Evidence map›Paper›PMID 38914586›Full record

ArticleScientific reports2024

Detection of COVID-19 by quantitative analysis of carbonyl compounds in exhaled breath.

Zhenzhen Xie, James D Morris, Jianmin Pan, Elizabeth A Cooke, Saurin R Sutaria, Dawn Balcom, Subathra Marimuthu, Leslie W Parrish, Holly Aliesky, Justin J Huang and 5 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
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

15 authors.

Zhenzhen XieDepartment of Chemical Engineering, University of Louisville, Louisville, KY, USA.
James D MorrisDepartment of Chemical Engineering, University of Louisville, Louisville, KY, USA.
Jianmin PanDivision of Biostatistics and Bioinformatics, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Elizabeth A CookeDepartment of Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY, USA.
Saurin R SutariaDepartment of Chemistry, University of Louisville, Louisville, KY, USA.
Dawn BalcomDivision of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY, USA.
Subathra MarimuthuDivision of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY, USA.
Leslie W ParrishDivision of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY, USA.
Holly AlieskyDivision of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY, USA.
Justin J HuangDuPont Manual High School, Louisville, KY, USA.
Shesh N RaiDivision of Biostatistics and Bioinformatics, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Forest W ArnoldDivision of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY, USA.
Jiapeng HuangDepartment of Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY, USA. jiapeng.huang@louisville.edu.
Michael H NantzDepartment of Chemistry, University of Louisville, Louisville, KY, USA. michael.nantz@louisville.edu.
Xiao-An FuDepartment of Chemical Engineering, University of Louisville, Louisville, KY, USA. xiaoan.fu@louisville.edu.

Funding

University of Louisville Center for Integrative Environmental Health SciencesP30ES030283 · NIEHS · UNIVERSITY OF LOUISVILLE · PI Amanda Jo LeBlanc · 2020 to 2026
$10.0M
NIEHS NIH HHS P30 ES030283
6 · The paper itself

Abstract

COVID-19 has caused a worldwide pandemic, creating an urgent need for early detection methods. Breath analysis has shown great potential as a non-invasive and rapid means for COVID-19 detection. The objective of this study is to detect patients infected with SARS-CoV-2 and even the possibility to screen between different SARS-CoV-2 variants by analysis of carbonyl compounds in breath. Carbonyl compounds in exhaled breath are metabolites related to inflammation and oxidative stress induced by diseases. This study included a cohort of COVID-19 positive and negative subjects confirmed by reverse transcription polymerase chain reaction between March and December 2021. Carbonyl compounds in exhaled breath were captured using a microfabricated silicon microreactor and analyzed by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). A total of 321 subjects were enrolled in this study. Of these, 141 (85 males, 60.3%) (mean ± SD age: 52 ± 15 years) were COVID-19 (55 during the alpha wave and 86 during the delta wave) positive and 180 (90 males, 50%) (mean ± SD age: 45 ± 15 years) were negative. Panels of a total of 34 ketones and aldehydes in all breath samples were identified for detection of COVID-19 positive patients. Logistic regression models indicated high accuracy/sensitivity/specificity for alpha wave (98.4%/96.4%/100%), for delta wave (88.3%/93.0%/84.6%) and for all COVID-19 positive patients (94.7%/90.1%/98.3%). The results indicate that COVID-19 positive patients can be detected by analysis of carbonyl compounds in exhaled breath. The technology for analysis of carbonyl compounds in exhaled breath has great potential for rapid screening and detection of COVID-19 and for other infectious respiratory diseases in future pandemics.

Indexed as

Breath TestsCOVID-19SARS-CoV-2AdultAgedAldehydesChromatography, High Pressure LiquidExhalationFemaleHumansMaleMass SpectrometryMiddle AgedAldehydesBreath analysisCarbonyl compoundsCOVID-19 detectionMicroreactorUHPLC-MS

Identifiers

PMID38914586
PMCPMC11196736

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.