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Virtanen J, Roine A, Kontunen A, Karjalainen M, Numminen J, Oksala N, Rautiainen M, Kivekäs I. The Detection of Bacteria in the Maxillary Sinus Secretion of Patients With Acute Rhinosinusitis Using an Electronic Nose: A Pilot Study. Ann Otol Rhinol Laryngol 2023; 132:1330-1335. [PMID: 36691987 PMCID: PMC10498650 DOI: 10.1177/00034894231151301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
OBJECTIVE Detecting bacteria as a causative pathogen of acute rhinosinusitis (ARS) is a challenging task. Electronic nose technology is a novel method for detecting volatile organic compounds (VOCs) that has also been studied in association with the detection of several diseases. The aim of this pilot study was to analyze maxillary sinus secretion with differential mobility spectrometry (DMS) and to determine whether the secretion demonstrates a different VOC profile when bacteria are present. METHODS Adult patients with ARS symptoms were examined. Maxillary sinus contents were aspirated for bacterial culture and DMS analysis. k-Nearest neighbor and linear discriminant analysis were used to classify samples as positive or negative, using bacterial cultures as a reference. RESULTS A total of 26 samples from 15 patients were obtained. After leave-one-out cross-validation, k-nearest neighbor produced accuracy of 85%, sensitivity of 67%, specificity of 94%, positive predictive value of 86%, and negative predictive value of 84%. CONCLUSIONS The results of this pilot study suggest that bacterial positive and bacterial negative sinus secretion release different VOCs and that DMS has the potential to detect them. However, as the results are based on limited data, further conclusions cannot be made. DMS is a novel method in disease diagnostics and future studies should examine whether the method can detect bacterial ARS by analyzing exhaled air.
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Affiliation(s)
- Jussi Virtanen
- Department of Otorhinolaryngology, Head and Neck Surgery, Tampere University Hospital, Tampere, Finland
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Pirkanmaa, Finland
| | - Antti Roine
- Department of Surgery, Tampere University Hospital, Hatanpää Hospital, Tampere, Finland
- Olfactomics Ltd., Tampere, Finland
| | - Anton Kontunen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Pirkanmaa, Finland
- Olfactomics Ltd., Tampere, Finland
| | - Markus Karjalainen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Pirkanmaa, Finland
- Olfactomics Ltd., Tampere, Finland
| | - Jura Numminen
- Department of Otorhinolaryngology, Head and Neck Surgery, Tampere University Hospital, Tampere, Finland
| | - Niku Oksala
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Pirkanmaa, Finland
- Olfactomics Ltd., Tampere, Finland
- Vascular Centre, Tampere University Hospital, Tampere, Finland
| | - Markus Rautiainen
- Department of Otorhinolaryngology, Head and Neck Surgery, Tampere University Hospital, Tampere, Finland
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Pirkanmaa, Finland
| | - Ilkka Kivekäs
- Department of Otorhinolaryngology, Head and Neck Surgery, Tampere University Hospital, Tampere, Finland
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Pirkanmaa, Finland
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Zhao C, Ma J, Jia W, Wang H, Tian H, Wang J, Zhou W. An Apple Fungal Infection Detection Model Based on BPNN Optimized by Sparrow Search Algorithm. BIOSENSORS 2022; 12:bios12090692. [PMID: 36140077 PMCID: PMC9496132 DOI: 10.3390/bios12090692] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 08/16/2022] [Accepted: 08/17/2022] [Indexed: 11/16/2022]
Abstract
To rapidly detect whether apples are infected by fungi, a portable electronic nose was used in this study to collect the gas information from apples, and the collected information was processed by smoothing filtering, data dimensionality reduction, and outlier removal. Following this, we utilized K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), a convolutional neural network (CNN), a back-propagation neural network (BPNN), a particle swarm optimization–back-propagation neural network (PSO-BPNN), a gray wolf optimization–backward propagation neural network (GWO-BPNN), and a sparrow search algorithm–backward propagation neural network (SSA-BPNN) model to discriminate apple samples, and adopted the 10-fold cross-validation method to evaluate the performance of each model. The results show that SSA can effectively optimize the performance of the BPNN, such that the recognition accuracy of the optimized SSA-BPNN model reaches 98.40%. This study provides an important reference value for the application of an electronic nose in the non-destructive and rapid detection of fungal infection in apples.
