The smell of lung disease: a review of the current status of electronic nose technology.

There is a need for timely, accurate diagnosis, and personalised management in lung diseases. Exhaled breath reflects inflammatory and metabolic processes in the human body, especially in the lungs. The analysis of exhaled breath using electronic nose (eNose) technology has gained increasing attention in the past years. This technique has great potential to be used in clinical practice as a real-time non-invasive diagnostic tool, and for monitoring disease course and therapeutic effects. To date, multiple eNoses have been developed and evaluated in clinical studies across a wide spectrum of lung diseases, mainly for diagnostic purposes. Heterogeneity in study design, analysis techniques, and differences between eNose devices currently hamper generalization and comparison of study results. Moreover, many pilot studies have been performed, while validation and implementation studies are scarce. These studies are needed before implementation in clinical practice can be realised. This review summarises the technical aspects of available eNose devices and the available evidence for clinical application of eNose technology in different lung diseases. Furthermore, recommendations for future research to pave the way for clinical implementation of eNose technology are provided.

Proof of concept for real-time detection of SARS CoV-2 infection with an electronic nose.

Rapid diagnosis is key to curtailing the Covid-19 pandemic. One path to such rapid diagnosis may rely on identifying volatile organic compounds (VOCs) emitted by the infected body, or in other words, identifying the smell of the infection. Consistent with this rationale, dogs can use their nose to identify Covid-19 patients. Given the scale of the pandemic, however, animal deployment is a challenging solution. In contrast, electronic noses (eNoses) are machines aimed at mimicking animal olfaction, and these can be deployed at scale. To test the hypothesis that SARS CoV-2 infection is associated with a body-odor detectable by an eNose, we placed a generic eNose in-line at a drive-through testing station. We applied a deep learning classifier to the eNose measurements, and achieved real-time detection of SARS CoV-2 infection at a level significantly better than chance, for both symptomatic and non-symptomatic participants. This proof of concept with a generic eNose implies that an optimized eNose may allow effective real-time diagnosis, which would provide for extensive relief in the Covid-19 pandemic.

Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research.

BACKGROUND: Ventilator-associated pneumonia (VAP) is a significant cause of mortality in the intensive care unit. Early diagnosis of VAP is important to provide appropriate treatment and reduce mortality. Developing a noninvasive and highly accurate diagnostic method is important. The invention of electronic sensors has been applied to analyze the volatile organic compounds in breath to detect VAP using a machine learning technique. However, the process of building an algorithm is usually unclear and prevents physicians from applying the artificial intelligence technique in clinical practice. Clear processes of model building and assessing accuracy are warranted. The objective of this study was to develop a breath test for VAP with a standardized protocol for a machine learning technique. METHODS: We conducted a case-control study. This study enrolled subjects in an intensive care unit of a hospital in southern Taiwan from February 2017 to June 2019. We recruited patients with VAP as the case group and ventilated patients without pneumonia as the control group. We collected exhaled breath and analyzed the electric resistance changes of 32 sensor arrays of an electronic nose. We split the data into a set for training algorithms and a set for testing. We applied eight machine learning algorithms to build prediction models, improving model performance and providing an estimated diagnostic accuracy. RESULTS: A total of 33 cases and 26 controls were used in the final analysis. Using eight machine learning algorithms, the mean accuracy in the testing set was 0.81 ± 0.04, the sensitivity was 0.79 ± 0.08, the specificity was 0.83 ± 0.00, the positive predictive value was 0.85 ± 0.02, the negative predictive value was 0.77 ± 0.06, and the area under the receiver operator characteristic curves was 0.85 ± 0.04. The mean kappa value in the testing set was 0.62 ± 0.08, which suggested good agreement. CONCLUSIONS: There was good accuracy in detecting VAP by sensor array and machine learning techniques. Artificial intelligence has the potential to assist the physician in making a clinical diagnosis. Clear protocols for data processing and the modeling procedure needed to increase generalizability.

Potentiometric E-Tongue System for Geosmin/Isoborneol Presence Monitoring in Drinkable Water.

A potentiometric E-tongue system based on low-selective polymeric membrane and chalcogenide-glass electrodes is employed to monitor the taste-and-odor-causing pollutants, geosmin (GE) and 2-methyl-isoborneol (MIB), in drinkable water. The developed approach may permit a low-cost monitoring of these compounds in concentrations near the odor threshold concentrations (OTCs) of 20 ng/L. The experiments demonstrate the success of the E-tongue in combination with partial least squares (PLS) regression technique for the GE/MIB concentration prediction, showing also the possibility to discriminate tap water samples containing these compounds at two concentration levels: the same OTC order from 20 to 100 ng/L and at higher concentrations from 0.25 to 10 mg/L by means of PLS-discriminant analysis (DA) method. Based on the results, developed multisensory system can be considered a promising easy-to-handle tool for express evaluation of GE/MIB species and to provide a timely detection of alarm situations in case of extreme pollution before the drinkable water is delivered to end users.

Training and Validating a Portable Electronic Nose for Lung Cancer Screening.

