Advertisement

Bioinformatics and Pharmacogenomics: Tools to Understand and Accelerate Infectious Disease Control

  • Ashwani Sharma
  • Subrata Sinha
  • Surabhi Johari
  • Bhaskar Mazumder
Chapter

Abstract

Population science provides a helpful hand to determine the impact of deadly infectious communicable diseases such as tuberculosis, among other viral and bacterial infections, on the population [1–3]. Timely information on these diseases aids in reducing risk, incidence and deaths associated with these diseases. It also helps to improve the quality of life for survivors. These research projects provided a common platform for clinical, basic and population scientists to work collectively to further improve individual and population health. Recent trends in genetic, epidemiology, [4–6] applied and surveillance researches provided useful clues to reduce the impact of spread of infectious diseases worldwide. Many studies, associated with population science in disease control, help in many ways to control the infectious diseases, such as:
  1. 1.

    It improves understanding of the influence of pathogenic deadly diseases on the population, including hereditary (genetic) and environmental factors that may influence a person’s risk of getting infected [7–10].

     
  2. 2.

    It may help in elucidating and understanding health problems among the population due to the influence of diseases and their pharmacological treatment or other preventive measures.

     
  3. 3.

    These studies facilitate the discovery of new treatments and the most effective ways to prevent diseases.

     
  4. 4.

    The study of population science enables rapid detection of infection among the population and also prospective cost-effectiveness analysis for treatment.

     

