Login Register Cart 0
Generic placeholder image

Current Pharmaceutical Analysis

Editor-in-Chief

ISSN (Print): 1573-4129
ISSN (Online): 1875-676X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Diagnosis Model of Paraquat Poisoning Based on Machine Learning

Author(s): Xianchuan Wang, Hongzhe Wang, Shuaishuai Yu and Xianqin Wang*

Volume 18, Issue 2, 2022

Published on: 02 March, 2021

Page: [176 - 181] Pages: 6

DOI: 10.2174/1573412917666210302150150

Price: $65

Open Access Journals Promotions 2
conference banner
Abstract

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 chi-square 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.

Keywords: Machine learning, SVM, metabolomics, gas chromatography-mass spectrometry, paraquat, poisoning.

« Previous Next »
Graphical Abstract

[1]
Lo, J.; Poon, L.Y.C. Systemic paraquat intoxication presenting with peripheral ulcerative keratitis: a case report and literature review. Ocul. Immunol. Inflamm., 2020, 28(6), 871-875.
[http://dx.doi.org/10.1080/09273948.2019.1640882] [PMID: 31411945]
[2]
Arab, T.M.; Malekzadegan, M.R.; Curiel, L.; Al Rramady, O.; Babury, M. Paraquat poisoning, case and review of literature. Am. J. Respir. Crit. Care Med., 2018, 197.
[3]
Xu, L.; Xu, J.; Wang, Z. Molecular mechanisms of paraquat-induced acute lung injury: a current review. Drug Chem. Toxicol., 2014, 37(2), 130-134.
[http://dx.doi.org/10.3109/01480545.2013.834361] [PMID: 24392656]
[4]
Delirrad, M.; Majidi, M.; Boushehri, B. Clinical features and prognosis of paraquat poisoning: a review of 41 cases. Int. J. Clin. Exp. Med., 2015, 8(5), 8122-8128.
[PMID: 26221379]
[5]
Tsai, W.T. A review on environmental exposure and health risks of herbicide paraquat. Toxicol. Environ. Chem., 2013, 95, 197-206.
[http://dx.doi.org/10.1080/02772248.2012.761999]
[6]
Mann, G.; Mitter, S. Paraquat poisoning. Clin. Toxicol., 2020, 58, 320-321.
[7]
Han, X.F.; Zhao, M. Effects of hemoperfusion on renal function in paraquat poisoning. Clin. Toxicol., 2020, 58, 335-335.
[8]
Yuan, G.; Li, R.; Zhao, Q.; Kong, X.; Wang, Y.; Wang, X.; Guo, R. Simultaneous determination of paraquat and diquat in human plasma by HPLC-DAD: Its application in acute poisoning patients induced by these two herbicides. J. Clin. Lab. Anal., 2020, e23669.
[PMID: 33296104]
[9]
Xiao, Q.; Wang, W.; Qi, H.; Gao, X.; Zhu, B.; Li, J.; Wang, P. Continuous hemoperfusion relieves pulmonary fibrosis in patients with acute mild and moderate paraquat poisoning. J. Toxicol. Sci., 2020, 45(10), 611-617.
[http://dx.doi.org/10.2131/jts.45.611] [PMID: 33012729]
[10]
Carter, R.A.; Pan, K.; Harville, E.W.; McRitchie, S.; Sumner, S. Metabolomics to reveal biomarkers and pathways of preterm birth: a systematic review and epidemiologic perspective. Metabolomics, 2019, 15(9), 124.
[http://dx.doi.org/10.1007/s11306-019-1587-1] [PMID: 31506796]
[11]
