Electronic Nose in the Detection of Wound Infection Bacteria from Bacterial Cultures: A Proof-of-Principle Study
| Autor: | Antti Roine, Maarit K Nieminen, Risto Vuento, Niku Oksala, Janne Aittoniemi, Terho Lehtimäki, Juha Kiiski, Pekka Kumpulainen, Lauri Hokkinen, Taavi Saviauk, Markus Karjalainen, Nelly N Tamminen |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Methicillin-Resistant Staphylococcus aureus Microbiological culture medicine.drug_class 030106 microbiology Antibiotics medicine.disease_cause Microbiology 03 medical and health sciences medicine Humans Blood culture Electronic Nose biology medicine.diagnostic_test Bacteria business.industry Pseudomonas aeruginosa Clostridium perfringens biology.organism_classification Methicillin-resistant Staphylococcus aureus 030104 developmental biology Staphylococcus aureus Wound Infection Surgery business |
| Zdroj: | European surgical research. Europaische chirurgische Forschung. Recherches chirurgicales europeennes. 59(1-2) |
| ISSN: | 1421-9921 |
| Popis: | Background: Soft tissue infections, including postoperative wound infections, result in a significant burden for modern society. Rapid diagnosis of wound infections is based on bacterial stains, cultures, and polymerase chain reaction assays, and the results are available earliest after several hours, but more often not until days after. Therefore, antibiotic treatment is often administered empirically without a specific diagnosis. Methods: We employed our electronic nose (eNose) system for this proof-of-concept study, aiming to differentiate the most relevant bacteria causing wound infections utilizing a set of clinical bacterial cultures on identical blood culture dishes, and established bacterial lines from the gaseous headspace. Results: Our eNose system was capable of differentiating both methicillin-sensitive Staphylococcus aureus (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA), Streptococcus pyogenes, Escherichia coli, Pseudomonas aeruginosa, and Clostridium perfringens with an accuracy of 78% within minutes without prior sample preparation. Most importantly, the system was capable of differentiating MRSA from MSSA with a sensitivity of 83%, a specificity of 100%, and an overall accuracy of 91%. Conclusions: Our results support the concept of rapid detection of the most relevant bacteria causing wound infections and ultimately differentiating MRSA from MSSA utilizing gaseous headspace sampling with an eNose. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|