and function, is known to be dysregulated in inflammatory conditions; however, its metabolic gene response to e-liquid exposure in human vocal fold fibroblasts (hVFFs) remains uncharacterized. Hence, it is critical to understand hyaluronan metabolic gene expression under e-liquid toxicity to develop novel drug discovery strategies for vocal fold inflammation. To avoid confounding effects from thermal degradation and aerosol variability in conventional vapor models, hVFFs were exposed to nicotine-containing unvaporized e-liquid (0.125–1 mg/mL) for 24 h, revealing concentration-dependent changes in cell morphology and viability (p < 0.05). The lethal concentration 50 (LC₅₀) was determined to be 0.437 mg/mL and used for short-term (24 h) and extended (72–96 h) exposures. Extended exposure induced intracellular reactive oxygen species (ROS), inflammation, suppressed collagenolysis, and increased the collagen 1 A: collagen 3 A ratio, suggesting fibrotic remodeling. Short-term exposure downregulated hyaluronan synthases (HAS1, HAS2, HAS3) and catabolic genes (HYAL2, CD44), reducing extracellular hyaluronan levels. In contrast, extended exposure repressed HAS1 and HAS2 while upregulating HAS3, CD44, and HYAL2, indicating enhanced hyaluronan degradation and accumulation of proinflammatory low molecular weight hyaluronan. CD44 silencing reduced IL-8 mRNA expression, confirming its role in hVFF inflammation. These findings provide the first mechanistic insight into unvaporized e-liquid-induced dysregulation of hyaluronan metabolism in hVFFs, offering a foundation for biomarker identification and therapeutic development targeting e-cigarette-associated vocal fold inflammation. • No targeted therapy for vocal fold inflammation from e-liquid toxicity. • Hyaluronan (HA) is vital for vocal fold structure, repair, and proper function. • No data on HA metabolic gene expression in vocal fold fibroblast (VFF) in e-liquid. • First study on unvaporized e-liquid’s effect on HA metabolic genes in human VFFs. • E-liquid boosts CD44-linked low MW HA levels, triggering VFF inflammation.
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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".