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Affiliation(s)
- Changtong Zhao
- Mechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China
| | - Jie Ma
- Mechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China
| | - Wenshen Jia
- Mechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China
- Institute of Quality Standard and Testing Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Correspondence: ; Tel.: +86-13521217121
| | - Huihua Wang
- Department of Food and Bioengineering, Beijing Vocational College of Agriculture, Beijing 102206, China
| | - Hui Tian
- Mechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China
| | - Jihua Wang
- Institute of Quality Standard and Testing Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Wei Zhou
- Hebei Food Safety Key Laboratory, Hebei Food Inspection and Research Institute, Shijiazhuang 050091, China
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Virtanen J, Kontunen A, Numminen J, Oksala N, Rautiainen M, Roine A, Kivekäs I. Identifying chronic rhinosinusitis without nasal polyps by analyzing aspirated nasal air with an electronic nose based on differential mobility spectrometry. Acta Otolaryngol 2022; 142:524-531. [PMID: 35787097 DOI: 10.1080/00016489.2022.2093397] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
BACKGROUND The diagnosis of chronic rhinosinusitis (CRS) is a complicated procedure. An electronic nose (eNose) is a novel method that detects disease from gas-phase mixtures, such as human breath. AIMS/OBJECTIVES To determine whether an eNose based on differential mobility spectrometry (DMS) can detect chronic rhinosinusitis without nasal polyps (CRSsNP) by analyzing aspirated nasal air. MATERIALS AND METHODS Adult patients with CRSsNP were examined. The control group consisted of patients with septal deviation. Nasal air was aspirated into a collection bag and analyzed with DMS. The DMS data were classified using regularized linear discriminant analysis (LDA) models with 10-fold cross-validation. RESULTS The accuracy of the DMS to distinguish CRSsNP from patients with septal deviation was 69%. Sensitivity and specificity were 67 and 70%, respectively. Bonferroni-corrected statistical differences were clearly noted. When a subgroup with more severe inflammatory disease was compared to controls, the classification accuracy increased to 82%. CONCLUSIONS The results of this feasibility study demonstrate that CRSsNP can potentially be differentiated distinguished from patients with similar nasal symptoms by analyzing the aspirated nasal air using DMS. Further research is warranted to evaluate the ability of this novel method in the differential diagnostics of CRS.
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Affiliation(s)
- Jussi Virtanen
- Department of Otorhinolaryngology, Head and Neck Surgery, Tampere University Hospital, Tampere, Finland.,Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Anton Kontunen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,Olfactomics Ltd, Tampere, Finland
| | - Jura Numminen
- Department of Otorhinolaryngology, Head and Neck Surgery, Tampere University Hospital, Tampere, Finland
| | - Niku Oksala
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,Olfactomics Ltd, Tampere, Finland.,Vascular Centre, Tampere University Hospital, Tampere, Finland
| | - Markus Rautiainen
- Department of Otorhinolaryngology, Head and Neck Surgery, Tampere University Hospital, Tampere, Finland.,Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Antti Roine
- Olfactomics Ltd, Tampere, Finland.,Department of Surgery, Tampere University Hospital, Hatanpää Hospital, Tampere, Finland
| | - Ilkka Kivekäs
- Department of Otorhinolaryngology, Head and Neck Surgery, Tampere University Hospital, Tampere, Finland.,Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
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Bax C, Robbiani S, Zannin E, Capelli L, Ratti C, Bonetti S, Novelli L, Raimondi F, Di Marco F, Dellacà RL. An Experimental Apparatus for E-Nose Breath Analysis in Respiratory Failure Patients. Diagnostics (Basel) 2022; 12:776. [PMID: 35453824 PMCID: PMC9026987 DOI: 10.3390/diagnostics12040776] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/18/2022] [Accepted: 03/19/2022] [Indexed: 02/01/2023] Open
Abstract
Background: Non-invasive, bedside diagnostic tools are extremely important for tailo ring the management of respiratory failure patients. The use of electronic noses (ENs) for exhaled breath analysis has the potential to provide useful information for phenotyping different respiratory disorders and improving diagnosis, but their application in respiratory failure patients remains a challenge. We developed a novel measurement apparatus for analysing exhaled breath in such patients. Methods: The breath sampling apparatus uses hospital medical air and oxygen pipeline systems to control the fraction of inspired oxygen and prevent contamination of exhaled gas from ambient Volatile Organic Compounds (VOCs) It is designed to minimise the dead space and respiratory load imposed on patients. Breath odour fingerprints were assessed using a commercial EN with custom MOX sensors. We carried out a feasibility study on 33 SARS-CoV-2 patients (25 with respiratory failure and 8 asymptomatic) and 22 controls to gather data on tolerability and for a preliminary assessment of sensitivity and specificity. The most significant features for the discrimination between breath-odour fingerprints from respiratory failure patients and controls were identified using the Boruta algorithm and then implemented in the development of a support vector machine (SVM) classification model. Results: The novel sampling system was well-tolerated by all patients. The SVM differentiated between respiratory failure patients and controls with an accuracy of 0.81 (area under the ROC curve) and a sensitivity and specificity of 0.920 and 0.682, respectively. The selected features were significantly different in SARS-CoV-2 patients with respiratory failure versus controls and asymptomatic SARS-CoV-2 patients (p < 0.001 and 0.046, respectively). Conclusions: the developed system is suitable for the collection of exhaled breath samples from respiratory failure patients. Our preliminary results suggest that breath-odour fingerprints may be sensitive markers of lung disease severity and aetiology.
