MétaCan
Menu
Back to cohort
Record W3120749237 · doi:10.1016/j.ebiom.2020.103183

A breath of fresh air – the potential for COVID-19 breath diagnostics

2021· article· en· W3120749237 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEBioMedicine · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute of Environmental Health SciencesNational Institutes of HealthCenter for Information Technology Research in the Interest of SocietyCanada Economic Development for Quebec RegionsTobacco-Related Disease Research Program
KeywordsFalse positive paradoxPandemicGold standard (test)MedicineCoronavirus disease 2019 (COVID-19)Public healthDiagnostic testSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Test (biology)Computer scienceVirologyRisk analysis (engineering)DiseasePathologyBiologyInfectious disease (medical specialty)Artificial intelligenceVeterinary medicine

Abstract

fetched live from OpenAlex

As the world continues to grapple with the ongoing SARS-CoV-2 pandemic, it remains clear that frequent and widespread virus testing is a valuable tool to understand disease spread and to guide public health actions by communities and governments. To date, most traditional diagnostic tests continue to rely on established polymerase chain reaction (PCR) technologies, which have proven to be quite robust as a tool for mass screening and remain the gold standard within modern medicine. When employed using standardized protocols, PCR typically has a high accuracy and high specificity (eg, low false positives and low false negatives). Early in the pandemic, there were challenges to quickly establish and distribute the best testing methods. Once resolved, the test was widely and successfully rolled out in protocols across the world. However, other challenges have emerged when using this as a tool to combat COVID-19 spread. For one, there are known sampling issues with nasopharyngeal PCR tests. While PCR itself is incredibly robust, it relies on collecting samples of actively amplifying viral genetic material. Though uncommon, it is possible to “miss” swabbing an area with active viral loads, which leads to a false-negative test result. There have been many more issues with the operational logistics and product supply chains that have strained testing systems during this public health crisis. The liquid reagents needed for the PCR test and the nasal swabs are in high demand, thus limiting availability in some locations causing alterations to planned testing protocols. Finally, although PCR is very reliable, there can be a significant time delay between sampling and when the results are available – hours-to-days, depending on processing capabilities of the test site. Recently, a new approach to viral diagnostics has been considered by examining exhaled breath for signatures of the host-response to infection [[1]Metabolomics of exhaled breath in critically ill COVID-19 patients: a pilot study.EBioMedicine. 2020; (in press)Google Scholar]. For several decades, it has been known that endogenously produced volatile organic compounds (VOCs) are present in exhaled breath, and these are frequent targets of breath diagnostics research and represent metabolic endpoints that can be quickly assessed for health information [[2]Beauchamp J. Davis C. Pleil J. Breathborne biomarkers and the human volatilome. Elsevier Science, 2020Google Scholar]. There are also reports of scent dogs being trained to detect human diseases, and observations of this phenomenon have also been recently expanded to include COVID-19 diagnosis [[3]Jendrny P. et al.Scent dog identification of samples from COVID-19 patients – a pilot study.BMC Infect Dis. 2020; 20: 536Crossref PubMed Scopus (112) Google Scholar, [4]Grandjean D, et al. Detection dogs as a help in the detection of COVID-19 Can the dog alert on COVID-19 positive persons by sniffing axillary sweat samples? Proof-of-concept study. bioRxiv, 2020: p. 2020.06.03.132134.Google Scholar]. While the cellular and molecular mechanisms and fundamental understandings of breath signature VOC generation are still being developed, some prior work in cell culture models pointed to viral-associated breath VOCs for both rhinovirus [[5]Schivo M. et al.Volatile emanations from in vitro airway cells infected with human rhinovirus.J Breath Res. 2014; 8037110Crossref Scopus (48) Google Scholar] and seasonal influenza respiratory tract infections [[6]Aksenov A.A. et al.Cellular Scent of Influenza Virus Infection.ChemBioChem. 