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  1. Article ; Online: Quantifying PG : VG ratio and nicotine content in commercially available e-liquids using handheld Raman spectroscopy.

    Richardson, Paul I C / Burke, Adam / Gotts, Nigel / Goodacre, Royston

    The Analyst

    2023  Volume 148, Issue 17, Page(s) 4002–4011

    Abstract: Electronic cigarettes are a popular nicotine consumption product that have risen in popularity as an alternative to cigarettes. However, their recent meteoric rise in market size and various controversies have resulted in the analyses of e-liquid ... ...

    Abstract Electronic cigarettes are a popular nicotine consumption product that have risen in popularity as an alternative to cigarettes. However, their recent meteoric rise in market size and various controversies have resulted in the analyses of e-liquid ingredients to be focused on powerful laboratory-based slow methods such as chromatography and mass spectrometry. Here we present a complementary technology based on Raman spectroscopy combined with chemometrics as a fast, inexpensive, and highly portable screening tool to detect and quantify the propylene glycol : glycerol (PG : VG) ratio and nicotine content of e-cigarette liquids. Through this, the PG : VG ratio of 20 out of 23 commercial samples was quantified to within 3% of their stated value, while nicotine was successfully quantified to within 1 mg g
    MeSH term(s) Nicotine/analysis ; Electronic Nicotine Delivery Systems ; Spectrum Analysis, Raman ; Propylene Glycol/chemistry ; Glycerol
    Chemical Substances Nicotine (6M3C89ZY6R) ; Propylene Glycol (6DC9Q167V3) ; Glycerol (PDC6A3C0OX)
    Language English
    Publishing date 2023-08-21
    Publishing country England
    Document type Journal Article
    ZDB-ID 210747-8
    ISSN 1364-5528 ; 0003-2654
    ISSN (online) 1364-5528
    ISSN 0003-2654
    DOI 10.1039/d3an00888f
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2423: Show issues Location:
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  2. Article ; Online: Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome.

    Roberts, Ivayla / Wright Muelas, Marina / Taylor, Joseph M / Davison, Andrew S / Xu, Yun / Grixti, Justine M / Gotts, Nigel / Sorokin, Anatolii / Goodacre, Royston / Kell, Douglas B

    Metabolomics : Official journal of the Metabolomic Society

    2021  Volume 18, Issue 1, Page(s) 6

    Abstract: Introduction: The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to ... ...

    Abstract Introduction: The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites, compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model.
    Objectives: Here we provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict the patient's infection severity (i.e., mild or severe) and potential outcome (i.e., discharged or deceased).
    Methods: High resolution untargeted UHPLC-MS/MS analysis was performed on patient serum using both positive and negative ionization modes. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model was created.
    Results: The predictors were selected for their relevant biological function and include deoxycytidine and ureidopropionate (indirectly reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on an additional 90 patients predicted outcome and severity at ROC AUC of 0.83 (CI 0.74-0.91) and 0.76 (CI 0.67-0.86).
    Conclusion: Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.
    MeSH term(s) Aged ; Biomarkers/blood ; COVID-19/blood ; Chromatography, Liquid/methods ; Female ; Humans ; Male ; Metabolomics/methods ; Middle Aged ; Prognosis ; SARS-CoV-2 ; Severity of Illness Index ; Tandem Mass Spectrometry/methods
    Chemical Substances Biomarkers
    Language English
    Publishing date 2021-12-20
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2250617-2
    ISSN 1573-3890 ; 1573-3882
    ISSN (online) 1573-3890
    ISSN 1573-3882
    DOI 10.1007/s11306-021-01859-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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