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GC/HRMS Analysis of E‑Liquids Complements In Vivo Modeling Methods and can Help to Predict Toxicity

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NIAID Data Ecosystem2026-05-02 收录
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https://figshare.com/articles/dataset/GC_HRMS_Analysis_of_E_Liquids_Complements_In_Vivo_Modeling_Methods_and_can_Help_to_Predict_Toxicity/25976287
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Tobacco smoking is a major risk factor for disease development, with the user inhaling various chemicals known to be toxic. However, many of these chemicals are absent before tobacco is “burned”. Similar, detailed data have only more recently being reported for the e-cigarette with regards to chemicals present before and after the e-liquid is “vaped.” Here, zebrafish were dosed with vaped e-liquids, while C57-BL/6J mice were vaped using nose-cone only administration. Preliminary assessments were made using e-liquids and GC/HRMS to identify chemical signatures that differ between unvaped/vaped and flavored/unflavored samples. Oxidative stress and inflammatory immune cell response assays were then performed using our in vivo models. Chemical signatures differed, e.g., between unvaped/vaped samples and also between unflavored/flavored e-liquids, with known chemical irritants upregulated in vaped and unvaped flavored e-liquids compared with unflavored e-liquids. However, when possible respiratory irritants were evaluated, these agents were predominantly present in only the vaped e-liquid. Both oxidative stress and inflammatory responses were induced by a menthol-flavored but not a tobacco-flavored e-liquid. Thus, chemical signatures differ between unvaped versus vaped e-liquid samples and also between unflavored versus flavored e-liquids. These flavors also likely play a significant role in the variability of e-liquid characteristics, e.g., pro-inflammatory and/or cytotoxic responses.
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