Xianchuan Wang, Hongzhe Wang, Shuaishuai Yu and Xianqin Wang* Pages 1 - 6 ( 6 )
Background: The objective of this research was to screen metabolites with specificity differences in the lung tissue of paraquat-poisoned rats by metabolomics technology and chi-square test method, to provide a theoretical basis for the study of the mechanisms of paraquat poisoning, and to use machine learning technology to construct a paraquat poisoning diagnosis model. This provided an intelligent decision-making method for the diagnosis of paraquat poisoning.
Methods: 18 paraquat-poisoned rats (36 mg/kg) and 16 positive control rats were selected. Lung tissue from each rat from both groups was extracted and analyzed by GC-MS. The chisquare test for feature evaluation was used to screen the difference in specific metabolites in the lung tissue between the paraquat-poisoned rats and the control group, and the SVM classification machine learning algorithm was used to construct an intelligent diagnosis model.
Results: In the end, a total of 14 significant metabolic differences were identified between the two groups (P < 0.05). The sensitivity, specificity, and accuracy of the constructed SVM paraquat poisoning diagnostic model reached 95%, 95% and 96.67%, respectively.
Conclusion: Based on metabolomics technology, the chi-square test for feature evaluation was used to successfully screen the changes of specific metabolites produced in the lungs after paraquat- poisoning, and the diagnosis model based on SVM was constructed to provide an intelligent decision for the diagnosis of paraquat poisoning.
Machine learning, SVM, metabolomics, gas chromatography-mass spectrometry, paraquat, poisoning.
Information Technology Center, Wenzhou Medical University, Wenzhou, Information Technology Center, Wenzhou Medical University, Wenzhou, School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou 325035, Analytical and Testing Center, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035