scholarly journals Phospholipids are A Potentially Important Source of Tissue Biomarkers for Hepatocellular Carcinoma: Results of a Pilot Study Involving Targeted Metabolomics

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 167
Author(s):  
Erin B. Evangelista ◽  
Sandi A. Kwee ◽  
Miles M. Sato ◽  
Lu Wang ◽  
Christoph Rettenmeier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Fatty Acids ◽  
Free Fatty Acids ◽  
Small Molecules ◽  
Bile Acids ◽  
Random Forests ◽  
Liver Tissue ◽  
Classification Performance ◽  
Tissue Biomarkers

Background: Hepatocellular carcinoma (HCC) pathogenesis involves the alteration of multiple liver-specific metabolic pathways. We systematically profiled cancer- and liver-related classes of metabolites in HCC and adjacent liver tissues and applied supervised machine learning to compare their potential yield for HCC biomarkers. Methods: Tumor and corresponding liver tissue samples were profiled as follows: Bile acids by ultra-performance liquid chromatography (LC) coupled to tandem mass spectrometry (MS), phospholipids by LC-MS/MS, and other small molecules including free fatty acids by gas chromatography—time of flight MS. The overall classification performance of metabolomic signatures derived by support vector machine (SVM) and random forests machine learning algorithms was then compared across classes of metabolite. Results: For each metabolite class, there was a plateau in classification performance with signatures of 10 metabolites. Phospholipid signatures consistently showed the highest discrimination for HCC followed by signatures derived from small molecules, free fatty acids, and bile acids with area under the receiver operating characteristic curve (AUC) values of 0.963, 0.934, 0.895, 0.695, respectively, for SVM-generated signatures comprised of 10 metabolites. Similar classification performance patterns were observed with signatures derived by random forests. Conclusion: Membrane phospholipids are a promising source of tissue biomarkers for discriminating between HCC tumor and liver tissue.

scholarly journals The effect of testosterone on the bile acid and bile lipid composition in rats

2021 ◽  
Author(s):  
I. S. Lupaina ◽  
◽  
A. M. Liashevych ◽  
Y. M. Reshetnik ◽  
S. P. Veselsky ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Free Fatty Acids ◽  
Bile Acids ◽  
Lipid Composition ◽  
Taurocholic Acid ◽  
The Body ◽  
Female Rats ◽  
Glycocholic Acid ◽  
Physiological Processes ◽  
Free Bile Acids

The study of sexual differences in the regulation of exocrine liver function is one of the topical areas in hepatology. After all, the liver serves as a mediator in a number of systemic effects of sex hormones on the body and is a key organ of their metabolism. In particular, the correlation between the concentration of steroid hormones can determine the direction of physiological processes and their possible distortions. Methods: physiological, biochemical, methods of mathematical statistics. Cholesecretion increased in female rats under the influence of testosterone. Testosterone raised the concentration of taurocholic acid and at the end of the acute experiment the level of taurohenodeoxycholic and taurodeoxycholic acids significantly increased. By comparison, the content of glycocholates decreased significantly immediately after the administration of the hormone but at the end of the experiment, the content of glycocholic acid increased significantly. The level of free bile acids increased under the testosterone. Testosterone affected the bile lipid composition, in particular, it raised the concentrations of phospholipids, cholesterol and its ethers, while the content of free fatty acids decreased under the studied hormone. Testosterone when administered intraperitoneally to female rats significantly affects the concentration of conjugated and free cholate, which may indicate its involvement in metabolic transformations and transport of bile acids to the primary bile ducts. The studied hormone raised the concentration of phospholipids, cholesterol and its ethers, but reduced the content of free fatty acids in the liver secretion of the studied animals.

scholarly journals Random Forests Highlight the Combined Effect of Environmental Heavy Metals Exposure and Genetic Damages for Cardiovascular Diseases

