• chronic liver disease
• non-invasive test
• volatile organic compound

• Humans
• Ion Mobility Spectrometry/methods
• Volatile Organic Compounds/analysis
• Gas Chromatography-Mass Spectrometry/methods
• Breath Tests/methods
• COVID-19/diagnosis
• Biomarkers/analysis
• Biomarkers/urine
• Neoplasms

• 1,2,3-Trichloropropane (TCP)
• Acrolein
• Hepatotoxicity
• Odor
• Spermine oxidase

• the Ministry of Food and Drug Safety
• the National Research Foundation of Korea

• Diagnostics
• Human Breath
• Meta-analysis
• Review
• VOC
• Volatile organic compounds

The objective of the present work was to analyze volatile compounds in alveolar air in patients with squamous cell lung cancer, lung adenocarcinoma or colon cancer, to prepare algorithms able to discriminate such specific pathological conditions. The concentration of 95 volatile compounds was measured in the alveolar air of 45 control subjects, 36 patients with lung adenocarcinoma, 25 patients with squamous cell lung cancer and 52 patients with colon cancer. Volatile compounds were measured with ion molecule reaction mass spectrometry (IMR-MS). An iterated least absolute shrinkage and selection operator multivariate logistic regression model was used to generate specific algorithms and discriminate control subjects from patients with different kinds of cancer. The final predictive models reached the following performance: by using 11 compounds, patients with lung adenocarcinoma were identified with a sensitivity of 86% and specificity of 84%; nine compounds allowed us to identify patients with lung squamous cell carcinoma with a sensitivity of 88% and specificity of 84%; patients with colon adenocarcinoma could be identified with a sensitivity of 96% and a specificity of 73% using a model comprising 13 volatile compounds. The different alveolar profiles of volatile compounds, obtained from patients with three different kinds of cancer, suggest dissimilar biological–biochemistry conditions; each kind of cancer has probably got a specific alveolar profile.

• alveolar air
• colon cancer
• ion-molecule reaction mass spectrometry (IMR-MS)
• lung adenocarcinoma
• squamous cell lung cancer
• volatile compounds

• Adenocarcinoma of Lung/metabolism
• Adult
• Aged
• Aged, 80 and over
• Breath Tests
• Carcinoma, Squamous Cell/metabolism
• Colonic Neoplasms/metabolism
• Female
• Humans
• Lung Neoplasms/metabolism
• Male
• Middle Aged
• Pulmonary Alveoli/metabolism
• Volatile Organic Compounds/analysis
• Volatile Organic Compounds/metabolism

• ensemble feature selection
• machine learning
• pain assessment
• stacked generalization

• R01 AT010413 NCCIH NIH HHS

• Breath print
• Electronic nose
• Exhaled breath analysis
• Gas chromatography
• Hepatic encephalopathy
• Liver cirrhosis
• Nonalcoholic fatty liver disease

• Breath Tests/instrumentation
• Breath Tests/methods
• Electronic Nose
• Exhalation
• Gastroenterology/instrumentation
• Gastroenterology/methods
• Gastroenterology/trends
• Humans
• Liver Diseases/diagnosis
• Volatile Organic Compounds/analysis

• Deep Forest
• Deep learning
• Ensemble feature selection
• Microbial biomarkers
• Microbiome-wide association studies

• breath analysis
• malignant pleural mesothelioma (MPM)
• volatile organic compounds (VOCs)

In the last decade many studies showed that the exhaled breath of subjects suffering from several pathological conditions has a peculiar volatile organic compound (VOC) profile. The objective of the present work was to analyse the VOCs in alveolar air to build a diagnostic tool able to identify the presence of pancreatic ductal adenocarcinoma in patients with histologically confirmed disease.

The concentration of 92 compounds was measured in the end-tidal breath of 65 cases and 102 controls. VOCs were measured with an ion-molecule reaction mass spectrometry. To distinguish between subjects with pancreatic adenocarcinomas and controls, an iterated Least Absolute Shrinkage and Selection Operator multivariate Logistic Regression model was elaborated.

The final predictive model, based on 10 VOCs, significantly and independently associated with the outcome had a sensitivity and specificity of 100 and 84% respectively, and an area under the ROC curve of 0.99. For further validation, the model was run on 50 other subjects: 24 cases and 26 controls; 23 patients with histological diagnosis of pancreatic adenocarcinomas and 25 controls were correctly identified by the model.