INTRODUCTION: Profiling volatile organic compounds in exhaled breath enables the diagnosis of several types of cancer. In this study we investigated whether a portable point-of-care version of an electronic nose (e-nose) (Aeonose, [eNose Company, Zutphen, the Netherlands]) is able to discriminate between patients with lung cancer and healthy controls on the basis of their volatile organic compound pattern. METHODS: In this study, we used five e-nose devices to collect breath samples from patients with lung cancer and healthy controls. A total of 60 patients with lung cancer and 107 controls exhaled through an e-nose for 5 minutes. Patients were assigned either to a training group for building an artificial neural network model or to a blinded control group for validating this model. RESULTS: For differentiating patients with lung cancer from healthy controls, the results showed a diagnostic accuracy of 83% with a sensitivity of 83%, specificity of 84%, and area under the curve of 0.84. Results for the blinded group showed comparable results, with a sensitivity of 88%, specificity of 86%, and diagnostic accuracy of 86%. CONCLUSION: This feasibility study showed that this portable e-nose can properly differentiate between patients with lung cancer and healthy controls. This result could have important implications for future lung cancer screening. Further studies with larger cohorts, including also more participants with early-stage tumors, should be performed to increase the robustness of this noninvasive diagnostic tool and to determine its added value in the diagnostic chain for lung cancer.

Recent advances in electronic nose techniques for monitoring of fermentation process.

Microbial fermentation process is often sensitive to even slight changes of conditions that may result in unacceptable end-product quality. Thus, the monitoring of the process is critical for discovering unfavorable deviations as early as possible and taking the appropriate measures. However, the use of traditional analytical techniques is often time-consuming and labor-intensive. In this sense, the most effective way of developing rapid, accurate and relatively economical method for quality assurance in microbial fermentation process is the use of novel chemical sensor systems. Electronic nose techniques have particular advantages in non-invasive monitoring of microbial fermentation process. Therefore, in this review, we present an overview of the most important contributions dealing with the quality control in microbial fermentation process using the electronic nose techniques. After a brief description of the fundamentals of the sensor techniques, some examples of potential applications of electronic nose techniques monitoring are provided, including the implementation of control strategies and the combination with other monitoring tools (i.e. sensor fusion). Finally, on the basis of the review, the electronic nose techniques are critically commented, and its strengths and weaknesses being highlighted. In addition, on the basis of the observed trends, we also propose the technical challenges and future outlook for the electronic nose techniques.

Use of digital technologies for nasal prosthesis manufacturing.

BACKGROUND AND AIM: Digital technology is becoming more accessible for common use in medical applications; however, their expansion in prosthetic and orthotic laboratories is not large because of the persistent image of difficult applicability to real patients. This article aims to offer real example in the area of human facial prostheses. TECHNIQUE: This article describes the utilization of optical digitization, computational modelling, rapid prototyping, mould fabrication and manufacturing of a nasal silicone prosthesis. This technical note defines the key points of the methodology and aspires to contribute to the introduction of a certified manufacturing procedure. DISCUSSION: The results show that the used technologies reduce the manufacturing time, reflect patient's requirements and allow the manufacture of high-quality prostheses for missing facial asymmetric parts. The methodology provides a good position for further development issues and is usable for clinical practice. Clinical relevance Utilization of digital technologies in facial prosthesis manufacturing process can be a good contribution for higher patient comfort and higher production efficiency but with higher initial investment and demands for experience with software tools.

Síntese de poli ( p-fenilenovinileno )s e desenvolvimento e otimização de um nariz eletrônico / Synthesis of poly ( p-phenylenevinylene )s and the development and optimization of an electronic nose

O presente trabalho envolve a síntese e caracterização de um polímero da família dos poli(p-fenilenovinileno)s (PPV), aplicável em camadas ativas de dispositivos como narizes eletrônicos, muito utilizados em várias áreas, como medicina, meio ambiente e indústria alimentícia. O trabalho visou também ao desenvolvimento de um nariz eletrônico, abrangendo o processo de preparação, o que incluiu a confecção dos eletrodos dos sensores de gases, o estudo referente ao equipamento de aquisição de dados e à comunicação com o microcomputador. Visou-se à otimização do equipamento que já vem sendo utilizado em medidas para várias detecções, estudando-se fatores como frequência da corrente alternada empregada, forma de onda (senoidal, quadrada e triangular), temperatura da amostra e distância entre os dígitos nos eletrodos. Verificou-se a possibilidade da utilização de nariz eletrônico na área de alimentos, estudando-se a identificação de méis de abelha de floradas diferentes e a detecção de fungos em laranjas pós-colheita

The present thesis involves the synthesis and characterization of a polymer of the poly(p-phenylenevinylene) (PPV) family, applicable as active layer of devices such as gas sensors and electronic noses, which are instruments widely used in several areas, including medicine, environmental sciences and food industry. This work also aimed the full development of an electronic nose, from the making of the electrodes and sensors to the study of the data acquisition and its communication with a personal computer. In order to optimize the equipment, the influence of several factors such as frequency of the applied alternating current, its waveform (sine, square and triangle), temperature of the sample, and spacing between the digits of the electrodes were investigated. Finally, the equipment was used for the identification of honeys from different blossoms and for the detection of fungi in post-harvest oranges

A comparison of sniff bottle staircase and olfactometer-based threshold tests.

Olfactometers have been gaining popularity as research tools, but they have yet to replace established testing procedures in a variety of laboratory and clinical settings, including absolute threshold tests. In this research, we designed and operated a simple olfactometer with which to assess threshold. To do this, we used a method-of-adjustment test that was compared to the three-alternative forced choice ascending sniff bottle staircase method, which is currently a standard threshold test procedure. We found that the olfactometer threshold test correlated highly with the staircase method, and that it possessed suitable test-retest reliability. The advantages of the olfactometer threshold test include faster test time and reduced cleaning and reassembly demands. Future use of the olfactometer in olfactory identification and/or detection thresholds amongst odors is also outlined.