RESEARCH ARTICLE

Biostatistical and Mathematical Analysis on Liver Disease during Covid-19 Pandemic

Xia Jiang.1 Bin Zhao.1,2* 

• .1Hospital, Hubei University of Technology, Wuhan, Hubei, China 
• .2School of Science, Hubei University of Technology, Wuhan, Hubei, China

Corresponding Author: Bin Zhao, School of Science, Hubei University of Technology, Wuhan, Hubei, China. Tel./Fax: +86 130 2851 7572. E-mail: [email protected] 

Received: October 22, 2022                                                            Published: November 10, 2022 

Citation: Bin Z. Biostatistical and Mathematical Analysis on Liver Disease during Covid-19 Pandemic. Int J Complement Intern Med. 2022;2(1):43–61. 

Copyright: ©2022 Zhao. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and build upon your work non-commercially.

Abstract 

The liver is the body's biggest organ and is involved in metabolic processes. The liver is involved in both metabolism and dehydration. It helps in the detoxification of harmful chemicals as well as the growth of microorganisms. For systemic removal and preservation of organisms and other creatures, the liver is the most essential organ. As a result, liver injury has significant ramifications. Liver disease is regarded as a serious health issue. Overuse of several medicines, which are occasionally prescribed as part of treatment, can cause organ damage. Hepatotoxicity is caused by other chemical agents, such as those employed in labs (thioacetamide, alcohol, etc.) and industry, as well as natural compounds (such as microcystin). Many over-the-counter medicines aren't prescribed to help people with liver disease, and they can harm the liver. As a result, plant-based treatments are utilized to treat liver illness. As a result, several human herbal treatments have been evaluated in experimental animal models for antioxidant and hepatoprotective liver function. Acanthus ilicifolius was used as Traditional Chinese medicine (TCM) and Traditional Indian medicine (TIM). The plants showed many clinical properties. Still, the neurological related functions and disorders are not well explored in this plant. Complex interplay of positive and negative emotions orchestrated by intricately associated neuronal circuits, neurotransmitters coupled with endocrinal in- fluence holds responsible for humanbehavior, considered as the root of human civilization, is currently facing existential crisis during COVID-19 pandemic. In the present study, an attempt was made to identify the interaction between A. ilicifolius natural compounds and Echinacoside as reference compounds were to study the neurotransmitters functions through biomathematical and computational method. Initially, in silico molecular docking was performed to identify the potent natural compounds against neurological disease. The results show among 8 natural compounds, 26.27-Di(nor)- cholest-5,7,23-trien-22-ol, 3-methoxymethoxy, Cholest-5-en-3-ol (3, Beta.)-, carbonochloridate, Cholesterol and Echinacoside exhibited maximum interaction with all the target proteins. Especially, Echinacoside exhibited the maximum interaction with (Serotonin) 5- hydroxytryptamine receptor 2A (-17.077), Sodiumdependent serotonin transporter (-15.810) and (Histamine) Histamine H2 receptor (-17.556). These two neurotransmitters act as a major concern related to the mental disorders and neurological functions. The natural compounds may potent inhibitor for neurological disorders.

Keywords: Liver, Herbs, Neurotransmitters, Acanthus ilicifolius, Echinacoside, In silico

References 

1. SE Hyman. Neurotransmitters. Curr Biol. 2005;15:R154–R158. 

2. RH Masland. Neuronal cell types. Curr Biol. 2004;14:R497–R500. 

3. EJ Nestler, SE Hyman, RJ Malenka. Molecular Neuropharmacology: Foundation for Clinical Neuroscience. New York: McGraw Hill, 2001. 

4. M Pestana, H Jardim, F Correia. Renal dopaminergic mechanisms in renal parenchymal diseases and hypertension. Nephrol Dial Transplant. 2001;16:53–59. 

5. AM Marko, JW Gerrard, DJ Buchan. Glutamic acid derivatives in adult celiac disease. II. Urinary total glutamic acid excretion. Can Med Assoc J. 1960;83:1324–1325. 