Pancreatic cancer is able to alter the concentration of some molecules in the blood and hence of VOCs in the alveolar air in equilibrium. The detection and statistical rendering of alveolar VOC composition can be useful for the clinical diagnostic approach of pancreatic neoplasms with excellent sensitivity and specificity.

The online version of this article (10.1186/s12885-018-4452-0) contains supplementary material, which is available to authorized users.

• Alveolar air
• IMR-MS
• LASSO logistic regression
• Pancreatic adenocarcinoma
• Volatile organic compounds

Inflammatory bowel diseases (IBD) profoundly affect quality of life and have been gradually increasing in incidence, prevalence and severity in many areas of the world, and in children in particular. Patients with suspected IBD require careful history and clinical examination, while definitive diagnosis relies on endoscopic and histological findings. The aim of the present study was to investigate whether the alveolar air of pediatric patients with IBD presents a specific volatile organic compounds’ (VOCs) pattern when compared to controls. Patients 10–17 years of age, were divided into four groups: Crohn’s disease (CD), ulcerative colitis (UC), controls with gastrointestinal symptomatology, and surgical controls with no evidence of gastrointestinal problems. Alveolar breath was analyzed by ion molecule reaction mass spectrometry. Four models were built starting from 81 molecules plus the age of subjects as independent variables, adopting a penalizing LASSO logistic regression approach: 1) IBDs vs. controls, finally based on 18 VOCs plus age (sensitivity = 95%, specificity = 69%, AUC = 0.925); 2) CD vs. UC, finally based on 13 VOCs plus age (sensitivity = 94%, specificity = 76%, AUC = 0.934); 3) IBDs vs. gastroenterological controls, finally based on 15 VOCs plus age (sensitivity = 94%, specificity = 65%, AUC = 0.918); 4) IBDs vs. controls, built starting from the 21 directly or indirectly calibrated molecules only, and finally based on 12 VOCs plus age (sensitivity = 94%, specificity = 71%, AUC = 0.888). The molecules identified by the models were carefully studied in relation to the concerned outcomes. This study, with the creation of models based on VOCs profiles, precise instrumentation and advanced statistical methods, can contribute to the development of new non–invasive, fast and relatively inexpensive diagnostic tools, with high sensitivity and specificity. It also represents a crucial step towards gaining further insights on the etiology of IBD through the analysis of specific molecules which are the expression of the particular metabolism that characterizes these patients.

• Adolescent
• Breath Tests
• Child
• Colitis, Ulcerative/diagnosis
• Colitis, Ulcerative/metabolism
• Colitis, Ulcerative/pathology
• Crohn Disease/diagnosis
• Crohn Disease/metabolism
• Crohn Disease/pathology
• Exhalation
• Female
• Humans
• Inflammatory Bowel Diseases/diagnosis
• Inflammatory Bowel Diseases/metabolism
• Inflammatory Bowel Diseases/physiopathology
• Male
• Mass Spectrometry
• Volatile Organic Compounds/isolation & purification
• Volatile Organic Compounds/metabolism

• Adult
• Aged
• Breath Tests/instrumentation
• Electronic Nose
• Female
• Humans
• Liver Cirrhosis/diagnosis
• Liver Diseases/classification
• Liver Diseases/pathology
• Male
• Middle Aged
• ROC Curve
• Sensitivity and Specificity

• Blood culture
• Chemical ionization
• Gram identification
• Mass spectrometry
• Prediction rule
• Volatile compound

• Feature selection
• Lasso
• Stability
• Supervised feature grouping

Recent advancements in the use of electronic-nose (e-nose) devices to analyze human breath profiles for the presence of specific volatile metabolites, known as biomarkers or chemical bio-indicators of specific human diseases, metabolic disorders and the overall health status of individuals, are providing the potential for new noninvasive tools and techniques useful to point-of-care clinical disease diagnoses. This exciting new area of electronic disease detection and diagnosis promises to yield much faster and earlier detection of human diseases and disorders, allowing earlier, more effective treatments, resulting in more rapid patient recovery from various afflictions. E-nose devices are particularly suited for the field of disease diagnostics, because they are sensitive to a wide range of volatile organic compounds (VOCs) and can effectively distinguish between different complex gaseous mixtures via analysis of electronic aroma sensor-array output profiles of volatile metabolites present in the human breath. This review provides a summary of some recent developments of electronic-nose technologies, particularly involving breath analysis, with the potential for providing many new diagnostic applications for the detection of specific human diseases associated with different organs in the body, detectable from e-nose analyses of aberrant disease-associated VOCs present in air expired from the lungs.

