Non-invasive diagnosis of non-alcoholic steatohepatitis and fibrosis with the use of omics and supervised learning: A proof of concept study

Metabolism ◽  
2019 ◽  
Vol 101 ◽  
pp. 154005 ◽  
Author(s):  
Nikolaos Perakakis ◽  
Stergios A. Polyzos ◽  
Alireza Yazdani ◽  
Aleix Sala-Vila ◽  
Jannis Kountouras ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Proof Of Concept ◽  
Concept Study ◽  
Non Invasive ◽  
Alcoholic Steatohepatitis

scholarly journals Non-Invasive Venous waveform Analysis (NIVA) for volume assessment during complex cranial vault reconstruction: A proof-of-concept study in children

PLoS ONE ◽  
2020 ◽  
Vol 15 (7) ◽  
pp. e0235933 ◽  
Author(s):  
Jenna H. Sobey ◽  
Srijaya K. Reddy ◽  
Kyle M. Hocking ◽  
Monica E. Polcz ◽  
Christy M. Guth ◽  
...  
Keyword(s):  
Cranial Vault ◽  
Waveform Analysis ◽  
Proof Of Concept ◽  
Concept Study ◽  
Non Invasive ◽  
Volume Assessment

scholarly journals Non-Invasive Estimation of Intracranial Pressure by Diffuse Optics: A Proof-of-Concept Study

2020 ◽  
Vol 37 (23) ◽  
pp. 2569-2579
Author(s):  
Jonas B. Fischer ◽  
Ameer Ghouse ◽  
Susanna Tagliabue ◽  
Federica Maruccia ◽  
Anna Rey-Perez ◽  
...  
Keyword(s):  
Intracranial Pressure ◽  
Proof Of Concept ◽  
Diffuse Optics ◽  
Concept Study ◽  
Non Invasive

scholarly journals ASC, IL-18 and Galectin-3 as Biomarkers of Non-Alcoholic Steatohepatitis: A Proof of Concept Study

2020 ◽  
Vol 21 (22) ◽  
pp. 8580
Author(s):  
Brianna Cyr ◽  
Robert W. Keane ◽  
Juan Pablo de Rivero Vaccari
Keyword(s):  
Area Under The Curve ◽  
Multivariate Logistic Regression Model ◽  
Proof Of Concept ◽  
Likelihood Ratios ◽  
Alcoholic Fatty Liver ◽  
Predictive Values ◽  
Concept Study ◽  
Alcoholic Steatohepatitis ◽  
Diagnostic Potential ◽  
Galectin 3

Non-alcoholic steatohepatitis (NASH) is a severe form of non-alcoholic fatty liver disease that is growing in prevalence. Symptoms of NASH become apparent when the disease has progressed significantly. Thus, there is a need to identify biomarkers of NASH in order to detect the disease earlier and to monitor disease severity. The inflammasome has been shown to play a role in liver diseases. Here, we performed a proof of concept study of biomarker analyses (cut-off points, positive and negative predictive values, receiver operating characteristic (ROC) curves, and likelihood ratios) on the serum of patients with NASH and healthy controls on apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC), interleukin (IL)-18, Galectin-3 (Gal-3), and C-reactive protein (CRP). ASC, IL-18, and Gal-3 were elevated in the serum of NASH patients when compared to controls. The area under the curve (AUC) for ASC was the highest (0.7317) with an accuracy of 68%, followed by IL-18 (0.7036) with an accuracy of 66% and Gal-3 (0.6891) with an accuracy of 61%. Moreover, we then fit a stepwise multivariate logistic regression model using ASC, IL-18, and Gal-3 to determine the probability of patients having a NASH diagnosis, which resulted in an AUC of 0.71 and an accuracy of 79%, indicating that combining these biomarkers increases their diagnostic potential for NASH. These results indicate that ASC, IL-18, and Gal-3 are reliable biomarkers of NASH and that combining these analytes increases the biomarker potential of these proteins.

Quantitative liver MRI including extracellular volume fraction for non-invasive quantification of liver fibrosis: a prospective proof-of-concept study

Gut ◽  
2017 ◽  
Vol 67 (3) ◽  
pp. 593-594 ◽  
Author(s):  
Julian A Luetkens ◽  
Sabine Klein ◽  
Frank Traeber ◽  
Frederic C Schmeel ◽  
Alois M Sprinkart ◽  
...  
Keyword(s):  
Liver Fibrosis ◽  
Volume Fraction ◽  
Extracellular Volume ◽  
Extracellular Volume Fraction ◽  
Proof Of Concept ◽  
Liver Mri ◽  
Concept Study ◽  
Non Invasive

scholarly journals Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept

2021 ◽  
Vol 9 (1) ◽  
pp. e002027
Author(s):  
Brinnae Bent ◽  
Peter J Cho ◽  
April Wittmann ◽  
Connie Thacker ◽  
Srikanth Muppidi ◽  
...  
Keyword(s):  
Heart Rate ◽  
Skin Temperature ◽  
Validation Cohort ◽  
Electrodermal Activity ◽  
External Validation ◽  
Glucose Variability ◽  
Proof Of Concept ◽  
Concept Study ◽  
Non Invasive ◽  
The Relationship

IntroductionDiabetes prevalence continues to grow and there remains a significant diagnostic gap in one-third of the US population that has pre-diabetes. Innovative, practical strategies to improve monitoring of glycemic health are desperately needed. In this proof-of-concept study, we explore the relationship between non-invasive wearables and glycemic metrics and demonstrate the feasibility of using non-invasive wearables to estimate glycemic metrics, including hemoglobin A1c (HbA1c) and glucose variability metrics.Research design and methodsWe recorded over 25 000 measurements from a continuous glucose monitor (CGM) with simultaneous wrist-worn wearable (skin temperature, electrodermal activity, heart rate, and accelerometry sensors) data over 8–10 days in 16 participants with normal glycemic state and pre-diabetes (HbA1c 5.2–6.4). We used data from the wearable to develop machine learning models to predict HbA1c recorded on day 0 and glucose variability calculated from the CGM. We tested the accuracy of the HbA1c model on a retrospective, external validation cohort of 10 additional participants and compared results against CGM-based HbA1c estimation models.ResultsA total of 250 days of data from 26 participants were collected. Out of the 27 models of glucose variability metrics that we developed using non-invasive wearables, 11 of the models achieved high accuracy (<10% mean average per cent error, MAPE). Our HbA1c estimation model using non-invasive wearables data achieved MAPE of 5.1% on an external validation cohort. The ranking of wearable sensor’s importance in estimating HbA1c was skin temperature (33%), electrodermal activity (28%), accelerometry (25%), and heart rate (14%).ConclusionsThis study demonstrates the feasibility of using non-invasive wearables to estimate glucose variability metrics and HbA1c for glycemic monitoring and investigates the relationship between non-invasive wearables and the glycemic metrics of glucose variability and HbA1c. The methods used in this study can be used to inform future studies confirming the results of this proof-of-concept study.

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