Identification of Staphylococcus aureus infections in hospital environment: electronic nose based approach

Dutta, Ritaban; Morgan, David W.; Baker, Nicky; Gardner, Julian W.; Hines, Evor L.

doi:10.1016/j.snb.2005.01.013

Cited by 82 publications

(43 citation statements)

References 6 publications

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“…Several previous studies on the detection of bacteria from gaseous headspace have been conducted using an eNose with entirely different operating principle [14, 17-19]. …”

Section: Discussionmentioning

confidence: 99%

Electronic Nose in the Detection of Wound Infection Bacteria from Bacterial Cultures: A Proof-of-Principle Study

et al. 2018

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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.

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“…Several previous studies on the detection of bacteria from gaseous headspace have been conducted using an eNose with entirely different operating principle [14, 17-19]. …”

Section: Discussionmentioning

confidence: 99%

Electronic Nose in the Detection of Wound Infection Bacteria from Bacterial Cultures: A Proof-of-Principle Study

et al. 2018

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“…The cluster centers were used as a basis to define a FIS which was subsequently used to explore and understand environmental patterns over the years. Set 1 data is used in the Wavelet -FCM -FIS based initial water data understanding and modelling where as Set 2 and Set 3 are used to measure the sensory drift over the years and to test prediction performance of the system [10][11][12][13].…”

Section: Data and Systemmentioning

confidence: 99%

Environmental Sensor Drift Correction using Wavelet-mFCM-FIS Architecture: An Unsupervised Machine Learning Approach

Dutta¹

2013

IJCA

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A novel hybrid sensor informatics architecture based on Discrete Wavelet Transform (DWT), Fuzzy logic based clustering (FCM) and Fuzzy Rule based Inference system (FIS) has been investigated and proposed to estimate dynamic generic sensor drift during physical sampling. DWT has been used for sensor pre-processing; data dimension reduction and feature extraction from sensor time series, where as DWT-FCM based approach has been used to estimate the cumulative drift in our sensory system. An intelligent generalized algorithm using multiple FCM maps (m-FCM) has been proposed to implement the drift correction on any future unknown data sets. In the next stage a newly proposed DWT-FCM-FIS architecture has been tested on multisensory environmental data sets covering three consecutive years (2009 -11) to predict probable month (environmental time of the year) with up to 93% accuracy. This novel hybrid data processing architecture have been implemented and tested upon a real time estuary sensory platform of temperature, conductivity and salinity sensors that have been deployed to monitor the Derwent Estuary in Hobart, Australia. This newly proposed approach could be an adaptive solution to tackle drifts in the sensor networks and improve overall monitoring quality.

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“…Two of the most promising health applications using electrical noses will be described below. Dutta et al, 2005). In this report, swabs from infected areas of patients were placed in 15 ml vials for 5 minutes, to allow the aromatic compounds to volatilize.…”

Section: Structure Of Electronic and Bioelectrical Noses For Biomedicmentioning

confidence: 99%

“…The resulting sa,ple was then "sniffed" by the electronic nose. Using innovative data clustering techniques, Dutta et al (2005) were able to identify bacterial classes with very high accuracy. If placed in hospital ventilation systems, this electronic nose could theoretically detect and prevent contamination of other patients or equipment.…”

Section: Structure Of Electronic and Bioelectrical Noses For Biomedicmentioning

confidence: 99%

Inspiration from Nature:Insights from Crustacean Chemical Sensors Can Lead to Successful Design of Artificial Chemical Sensors

Mead¹

2012

Advances in Chemical Sensors

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Identification of Staphylococcus aureus infections in hospital environment: electronic nose based approach

Cited by 82 publications

References 6 publications

Electronic Nose in the Detection of Wound Infection Bacteria from Bacterial Cultures: A Proof-of-Principle Study

Electronic Nose in the Detection of Wound Infection Bacteria from Bacterial Cultures: A Proof-of-Principle Study

Environmental Sensor Drift Correction using Wavelet-mFCM-FIS Architecture: An Unsupervised Machine Learning Approach

Inspiration from Nature:Insights from Crustacean Chemical Sensors Can Lead to Successful Design of Artificial Chemical Sensors

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