Bitencourt, A.G.V.; Goldberg, J.; Pinker, K.; Thakur, S.B. Clinical applications of breast cancer metabolomics using high-resolution magic angle spinning proton magnetic resonance spectroscopy (HRMAS 1H MRS): systematic scoping review. Metabolomics, 2019, 15(11), 148.
[http://dx.doi.org/10.1007/s11306-019-1611-5] [PMID: 31696341]
[12]
Gramatyka, M.; Sokół, M. Radiation metabolomics in the quest of cardiotoxicity biomarkers: the review. Int. J. Radiat. Biol., 2020, 96(3), 349-359.
[http://dx.doi.org/10.1080/09553002.2020.1704299] [PMID: 31976800]
[13]
Yang, Q.; Zhang, A.H.; Miao, J.H.; Sun, H.; Han, Y.; Yan, G.L.; Wu, F.F.; Wang, X.J. Metabolomics biotechnology, applications, and future trends: a systematic review. RSC Advances, 2019, 9, 37245-37257.
[http://dx.doi.org/10.1039/C9RA06697G]
[14]
Struck-Lewicka, W.; Wawrzyniak, R.; Artymowicz, M.; Kordalewska, M.; Markuszewski, M.; Matuszewski, M.; Gutknecht, P.; Siebert, J.; Markuszewski, M.J. GC-MS-based untargeted metabolomics of plasma and urine to evaluate metabolic changes in prostate cancer. J. Breath Res., 2020, 14(4), 047103.
[http://dx.doi.org/10.1088/1752-7163/abaeca] [PMID: 32969349]
[15]
Zhang, Z.; Zhou, Y.; Lin, Y.; Li, Y.; Xia, B.; Lin, L.; Liao, D. GC-MS-based metabolomics research on the anti-hyperlipidaemic activity of Prunella vulgaris L. polysaccharides. Int. J. Biol. Macromol., 2020, 159, 461-473.
[http://dx.doi.org/10.1016/j.ijbiomac.2020.05.003] [PMID: 32387363]
[16]
Matyushin, D.D.; Sholokhova, A.Y.; Buryak, A.K. Deep learning driven gc-ms library search and its application for metabolomics. Anal. Chem., 2020, 92(17), 11818-11825.
[http://dx.doi.org/10.1021/acs.analchem.0c02082] [PMID: 32867500]
[17]
Jiang, X.J.; Bao, X.; Ma, J.S.; Wen, C.C.; Wang, X.Q.; Ye, Y.Z. Effect of curcumin on acute paraquat poisoning by metabolomics. Curr. Pharm. Anal., 2018, 14, 635-643.
[http://dx.doi.org/10.2174/1573412914666180627142952]
[18]
Chen, L.G.; Tu, X.T.; Chen, B.B.; Zhang, J.; Ma, J.S.; Wang, X.Q. Effect of pirfenidone on rats with acute paraquat poisoning by urine metabolomics. Lat. Am. J. Pharm., 2018, 37, 420-424.
[19]
Liu, W.; Li, S.; Wu, Y.K.; Yan, X.; Zhu, Y.M.; Jiang, F.Y.; Jiang, Y.; Zou, L.H.; Wang, T.T. Metabolic profiling of rats poisoned with paraquat and treated with Xuebijing using a UPLC-QTOF-MS/MS metabolomics approach. Anal. Methods, 2020, 12(37), 4562-4571.
[http://dx.doi.org/10.1039/D0AY00968G] [PMID: 33001064]
[20]
Wen, C.; Lin, F.; Huang, B.; Zhang, Z.; Wang, X.; Ma, J.; Lin, G.; Chen, H.; Hu, L. Metabolomics analysis in acute paraquat poisoning patients based on UPLC-Q-TOF-MS and machine learning approach. Chem. Res. Toxicol., 2019, 32(4), 629-637.
[http://dx.doi.org/10.1021/acs.chemrestox.8b00328] [PMID: 30807114]
[21]
Gao, L.N.; Wang, G.; Yuan, H.Y.; Xu, E.Y.; Liu, G.J.; Liu, J.T. Serum metabolomics in mice after paraquat posioning. Mol. Cell. Toxicol., 2019, 15, 453-458.
[http://dx.doi.org/10.1007/s13273-019-0049-1]
[22]
