scholarly journals Real Time Breath Analysis Using Portable Gas Chromatography for Adult Asthma Phenotypes

Metabolites ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 265
Author(s):  
Ruchi Sharma ◽  
Wenzhe Zang ◽  
Menglian Zhou ◽  
Nicole Schafer ◽  
Lesa A. Begley ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Real Time ◽  
Rapid Assessment ◽  
Principal Component ◽  
Corticosteroid Treatment ◽  
Breath Analysis ◽  
Blood Eosinophil ◽  
Exhaled Breath ◽  
Respiratory Illnesses ◽  
Linear Discriminant

Asthma is heterogeneous but accessible biomarkers to distinguish relevant phenotypes remain lacking, particularly in non-Type 2 (T2)-high asthma. Moreover, common clinical characteristics in both T2-high and T2-low asthma (e.g., atopy, obesity, inhaled steroid use) may confound interpretation of putative biomarkers and of underlying biology. This study aimed to identify volatile organic compounds (VOCs) in exhaled breath that distinguish not only asthmatic and non-asthmatic subjects, but also atopic non-asthmatic controls and also by variables that reflect clinical differences among asthmatic adults. A total of 73 participants (30 asthma, eight atopic non-asthma, and 35 non-asthma/non-atopic subjects) were recruited for this pilot study. A total of 79 breath samples were analyzed in real-time using an automated portable gas chromatography (GC) device developed in-house. GC-mass spectrometry was also used to identify the VOCs in breath. Machine learning, linear discriminant analysis, and principal component analysis were used to identify the biomarkers. Our results show that the portable GC was able to complete breath analysis in 30 min. A set of nine biomarkers distinguished asthma and non-asthma/non-atopic subjects, while sets of two and of four biomarkers, respectively, further distinguished asthmatic from atopic controls, and between atopic and non-atopic controls. Additional unique biomarkers were identified that discriminate subjects by blood eosinophil levels, obese status, inhaled corticosteroid treatment, and also acute upper respiratory illnesses within asthmatic groups. Our work demonstrates that breath VOC profiling can be a clinically accessible tool for asthma diagnosis and phenotyping. A portable GC system is a viable option for rapid assessment in asthma.

scholarly journals Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable 2-dimensional gas chromatography device

2019 ◽  
Author(s):  
Menglian Zhou ◽  
Ruchi Sharma ◽  
Hongbo Zhu ◽  
Jiliang Li ◽  
Shiyu Wang ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Predictive Value ◽  
Distress Syndrome ◽  
Principal Component ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Linear Discriminant

AbstractAcute respiratory distress syndrome (ARDS) is the most severe form of acute lung injury, responsible for high mortality and long-term morbidity. As a dynamic syndrome with multiple etiologies its timely diagnosis is difficult as is tracking the course of the syndrome. Therefore, there is a significant need for early, rapid detection and diagnosis as well as clinical trajectory monitoring of ARDS. Here we report our work on using human breath to differentiate ARDS and non-ARDS causes of respiratory failure. A fully automated portable 2-dimensional gas chromatography device with high peak capacity, high sensitivity, and rapid analysis capability was designed and made in-house for on-site analysis of patients’ breath. A total of 85 breath samples from 48 ARDS patients and controls were collected. Ninety-seven elution peaks were separated and detected in 13 minutes. An algorithm based on machine learning, principal component analysis (PCA), and linear discriminant analysis (LDA) was developed. As compared to the adjudications done by physicians based on the Berlin criteria, our device and algorithm achieved an overall accuracy of 87.1% with 94.1% positive predictive value and 82.4% negative predictive value. The high overall accuracy and high positive predicative value suggest that the breath analysis method can accurately diagnose ARDS. The ability to continuously and non-invasively monitor exhaled breath for early diagnosis, disease trajectory tracking, and outcome prediction monitoring of ARDS may have a significant impact on changing practice and improving patient outcomes.

