scholarly journals Machine Learning Analysis of Volatolomic Profiles in Breath Can Identify Non-invasive Biomarkers of Liver Disease

2021 ◽  
Author(s):  
Jonathan Thomas ◽  
Joanna Roopkumar ◽  
Tushar Patel
Keyword(s):  
Machine Learning ◽  
Portal Hypertension ◽  
Liver Disease ◽  
Hepatic Metabolism ◽  
Molecular Feature ◽  
Non Invasive ◽  
Molecular Features ◽  
Signature Pattern ◽  
Ion Mobility Spectroscopy ◽  
Learning Analysis

Abstract Disease-related effects on hepatic metabolism can alter the composition of chemicals in the circulation and subsequently in breath. The presence of disease related alterations in exhaled volatile organic compounds (VOC) could provide a basis for non-invasive biomarkers of hepatic disease. This study examined the feasibility of combining global VOC (volatolomic) profiles from breath analysis and machine learning to develop signature pattern-based biomarkers for cirrhosis. Breath samples were analyzed using thermal desorption-gas chromatography-field asymmetric ion mobility spectroscopy to generate volatolomic profiles. Samples were collected from 35 persons with cirrhosis, 4 with non-cirrhotic portal hypertension, and 11 healthy participants. Molecular features of interest were identified to determine their ability to classify cirrhosis or portal hypertension. A molecular feature score was derived that increased with the stage of cirrhosis and had an AUC of 0.78 for detection. Chromatographic breath profiles were utilized to generate machine learning-based classifiers. Algorithmic models could discriminate presence or stage of cirrhosis with a sensitivity of 88-92% and specificity of 75%. These results demonstrate the feasibility of volatolomic profiling to classify clinical phenotypes without identifying specific compounds. These studies will pave the way in developing non-invasive biomarkers of liver disease based on volatolomic signatures found in breath.

scholarly journals Machine learning analysis of volatolomic profiles in breath can identify non-invasive biomarkers of liver disease: A pilot study

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260098
Author(s):  
Jonathan N. Thomas ◽  
Joanna Roopkumar ◽  
Tushar Patel
Keyword(s):  
Machine Learning ◽  
Portal Hypertension ◽  
Liver Disease ◽  
Hepatic Metabolism ◽  
Supervised Machine Learning ◽  
Molecular Feature ◽  
Non Invasive ◽  
Molecular Features ◽  
Signature Pattern ◽  
Ion Mobility Spectroscopy

Disease-related effects on hepatic metabolism can alter the composition of chemicals in the circulation and subsequently in breath. The presence of disease related alterations in exhaled volatile organic compounds could therefore provide a basis for non-invasive biomarkers of hepatic disease. This study examined the feasibility of using global volatolomic profiles from breath analysis in combination with supervised machine learning to develop signature pattern-based biomarkers for cirrhosis. Breath samples were analyzed using thermal desorption-gas chromatography-field asymmetric ion mobility spectroscopy to generate breathomic profiles. A standardized collection protocol and analysis pipeline was used to collect samples from 35 persons with cirrhosis, 4 with non-cirrhotic portal hypertension, and 11 healthy participants. Molecular features of interest were identified to determine their ability to classify cirrhosis or portal hypertension. A molecular feature score was derived that increased with the stage of cirrhosis and had an AUC of 0.78 for detection. Chromatographic breath profiles were utilized to generate machine learning-based classifiers. Algorithmic models could discriminate presence or stage of cirrhosis with a sensitivity of 88–92% and specificity of 75%. These results demonstrate the feasibility of volatolomic profiling to classify clinical phenotypes using global breath output. These studies will pave the way for the development of non-invasive biomarkers of liver disease based on volatolomic signatures found in breath.

Portal hypertension and its management in children

2017 ◽  
Vol 103 (2) ◽  
pp. 186-191 ◽  
Author(s):  
Tassos Grammatikopoulos ◽  
Patrick James McKiernan ◽  
Anil Dhawan
Keyword(s):  
Portal Hypertension ◽  
Liver Disease ◽  
Gastrointestinal Bleeding ◽  
Surgical Procedures ◽  
Vascular Occlusion ◽  
Portal System ◽  
Mortality And Morbidity ◽  
Non Invasive ◽  
Intravascular Pressure

Portal hypertension (PHT), defined as raised intravascular pressure in the portal system, is a complication of chronic liver disease or liver vascular occlusion. Advances in our ability to diagnose and monitor the condition but also predict the risk of gastrointestinal bleeding have enabled us to optimise the management of children with PHT either at a surveillance or at a postbleeding stage. A consensus among paediatric centres in the classification of varices can be beneficial in streamlining future paediatric studies. New invasive (endoscopic and surgical procedures) and non-invasive (pharmacotherapy) techniques are currently used enabling clinicians to reduce mortality and morbidity in children with PHT.

