Evidence map›Paper›PMID 39319329›Full record

ArticlePNAS nexus2024

A breath-based in vitro diagnostic assay for the detection of lower respiratory tract infections.

Dapeng Chen, Marek A Mirski, Shuo Chen, Wayne A Bryden, Michael McLoughlin, Kiana M Kiser, Emily R Caton, Caroline R Haddaway, Maximilian S Cetta, Yezhi Pan

Abstract read
In one paragraph

Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Dapeng ChenZeteo Tech, Inc., Sykesville, MD 21784, USA.ORCID https://orcid.org/0000-0001-8412-581X
Marek A MirskiDepartment of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Shuo ChenDivision of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD 21205, USA.
Wayne A BrydenZeteo Tech, Inc., Sykesville, MD 21784, USA.ORCID https://orcid.org/0000-0001-5477-7809
Michael McLoughlinZeteo Tech, Inc., Sykesville, MD 21784, USA.ORCID https://orcid.org/0000-0002-3607-5739
Kiana M KiserZeteo Tech, Inc., Sykesville, MD 21784, USA.ORCID https://orcid.org/0009-0008-5849-6316
Emily R CatonZeteo Tech, Inc., Sykesville, MD 21784, USA.
Caroline R HaddawayZeteo Tech, Inc., Sykesville, MD 21784, USA.ORCID https://orcid.org/0009-0008-9886-4363
Maximilian S CettaZeteo Tech, Inc., Sykesville, MD 21784, USA.
Yezhi PanDepartment of Psychiatry, Maryland Psychiatric Research Center, School of Medicine, University of Maryland, Baltimore, MD 21201, USA.ORCID https://orcid.org/0009-0001-4014-0302

Funding

Noninvasive Diagnostic Markers of Lower Respiratory Tract Infection in Mechanically Ventilated PatientsR44AI177245 · NIAID · ZETEO TECH, INC. · PI CHEN, DAPENG · 2023 to 2024
$1.9M
NIAID NIH HHS R44 AI177245
6 · The paper itself

Abstract

An accurate diagnosis is critical to reducing mortality in people with lower respiratory tract infections (LRTIs). Current microbiological culture is time-consuming, and nucleic acid amplification-based molecular technologies cannot distinguish between colonization and infection. Previously, we described developing a sampling system for effectively capturing biomolecules from human breath. We identified a new class of proteoform markers of protease activation, termed proteolytic products of infection, for detecting LRTIs in people with mechanical ventilation. Here, we further developed an in vitro assay by designing a specific substrate sensor for human neutrophil elastase (HNE) to detect LRTIs in breath samples. In the proof-of-concept study, we then applied this in vitro assay to breath samples collected from intubated patients and healthy volunteers. The findings revealed that the LRTI group demonstrated a significant mean differential, showing a 9.8-fold elevation in measured HNE activity compared with the non-LRTI group and a 9.2-fold compared with healthy volunteers. The in vitro assay's diagnostic potential was assessed by constructing a receiver operating characteristic curve, resulting in an area under the curve of 0.987. Using an optimal threshold for HNE at 0.2 pM, the sensitivity was determined to be 1.0 and the specificity to be 0.867. Further correlation analysis revealed a strong positive relationship between the measured HNE activity and the protein concentration in the breath samples. Our results demonstrate that this breath-based in vitro assay provides high diagnostic performance for LRTIs, suggesting that the technology may be useful in the near term for the accurate diagnosis of LRTIs.

Indexed as

human breathlower respiratory tract infectionmass spectrometrynoninvasive diagnostics

Identifiers

PMID39319329
PMCPMC11421151

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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.