A ROBUST AND FRIENDLY HUMAN–ROBOT INTERFACE SYSTEM BASED ON NATURAL HUMAN GESTURES

2010 ◽  
Vol 24 (06) ◽  
pp. 847-866 ◽  
Author(s):  
MING-SHAUNG CHANG ◽  
JUNG-HUA CHOU
Keyword(s):  
Real Time ◽  
Face Detection ◽  
Fuzzy Logic Controller ◽  
Tracking System ◽  
Back Propagation ◽  
Principal Component ◽  
Working Environment ◽  
Expression Recognition ◽  
Linear Discriminant ◽  
Human Gestures

In this paper, we design a robust and friendly human–robot interface (HRI) system for our intelligent mobile robot based only on natural human gestures. It consists of a triple-face detection method and a fuzzy logic controller (FLC)-Kalman filter tracking system to check the users and predict their current position in a dynamic and cluttered working environment. In addition, through the combined classifier of the principal component analysis (PCA) and back-propagation artificial neural network (BPANN), single and successive commands defined by facial positions and hand gestures are identified for real-time command recognition after dynamic programming (DP). Therefore, the users can instruct this HRI system to make member recognition or expression recognition corresponding to their gesture commands, respectively based on the linear discriminant analysis (LDA) and BPANN. The experimental results prove that the proposed HRI system perform accurately in real-time face detection and tracking, and robustly react to the corresponding gesture commands at eight frames per second (fps).

Exhaled Breath Analysis by Real-Time Mass Spectrometry

2019 ◽  
Author(s):  
L. Bryant ◽  
R. Cordell ◽  
M.J. Wilde ◽  
L. Carr ◽  
W. Ibrahim ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Real Time ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis

Applications of Machine Learning in Real-Time Tumor Localization

2012 ◽  
pp. 419-431
Author(s):  
Ruijiang Li ◽  
Steve B. Jiang
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Tumor Localization ◽  
Linear Discriminant ◽  
Learning Techniques ◽  
Domain Specific Knowledge ◽  
Applications Of Machine Learning

Recently, machine learning has gained great popularity in many aspects of radiation therapy. In this chapter, the authors will demonstrate the applications of various machine learning techniques in the context of real-time tumor localization in lung cancer radiotherapy. These cover a wide range of well established machine learning techniques, including principal component analysis, linear discriminant analysis, artificial neural networks, and support vector machine, etc. Respiratory gating, as a special case of tumor localization, will also be discussed. The chapter will demonstrate how domain specific knowledge and prior information can be useful in achieving more accurate and robust tumor localization. Future research directions in machine learning that can further improve the accuracy for tumor localization are also discussed.

Real-time exhaled breath analysis identifies altered metabolic signature in cystic fibrosis

2017 ◽  
Author(s):  
Thomas Gaisl ◽  
Lukas Bregy ◽  
Nina Stebler ◽  
Martin Gaugg ◽  
Tobias Bruderer ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Real Time ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Metabolic Signature ◽  
Exhaled Breath Analysis

119 Exhaled breath analysis by real-time mass spectrometry in patients with pulmonary fibrosis

CHEST Journal ◽  
2017 ◽  
Vol 151 (5) ◽  
pp. A16
Author(s):  
P. Sinues ◽  
Y. Nussbaumer-Ochsner ◽  
M.T. Gaugg ◽  
L. Bregy ◽  
A. Engler ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Pulmonary Fibrosis ◽  
Real Time ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis

scholarly journals An Authentication Study on Grappa Spirit: The Use of Chemometrics to Detect a Food Fraud

Analytica ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 84-92
Author(s):  
Silvia Arduini ◽  
Alessandro Zappi ◽  
Marcello Locatelli ◽  
Salvatore Sgrò ◽  
Dora Melucci
Keyword(s):  
Gas Chromatography ◽  
Principal Component ◽  
Volatile Fraction ◽  
Alcohol Content ◽  
Food Fraud ◽  
Linear Discriminant ◽  
Grape Marc ◽  
Class A ◽  
Flame Ionization ◽  
One Class Classification

An authenticity study on Italian grape marc spirit was carried out by gas chromatography (GC) and chemometrics. A grape marc spirit produced in Italy takes the particular name of “grappa”, a product which has peculiar tradition and production in its country of origin. Therefore, the evaluation of its authenticity plays an important role for its consumption in Italy, as well as for its exportation all around the world. For the present work, 123 samples of grappa and several kinds of spirits were analyzed in their alcohol content by electronic densimetry, and in their volatile fraction by gas-chromatography with a flame-ionization detector. Part of these samples (94) was employed as a training set to compute a chemometric model (by linear discriminant analysis, LDA) and the other part (29 samples) was used as a test set to validate it. Finally, two grappa samples seized from the market by the Italian Customs and Monopolies Agency and considered suspicious due to their aroma reported as non-compliant were projected onto the LDA model to evaluate the compliance with the “grappa” class. A further one-class classification method by principal component analysis (PCA) was carried out to evaluate the compliance with other classes. Results showed that the suspicious samples were not recognized as belonging to any of the analyzed spirit classes, confirming the starting hypothesis that they could be grappa samples adulterated in some way.

