Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

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

ABSTRACT: This article presents a database which was obtained by acquiring measurements through a multisensory device called Electronic Nose (E-nose) based on a matrix of metal oxide sensors, in order to discriminate and classify a group of people affected by the respiratory disease Chronic Obstructive Pulmonary Disease (COPD), smokers and healthy control people through exhaled breath analysis. The database consists of 4 groups of measurements which were acquired through the E-nose system: 10 control samples (healthy people), 20 samples of people with COPD, 4 samples of smokers and 10 air samples, where in each group two samples of exhaled breath per person were acquired giving a total of 78 samples (40 from COPD, 20 from control, 8 from smokers and 10 from the air).

SUBMITTER: Duran Acevedo CM

PROVIDER: S-EPMC7838708 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

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

Durán Acevedo Cristhian Manuel CM Cuastumal Vasquez Carlos A CA Carrillo Gómez Jeniffer Katerine JK

Data in brief 20210118

This article presents a database which was obtained by acquiring measurements through a multisensory device called Electronic Nose (E-nose) based on a matrix of metal oxide sensors, in order to discriminate and classify a group of people affected by the respiratory disease Chronic Obstructive Pulmonary Disease (COPD), smokers and healthy control people through exhaled breath analysis. The database consists of 4 groups of measurements which were acquired through the E-nose system: 10 control samp ...[more]

PMID: 33537382

Similar Datasets

Detection of Ovarian Cancer through Exhaled Breath by Electronic Nose: A Prospective Study.

Project description:Diagnostic methods for the early identification of ovarian cancer (OC) represent an unmet clinical need, as no reliable diagnostic tools are available. Here, we tested the feasibility of electronic nose (e-nose), composed of ten metal oxide semiconductor (MOS) sensors, as a diagnostic tool for OC detection. Women with suspected ovarian masses and healthy subjects had volatile organic compounds analysis of the exhaled breath using e-nose. E-nose analysis was performed on breath samples collected from 251 women divided into three groups: 86 OC cases, 51 benign masses, and 114 controls. Data collected were analyzed by Principal Component Analysis (PCA) and K-Nearest Neighbors' algorithm (K-NN). A first 1-K-NN (cases vs. controls) model has been developed to discriminate between OC cases and controls; the model performance tested in the prediction gave 98% of sensitivity and 95% of specificity, when the strict class prediction was applied; a second 1-K-NN (cases vs. controls + benign) model was built by grouping the non-cancer groups (controls + benign), thus considering two classes, cases and controls + benign; the model performance in the prediction was of 89% for sensitivity and 86% for specificity when the strict class prediction was applied. Our preliminary results suggested the potential role of e-nose for the detection of OC. Further studies aiming to test the potential adoption of e-nose in the early diagnosis of OC are needed.

| S-EPMC7565069 | biostudies-literature

Profiling the proteome of exhaled breath condensate in healthy smokers and COPD patients by LC-MS/MS.

Project description:Three pools of exhaled breath condensate (EBC) from non-smokers plus healthy smokers (NS + HS, n = 45); chronic obstructive pulmonary disease (COPD) without emphysema (COPD, n = 15) and subjects with pulmonary emphysema associated with α(1)-antitrypsin deficiency (AATD, n = 23) were used for an exploratory proteomic study aimed at generating fingerprints of these groups that can be used in future pathophysiological and perhaps even clinical research. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was the platform applied for this hypothesis-free investigation. Analysis of pooled specimens resulted in the production of a "fingerprint" made of 44 proteins for NS/HS; 17 for COPD and 15 for the group of AATD subjects. Several inflammatory cytokines (IL-1α, IL-1β, IL-2; IL-12, α and β subunits, IL-15, interferon α and γ, tumor necrosis factor α); Type I and II cytokeratins; two SP-A isoforms; Calgranulin A and B and α1-antitrypsin were detected and validated through the use of surface enhanced laser-desorption ionization mass spectrometry (SELDI-MS) and/or by Western blot (WB) analysis. These results are the prelude of quantitative studies aimed at identifying which of these proteins hold promise as identifiers of differences that could distinguish healthy subjects from patients.

| S-EPMC3509556 | biostudies-literature

Clinical studies of detecting COVID-19 from exhaled breath with electronic nose.

Project description:The COVID-19 pandemic has attracted numerous research studies because of its impact on society and the economy. The pandemic has led to progress in the development of diagnostic methods, utilizing the polymerase chain reaction (PCR) as the gold standard for coronavirus SARS-CoV-2 detection. Numerous tests can be used at home within 15 min or so but of with lower accuracy than PCR. There is still a need for point-of-care tests available for mass daily screening of large crowds in airports, schools, and stadiums. The same problem exists with fast and continuous monitoring of patients during their medical treatment. The rapid methods can use exhaled breath analysis which is non-invasive and delivers the result quite fast. Electronic nose can detect a cocktail of volatile organic com-pounds (VOCs) induced by virus infection and disturbed metabolism in the human body. In our exploratory studies, we present the results of COVID-19 detection in a local hospital by applying the developed electronic setup utilising commercial VOC gas sensors. We consider the technical problems noticed during the reported studies and affecting the detection results. We believe that our studies help to advance the proposed technique to limit the spread of COVID-19 and similar viral infections.

| S-EPMC9512806 | biostudies-literature

Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition.

