Evidence map›Paper›PMID 40132411›Full record

ReviewTalanta2025

Volatile organic compounds (VOCs) detection for the identification of bacterial infections in clinical wound samples.

Mostafa Azimzadeh, Patricia Khashayar, Marziyeh Mousazadeh, Maryam Daneshpour, Mohammad Rostami, David R Goodlett, Karim Manji, Somayeh Fardindoost, Mohsen Akbari, Mina Hoorfar

Abstract readReview
In one paragraph

Review in Talanta, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Microbial volatile organic compounds and olfactory receptors in wound malodor.Frontiers in cellular and infection microbiology · 2026
    Pooled it
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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

10 authors.

Mostafa AzimzadehLaboratory for Innovations in Microengineering (LiME), Department of Mechanical Engineering, University of Victoria, Victoria, BC, V8P 5C2, Canada; Department of Mechanical Engineering, University of Victoria, Victoria, BC, V8P 5C2, Canada.
Patricia KhashayarInternational Institute for Biosensing, University of Minnesota, Minnesota, USA.
Marziyeh MousazadehBehzsaz Company, Tehran, Iran.
Maryam DaneshpourASA Kimyasal Company, Istanbul, Turkey.
Mohammad RostamiDepartment of Computer Science, University of Southern California, Los Angeles, CA, 90089, USA.
David R GoodlettDepartment of Biochemistry and Microbiology, University of Victoria, Victoria, BC, Canada; University of Victoria Genome British Columbia Proteomics Center, University of Victoria, Victoria, BC, Canada.
Karim ManjiDepartment of Surgery, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Somayeh FardindoostDepartment of Mechanical Engineering, University of Victoria, Victoria, BC, V8P 5C2, Canada.
Mohsen AkbariLaboratory for Innovations in Microengineering (LiME), Department of Mechanical Engineering, University of Victoria, Victoria, BC, V8P 5C2, Canada; Department of Mechanical Engineering, University of Victoria, Victoria, BC, V8P 5C2, Canada; Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, USA; School of Biomedical Engineering, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada. Electronic address: makbari@uvic.ca.
Mina HoorfarDepartment of Mechanical Engineering, University of Victoria, Victoria, BC, V8P 5C2, Canada. Electronic address: mhoorfar@uvic.ca.

Funding

MS Diagnostic Bacterial Identification LibraryR01AI147314 · NIAID · UNIVERSITY OF MARYLAND BALTIMORE · PI ERNST, ROBERT K, GOODLETT, DAVID ROBINSON · 2020 to 2024
$2.3M
NIAID NIH HHS R01 AI147314
6 · The paper itself

Abstract

Early detection of wound infections is critical for timely intervention and prevention of possible complications since prompt treatment can help lower pathogen spread and enhance faster healing. Early detection also helps reduce the risk of serious infections requiring extensive medical interventions or life-threatening diseases such as sepsis. Culture-based approaches currently used for bacterial identification have limited sensitivity and specificity. At the same time, they are time-consuming, resulting in delays in therapy and, therefore, having a negative impact on the treatment outcomes. Quantifying the volatile organic compounds (VOCs) released by bacteria residing in wounds is a promising, non-invasive option for detecting infections at early stages. This method allows for continuous monitoring without requiring invasive procedures, thereby reducing patient discomfort and the risk of further complications. Spectroscopy methods and sensors are the primary VOC detection and quantification approaches, but sensors are more rapid, cost-effective, non-invasive, and precise. This review highlights the significance of the early detection of wound infection to enable timely intervention and prevent complications, emphasizing the limitations of culture-based approaches. It also explores the potential of quantifying VOCs using different methods and discusses the correlation between their levels and the rate of bacterial infections in wounds. Additionally, the review evaluates current VOC-based monitoring methods for wound management, identifies gaps in the field, and advocates for further research to advance wound care and enhance patient outcomes.

Indexed as

Bacterial InfectionsVolatile Organic CompoundsWound InfectionBacteriaHumansVolatile Organic CompoundsBacterial infectionsChronic woundsSensorVOCVolatile organic compoundsWound monitoring

Identifiers

PMID40132411
PMCPMC13262938

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.