References

  1. 1.
    Amin Z (2006) Clinical tuberculosis problems and management. Acta Med Indones 38(2):109–116PubMedGoogle Scholar
  2. 2.
    Mouchet J, Manguin S et al (1998) Evolution of malaria in Africa for the past 40 years: impact of climatic and human factors. J Am Mosq Control Assoc 14(2):121–130PubMedGoogle Scholar
  3. 3.
    Subramaniam J, Murugan K et al (2015) Eco-friendly control of malaria and arbovirus vectors using the mosquitofish Gambusia Affinis and ultra-low dosages of Mimusops elengi-synthesized silver nanoparticles: towards an integrative approach? Environ Sci Pollut Res Int 22(24):20067–20083CrossRefPubMedGoogle Scholar
  4. 4.
    Al-Afasy HH, Al-Obaidan MA et al (2013) Risk factors for multiple sclerosis in Kuwait: a population-based case-control study. Neuroepidemiology 40(1):30–35CrossRefPubMedGoogle Scholar
  5. 5.
    Bilinski P, Wojtyla A et al (2012) Epigenetic regulation in drug addiction. Ann Agric Environ Med 19(3):491–496PubMedGoogle Scholar
  6. 6.
    Seltman RE, Matthews BR (2012) Frontotemporal lobar degeneration: epidemiology, pathology, diagnosis and management. CNS Drugs 26(10):841–870CrossRefPubMedGoogle Scholar
  7. 7.
    Cunha MP, Lieberknecht V et al (2016) Creatine affords protection against glutamate-induced nitrosative and oxidative stress. Neurochem Int 95:4–14CrossRefPubMedGoogle Scholar
  8. 8.
    Kobayashi H, Naito M et al (2016) Circulating fibrocytes correlate with the asthma control test score. Allergol Immunopathol (Madr) 44(3):191–196CrossRefGoogle Scholar
  9. 9.
    Kumar A, Guardia A et al (2015) A focused screen identifies antifolates with activity on mycobacterium tuberculosis. ACS Infect Dis 1(12):604–614CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Madoff DC, Gaba RC et al (2016) Portal venous interventions: state of the art. Radiology 278(2):333–353CrossRefPubMedGoogle Scholar
  11. 11.
    Knowlson S, Burlison J et al (2015) New strains intended for the production of inactivated polio vaccine at low-containment after eradication. PLoS Pathog 11(12):e1005316CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Norback D, Hashim JH et al (2016) Rhinitis, ocular, throat and dermal symptoms, headache and tiredness among students in schools from johor bahru, malaysia: associations with fungal DNA and mycotoxins in classroom dust. PLoS One 11(2):e0147996CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Tomljenovic D, Baudoin T et al (2016) Nasal and ocular responses after specific and nonspecific nasal challenges in seasonal allergic rhinitis. Ann Allergy Asthma Immunol 116(3):199–205CrossRefPubMedGoogle Scholar
  14. 14.
    Sahadevan A, Cusack R et al (2015) Safety of grass pollen sublingual immunotherapy for allergic rhinitis in concomitant asthma. Ir Med J 108(10):304–307PubMedGoogle Scholar
  15. 15.
    Antonicelli L, Marchetti P et al (2015) The heterogeneity hidden in allergic rhinitis and its impact on co-existing asthma in adults: a population-based survey. Int Arch Allergy Immunol 168(3):205–212CrossRefPubMedGoogle Scholar
  16. 16.
    Dilek F, Gultepe B et al (2016) Beyond anti-microbial properties: the role of cathelicidin in allergic rhinitis. Allergol Immunopathol (Madr) 44(4):297–302CrossRefGoogle Scholar
  17. 17.
    Jaruvongvanich V, Mongkolpathumrat P et al (2016) Extranasal symptoms of allergic rhinitis are difficult to treat and affect quality of life. Allergol Int 65(2):199–203CrossRefPubMedGoogle Scholar
  18. 18.
    Wang ZY, Jiang MJ et al (2015) Classification of non-allergic rhinitis based on inflammatory characteristics. Int J Clin Exp Med 8(10):17523–17529PubMedPubMedCentralGoogle Scholar
  19. 19.
    Ehteshami-Afshar S, FitzGerald JM et al (2016) The global economic burden of asthma and chronic obstructive pulmonary disease. Int J Tuberc Lung Dis 20(1):11–23CrossRefPubMedGoogle Scholar
  20. 20.
    Yao CW, Shen TC et al (2016) Asthma is associated with a subsequent risk of peripheral artery disease: a longitudinal population-based study. Medicine (Baltimore) 95(3):e2546CrossRefGoogle Scholar
  21. 21.
    Domingues M, Amaral R et al (2016) Assessment of asthma control using CARAT in patients with and without allergic rhinitis: a pilot study in primary care. Rev Port Pneumol (2006) 22(3):163–166Google Scholar
  22. 22.
    Wang C (2015) Impact of chronic rhinitis and rhinosinusitis on the asthma control. Zhonghua Yi Xue Za Zhi 95(38):3094–3095PubMedGoogle Scholar
  23. 23.
    Ding B, Enstone A (2016) Asthma and chronic obstructive pulmonary disease overlap syndrome (ACOS): structured literature review and physician insights. Expert Rev Respir Med 10(3):363–371CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Lin J, Li N (2015) A better understanding in patients with asthma is the cornerstone to improve their overall disease control. Zhonghua Nei Ke Za Zhi 54(8):665–666PubMedGoogle Scholar
  25. 25.
    See KC, Phua J et al (2015) Trigger factors in asthma and chronic obstructive pulmonary disease: a single-centre cross-sectional study. Singap Med J 57(10):561–565CrossRefGoogle Scholar
  26. 26.
    Carneiro HA, Mylonakis E (2009) Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clin Infect Dis 49(10):1557–1564CrossRefPubMedGoogle Scholar
  27. 27.
    Alicino C, Bragazzi NL et al (2015) Assessing Ebola-related web search behaviour: insights and implications from an analytical study of google trends-based query volumes. Infect Dis Poverty 4:54CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Bragazzi NL, Bacigaluppi S et al (2016) Infodemiology of status epilepticus: a systematic validation of the google trends-based search queries. Epilepsy Behav 55:120–123CrossRefPubMedGoogle Scholar
  29. 29.
    Fond G, Gaman A et al (2015) Google trends: ready for real-time suicide prevention or just a zeta-Jones effect? An exploratory study. Psychiatry Res 228(3):913–917CrossRefPubMedGoogle Scholar
  30. 30.
    Pollett S, Wood N et al (2015) Validating the use of google trends to enhance pertussis surveillance in California. PLoS Curr 7Google Scholar
  31. 31.
    Reed DD (2015) Google search trends for tanning salons: temporal patterns indicate peak interest in mid spring. J Am Acad Dermatol 73(6):1055–1056CrossRefPubMedGoogle Scholar
  32. 32.
    Toosi B, Kalia S (2015) Seasonal and geographic patterns in tanning using real-time data from google trends. JAMA Dermatol 152(2):1–2Google Scholar
  33. 33.
    Wang HW, Chen DR et al (2015) Forecasting the incidence of dementia and dementia-related outpatient visits with google trends: evidence from Taiwan. J Med Internet Res 17(11):e264CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Zou X, Zhu W et al (2015) Google flu trends--the initial application of big data in public health. Zhonghua Yu Fang Yi Xue Za Zhi 49(6):581–584PubMedGoogle Scholar
  35. 35.
    National Electronic Disease Surveillance System Working, G (2001) National Electronic Disease Surveillance System (NEDSS): a standards-based approach to connect public health and clinical medicine. J Public Health Manag Pract 7(6):43–50CrossRefGoogle Scholar
  36. 36.
    Erturk E, Wouters S et al (2016) Association of ADHD and celiac disease: what is the evidence? A systematic review of the literature. J Atten Disord:108705471561149Google Scholar
  37. 37.
    Sollid LM, Jabri B (2013) Triggers and drivers of autoimmunity: lessons from coeliac disease. Nat Rev Immunol 13(4):294–302CrossRefPubMedGoogle Scholar
  38. 38.
    Sollid LM (2002) Coeliac disease: dissecting a complex inflammatory disorder. Nat Rev Immunol 2(9):647–655CrossRefPubMedGoogle Scholar
  39. 39.
    Tye-Din JA, Stewart JA et al (2010) Comprehensive, quantitative mapping of T cell epitopes in gluten in celiac disease. Sci Transl Med 2(41):41–51CrossRefGoogle Scholar
  40. 40.
    Matthias T, Neidhofer S et al (2011) Novel trends in celiac disease. Cell Mol Immunol 8(2):121–125CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Magalhaes WC, Rodrigues MR et al (2012) DIVERGENOME: a bioinformatics platform to assist population genetics and genetic epidemiology studies. Genet Epidemiol 36(4):360–367CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Ashwani Sharma
    • 1
    • 2
  • Subrata Sinha
    • 3
  • Surabhi Johari
    • 3
  • Bhaskar Mazumder
    • 4
  1. 1.Laboratoire d’Electrochimie Moléculaire, LEM(UMR CNRS - P7 7591, Université Paris Diderot - Paris 7Paris Cedex 13France
  2. 2.Institut de Recherche de Chimie Paris (IRCP), Equipe de Chimie Theorique et Modelisation (CTM), École nationale supérieure de chimie de Paris (ENSCP), UMR 8247, CNRS/Chimie Paris Tech)Paris Cedex 05France
  3. 3.Centre for Bioinformatics StudiesDibrugarh UniversityDibrugarhIndia
  4. 4.Department of Pharmaceutical ScienceDibrugarh UniversityDibrugarhIndia

Personalised recommendations