Wang, Z.; Ma, J.; Zhang, M.; Wen, C.; Huang, X.; Sun, F.; Wang, S.; Hu, L.; Lin, G.; Wang, X. Serum metabolomics in rats after acute paraquat poisoning. Biol. Pharm. Bull., 2015, 38(7), 1049-1053.
[http://dx.doi.org/10.1248/bpb.b15-00147] [PMID: 26133715]
[23]
Gao, L.; Yuan, H.; Xu, E.; Liu, J. Toxicology of paraquat and pharmacology of the protective effect of 5-hydroxy-1-methylhydantoin on lung injury caused by paraquat based on metabolomics. Sci. Rep., 2020, 10(1), 1790.
[http://dx.doi.org/10.1038/s41598-020-58599-y] [PMID: 32019966]
[24]
Liu, Y.Z.; Qian, Y.; Jiang, Y.C.; Shang, J. Using favorite data to analyze asymmetric competition: Machine learning models. Eur. J. Oper. Res., 2020, 287, 600-615.
[http://dx.doi.org/10.1016/j.ejor.2020.03.074]
[25]
Richmond, M.; Sobey, A.; Pandit, R.; Kolios, A. Stochastic assessment of aerodynamics within offshore wind farms based on machine-learning. Renew. Energy, 2020, 161, 650-661.
[http://dx.doi.org/10.1016/j.renene.2020.07.083]
[26]
Vesselinov, V.V.; Alexandrov, B.S.; O’Malley, D. Contaminant source identification using semi-supervised machine learning. J. Contam. Hydrol., 2018, 212, 134-142.
[http://dx.doi.org/10.1016/j.jconhyd.2017.11.002] [PMID: 29174719]
[27]
Raza, A.; Bardhan, S.; Xu, L.; Yamijala, S.S.R.K.C.; Wong, B.M. A machine learning approach for predicting defluorination of Per- and Polyfluoroalkyl Substances (PFAS) for their efficient treatment and removal. Environ. Sci. Technol. Lett., 2019, 6, 624-629.
[http://dx.doi.org/10.1021/acs.estlett.9b00476]
[28]
Thakur, R.K.; Deshpande, M.V. Kernel optimized-support vector machine and mapreduce framework for sentiment classification of train reviews. Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2019, 27, 1025-1050.
[http://dx.doi.org/10.1142/S0218488519500454]
[29]
Toledo-Perez, D.C.; Rodriguez-Resendiz, J.; Gomez-Loenzo, R.A.; Jauregui-Correa, J.C. Support Vector Machine-Based EMG Signal Classification Techniques: A Review. Applied Sciences-Basel, 2019, 9.
[30]
Nalepa, J.; Kawulok, M. Selecting training sets for support vector machines: a review. Artif. Intell. Rev., 2019, 52, 857-900.
[http://dx.doi.org/10.1007/s10462-017-9611-1]
[31]
Wang, S.H.; Zhang, L.J.; Wang, X.Q.; Wang, Z.Y.; Wen, C.C.; Ma, J.S.; Gao, Z.M.; Hu, L.F. Metabolic changes in rat lung after acute paraquat poisoning by gas chromatography-mass spectrometry. Int. J. Clin. Exp. Med., 2016, 9, 21514-21520.
[32]
Venkatesan, N. Pulmonary protective effects of curcumin against paraquat toxicity. Life Sci., 2000, 66(2), PL21-PL28.
[PMID: 10666014]
[33]
Orito, K.; Suzuki, Y.; Matsuda, H.; Shirai, M.; Akahori, F. Chymase is activated in the pulmonary inflammation and fibrosis induced by paraquat in hamsters. Tohoku J. Exp. Med., 2004, 203(4), 287-294.
[http://dx.doi.org/10.1620/tjem.203.287] [PMID: 15297733]

Rights & Permissions Print Cite
Article Metrics
21
1
Wayfinder Image
Open Access Journals Promotions
© 2024 Bentham Science Publishers | Privacy Policy