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Affiliation(s)
- Carmen Bax
- Department of Chemistry, Materials and Chemical Engineering “Giulio Natta” (DCMC), Politecnico di Milano, 20133 Milano, Italy; (C.B.); (C.R.)
| | - Stefano Robbiani
- TechRes Lab, Department of Electronics Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milano, Italy; (S.R.); (E.Z.); (R.L.D.)
| | - Emanuela Zannin
- TechRes Lab, Department of Electronics Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milano, Italy; (S.R.); (E.Z.); (R.L.D.)
| | - Laura Capelli
- Department of Chemistry, Materials and Chemical Engineering “Giulio Natta” (DCMC), Politecnico di Milano, 20133 Milano, Italy; (C.B.); (C.R.)
| | - Christian Ratti
- Department of Chemistry, Materials and Chemical Engineering “Giulio Natta” (DCMC), Politecnico di Milano, 20133 Milano, Italy; (C.B.); (C.R.)
| | - Simone Bonetti
- Unit of Pneumology, Azienda Ospedaliera Socio Sanitaria Territoriale Papa Giovanni XXIII, 24127 Bergamo, Italy; (S.B.); (L.N.); (F.R.); (F.D.M.)
- Department of Health Sciences, Università degli Studi di Milano, 20142 Milano, Italy
| | - Luca Novelli
- Unit of Pneumology, Azienda Ospedaliera Socio Sanitaria Territoriale Papa Giovanni XXIII, 24127 Bergamo, Italy; (S.B.); (L.N.); (F.R.); (F.D.M.)
| | - Federico Raimondi
- Unit of Pneumology, Azienda Ospedaliera Socio Sanitaria Territoriale Papa Giovanni XXIII, 24127 Bergamo, Italy; (S.B.); (L.N.); (F.R.); (F.D.M.)
| | - Fabiano Di Marco
- Unit of Pneumology, Azienda Ospedaliera Socio Sanitaria Territoriale Papa Giovanni XXIII, 24127 Bergamo, Italy; (S.B.); (L.N.); (F.R.); (F.D.M.)
- Department of Health Sciences, Università degli Studi di Milano, 20142 Milano, Italy
| | - Raffaele L. Dellacà
- TechRes Lab, Department of Electronics Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milano, Italy; (S.R.); (E.Z.); (R.L.D.)
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Li S, Feng L, Ge Y, Zhu L, Zhao L. An Ensemble Learning Method for Robot Electronic Nose with Active Perception. SENSORS 2021; 21:s21113941. [PMID: 34200495 PMCID: PMC8200962 DOI: 10.3390/s21113941] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 05/27/2021] [Accepted: 06/01/2021] [Indexed: 11/23/2022]
Abstract
The electronic nose is the olfactory organ of the robot, which is composed of a large number of sensors to perceive the smell of objects through free diffusion. Traditionally, it is difficult to realize the active perception function, and it is difficult to meet the requirements of small size, low cost, and quick response that robots require. In order to address these issues, a novel electronic nose with active perception was designed and an ensemble learning method was proposed to distinguish the smell of different objects. An array of three MQ303 semiconductor gas sensors and an electrochemical sensor DART-2-Fe5 were used to construct the novel electronic nose, and the proposed ensemble learning method with four algorithms realized the active odor perception function. The experiment results verified that the accuracy of the active odor perception can reach more than 90%, even though it used 30% training data. The novel electronic nose with active perception based on the ensemble learning method can improve the efficiency and accuracy of odor data collection and olfactory perception.
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Affiliation(s)
- Shengming Li
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China; (S.L.); (L.F.); (Y.G.)
