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Recent Edition
Editorial
Volume 2 Issue 3 - 2018
Human Suicide Study from Mathematical Approaches
Da-Yong Lu1*, Ting-Ren Lu1 and Nagendra Sastry Yarla2
1Shanghai University, Shanghai 200444, PRC, china
2Divisions of Biochemistry & Chemistry, City University of New York School of Medicine, 160 Convent Avenue, New York, NY10031, USA
*Corresponding Author: Dr. Da-Yong Lu, Shanghai University, Shanghai 200444, PRC, china.
Received: May 21, 2018; Published: May 26, 2018
Abstract
Human suicide has a high human mortality (2% of human mortality worldwide). The mechanisms of action for drugs and clinical therapeutics remain to be established. The patho-therapeutic relationships between different causality and suicidal risks need to improve. To deal with the hot topic of suicide prevention and treatments by mathematical approaches seem revolutionary ideas. We hope this can lead to reduce human suicides in the future.
Keywords: Human genome; Human suicide; Mental disorder; Genetic diagnostics; Drug developments; Suicide prevention; Suicide treatments; Molecular target
Introduction
Human suicide is the causality of a great number of human mortality (2% human mortality worldwide) [1], which is a common symptom of human depression and many other disease origins worldwide [1-8]. In order to reduce human suicide, some good examples and paradigms have been speculated and systematical investigated. 
Causality of human suicide
The causality of human suicides can be diversified; several major risk factors are enlisted as;
Different types of human mental illness [9];
A history of past physical or psychiatric traumas [10];
Environmental/economical burden, pressures and influences [11];
Human genetic changes [1, 5-6, 12-14];
Current therapeutics shows some positive outcomes in suicidal/mental illness treatments [15].
Mathematical Study of Human Suicide
Given the fact that a lot of inherent/environmental factors can induce human suicide [8-14], suicide prevention and treatments are very difficult from great diversity of human suicide origins. Mathematical approaches can help to find these relations and focus on important matter. In the previous study, we give an equation of their possibilities [14].
Ptotal = Pgene + Pdrug + Penvironment                 (Eq 1)
In order to find the overlap of their interactions, we offer a new equation;
X = ƒ (U1, ···, Uk)            (Eq 2)
X: Total suicide rates
U: Individual causality
Furthermore, it can be calculated in equation 3.
T (P1, ···, Pn) = θ (Pn)             (Eq 3)
Overall, we believe that we can learn more after these computational calculation and statistics [16].
Conclusion
There is no well-established ideology for suicide causality and treatment now. Several types of diseases and long-term physical handicaps, such as mental disorders are possible targets for drug intervention [1-8, 17-18]. In the future, modern diagnostics for suicide and treatments in the clinic must be strengthened and categorized. Increasing mandatory genetic/molecular/image diagnostic trials are required for advanced technology, including mathematical ones.
References
  1. Bondy B., et al. “Genetics of suicide”. Molecular Psychiatry 11.4 (2006): 336-351.
  2. Lu DY., “Suicide Risks and Treatments, New Ideas and Future Perspectives”. Nova Science Publishers (2017).
  3. Lu DY., et al. “Introduction for suicide study”. Journal Metabolic Syndrome 6.2 (2017): 227.
  4. Lu DY., et al. “Historical analysis of suicide/mental disorder and current diagnostics in clinics. Suicide Risks and Treatments, New Ideas and Future Perspectives”. Nova Science Publishers (2017): 1-12.
  5. Lu DY., et al. “Genetics and bioinformatics studies of antidepressant drug therapeutic efficacies and toxicities, a current overview”. Recent Patents on CNS Drug Discovery 9.3 (2014): 193-199.
  6. Lu D.Y., et al. “Genetics and bioinformatics study of antidepressant drugs, recent advancements and future trends Suicidal Ideation: Predictors, Prevalence and Prevention”. Nova Science Publishing 3 (2015): 57-71.
  7. Lu DY., et al. “Current suicide prediction, prevention and treatments.Suicide Risks and Treatments, New Ideas and Future Perspectives”. Nova Science Publishers (2017): 25-38.
  8. Serafini G., et al. “Suicidal ideation: a comprehensive overview. Suicidal Ideation: Predictors, Prevalence and Prevention”. Nova Science Publishing 1 (2015): 1- 42.
  9. Lu DY., et al. “Human suicide study, is there an associations between suicide and mental illness”. Metabolomics 6 (2016): 186.
  10. Cornelius JR., et al. “Suicidal symptoms among veterans with chronic PTSD evaluated for treatment at a VA hospital. Suicidal Ideation: Predictors, Prevalence and Prevention. Nova Science Publishing, Chapter 2 (2015): 43-56
  11. Lu DY., et al. “The efficacies and toxicities of antidepressant drugs in clinics, building the relationship between Chemo-Genetics and Socio-Environments”. Central Nervous System Agents in Medicinal Chemistry 16.1 (2016): 12-18.
  12. Lu DY., et al. “Therapeutic drug developments and clinical utilities.Suicide Risks and Treatments, New Ideas and Future Perspectives”. Nova Science Publishers 6 (2017): 63-72.
  13. Lu DY., et al. “How can we pinpoint genetic involvement in antidepressant-induced suicide?”  Advances in Pharmacoepidemiology and Drug Safety 1.1 (2012).
  14. Lu DY., et al. “Pharmacogenetics in neural toxicities of drugs”. Pharmacogenomics 14.10 (2013): 1129-1131.
  15. While D., et al. “Implementation of mental health service recommendations in England and Wales and suicide rates, 1997-2006: a cross-sectional and before-and-after observational study”. Lancet 379 (2012): 1005-1012.
  16. Gentle JE. “Elements of Computational Statistics”. Springer Science Germany (2002).
  17. Fleischmann A. “Suicidal behavior prevention: WHO perspective on research”. American Journal of Medical Genetics 133 (2005): 8-12.
  18. Pashak TJ. “Mental disorders are not real: using skepticism and critical thinking to challenge key myths in the science of mental health”. Journal of Mental Disorders and Treatment 31 (2017): 140
Citation: Da-Yong Lu., et al. “Human Suicide Study from Mathematical Approaches”. Clinical Biotechnology and Microbiology 2.3 (2018): 361-363.
Copyright: © 2018 Da-Yong Lu., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.