Aminopyrine demethylation measured by breath analysis in cirrhosis

1976 ◽  
Vol 20 (4) ◽  
pp. 484-492 ◽  
Author(s):  
Johannes Bircher ◽  
Adrian Küpfer ◽  
Iva Gikalov ◽  
Rudolf Preisig ◽  
Rudolf Preisig
Keyword(s):  
Breath Analysis ◽  
Aminopyrine Demethylation

Breath odor and disease: What will be promised with breath analysis?

2005 ◽  
Vol 36 (5) ◽  
pp. 270-274
Author(s):  
Hideo UEDA ◽  
Kyoichi KOBASHI
Keyword(s):  
Breath Analysis

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 Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique, Modular eNose Concept for Breath Diagnostics and Utilization of Calibration Transfer Methods in Breath Analysis Studies

Molecules ◽  
2021 ◽  
Vol 26 (12) ◽  
pp. 3776
Author(s):  
Carsten Jaeschke ◽  
Marta Padilla ◽  
Johannes Glöckler ◽  
Inese Polaka ◽  
Martins Leja ◽  
...  
Keyword(s):  
Real Life ◽  
Sensor Arrays ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Calibration Transfer ◽  
Early Disease Detection ◽  
Exhaled Breath Analysis ◽  
Breath Analyzer ◽  
Analysis Experiment ◽  
Before And After

Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study (in our case meal/no meal) are needed, although some CT methods present reasonably good results with only one class.

scholarly journals Electronic nose dataset for COPD detection from smokers and healthy people through exhaled breath analysis

Data in Brief ◽  
2021 ◽  
Vol 35 ◽  
pp. 106767
Author(s):  
Cristhian Manuel Durán Acevedo ◽  
Carlos A. Cuastumal Vasquez ◽  
Jeniffer Katerine Carrillo Gómez
Keyword(s):  
Electronic Nose ◽  
Breath Analysis ◽  
Healthy People ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis

scholarly journals Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Muhammad Aqeel Aslam ◽  
Cuili Xue ◽  
Yunsheng Chen ◽  
Amin Zhang ◽  
Manhua Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Gastric Cancer ◽  
Feature Extraction ◽  
Early Gastric Cancer ◽  
Breath Analysis ◽  
Cancer Classification ◽  
Unlabeled Data ◽  
Softmax Classifier ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

AbstractDeep learning is an emerging tool, which is regularly used for disease diagnosis in the medical field. A new research direction has been developed for the detection of early-stage gastric cancer. The computer-aided diagnosis (CAD) systems reduce the mortality rate due to their effectiveness. In this study, we proposed a new method for feature extraction using a stacked sparse autoencoder to extract the discriminative features from the unlabeled data of breath samples. A Softmax classifier was then integrated to the proposed method of feature extraction, to classify gastric cancer from the breath samples. Precisely, we identified fifty peaks in each spectrum to distinguish the EGC, AGC, and healthy persons. This CAD system reduces the distance between the input and output by learning the features and preserve the structure of the input data set of breath samples. The features were extracted from the unlabeled data of the breath samples. After the completion of unsupervised training, autoencoders with Softmax classifier were cascaded to develop a deep stacked sparse autoencoder neural network. In last, fine-tuning of the developed neural network was carried out with labeled training data to make the model more reliable and repeatable. The proposed deep stacked sparse autoencoder neural network architecture exhibits excellent results, with an overall accuracy of 98.7% for advanced gastric cancer classification and 97.3% for early gastric cancer detection using breath analysis. Moreover, the developed model produces an excellent result for recall, precision, and f score value, making it suitable for clinical application.

scholarly journals Socio-economic demands and challenges for non-invasive disease diagnosis through a portable breathalyzer by the incorporation of 2D nanosheets and SMO nanocomposites

RSC Advances ◽  
2021 ◽  
Vol 11 (35) ◽  
pp. 21216-21234
Author(s):  
Ramji Kalidoss ◽  
Radhakrishnan Kothalam ◽  
A. Manikandan ◽  
Saravana Kumar Jaganathan ◽  
Anish Khan ◽  
...  
Keyword(s):  
Breath Analysis ◽  
Disease Diagnosis ◽  
Invasive Disease ◽  
Research Community ◽  
Clinical Diagnostics ◽  
Non Invasive ◽  
Technological Developments ◽  
2D Nanosheets

Breath analysis for non-invasive clinical diagnostics and treatment progression has penetrated the research community owing to the technological developments in novel sensing nanomaterials.

Breath analysis in patients with end-stage renal disease: effect of haemodialysis

2008 ◽  
Vol 38 (10) ◽  
pp. 728-733 ◽  
Author(s):  
G. Rolla ◽  
M. Bruno ◽  
L. Bommarito ◽  
E. Heffler ◽  
N. Ferrero ◽  
...  
Keyword(s):  
Renal Disease ◽  
End Stage Renal Disease ◽  
Breath Analysis ◽  
Disease Effect ◽  
Stage Renal Disease ◽  
End Stage
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