Project description:The reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach has been widely used to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, instead of using it alone, clinicians often prefer to diagnose the coronavirus disease 2019 (COVID-19) by utilizing a combination of clinical signs and symptoms, laboratory test, imaging measurement (e.g., chest computed tomography scan), and multivariable clinical prediction models, including the electronic nose. Here, we report on the development and use of a low cost, noninvasive method to rapidly sniff out COVID-19 based on a portable electronic nose (GeNose C19) integrating an array of metal oxide semiconductor gas sensors, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total of 615 breath samples composed of 333 positive and 282 negative samples. The samples were obtained from 43 positive and 40 negative COVID-19 patients, respectively, and confirmed with RT-qPCR at two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis, support vector machine, stacked multilayer perceptron, and deep neural network) were utilized to identify the top-performing pattern recognition methods and to obtain a high system detection accuracy (88-95%), sensitivity (86-94%), and specificity (88-95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening.

| S-EPMC9379872 | biostudies-literature

Electronic nose dataset for detection of wine spoilage thresholds.

Project description:In this data article, we provide a time series dataset obtained for an application of wine quality detection focused on spoilage thresholds. The database contains 235 recorded measurements of wines divided into three groups and labeled as high quality (HQ), average quality (AQ) and low quality (LQ), in addition to 65 ethanol measurements. This dataset was collected using an electronic nose system (E-Nose) based on Metal Oxide Semiconductor (MOS) gas sensors, self-developed at the Universidade Federal Rural de Pernambuco (Brazil). The dataset is related to the research article entitled "Wine quality rapid detection using a compact electronic nose system: application focused on spoilage thresholds by acetic acid" by Rodriguez Gamboa et al., 2019. The dataset can be accessed publicly at the repository: https://data.mendeley.com/datasets/vpc887d53s/.

| S-EPMC6624639 | biostudies-literature

Exhaled breath analysis using electronic nose in cystic fibrosis and primary ciliary dyskinesia patients with chronic pulmonary infections.

Project description:The current diagnostic work-up and monitoring of pulmonary infections may be perceived as invasive, is time consuming and expensive. In this explorative study, we investigated whether or not a non-invasive exhaled breath analysis using an electronic nose would discriminate between cystic fibrosis (CF) and primary ciliary dyskinesia (PCD) with or without various well characterized chronic pulmonary infections. We recruited 64 patients with CF and 21 with PCD based on known chronic infection status. 21 healthy volunteers served as controls. An electronic nose was employed to analyze exhaled breath samples. Principal component reduction and discriminant analysis were used to construct internally cross-validated receiver operator characteristic (ROC) curves. Breath profiles of CF and PCD patients differed significantly from healthy controls p = 0.001 and p = 0.005, respectively. Profiles of CF patients having a chronic P. aeruginosa infection differed significantly from to non-chronically infected CF patients p = 0.044. We confirmed the previously established discriminative power of exhaled breath analysis in separation between healthy subjects and patients with CF or PCD. Furthermore, this method significantly discriminates CF patients suffering from a chronic pulmonary P. aeruginosa (PA) infection from CF patients without a chronic pulmonary infection. Further studies are needed for verification and to investigate the role of electronic nose technology in the very early diagnostic workup of pulmonary infections before the establishment of a chronic infection.

| S-EPMC4277311 | biostudies-literature

Global metabolome profiling of exhaled breath condensates in male smokers with asthma COPD overlap and prediction of the disease.

Project description:Asthma-chronic obstructive pulmonary disease (COPD) overlap, termed as ACO, is a complex heterogeneous disease characterised by persistent airflow limitation, which manifests features of both asthma and COPD. These patients have a worse prognosis, in terms of more frequent and severe exacerbations, more frequent symptoms, worse quality of life, increased comorbidities and a faster lung function decline. In absence of clear diagnostic or therapeutic guidelines, ACO presents as a challenge to clinicians. The present study aims to investigate whether ACO patients have a distinct exhaled breath condensate (EBC) metabolic profile in comparison to asthma and COPD. A total of 132 age and BMI matched male smokers were recruited in the exploratory phase which consisted of (i) controls = 33 (ii) asthma = 34 (iii) COPD = 30 and (iv) ACO = 35. Using nuclear magnetic resonance (NMR) metabolomics, 8 metabolites (fatty acid, propionate, isopropanol, lactate, acetone, valine, methanol and formate) were identified to be significantly dysregulated in ACO subjects when compared to both, asthma and COPD. The expression of these dysregulated metabolites were further validated in a fresh patient cohort consisting of (i) asthma = 32 (ii) COPD = 32 and (iii) ACO = 40, which exhibited a similar expression pattern. Multivariate receiver operating characteristic (ROC) curves generated using these metabolites provided a robust ACO classification model. The findings were also integrated with previously identified serum metabolites and inflammatory markers to develop a robust predictive model for differentiation of ACO. Our findings suggest that NMR metabolomics of EBC holds potential as a platform to identify robust, non-invasive biomarkers for differentiating ACO from asthma and COPD.

| S-EPMC8370999 | biostudies-literature

Metabolomics of COPD Pulmonary Rehabilitation Outcomes via Exhaled Breath Condensate.

