Evidence map›Paper›PMID 34722460›Full record

ArticleFrontiers in chemistry2021

Open Characterization of Vaping Liquids in Canada: Chemical Profiles and Trends.

Ivana Kosarac, Cariton Kubwabo, Xinghua Fan, Shabana Siddique, Dora Petraccone, Wei He, Jun Man, Matthew Gagne, Kelly R Thickett, Trevor K Mischki

Abstract read
In one paragraph

Article in Frontiers in chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Ivana KosaracOffice of Research and Surveillance, Tobacco Control Directorate, Controlled Substances and Cannabis Branch, Health Canada, Ottawa, ON, Canada.
Cariton KubwaboExposure and Biomonitoring Division, Environmental and Radiation Sciences Directorate, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada.
Xinghua FanExposure and Biomonitoring Division, Environmental and Radiation Sciences Directorate, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada.
Shabana SiddiqueExposure and Biomonitoring Division, Environmental and Radiation Sciences Directorate, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada.
Dora PetracconeOffice of Research and Surveillance, Tobacco Control Directorate, Controlled Substances and Cannabis Branch, Health Canada, Ottawa, ON, Canada.
Wei HeExposure and Biomonitoring Division, Environmental and Radiation Sciences Directorate, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada.
Jun ManFood Research Division, Food Directorate, Health Products and Food Branch, Health Canada, Ottawa, ON, Canada.
Matthew GagneHazard Methodology Division, Safe Environments Directorate, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada.
Kelly R ThickettOffice of Research and Surveillance, Tobacco Control Directorate, Controlled Substances and Cannabis Branch, Health Canada, Ottawa, ON, Canada.
Trevor K MischkiOffice of Research and Surveillance, Tobacco Control Directorate, Controlled Substances and Cannabis Branch, Health Canada, Ottawa, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Currently, there is a lack of comprehensive data on the diversity of chemicals present in vaping liquids. To address this gap, a non-targeted analysis of 825 vaping liquids collected between 2017 and 2019 from Canadian retailers was conducted. Prior to mass spectrometry analysis, samples were diluted 1:500 v/v with methanol or acetonitrile. Chemical compound separation and analysis was carried out using gas chromatography and triple quadrupole mass spectrometry (GC-MS/MS) systems operated in the full scan mode and mass range of 35-450 m/z. Mass spectrum for each sample was obtained in electron ionization at 70 eV and processed. Non-targeted identification workflow included use of automated mass spectral deconvolution and identification system (AMDIS), where required, as well as a number of commercially available spectral libraries. In order to validate identities, an in-house database of expected compounds previously detected in vaping liquids was used along with genuine analytical standards for compounds of interest. This resulted in a dataset of over 1,500 unique detected chemicals. Approximately half of these chemical compounds were detected only once in a single product and not in multiple products analyzed. For any sample analyzed, on average, 40% of the chemical constituents appeared to have flavouring properties. The remainder were nicotine and related alkaloids, processing, degradation or indirect additives, natural extractives and compounds with unknown roles. Data published here from the project on the Open Characterization of vaping liquids is unique as it offers a detailed understanding of products' flavour chemical profiles, the presence and frequency of chemicals of potential health concern, as well as trends and changes in products' chemical complexity over a three-year period. Non-targeted chemical surveillance such as this present valuable tools to public health officials and researchers in responding to emergent issues such as vaping associated lung injury or informing chemical based strategies which may be aimed at addressing product safety or appeal.

Indexed as

E-cigarettesflavoursgc msnicotinenon-targeted analysispodsvapingvaping liquids

Identifiers

PMID34722460
PMCPMC8551961

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.