2021 ◽  
Vol 11 (18) ◽  
pp. 8405
Author(s):  
Alfonso Monaco ◽  
Antonio Lacalamita ◽  
Nicola Amoroso ◽  
Armando D’Orta ◽  
Andrea Del Buono ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heavy Metals ◽  
Cardiovascular Diseases ◽  
Random Forests ◽  
Classification Problem ◽  
Classification Performance ◽  
Cvd Risk ◽  
Learning Framework ◽  
Clinic Foundation ◽  
Highly Correlated

Heavy metals are a dangerous source of pollution due to their toxicity, permanence in the environment and chemical nature. It is well known that long-term exposure to heavy metals is related to several chronic degenerative diseases (cardiovascular diseases, neoplasms, neurodegenerative syndromes, etc.). In this work, we propose a machine learning framework to evaluate the severity of cardiovascular diseases (CVD) from Human scalp hair analysis (HSHA) tests and genetic analysis and identify a small group of these clinical features mostly associated with the CVD risk. Using a private dataset provided by the DD Clinic foundation in Caserta, Italy, we cross-validated the classification performance of a Random Forests model with 90 subjects affected by CVD. The proposed model reached an AUC of 0.78 ± 0.01 on a three class classification problem. The robustness of the predictions was assessed by comparison with different cross-validation schemes and two state-of-the-art classifiers, such as Artificial Neural Network and General Linear Model. Thus, is the first work that studies, through a machine learning approach, the tight link between CVD severity, heavy metal concentrations and SNPs. Then, the selected features appear highly correlated with the CVD phenotype, and they could represent targets for future CVD therapies.

scholarly journals Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3085 ◽  
Author(s):  
Raluca Brehar ◽  
Delia-Alexandrina Mitrea ◽  
Flaviu Vancea ◽  
Tiberiu Marita ◽  
Sergiu Nedevschi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Deep Learning ◽  
Binary Classification ◽  
Classification Performance ◽  
Ultrasound Images ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Segmentation Accuracy ◽  
Conventional Machine

The emergence of deep-learning methods in different computer vision tasks has proved to offer increased detection, recognition or segmentation accuracy when large annotated image datasets are available. In the case of medical image processing and computer-aided diagnosis within ultrasound images, where the amount of available annotated data is smaller, a natural question arises: are deep-learning methods better than conventional machine-learning methods? How do the conventional machine-learning methods behave in comparison with deep-learning methods on the same dataset? Based on the study of various deep-learning architectures, a lightweight multi-resolution Convolutional Neural Network (CNN) architecture is proposed. It is suitable for differentiating, within ultrasound images, between the Hepatocellular Carcinoma (HCC), respectively the cirrhotic parenchyma (PAR) on which HCC had evolved. The proposed deep-learning model is compared with other CNN architectures that have been adapted by transfer learning for the ultrasound binary classification task, but also with conventional machine-learning (ML) solutions trained on textural features. The achieved results show that the deep-learning approach overcomes classical machine-learning solutions, by providing a higher classification performance.

Influence of bile acids and free fatty acids on physicochemical properties of LP-X

1978 ◽  
Vol 172 (3) ◽  
pp. 211-221 ◽  
Author(s):  
G. Baggio ◽  
P. Müller ◽  
H. Wieland ◽  
P. D. Niedmann ◽  
D. Seidel
Keyword(s):  
Fatty Acids ◽  
Physicochemical Properties ◽  
Free Fatty Acids ◽  
Bile Acids

scholarly journals Exploring the interactions between serum free fatty acids and fecal microbiota in obesity through a machine learning algorithm

2019 ◽  
Vol 121 ◽  
pp. 533-541 ◽  
Author(s):  
Tania Fernández-Navarro ◽  
Irene Díaz ◽  
Isabel Gutiérrez-Díaz ◽  
Javier Rodríguez-Carrio ◽  
Ana Suárez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fatty Acids ◽  
Free Fatty Acids ◽  
Learning Algorithm ◽  
Fecal Microbiota ◽  
Machine Learning Algorithm ◽  
Serum Free Fatty Acids ◽  
Serum Free
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