Evidence map›Paper›PMID 39731069›Full record

ArticleBMC infectious diseases2024

Early detection of bacterial pneumonia by characteristic induced odor signatures.

Kim Arnold, Alejandro Gómez-Mejia, Miguel de Figueiredo, Julien Boccard, Kapil Dev Singh, Serge Rudaz, Pablo Sinues, Annelies S Zinkernagel

Abstract read
In one paragraph

Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Construction of Baicalein-Loaded Chitosan Nanoparticles forInternational journal of nanomedicine · 2025
    Article
  2. 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

8 authors.

Kim Arnold *University Children's Hospital Basel (UKBB), Basel, 4056, Switzerland.
Alejandro Gómez-Mejia *Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zürich, Zurich, 8097, Switzerland.
Miguel de FigueiredoSchool of Pharmaceutical Sciences, University of Geneva, Geneva, 1206, Switzerland.
Julien BoccardSchool of Pharmaceutical Sciences, University of Geneva, Geneva, 1206, Switzerland.
Kapil Dev SinghUniversity Children's Hospital Basel (UKBB), Basel, 4056, Switzerland.
Serge RudazSchool of Pharmaceutical Sciences, University of Geneva, Geneva, 1206, Switzerland.
Pablo SinuesUniversity Children's Hospital Basel (UKBB), Basel, 4056, Switzerland. pablo.sinues@unibas.ch.
Annelies S ZinkernagelDepartment of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zürich, Zurich, 8097, Switzerland. annelies.zinkernagel@usz.ch.

Funding

Fondation Botnar 320030_173168Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 31003A_176252Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung PCEGP3-181300
6 · The paper itself

Abstract

introductionThe ability to detect pathogenic bacteria before the onsets of severe respiratory symptoms and to differentiate bacterial infection allows to improve patient-tailored treatment leading to a significant reduction in illness severity, comorbidity as well as antibiotic resistance. As such, this study refines the application of the non-invasive Secondary Electrospray Ionization-High Resolution Mass Spectrometry (SESI-HRMS) methodology for real-time and early detection of human respiratory bacterial pathogens in the respiratory tract of a mouse infection model.

methodsA real-time analysis of changes in volatile metabolites excreted by mice undergoing a lung infection by Staphylococcus aureus or Streptococcus pneumoniae were evaluated using a SESI-HRMS instrument. The infection status was confirmed using classical CFU enumeration and tissue histology. The detected VOCs were analyzed using a pre- and post-processing algorithm along with ANOVA and RASCA statistical evaluation methods.

resultsCharacteristic changes in the VOCs emitted from the mice were detected as early as 4-6 h post-inoculation. Additionally, by using each mouse as its own baseline, we mimicked the inherent variation within biological organism and reported significant variations in 25 volatile organic compounds (VOCs) during the course of a lung bacterial infection.

conclusionthe non-invasive SESI-HRMS enables real-time detection of infection specific VOCs. However, further refinement of this technology is necessary to improve clinical patient management, treatment, and facilitate decisions regarding antibiotic use due to early infection detection.

Indexed as

Early DiagnosisOdorantsStaphylococcus aureusStreptococcus pneumoniaeVolatile Organic CompoundsAnimalsDisease Models, AnimalFemaleLungMicePneumonia, BacterialSpectrometry, Mass, Electrospray IonizationVolatile Organic CompoundsExhalomic analysisLung infection VOCSESI-HRMSStaphylococcus aureusStreptococcus pneumoniae

Identifiers

PMID39731069
PMCPMC11681641

What Socratic holds

Textmetadata
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 Socratic graph.