2014; 15: 1040-1048Crossref PubMed Scopus (62) Google Scholar]. Specific oxidative stress VOCs were also observed post-vaccination in another previous study [[7]Phillips M. et al.Effect of influenza vaccination on oxidative stress products in breath.J Breath Res. 2010; 4026001Crossref Scopus (52) Google Scholar], when a nasally-delivered attenuated live virus vaccine was used. This was also followed by at least one porcine animal study that looked at breath VOC signatures in influenza infected animals [[8]Traxler S. et al.VOC breath profile in spontaneously breathing awake swine during Influenza A infection.Sci Rep. 2018; 8: 14857Crossref PubMed Scopus (47) Google Scholar]. Earlier this year, two other reports have also tentatively linked specific breath VOCs with SARS-CoV-2 infections [[9]Ruszkiewicz D.M. et al.Diagnosis of COVID-19 by analysis of breath with gas chromatography-ion mobility spectrometry - a feasibility study.EClin Med. 2020; 100609Google Scholar, [10]Shan B. et al.Multiplexed nanomaterial-based sensor array for detection of COVID-19 in exhaled breath.ACS Nano. 2020; 14: 12125-12132Crossref PubMed Scopus (216) Google Scholar], and clearly this emerging area is likely to continue to yield interesting results. A study recently published in EBioMedicine by Grassin-Delyle et al. [[1]Metabolomics of exhaled breath in critically ill COVID-19 patients: a pilot study.EBioMedicine. 2020; (in press)Google Scholar] measured very specific VOCs in exhaled breath from mechanically ventilated adults with COVID-19 and compared that signature to control ventilated patients with non-COVID acute respiratory distress syndrome. As in the studies published by Ruszkiewicz et al. and Shan et al., this third group has also shown that COVID-19 associated breath signatures can specifically distinguish infection – in this case with 93% accuracy. While ongoing work is still needed to confirm these results in larger cohorts, it represents a potential rapid near-real-time test that could be deployed to augment PCR testing strategies. Even if not used as a final confirmatory measure, the rapid nature of this reagent-free, logistically simple test may be useful for high throughput screening of asymptomatic cases in large or unique populations (eg, prior to boarding an airplane, or entering a sports stadium). While still a very new approach, there are benefits to considering breath testing for SARS-CoV-2 infections. When coupled with several of the near-real-time VOC detectors that are under development or recently available, it may provide a quick test – potentially yielding results in minutes, before a subject has left the testing area. This could lead to higher quarantine compliance and limit community spread, as there is no latency lag-time for asymptomatic or pre-symptomatic individuals. While the promise of vaccine deployment is tantalizingly close, it is clear that society will need to continue to test for SARS-CoV-2 infections for some time. These rapid breath-based tests could be a key part of the international response strategy. Breath analysis research teams need to collectively meet this global need. The authors confirm sole responsibility for the conception and preparation of this invited Commentary. Dr. Davis reports patents #10,111,606 and #10,067,119 and #9398,881 and PCT/US2017/063,018 licensed to SensIT Ventures, Inc., and a patent #9824,870 issued and Prof. Davis is a co-founder of the start-up company SensIT Ventures, Inc. Dr. Kenyon reports patents #10,111,606 and #10,067,119 and #9398,881 and PCT/US2017/063,018 licensed to SensIT Ventures, Inc. Dr. Schivo reports patent PCT/US2017/063,018 licensed to SensIT Ventures, Inc. The authors are supported by NIH National centre for Advancing Translational Sciences (NCATS) through award UL1 TR001860 (CED, NJK); NIH award UG3-OD023365 (CED, NJK); NIH award 1P30ES023513-01A1 (CED, NJK), the Veteran's Administration (CED, MS, NJK), the University of California Tobacco-Related Disease Research Program award T31IR1614 (CED, NJK), and a University of California CITRIS award 19-0092 (CED, MS, NK). The contents of this commentary are solely the responsibility of the authors and do not necessarily represent the official views of the funding agencies. Metabolomics of exhaled breath in critically ill COVID-19 patients: A pilot studyThe real-time, non-invasive detection of methylpent-2-enal, 2,4-octadiene 1-chloroheptane, and nonanal in exhaled breath may identify ARDS patients with COVID-19. Full-Text PDF Open Access

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.255
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it