scholarly journals Magnetic resonance parametric mapping of the spleen for non-invasive assessment of portal hypertension

2020 ◽  
Vol 31 (1) ◽  
pp. 85-93
Author(s):  
Narine Mesropyan ◽  
Alexander Isaak ◽  
Anton Faron ◽  
Michael Praktiknjo ◽  
Christian Jansen ◽  
...  
Keyword(s):  
Portal Hypertension ◽  
Liver Disease ◽  
Diagnostic Performance ◽  
Volume Fraction ◽  
Portal Pressure ◽  
Extracellular Volume ◽  
Relaxation Times ◽  
Pressure Measurements ◽  
Non Invasive ◽  
T1 And T2

Abstract Objectives In patients with advanced liver disease, portal hypertension is an important risk factor, leading to complications such as esophageal variceal bleeding, ascites, and hepatic encephalopathy. This study aimed to determine the diagnostic value of T1 and T2 mapping and extracellular volume fraction (ECV) for the non-invasive assessment of portal hypertension. Methods In this prospective study, 50 participants (33 patients with indication for trans-jugular intrahepatic portosystemic shunt (TIPS) and 17 healthy volunteers) underwent MRI. The derivation and validation cohorts included 40 and 10 participants, respectively. T1 and T2 relaxation times and ECV of the liver and the spleen were assessed using quantitative mapping techniques. Direct hepatic venous pressure gradient (HVPG) and portal pressure measurements were performed during TIPS procedure. ROC analysis was performed to compare diagnostic performance. Results Splenic ECV correlated with portal pressure (r = 0.72; p < 0.001) and direct HVPG (r = 0.50; p = 0.003). No significant correlations were found between native splenic T1 and T2 relaxation times with portal pressure measurements (p > 0.05, respectively). In the derivation cohort, splenic ECV revealed a perfect diagnostic performance with an AUC of 1.000 for the identification of clinically significant portal hypertension (direct HVPG ≥ 10 mmHg) and outperformed other parameters: hepatic T2 (AUC, 0.731), splenic T2 (AUC, 0.736), and splenic native T1 (AUC, 0.806) (p < 0.05, respectively). The diagnostic performance of mapping parameters was comparable in the validation cohort. Conclusion Splenic ECV was associated with portal pressure measurements in patients with advanced liver disease. Future studies should explore the diagnostic value of parametric mapping accross a broader range of pressure values. Key Points • Non-invasive assessment and monitoring of portal hypertension is an area of unmet interest. • Splenic extracellular volume fraction is strongly associated with portal pressure in patients with end-stage liver disease. • Quantitative splenic and hepatic MRI-derived parameters have a potential to become a new non-invasive diagnostic parameter to assess and monitor portal pressure.

scholarly journals Age and life expectancy clocks based on machine learning analysis of mouse frailty

2019 ◽  
Author(s):  
Michael B Schultz ◽  
Alice E Kane ◽  
Sarah J Mitchell ◽  
Michael R MacArthur ◽  
Elisa Warner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Life Expectancy ◽  
Strong Predictor ◽  
Cost Effective ◽  
Chronological Age ◽  
Random Forest Regression ◽  
Non Invasive ◽  
Longevity Genes ◽  
Clinical Models ◽  
Learning Analysis

ABSTRACTThe identification of genes and interventions that slow or reverse aging is hampered by the lack of non-invasive metrics that can predict life expectancy of pre-clinical models. Frailty Indices (FIs) in mice are composite measures of health that are cost-effective and non-invasive, but whether they can accurately predict health and lifespan is not known. Here, mouse FIs were scored longitudinally until death and machine learning was employed to develop two clocks. A random forest regression was trained on FI components for chronological age to generate the FRIGHT (Frailty Inferred Geriatric Health Timeline) clock, a strong predictor of chronological age. A second model was trained on remaining lifespan to generate the AFRAID (Analysis of Frailty and Death) clock, which accurately predicts life expectancy and the efficacy of a lifespan-extending intervention up to a year in advance. Adoption of these clocks should accelerate the identification of novel longevity genes and aging interventions.

scholarly journals Inexpensive, non-invasive biomarkers predict Alzheimer transition using machine learning analysis of the Alzheimer’s Disease Neuroimaging (ADNI) database

PLoS ONE ◽  
2020 ◽  
Vol 15 (7) ◽  
pp. e0235663
Author(s):  
Juan Felipe Beltrán ◽  
Brandon Malik Wahba ◽  
Nicole Hose ◽  
Dennis Shasha ◽  
Richard P. Kline ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Non Invasive ◽  
Learning Analysis
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