6. R Belanger, N Chandramohan, R Misbin. Tyrosine and glutamic acid in plasma and urine of patients with altered thyroid function. Metabolism. 1972;21:855–865. 

7. C Ragginer, A Lechner, C Bernecker. Reduced urinary glutamate levels are associated with the frequency of migraine attacks in females. Eur J Neurol. 2012;19:1146–1150. 

8. T Field, M Diego, M Hernandez Reif. Comorbid depression and anxiety effects on pregnancy and neonatal outcome. Infant Behav Dev. 2010;33:23–29. 

9. A Ghaddar, KH Omar, M Dokmak. Work related stress and urinary catecholamines among laboratory technicians. J Occup Health. 2014;55:398–404. 

10. L Liu, Q Li, N Li. Simultaneous determination of catecholamines and their metabolites related to Alzheimer’s disease in human urine. J Sep Sci. 2011;34:1198–1204. 

11. NJ Paine, LL Watkins, JA Blumenthal. Association of depressive and anxiety symptoms with 24–hour urinary catecholamines in individuals with untreated high blood pressure. Psychosom Med. 2015;77:136–144. 

12. JW Hughes, L Watkins, JA Blumenthal. Depression and anxiety symptoms are related to increased 24–hour urinary norepinephrine excretion among healthy middle– aged women. J Psychosom Res. 2004;57:353– 358. 

13. SH Koslow, JW Maas, CL Bowden. CSF and urinary biogenic amines and metabolites in depression and mania. A controlled, univariate analysis. Arch Gen Psychiatry. 1983;40:999– 1010. 

14. CS Nicholson Guthrie, GD Guthrie, GP Sutton. Urine GABA levels in ovarian cancer patients: elevated GABA in malignancy. Cancer Lett. 2001;162:27–30. 

15. MI Nichkova, H Huisman, PM Wynveen. Evaluation of a novel ELISA for serotonin: urinary serotonin as a potential biomarker for depression. Anal Bioanal Chem. 2012;402:1593– 1600. 

16. T Aruoma, L Bahorun, S Jen. Neuroprotection by bioactive components in medicinal and food plant extracts. Mutation Research. 2003;544(2– 3):203–215. 

17. Bin Zhao, Thirumalai Diraviyam, Xiaoying Zhang. A bio–mathematical approach: Speculations to construct virtual placenta. Applied Mathematics and Computation. 2015;256:344–351. 

18. Cumpson P Sano, Naoko. “Stability of reference masses V: UV/ozone treatment of gold and platinum surfaces”. Metrologia. 2013;50(1):27– 36. 

19. S Ganesh, J Jannet Vennila, A Annie Mercy. Effects of Phytochemicals extracted from Acanthus Ilicifolius against Staphylococcus aureus: An In–silico approach. American Journal of Drug Discovery and Development. 2013;3(4):293–297. 

20. Tian Wang, Xiaoying Zhang, Wenyan Xie. “Desert Ginseng”: A Review. The American Journal of Chinese Medicine. 2012;40(6):1123– 1141. 

21. K Meraj, MK Mahto, NB Christina. Molecular modeling, docking and ADMET studies towards development of novel Disopyramide analogs for potential inhibition of human voltage gated sodium channel proteins. Bioinformation. 2012;8(23):1139–1146. 

22. JE William. Computational Models for ADME. Annual Reports in Medicinal Chemistry. 2007;42:449–467. 

23. H Pollard, JC Schwartz. Histamine neuronal pathways and their functions. Trends Neurosci. 1987;10:86–89. 

24. JT Schmidt. The Laminar organization of optic nerve fibres in the tectum of goldfish. Proc R Soc London Ser B. 1979;205:287–306. 

25. JT Schmidt, JA Freeman. Brain Res. 1980;187:129–142. 

26. PG Strange. In Dopamine Receptors Fraser CM & Creese I eds, AR Liss. NewYork. 1987. 

27. T Asano, M Ui, N Ogasawara. Immuno-Affinity Purification and Characterization of the a Subunits of Go Type G Proteins from Various Bovine Tissues. J Biol Chem. 1985;260:12653– 12658.