• Breath print
• GC–MS
• Leave-one-out validation
• Principal components analysis
• Volatile organic compounds

• bioaerosol
• electronic nose
• global health
• point-of-care testing
• volatile organic compounds

• Amino Acid Sequence
• Artificial Intelligence
• Computational Biology/methods
• Epitopes, B-Lymphocyte/chemistry
• Epitopes, B-Lymphocyte/immunology
• Protein Conformation

Identifying group-specific characteristics in metabolic networks can provide better insight into evolutionary developments. Here, we present an approach to classify the three domains of life using topological information about the underlying metabolic networks. These networks have been shown to share domain-independent structural similarities, which pose a special challenge for our endeavour. We quantify specific structural information by using topological network descriptors to classify this set of metabolic networks. Such measures quantify the structural complexity of the underlying networks. In this study, we use such measures to capture domain-specific structural features of the metabolic networks to classify the data set. So far, it has been a challenging undertaking to examine what kind of structural complexity such measures do detect. In this paper, we apply two groups of topological network descriptors to metabolic networks and evaluate their classification performance. Moreover, we combine the two groups to perform a feature selection to estimate the structural features with the highest classification ability in order to optimize the classification performance.

By combining the two groups, we can identify seven topological network descriptors that show a group-specific characteristic by ANOVA. A multivariate analysis using feature selection and supervised machine learning leads to a reasonable classification performance with a weighted F-score of 83.7% and an accuracy of 83.9%. We further demonstrate that our approach outperforms alternative methods. Also, our results reveal that entropy-based descriptors show the highest classification ability for this set of networks.

Our results show that these particular topological network descriptors are able to capture domain-specific structural characteristics for classifying metabolic networks between the three domains of life.

• Biomarkers/analysis
• Biomarkers/chemistry
• Computational Biology/methods
• High-Throughput Screening Assays
• Mass Spectrometry/methods
• Proteomics/methods

Lung cancer is the leading cause of death from cancer in the world and its treatment is dependant on the type and stage of cancer detected in the patient. Molecular biomarkers that can characterize the cancer phenotype are thus a key tool in planning a therapeutic response. A common protocol for identifying such biomarkers is to employ genomic microarray analysis to find genes that show differential expression according to disease state or type. Data-mining techniques such as feature selection are often used to isolate, from among a large manifold of genes with differential expression, those specific genes whose differential expression patterns are of optimal value in phenotypic differentiation. One such technique, Biomarker Identifier (BMI), has been developed to identify features with the ability to distinguish between two data groups of interest, which is thus highly applicable for such studies.

Microarray data with validated genes was used to evaluate the utility of BMI in identifying markers for lung cancer. This data set contains a set of 129 gene expression profiles from large-airway epithelial cells (60 samples from smokers with lung cancer and 69 from smokers without lung cancer) and 7 genes from this data have been confirmed to be differentially expressed by quantitative PCR. Using this data set, BMI was compared with various well-known feature selection methods and was found to be more successful than other methods in finding useful genes to classify cancerous samples. Also it is evident that genes selected by BMI (given the same number of genes and classification algorithms) showed better discriminative power than those from the original study. After pathway analysis on the selected genes by BMI, we have been able to correlate the selected genes with well-known cancer-related pathways.

Our results show that BMI can be used to analyze microarray data and to find useful genes for classifying samples. Pathway analysis suggests that BMI is successful in identifying biomarker-quality cancer-related genes from the data.

• artificial olfaction
• disease diagnoses
• e-nose
• electronic aroma detection
• healthcare applications

• Delivery of Health Care
• Drug Carriers/chemistry
• Drug Carriers/metabolism
• Humans
• Infections/diagnosis
• Metabolic Diseases/diagnosis
• Neoplasms/diagnosis
• Smell
• Telemedicine
• Transistors, Electronic
• Volatile Organic Compounds/chemistry

• lung cancer
• microarrays
• data mining
• feature selection

• Computational Biology/methods
• Gene Expression Profiling
• Humans
• Lung Neoplasms/genetics
• Metabolic Networks and Pathways
• Oncogenes
• Stochastic Processes

• P20 RR016475 NCRR NIH HHS