Evidence map›Paper›PMID 39370469›Full record

ArticleScientific reports2024

COVID-19 detection from exhaled breath.

Nicolò Bellarmino, Riccardo Cantoro, Michele Castelluzzo, Raffaele Correale, Giovanni Squillero, Giorgio Bozzini, Francesco Castelletti, Carla Ciricugno, Daniela Dalla Gasperina, Francesco Dentali and 3 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 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. 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

13 authors.

Nicolò Bellarmino *Politecnico di Torino, Torino, Italy. nicolo.bellarmino@polito.it.
Riccardo Cantoro *Politecnico di Torino, Torino, Italy.
Michele Castelluzzo *NanoTech Analysis Srl, Torino, Italy.
Raffaele Correale *NanoTech Analysis Srl, Torino, Italy.
Giovanni Squillero *Politecnico di Torino, Torino, Italy.
Giorgio BozziniHospital ASST Lariana, Como, Italy.
Francesco CastellettiUniversità degli Studi dell'Insubria, Varese, Italy.
Carla CiricugnoNanoTech Analysis Srl, Torino, Italy.
Daniela Dalla GasperinaUniversità degli Studi dell'Insubria, Varese, Italy.
Francesco DentaliUniversità degli Studi dell'Insubria, Varese, Italy.
Giovanni PoggialiniUniversità degli Studi dell'Insubria, Varese, Italy.
Piergiorgio SalernoUniversità degli Studi dell'Insubria, Varese, Italy.
Stefano TaborelliUniversità degli Studi dell'Insubria, Varese, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The SARS-CoV-2 coronavirus emerged in 2019 causing a COVID-19 pandemic that resulted in 7 million deaths out of 770 million reported cases over the next 4 years. The global health emergency called for unprecedented efforts to monitor and reduce the rate of infection, pushing the study of new diagnostic methods. In this paper, we introduce a cheap, fast, and non-invasive COVID-19 detection system, which exploits only exhaled breath. Specifically, provided an air sample, the mass spectra in the 10-351 mass-to-charge range are measured using an original micro and nano-sampling device coupled with a high-precision spectrometer; then, the raw spectra are processed by custom software algorithms; the clean and augmented data are eventually classified using state-of-the-art machine-learning algorithms. An uncontrolled clinical trial was conducted between 2021 and 2022 on 302 subjects who were concerned about being infected, either due to exhibiting symptoms or having recently recovered from illness. Despite the simplicity of use, our system showed a performance comparable to the traditional polymerase-chain-reaction and antigen testing in identifying cases of COVID-19 (that is, 95% accuracy, 94% recall, 96% specificity, and 92% [Formula: see text]-score). In light of these outcomes, we think that the proposed system holds the potential for substantial contributions to routine screenings and expedited responses during future epidemics, as it yields results comparable to state-of-the-art methods, providing them in a more rapid and less invasive manner.

Indexed as

Breath TestsCOVID-19SARS-CoV-2AdultAgedAlgorithmsCOVID-19 TestingExhalationFemaleHumansMachine LearningMaleMass SpectrometryMiddle AgedSensitivity and Specificity

Identifiers

PMID39370469
PMCPMC11456604

What OpenQuestion holds

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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.