Stool microbiota are superior to saliva in distinguishing cirrhosis and hepatic encephalopathy using machine learning

2021 ◽  
Author(s):  
Krishnakant Saboo ◽  
Nikita V. Petrakov ◽  
Amirhossein Shamsaddini ◽  
Andrew Fagan ◽  
Edith A. Gavis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatic Encephalopathy ◽  
Stool Microbiota

scholarly journals Colonic mucosal microbiome differs from stool microbiome in cirrhosis and hepatic encephalopathy and is linked to cognition and inflammation

2012 ◽  
Vol 303 (6) ◽  
pp. G675-G685 ◽  
Author(s):  
Jasmohan S. Bajaj ◽  
Phillip B. Hylemon ◽  
Jason M. Ridlon ◽  
Douglas M. Heuman ◽  
Kalyani Daita ◽  
...  
Keyword(s):  
Hepatic Encephalopathy ◽  
Cognitive Testing ◽  
Disease Score ◽  
Network Analyses ◽  
Microbiome Analysis ◽  
End Stage Liver Disease ◽  
Cirrhotic Patients ◽  
End Stage ◽  
Stool Microbiota ◽  
Poor Cognition

Although hepatic encephalopathy (HE) is linked to the gut microbiota, stool microbiome analysis has not found differences between HE and no-HE patients. This study aimed to compare sigmoid mucosal microbiome of cirrhotic patients to controls, between HE vs. no-HE patients, and to study their linkage with cognition and inflammation. Sixty cirrhotic patients (36 HE and 24 no-HE) underwent cognitive testing, stool collection, cytokine (Th1, Th2, Th17, and innate immunity), and endotoxin analysis. Thirty-six patients (19 HE and 17 no-HE) and 17 age-matched controls underwent sigmoid biopsies. Multitag pyrosequencing (including autochthonous genera, i.e., Blautia, Roseburia, Fecalibacterium, Dorea) was performed on stool and mucosa. Stool and mucosal microbiome differences within/between groups and correlation network analyses were performed. Controls had significantly higher autochthonous and lower pathogenic genera compared with cirrhotic patients, especially HE patients. HE patients had worse MELD (model for end-stage liver disease) score and cognition and higher IL-6 and endotoxin than no-HE. Mucosal microbiota was different from stool within both HE/no-HE groups. Between HE/no-HE patients, there was no difference in stool microbiota but mucosal microbiome was different with lower Roseburia and higher Enterococcus, Veillonella, Megasphaera, and Burkholderia abundance in HE. On network analysis, autochthonous genera ( Blautia, Fecalibacterium, Roseburia, and Dorea) were associated with good cognition and decreased inflammation in both HE/no-HE, whereas genera overrepresented in HE ( Enterococcus, Megasphaera, and Burkholderia) were linked to poor cognition and inflammation. Sigmoid mucosal microbiome differs significantly from stool microbiome in cirrhosis. Cirrhotic, especially HE, patients' mucosal microbiota is significantly different from controls with a lack of potentially beneficial autochthonous and overgrowth of potentially pathogenic genera, which are associated with poor cognition and inflammation.

CT-Based radiomics model for preoperative prediction of hepatic encephalopathy after transjugular intrahepatic portosystemic shunt

2022 ◽  
Author(s):  
Sihang Cheng ◽  
Xiang Yu ◽  
Xinyue Chen ◽  
Zhengyu Jin ◽  
Huadan Xue ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatic Encephalopathy ◽  
Random Forest ◽  
Transjugular Intrahepatic Portosystemic Shunt ◽  
Ct Images ◽  
Portosystemic Shunt ◽  
Random Forest Algorithm ◽  
Clinical Model ◽  
Original Dataset ◽  
Intrahepatic Portosystemic Shunt

Objective: To develop and evaluate a machine learning-based CT radiomics model for the prediction of hepatic encephalopathy (HE) after transjugular intrahepatic portosystemic shunt (TIPS). Methods: A total of 106 patients who underwent TIPS placement were consecutively enrolled in this retrospective study. Region of interests (ROIs) were drawn on unenhanced, arterial phase, and portal venous Phase CT images, and radiomics features were extracted, respectively. A radiomics model was established to predict the occurrence of HE after TIPS by using random forest algorithm and ten-fold cross-validation. Receiver operating characteristic (ROC) curves were performed to validate the capability of the radiomics model and clinical model on the training, test and original datasets, respectively. Results: The radiomics model showed favorable discriminatory ability in the training cohort with an area under the curve (AUC) of 0.899 (95% CI, 0.848 to 0.951), while in the test cohort, it was confirmed with an AUC of 0.887 (95% CI, 0.760 to 1.00). After applying this model to original dataset, it had an AUC of 0.955 (95% CI, 0.896 to 1.00). A clinical model was also built with an AUC of 0.649 (95% CI, 0.530 to 0.767) in the original dataset, and a Delong test demonstrated its relative lower efficiency when compared with the radiomics model (p < 0.05). Conclusion: Machine learning-based CT radiomics model performed better than traditional clinical parameter-based models in the prediction of post-TIPS HE. Advances in knowledge: Radiomics model for the prediction of post-TIPS HE was built based on feature extraction from routine acquired preoperative CT images and feature selection by random forest algorithm, which showed satisfied performance and proved the advantages of machine learning in this field.

scholarly journals Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning

2021 ◽  
Vol 14 ◽  
Author(s):  
Gaoyan Zhang ◽  
Yuexuan Li ◽  
Xiaodong Zhang ◽  
Lixiang Huang ◽  
Yue Cheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatic Encephalopathy ◽  
Visual Memory ◽  
Memory Function ◽  
Brain Network ◽  
Network Evolution ◽  
Cognitive Networks ◽  
Support Vector ◽  
Graph Properties ◽  
Modular Algorithm

Hepatic encephalopathy (HE) is a neurocognitive dysfunction based on metabolic disorders caused by severe liver disease, which has a high one-year mortality. Mild hepatic encephalopathy (MHE) has a high risk of converting to overt HE, and thus the accurate identification of MHE from cirrhosis with no HE (noHE) is of great significance in reducing mortality. Previously, most studies focused on studying abnormality in the static brain networks of MHE to find biomarkers. In this study, we aimed to use multi-layer modular algorithm to study abnormality in dynamic graph properties of brain network in MHE patients and construct a machine learning model to identify individual MHE from noHE. Here, a time length of 500-second resting-state functional MRI data were collected from 41 healthy subjects, 32 noHE patients and 30 MHE patients. Multi-layer modular algorithm was performed on dynamic brain functional connectivity graph. The connection-stability score was used to characterize the loyalty in each brain network module. Nodal flexibility, cohesion and disjointness were calculated to describe how the node changes the network affiliation across time. Results show that significant differences between MHE and noHE were found merely in nodal disjointness in higher cognitive network modules (ventral attention, fronto-parietal, default mode networks) and these abnormalities were associated with the decline in patients’ attention and visual memory function evaluated by Digit Symbol Test. Finally, feature extraction from node disjointness with the support vector machine classifier showed an accuracy of 88.71% in discrimination of MHE from noHE, which was verified by different window sizes, modular partition parameters and machine learning parameters. All these results show that abnormal nodal disjointness in higher cognitive networks during brain network evolution can be seemed as a biomarker for identification of MHE, which help us understand the disease mechanism of MHE at a fine scale.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning
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