ArticleJournal of applied microbiology2022
Real-time detection of volatile metabolites enabling species-level discrimination of bacterial biofilms associated with wound infection.
Article in Journal of applied microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 17 citations in OpenAlex.
- Untargeted GC-IMS Metabolomics of Wound Headspace for Bacterial Infection Biomarker Discovery.Metabolites · 2026Article
- Stimuli-Responsive Nanomaterial-Based Biosensor Structures for Wound Care: pH, ROS, and Temperature Sensing Strategies.Micromachines · 2026Review
- The Role and Mechanism of Nanozymes in Burn Wound Healing.International journal of nanomedicine · 2026Review
- Infection-driven proliferative phase impairment in chronic wounds: a mechanistic framework for precision regenerative therapy.Frontiers in immunology · 2026Review
- Volatile organic compounds (VOCs) detection for the identification of bacterial infections in clinical wound samples.Talanta · 2025Review
- New-generation biofilm effective antimicrobial peptides and a real-time anti-biofilm activity assay: CoMIC.Applied microbiology and biotechnology · 2024Article
- Identification of trans-genus biomarkers for early diagnosis of intestinal schistosomiasis and progression of gut pathology in a mouse model using metabolomics.PLoS neglected tropical diseases · 2024Article
- The Effect of Mesenchymal Stem Cells on the Wound Infection.Current stem cell research & therapy · 2024Review
- Volatilomes of human infection.Analytical and bioanalytical chemistry · 2024Review
- Current Limitations of Staph Infection Diagnostics, and the Role for VOCs in Achieving Culture-Independent Detection.Pathogens (Basel, Switzerland) · 2023Review
- A growing battlefield in the war against biofilm-induced antimicrobial resistance: insights from reviews on antibiotic resistance.Frontiers in cellular and infection microbiology · 2023Review
- A Host-Directed Approach to the Detection of Infection in Hard-to-Heal Wounds.Diagnostics (Basel, Switzerland) · 2022Review
- Multi-Strain and -Species Investigation of Volatile Metabolites Emitted from Planktonic and BiofilmMetabolites · 2022Article
- Real-time detection of volatile metabolites enabling species-level discrimination of bacterial biofilms associated with wound infection.Journal of applied microbiology · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsThe main aim of this study was to investigate the real-time detection of volatile metabolites for the species-level discrimination of pathogens associated with clinically relevant wound infection, when grown in a collagen wound biofilm model. METHODS AND
resultsThis work shows that Staphylococcus aureus, Pseudomonas aeruginosa and Streptococcus pyogenes produce a multitude of volatile compounds when grown as biofilms in a collagen-based biofilm model. The real-time detection of these complex volatile profiles using selected ion flow tube mass spectrometry and the use of multivariate statistical analysis on the resulting data can be used to successfully differentiate between the pathogens studied.
conclusionsThe range of bacterial volatile compounds detected between the species studied vary and are distinct. Discrimination between bacterial species using real-time detection of volatile metabolites and multivariate statistical analysis was successfully demonstrated. SIGNIFICANCE AND IMPACT OF THE STUDY: Development of rapid point-of-care diagnostics for wound infection would improve diagnosis and patient care. Such technological approaches would also facilitate the appropriate use of antimicrobials, minimizing the emergence of antimicrobial resistance. This study further develops the use of volatile metabolite detection as a new diagnostic approach for wound infection.
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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.