28. DT Monaghan, CW Cotman. Proc Natl Acad Sci. USA. 1986;83:7533–7536. 

29. FE Bloom. The Neurosciences, Fourth Study Program Schmitt F0 & Worden FG, eds. pp. 51–58. MITPress, Cambridge, MA. 1979. 

30. Daniel Okoh, Oluwafisayo Owolabi, Christopher Ekechukwu, et al. A regional GNSS–VTEC model over Nigeria using neural networks: A novel approach. Geodesy and Geodynamics. 2016;7(1):19–31. 

31. Shen J, Tan C, Zhang Y. "Discovery of Potent Ligands for Estrogen Receptor β by Structure–Based Virtual Screening" J Med Chem. 2010;53:5361–5365. 

32. Xiu ling LI, Jun jie WEI. Stability and Bifurcation Analysis in a System of Four Coupled Neurons with Multiple Delays. Acta Mathematicae Applicatae Sinica. 2013;29(2):425–448. 

33. Sheng Chen, Xiao Jie Zhang, Li Xi Li. Histone deacetylase 6 delays motor neuron degeneration by ameliorating the autophagic flux defect in a transgenic mouse model of amyotrophic lateral sclerosis. Neuroscience Bulletin. 2015;31(4):459–468. 

34. M Urbanska, M Blazejczyk, J Jaworski. Molecular basis of dendritic arborization. Acta neurobiologiae experimentalis. 2008;68(2):264–288. 

35. Fakhri Yousefi, Zeynab Amoozandeh. Statistical mechanics and artificial intelligence to model the thermodynamic properties of pure and mixture of ionic liquids. Chinese Journal of Chemical Engineering. 2016;24(12):1761–1771. 

36. F Yousefi, Z Amoozandeh. A new model to predict the densities of nanofluids using statistical mechanics and artificial intelligent plus principal component analysis. Chinese Journal of Chemical Engineering. 2017;25(9):1273–1281. 

37. XIE Yong, KANG Yan Mei, LIU Yong. Firing properties and synchronization rate in fractional–order Hindmarsh–Rose model neurons. Science China. 2014;57(5):914–922. 

38. Jianxin Zhao, Huazhou Xu, Yuanxiang Tian. Effect of electroacupuncture on brain–derived neurotrophic factor mRNA expression in mouse hippocampus following cerebral ischemia–reperfusion injury. Journal of Traditional Chinese Medicine. 2013;33(2):253–257. 

39. CUI Hong yan, FENG Chen, LIU Yun jie, Analysis of prediction performance in waveletminimum complexity echo state network. The Journal of China Universities of Posts and Telecommunications. 2013;20(4):59–66. 

40. JL Barker, RM McBurney. Receptors for Amino Acid Transmitters. Proc R Soc London Ser B. 1979;206:319– 327. 

41. BE Kosofsky, ME Mollwer, JH Morrison. J Comp Neurol. 1984;230:168–178. 

42. DA Brown. Neuropharmacology: Acetylcholine and brain cells. Nature. 1986;319:358–359. 

43. JR Brown, Arbuthnott. Neuroscience. 1983;10:349–355. 

44. RY Moore, FE Bloom. Central catecholamine neuron systems: anatomy and physiology of the norepinephrine and epinephrine systems. Annu Rev Neurosci. 1979;2:113–168. 

45. CW Cotman, LL Iversen. Excitatory amino acids in the brain - focus on NMDA receptors. Trends Neurosci. 1987;10:263–265. 

46. J Shen, C Tan, Y Zhang. Discovery of Potent Ligands for Estrogen Receptor β by Structure– Based Virtual Screening. J Med Chem. 2010;53:5361–5365. 

47. Cesar Razquin A, Snijder B, Frappier Brinton, et al. A Call for Systematic Research on Solute Carriers. Cell. 2015;162: 478–487. 

48. Chambers JK, Macdonald LE, Sarau HM, et al. A G protein–coupled receptor for UDP–glucose. Journal of Biological Chemistry. 2000;275:10767– 10771.