Detection of Volatile Compounds Emitted by Bacteria in Wounds Using Gas Sensors

In this paper we analyze an experiment for the use of low-cost gas sensors intended to detect bacteria in wounds using a non-intrusive technique. Seven different genera/species of microbes tend to be present in most wound infections. Detection of these bacteria usually requires sample and laboratory testing which is costly, inconvenient and time-consuming. The validation processes for these sensors with nineteen types of microbes (1 Candida, 2 Enterococcus, 6 Staphylococcus, 1 Aeromonas, 1 Micrococcus, 2 E. coli and 6 Pseudomonas) are presented here, in which four sensors were evaluated: TGS-826 used for ammonia and amines, MQ-3 used for alcohol detection, MQ-135 for CO2 and MQ-138 for acetone detection. Validation was undertaken by studying the behavior of the sensors at different distances and gas concentrations. Preliminary results with liquid cultures of 108 CFU/mL and solid cultures of 108 CFU/cm2 of the 6 Pseudomonas aeruginosa strains revealed that the four gas sensors showed a response at a height of 5 mm. The ammonia detection response of the TGS-826 to Pseudomonas showed the highest responses for the experimental samples over the background signals, with a difference between the values of up to 60 units in the solid samples and the most consistent and constant values. This could suggest that this sensor is a good detector of Pseudomonas aeruginosa, and the recording made of its values could be indicative of the detection of this species. All the species revealed similar CO2 emission and a high response rate with acetone for Micrococcus, Aeromonas and Staphylococcus.

[1]  Heather D. Bean,et al.  Comparative analysis of the volatile metabolomes of Pseudomonas aeruginosa clinical isolates , 2016, Journal of breath research.

[2]  Daniel Sierra-Sosa,et al.  Pressure ulcer image segmentation technique through synthetic frequencies generation and contrast variation using toroidal geometry , 2017, Biomedical engineering online.

[3]  Wojciech Kukwa,et al.  Detecting Breathing and Snoring Episodes Using a Wireless Tracheal Sensor—A Feasibility Study , 2017, IEEE Journal of Biomedical and Health Informatics.

[4]  F H Silver,et al.  A review of the etiology and treatment of skin ulcers with wound dressings: comparison of the effects of occlusive and nonocclusive dressings. , 1992, Journal of long-term effects of medical implants.

[5]  K. Webb,et al.  Variation in hydrogen cyanide production between different strains of Pseudomonas aeruginosa , 2011, European Respiratory Journal.

[6]  César Raúl Aguilar-García Infección de piel y tejidos blandos por el género Aeromonas , 2015 .

[7]  S. Foster,et al.  Human skin commensals augment Staphylococcus aureus pathogenesis , 2018, Nature Microbiology.

[8]  Joseph R. Stetter,et al.  Detection and discrimination of coliform bacteria with gas sensor arrays , 2000 .

[9]  David Smith,et al.  Detection of volatile compounds emitted by Pseudomonas aeruginosa using selected ion flow tube mass spectrometry , 2005, Pediatric pulmonology.

[10]  Yang Hao,et al.  Detecting Vital Signs with Wearable Wireless Sensors , 2010, Sensors.

[11]  Ayman El-Baz,et al.  Tissues Classification for Pressure Ulcer Images Based on 3D Convolutional Neural Network , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

[12]  Paul Anthony Iaizzo,et al.  Wound status evaluation using color image processing , 1997, IEEE Transactions on Medical Imaging.

[13]  Dermot Diamond,et al.  Glucose Sensing for Diabetes Monitoring: Recent Developments , 2017, Sensors.

[14]  I. Shrira,et al.  The smell of death: evidence that putrescine elicits threat management mechanisms , 2015, Front. Psychol..

[15]  Lina Pan,et al.  A background elimination method based on wavelet transform in wound infection detection by electronic nose , 2011 .

[16]  Kazumasa Fukuda,et al.  Small and Rough Colony Pseudomonas aeruginosa with Elevated Biofilm Formation Ability Isolated in Hospitalized Patients , 2007, Microbiology and immunology.

[17]  K. Tu,et al.  Predicting the growth situation of Pseudomonas aeruginosa on agar plates and meat stuffs using gas sensors , 2016, Scientific Reports.

[18]  Ayman El-Baz,et al.  Classification of pressure ulcer tissues with 3D convolutional neural network , 2018, Medical & Biological Engineering & Computing.

[19]  Pengfei Jia,et al.  Improving the performance of electronic nose for wound infection detection using orthogonal signal correction and particle swarm optimization , 2014 .

[20]  J. Latgé,et al.  Volatile Compounds Emitted by Pseudomonas aeruginosa Stimulate Growth of the Fungal Pathogen Aspergillus fumigatus , 2016, mBio.

[21]  Heather D. Bean,et al.  Volatile molecules from bronchoalveolar lavage fluid can ‘rule-in’ Pseudomonas aeruginosa and ‘rule-out’ Staphylococcus aureus infections in cystic fibrosis patients , 2018, Scientific Reports.

[22]  B. Iglewski,et al.  P. aeruginosa Biofilms in CF Infection , 2008, Clinical reviews in allergy & immunology.