Project description:Chronic obstructive pulmonary disease (COPD) is characterized by different phenotypes and clinical presentations. Therefore, a single strategy of pulmonary rehabilitation (PR) does not always yield the expected clinical outcomes as some individuals respond excellently, others discreetly, or do not respond at all. Fifty consecutive COPD patients were enrolled. Of them, 35 starting a 5-week PR program were sampled at admission (T0), after 2 (T2W) and 5 (T5W) weeks, while 15 controls not yet on PR were tested at T0 and T5W. Nuclear magnetic resonance (NMR) profiling of exhaled breath condensate (EBC) and multivariate statistical analysis were applied to investigate the relationship between biomarkers and clinical parameters. The model including the three classes correctly located T2W between T0 and T5W, but 38.71% of samples partially overlapped with T0 and 32.26% with T5W, suggesting that for some patients PR is already beneficial at T2W (32.26% overlapping with T5W), while for others (38.71% overlapping with T0) more time is required. Rehabilitated patients presented several altered biomarkers. In particular, methanol from T0 to T5W decreased in parallel with dyspnea and fatigue, while the walk distance increased. Methanol could be ascribed to lung inflammation. We demonstrated that the metabolic COPD phenotype clearly evolves during PR, with a strict relationship between clinical and molecular parameters. Methanol, correlating with clinical parameters, represents a useful biomarker for monitoring personalized outcomes and establishing more targeted protocols.

| S-EPMC8834302 | biostudies-literature

Exhaled-Breath Testing Using an Electronic Nose during Spinal Cord Stimulation in Patients with Failed Back Surgery Syndrome: An Experimental Pilot Study.

Project description:The increased awareness of discrepancies between self-reporting outcome measurements and objective outcome measurements within the field of neuromodulation has accelerated the search towards more objective measurements. The aim of this study was to evaluate whether an electronic nose can differentiate between chronic pain patients in whom Spinal Cord Stimulation (SCS) was activated versus deactivated. Twenty-seven patients with Failed Back Surgery Syndrome (FBSS) participated in this prospective pilot study. Volatile organic compounds in exhaled breath were measured with electronic nose technology (Aeonose™) during SCS on and off states. Random forest was used with a leave-10%-out cross-validation method to determine accuracy of discriminating between SCS on and off states. Our random forest showed an accuracy of 0.56, with an area under the curve of 0.62, a sensitivity of 62% (95% CI: 41-79%) and a specificity of 50% (95% CI: 30-70%). Pain intensity scores were significantly different between both SCS states. Our findings indicate that we cannot discriminate between SCS off and on states based on exhaled breath with the Aeonose™ in patients with FBSS. In clinical practice, these findings imply that with a noninvasive electronic nose, exhaled breath cannot be used as an additional marker of the effect of neuromodulation.

| S-EPMC8269089 | biostudies-literature

Noninvasive detection of lung cancer using exhaled breath.

Project description:Early detection of lung cancer is a key factor for increasing the survival rates of lung cancer patients. The analysis of exhaled breath is promising as a noninvasive diagnostic tool for diagnosis of lung cancer. We demonstrate the quantitative analysis of carbonyl volatile organic compounds (VOCs) and identification of lung cancer VOC markers in exhaled breath using unique silicon microreactor technology. The microreactor consists of thousands of micropillars coated with an ammonium aminooxy salt for capture of carbonyl VOCs in exhaled breath by means of oximation reactions. Captured aminooxy-VOC adducts are analyzed by nanoelectrospray Fourier transform-ion cyclotron resonance (FT-ICR) mass spectrometry (MS). The concentrations of 2-butanone, 2-hydroxyacetaldehyde, 3-hydroxy-2-butanone, and 4-hydroxyhexenal (4-HHE) in the exhaled breath of lung cancer patients (n = 97) were significantly higher than in the exhaled breath of healthy smoker and nonsmoker controls (n = 88) and patients with benign pulmonary nodules (n = 32). The concentration of 2-butanone in exhaled breath of patients (n = 51) with stages II though IV non-small cell lung cancer (NSCLC) was significantly higher than in exhaled breath of patients with stage I (n = 34). The carbonyl VOC profile in exhaled breath determined using this new silicon microreactor technology provides for the noninvasive detection of lung cancer.

| S-EPMC3930402 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data