scholarly journals Identification of recent exacerbations in COPD patients by electronic nose

2020 ◽  
Vol 6 (4) ◽  
pp. 00307-2020
Author(s):  
Job J.M.H. van Bragt ◽  
Paul Brinkman ◽  
Rianne de Vries ◽  
Susanne J.H. Vijverberg ◽  
Els J.M. Weersink ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Inhaled Corticosteroids ◽  
Ambient Air ◽  
Breath Analysis ◽  
High Accuracy ◽  
Exhaled Breath ◽  
Molecular Fingerprint ◽  
Linear Discriminant ◽  
Copd Patients ◽  
Exhaled Breath Analysis

Molecular profiling of exhaled breath by electronic nose (eNose) might be suitable as a noninvasive tool that can help in monitoring of clinically unstable COPD patients. However, supporting data are still lacking. Therefore, as a first step, this study aimed to determine the accuracy of exhaled breath analysis by eNose to identify COPD patients who recently exacerbated, defined as an exacerbation in the previous 3 months.Data for this exploratory, cross-sectional study were extracted from the multicentre BreathCloud cohort. Patients with a physician-reported diagnosis of COPD (n=364) on maintenance treatment were included in the analysis. Exacerbations were defined as a worsening of respiratory symptoms requiring treatment with oral corticosteroids, antibiotics or both. Data analysis involved eNose signal processing, ambient air correction and statistics based on principal component (PC) analysis followed by linear discriminant analysis (LDA).Before analysis, patients were randomly divided into a training (n=254) and validation (n=110) set. In the training set, LDA based on PCs 1–4 discriminated between patients with a recent exacerbation or no exacerbation with high accuracy (receiver operating characteristic (ROC)–area under the curve (AUC)=0.98, 95% CI 0.97–1.00). This high accuracy was confirmed in the validation set (AUC=0.98, 95% CI 0.94–1.00). Smoking, health status score, use of inhaled corticosteroids or vital capacity did not influence these results.Exhaled breath analysis by eNose can discriminate with high accuracy between COPD patients who experienced an exacerbation within 3 months prior to measurement and those who did not. This suggests that COPD patients who recently exacerbated have their own exhaled molecular fingerprint that could be valuable for monitoring purposes.

Real-time exhaled breath analysis in patients with cystic fibrosis and controls

2018 ◽  
Vol 12 (3) ◽  
pp. 036013 ◽  
Author(s):  
Thomas Gaisl ◽  
Lukas Bregy ◽  
Nina Stebler ◽  
Martin T Gaugg ◽  
Tobias Bruderer ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Real Time ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis

scholarly journals Exhaled Breath Analysis in Diagnostics of Cardiovascular Diseases

Kardiologiia ◽  
2019 ◽  
Vol 59 (7) ◽  
pp. 61-67 ◽  
Author(s):  
A. A. Bykova ◽  
L. K. Malinovskaya ◽  
P. Sh. Chomakhidze ◽  
O. V. Trushina ◽  
Y. R. Shaltaeva ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Clinical Practice ◽  
Cardiovascular Diseases ◽  
Real Time ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Secretory Function ◽  
Pathological Conditions ◽  
Exhaled Breath Analysis ◽  
Modern Technologies

Exhaled breath analysis is a novel tool for diagnostics of different diseases. Taking into account the secretory function of the lungs, the composition of exhaled breath is different in physiological and pathological conditions. In this review we consider of some substances which content vary in cardiovascular diseases – pentane, isoprene, carbon monoxide and trimethylamine. Modern technologies allow to move the analysis of exhaled breath from research laboratories into clinical practice. Thus, a new tool for real time of screening various cardiovascular diseases has appeared in the arsenal of physicians.

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