Evidence map›Paper›PMID 40762732›Full record

ArticleAnalytical and bioanalytical chemistry2025

Systematic comparison of methods for offline breath sampling.

Mark Woollam, Andrew Christensen, Eray Schulz, Serenidy Eckerle, Michael D Davis, Don B Sanders, Mangilal Agarwal

Registry-linked trialAbstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Analytical and bioanalytical chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07696273 (Miniaturized Smart Sensor Device for the Detection of Hypo- and Hyperglycemic Events in Persons Diagnosed With Diabetes), which is not on this 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.

NCT07696273 narecruitingnot on this mapstarted 2026, after this paper: background citation

Miniaturized Smart Sensor Device for the Detection of Hypo- and Hyperglycemic Events in Persons Diagnosed With Diabetes

TypeinterventionalSponsorIndiana UniversityRan2026 to 2027Enrolled40ConditionsDiabetes, Hyperglycemia, Hypoglycemia (Diabetic)ArmsSensing Device and Tedlar Bags
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
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

7 authors.

Mark WoollamDepartment of Chemistry & Chemical Biology, Indiana University Indianapolis, Indianapolis, IN, 46202, USA. mwoollam@iu.edu.
Andrew ChristensenIntegrated Nanosystems Development Institute, Indiana University Indianapolis, Indianapolis, IN, 46202, USA.
Eray SchulzDepartment of Chemistry & Chemical Biology, Indiana University Indianapolis, Indianapolis, IN, 46202, USA.
Serenidy EckerleIntegrated Nanosystems Development Institute, Indiana University Indianapolis, Indianapolis, IN, 46202, USA.
Michael D DavisDivision of Pulmonology, Allergy, and Sleep Medicine, Riley Hospital for Children at Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
Don B SandersDivision of Pulmonology, Allergy, and Sleep Medicine, Riley Hospital for Children at Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
Mangilal AgarwalDepartment of Chemistry & Chemical Biology, Indiana University Indianapolis, Indianapolis, IN, 46202, USA. agarwal@iu.edu.

Funding

SCH: Smart Breath- based Diagnosis of Pulmonary Exacerbations in Children with Cystic Fibrosis through Machine Learning: Towards Noninvasive Health Monitoring in Real-TimeR01HL177812 · NHLBI · INDIANA UNIVERSITY INDIANAPOLIS · PI Mangilal Agarwal · 2024 to 2026
$783k
NHLBI NIH HHS 1R01HL177812-01NHLBI NIH HHS R01 HL177812
6 · The paper itself

Abstract

Harnessing the potential of exhaled breath analysis is an emerging frontier in medical diagnostics, given breath is a rich source of volatile organic compound (VOC) biomarkers for different medical conditions. A current downfall in this field, however, is the lack of standardized and widely available methods for offline sampling of exhaled VOCs. Herein, strides are taken toward the standardization of breath sampling in Tedlar bags by exploring several factors that can impact VOC heterogeneity, including tubing material, chemical composition of collection bags, breath fractionation, exhalation volume, and transfer flow rate. After bag-based sampling standardization, performance was benchmarked using two offline breath sampling methods, Tedlar bags and the Respiration Collector for In Vitro Analysis (ReCIVA). Three volunteers from the laboratory with no known respiratory diseases donated ≥ n = 5 samples collected onto adsorption tubes via each method, which were analyzed through thermal desorption (TD) coupled with gas chromatography-mass spectrometry (GC-MS). Data processing revealed a set of 15 highly reliable on-breath VOCs detected across volunteers, and most analytes (except indole) demonstrated higher sensitivity using Tedlar bags. Calculating relative standard deviation (RSD) values showed Tedlar bags were also significantly more reproducible compared to the ReCIVA (p < 0.03). Agreement between the two methods was demonstrated through correlating VOC signals with high statistical significance (R

Indexed as

Gas Chromatography-Mass SpectrometryVolatile Organic CompoundsAdultBiomarkersBreath TestsExhalationHumansMaleReproducibility of ResultsBiomarkersVolatile Organic CompoundsExhaled breathGas chromatography-mass spectrometry (GC–MS)Method standardizationThermal desorptionVolatile organic compounds (VOCs)

Identifiers

PMID40762732
PMCPMC12402010

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Registered trials

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.