- School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian 116024, China
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China;
| | - Lin Feng
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China; (S.L.); (L.F.); (Y.G.)
- School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian 116024, China
| | - Yunfei Ge
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China; (S.L.); (L.F.); (Y.G.)
| | - Li Zhu
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China;
- School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
- Correspondence:
| | - Liang Zhao
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China;
- School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
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Giarratana N, Rajan B, Kamala K, Mendenhall M, Reiner G. A sprayable Acid-Oxidizing solution containing hypochlorous acid (AOS2020) efficiently and safely inactivates SARS-Cov-2: a new potential solution for upper respiratory tract hygiene. Eur Arch Otorhinolaryngol 2021; 278:3099-3103. [PMID: 33575830 PMCID: PMC7877500 DOI: 10.1007/s00405-021-06644-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 01/21/2021] [Indexed: 01/12/2023]
Abstract
Introduction To eliminate the COVID-19 pandemic, the transmission of the virus SARS-CoV-2 among the population needs to be blocked and/or at least reduced. Upper respiratory tract viral loads are highest in the early stages of the disease, and high loads are associated with higher mortality rates. This study aims to evaluate the virucidal efficacy of AOS2020, a novel sprayable Acid-Oxidizing solution containing pure and stable hypochlorous acid (HClO), on human coronavirus SARS-Cov-2 in vitro, and the tolerability profile on nasal and oral mucosa suggesting to be a potential solution for upper respiratory hygiene. Method Virucidal assays and intranasal and oral irritation tests were undertaken in accordance with relevant national and international guidance and methods. Results In pre-clinical tests, the AOS2020, showed > 99.8% virucidal efficacy in < 1 min against SARS-Cov-2. The safety profile testing on both the nasal and oral mucosa indicates that AOS2020 is non-irritant. Conclusion These initial results indicate that this product has the potential treatment to reduce viral load in the upper respiratory tract.
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Affiliation(s)
| | | | | | | | - Giorgio Reiner
- APR Applied Pharma Research S.A, 6828, Balerna, Switzerland
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Hokkinen L, Kesti A, Lepomäki J, Anttalainen O, Kontunen A, Karjalainen M, Aittoniemi J, Vuento R, Lehtimäki T, Oksala N, Roine A. Differential mobility spectrometry classification of bacteria. Future Microbiol 2020; 15:233-240. [DOI: 10.2217/fmb-2019-0192] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Aim: Rapid identification of bacteria would facilitate timely initiation of therapy and improve cost–effectiveness of treatment. Traditional methods (culture, PCR) require reagents, consumables and hours to days to complete the identification. In this study, we examined whether differential mobility spectrometry could classify most common bacterial species, genera and between Gram status within minutes. Materials & methods: Cultured bacterial sample gaseous headspaces were measured with differential mobility spectrometry and data analyzed using k-nearest-neighbor and leave-one-out cross-validation. Results: Differential mobility spectrometry achieved a correct classification rate 70.7% for all bacterial species. For bacterial genera, the rate was 77.6% and between Gram status, 89.1%. Conclusion: Largest difficulties arose in distinguishing bacteria of the same genus. Future improvement of the sensor characteristics may improve the classification accuracy.
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Affiliation(s)
- Lauri Hokkinen
- Faculty of Medicine & Health Technology, Tampere University, Tampere, Finland
| | - Artturi Kesti
- Faculty of Medicine & Health Technology, Tampere University, Tampere, Finland
| | - Jaakko Lepomäki
- Faculty of Medicine & Health Technology, Tampere University, Tampere, Finland
| | - Osmo Anttalainen
- Vascular & interventional Center, Tampere University Hospital, Tampere, Finland
| | - Anton Kontunen
- Faculty of Medicine & Health Technology, Tampere University, Tampere, Finland
| | - Markus Karjalainen
- Faculty of Medicine & Health Technology, Tampere University, Tampere, Finland
| | | | | | - Terho Lehtimäki
- Faculty of Medicine & Health Technology, Tampere University, Tampere, Finland
- Fimlab Laboratories, Tampere, Finland
- Finnish Cardiovascular Research Center – Tampere, Tampere University, Tampere, Finland
| | - Niku Oksala
- Vascular & interventional Center, Tampere University Hospital, Tampere, Finland
- Department of Surgery, Faculty of Medicine & Health Technology, Tampere University, Tampere, Finland
| | - Antti Roine
- Faculty of Medicine & Health Technology, Tampere University, Tampere, Finland
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