General machine learning model, review, and experimental-theoretic study of magnolol activity in enterotoxigenic induced oxidative stress
| العنوان: | General machine learning model, review, and experimental-theoretic study of magnolol activity in enterotoxigenic induced oxidative stress |
|---|---|
| المؤلفون: | Zhiliang Tan, Lucas Anton Pastur-Romay, Cristian R. Munteanu, Xuefeng Han, Wenjun Xiao, José M. Vázquez-Naya, Chuanshe Zhou, Shaoxun Tang, Javier Pereira Loureiro, Yong Liu, Yanli Deng |
| المصدر: | RUC. Repositorio da Universidade da Coruña instname |
| بيانات النشر: | Bentham Science, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | 0301 basic medicine, medicine.disease_cause, Machine learning, computer.software_genre, Lignans, Superoxide dismutase, Machine Learning, 03 medical and health sciences, chemistry.chemical_compound, Mice, Enterotoxigenic Escherichia coli, Drug Discovery, medicine, Animals, QSAR models, Escherichia coli Infections, chemistry.chemical_classification, Reactive oxygen species, biology, business.industry, Glutathione peroxidase, Biphenyl Compounds, General Medicine, Glutathione, Malondialdehyde, Magnolol, Enteritis, Oxidative Stress, Antioxidative activity, 030104 developmental biology, chemistry, biology.protein, Artificial intelligence, business, computer, Oxidative stress, Random forest |
| الوصف: | [Abstract] This study evaluated the antioxidative effects of magnolol based on the mouse model induced by Enterotoxigenic Escherichia coli (E. coli, ETEC). All experimental mice were equally treated with ETEC suspensions (3.45×109 CFU/ml) after oral administration of magnolol for 7 days at the dose of 0, 100, 300 and 500 mg/kg Body Weight (BW), respectively. The oxidative metabolites and antioxidases for each sample (organism of mouse) were determined: Malondialdehyde (MDA), Nitric Oxide (NO), Glutathione (GSH), Myeloperoxidase (MPO), Catalase (CAT), Superoxide Dismutase (SOD), and Glutathione Peroxidase (GPx). In addition, we also determined the corresponding mRNA expressions of CAT, SOD and GPx as well as the Total Antioxidant Capacity (T-AOC). The experiment was completed with a theoretical study that predicts a series of 79 ChEMBL activities of magnolol with 47 proteins in 18 organisms using a Quantitative Structure- Activity Relationship (QSAR) classifier based on the Moving Averages (MAs) of Rcpi descriptors in three types of experimental conditions (biological activity with specific units, protein target and organisms). Six Machine Learning methods from Weka software were tested and the best QSAR classification model was provided by Random Forest with True Positive Rate (TPR) of 0.701 and Area under Receiver Operating Characteristic (AUROC) of 0.790 (test subset, 10-fold crossvalidation). The model is predicting if the new ChEMBL activities are greater or lower than the average values for the magnolol targets in different organisms. National Natural Science Foundation of China; 30972166 Hunan Provincial Education Department; 08A031 Hunan Provincial Innovation Foundation for Postgraduate; CX2011B304 Hunan Provincial Innovation Foundation for Postgraduate; CX2014B300 Xunta de Galicia; R2014/039 Xunta de Galicia; GRC2014/049 Ministerio de Economía y Competitividad; UNLC08-1E-002 Ministerio de Economía y Competitividad; UNLC13-13-3503 |
| اللغة: | English |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54f45e92b3690e8a04d33c5c16e48060Test https://hdl.handle.net/2183/22817Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....54f45e92b3690e8a04d33c5c16e48060 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |