Skip to main content
SpringerLink
Log in

Effects of SARS-COV-2 on Blood

  • Conference paper
  • First Online:
Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1393))

Abstract

At present, the world is suffering from coronavirus disease, or you can say COVID-19. This disease is spread worldwide because of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The world’s first case of COVID-19 came to Wuhan city in China (late December 2019). All over the world, 9,586,769 people have been affected by it, and 488,824 people passed away due to COVID-19 now. WHO already announced an emergency due to COVID-19. The study aims to determine the effects of the SARS-CoV-2 virus on the blood; then we will describe the blood classification. After that, we will determine the effects of the SARS-CoV-2 virus on the blood cells (RBCs and WBCs) and the blood plasma. With the help of a linear classification algorithm, we will determine the speed of increasing antibody-secreting cells (ASCs) in the human body.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Soo YOY, Cheng Y, Wong R, Hui DS, Lee CK, Tsang KKS, Sung JJY (2004) Retrospective comparison of convalescent plasma with continuing high-dose methylprednisolone treatment in SARS patients. Clin Microbiol Infect 10(7):676–678

    Article  Google Scholar 

  2. Ksiazek TG, Erdman D, Goldsmith CS, Zaki SR, Peret T, Emery S, Rollin PE (2003) A novel coronavirus associated with severe acute respiratory syndrome. N Engl J Med 348(20):1953–1966

    Article  Google Scholar 

  3. Darnell ME, Taylor DR (2006) Evaluation of inactivation methods for severe acute respiratory syndrome coronavirus in noncellular blood products. Transfusion 46(10):1770–1777

    Article  Google Scholar 

  4. Ding Y, Wang H, Shen H, Li Z, Geng J, Han H, Lu Y (2003) The clinical pathology of severe acute respiratory syndrome (SARS): a report from China. J Pathol: J Pathol Soc Great Britain Ireland 200(3):282–289

    Article  Google Scholar 

  5. Romanelli F, Smith KM, Hoven AD (2004) Chloroquine and hydroxychloroquine as inhibitors of human immunod eficiency virus (HIV-1) activity. Curr Pharm Des 10(21):2643–2648

    Article  Google Scholar 

  6. Ludvigsson JF (2020) Systematic review of COVID-19 in children shows milder cases and a better prognosis than adults. Acta Paediatr 109(6):1088–1095

    Article  Google Scholar 

  7. Yao X, Ye F, Zhang M, Cui C, Huang B, Niu P, Zhan S (2020) In vitro antiviral activity and projection of optimized dosing design of hydroxychloroquine for the treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Clinical Infectious Diseases

    Google Scholar 

  8. Chen L, Xiong J, Bao L, Shi Y (2020) Convalescent plasma as a potentialtherapy for COVID-19. Lancet Infect Dis 20(4):398–400

    Article  Google Scholar 

  9. McCreary EK, Pogue JM (2020) Coronavirus disease 2019 treatment: a review of early and emerging options. In Open Forum infectious diseases (Vol 7, No 4, p. of a a105). US: Oxford University Press

    Google Scholar 

  10. Zhang B, Liu S, Tan T, Huang W, DongY. Chen L, Zou Y (2020) Treatment with convalescent plasma for critically ill patients with SARS-CoV-2 infection. Chest

    Google Scholar 

  11. Imai Y, Kuba K, Penninger JM (2008) The discovery of angiotensin-converting enzyme 2 and its role in acute lung injury in mice. Exp Physiol 93(5):543–548

    Article  Google Scholar 

  12. Corman VM, MuthD, Niemeyer D, Drosten C (2018) Hosts and sources of endemic human coronaviruses. In: Advances in virus research (Vol 100, pp 163–188). Academic Press

    Google Scholar 

  13. Zaigham M, Andersson O (2020) Maternal and perinatal outcomes with COVID‐19: a systematic review of 108 pregnancies. Acta Obstetrician Etgynecologica Scandinavica

    Google Scholar 

  14. McIntosh K, Hirsch MS, Bloom A (2020) Coronavirus disease 2019 (COVID-19). Up To Date Hirsch MS Bloom 5

    Google Scholar 

  15. Program NHBPE (2000) Report of the national high blood pressure education program working group on high blood pressure in pregnancy. Am J Obstet Gynecol 183(1):s1–s22

    Article  Google Scholar 

  16. Gu J, Korteweg C (2007) Pathology and pathogenesis of severe acute respiratory syndrome. Am J Pathol 170(4):1136–1147

    Article  Google Scholar 

  17. Zhang JJ, Dong X, Cao YY, Yuan YD, Yang YB, Yan YQ, Gao YD (2020) Clinical characteristics of 140 patients infected with SARS‐CoV‐2 in Wuhan, China. Allergy

    Google Scholar 

  18. LaVignera S, Cannarella R, Condorelli RA, Torre F, Aversa A, Calogero A

    Google Scholar 

  19. Sex-specific SARS-CoV-2 mortality: among hormone-modulated ACE2 expression, risk of venous thromboembolism and hypovitaminosisd. Int J Molec sciences, 21(8):2948

    Google Scholar 

  20. Zhou Y, Hou Y, Shen J, Huang Y, Martin W, Cheng F (2020) Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2. Cell Discovery 6(1):1–18

    Google Scholar 

  21. Ziegler CG, Allon SJ, Nyquist SK, Mbano IM, Miao VN, Tzouanas CN, Feldman J (2020) SARS-CoV-2 receptor ACE2 is an interferon-stimulated gene in human airway epithelial cells and is detected in specific cell subsets across tissues. Cell

    Google Scholar 

  22. Huang K, Yang T, Xu J, Yang L, Zhao J, Zhang X, Wen F (2019) Prevalence, risk factors, and management of asthma in China: a national cross-sectional study. Lancet 394(10196):407–418. Huang K, Yang T, Xu J, Yang L, Zhao J, Zhang X, Wen F (2019) Prevalence, risk factors, and management of asthma in China: a national cross-sectional study. Lancet 394(10196):407–418

    Google Scholar 

  23. Bhardwaj A, Aruna T (2013) A novel genetic programming based classifier design using a new constructive crossover operator with a local search technique. International conference on intelligent computing. Springer, Berlin, Heidelberg (2013)

    Google Scholar 

  24. Tiwari S, Goel S, Bhardwaj A (2020) Machine Learning approach for the classification of EEG signals of multiple imagery tasks. 2020 11th International conference on computing, communication and networking technologies (ICCCNT). IEEE

    Google Scholar 

  25. Roberts JM, Pearson G, Cutler J, Lindheimer M (2003) Summary of the NHLBI working group on research on hypertension during pregnancy. Hypertension 41(3):437–445

    Article  Google Scholar 

  26. Monteil V, Kwon H, Prado P, Hagelkrüys A, Wimmer RA, Stahl M, Romero JP (2020) Inhibition of SARS-CoV-2 infections in engineered human tissues using clinical-grade soluble human ACE2. Cell

    Google Scholar 

  27. Kumar A, Sinha N, Bhardwaj A (2020) A novel fitness function in genetic programming for medical data classification. J Biomed Inform, 103623

    Google Scholar 

  28. Acharya D et al (2020) A long short term memory deep learning network for the classification of negative emotions using EEG signals. 2020 International joint conference on neural networks (IJCNN). IEEE

    Google Scholar 

  29. Bhardwaj H, et al (2018) Breast cancer diagnosis using simultaneous feature selection and classification: A genetic programming approach. 2018 IEEE symposium series on computational intelligence (SSCI). IEEE

    Google Scholar 

  30. Acharya D et al (2020) A novel fitness function in genetic programming to handle unbalanced emotion recognition data. Pattern Recogn Lett

    Google Scholar 

  31. Acharya D et al (2020) Emotion recognition using fourier transform and genetic programming. Appl Acoust 164:107260

    Google Scholar 

  32. Bhardwaj H et al (2019) Classification of electroencephalogram signal for the detection of epilepsy using Innovative Genetic Programming. Expert Syst 36.1:e12338

    Google Scholar 

  33. Bhardwaj A, Tiwari A (2013) A novel genetic programming based classifier design using a new constructive crossover operator with a local search technique. In: International conference on intelligent computing (pp 86–95). Springer, Berlin, Heidelberg

    Google Scholar 

  34. Oudit GY, Kassiri Z, Jiang C, Liu PP, Poutanen SM, Penninger JM, Butany J (2009) SARS-coronavirus modulation of myocardial ACE2 expression and inflammation in patients with SARS. Eur J Clin Invest 39(7):618–625

    Article  Google Scholar 

  35. Li Z, Yi Y, Luo X, Xiong N, Liu Y, Li S, Zhang Y (2020) Development and clinical application of a rapid IgM‐IgG combined antibody test for SARS‐CoV‐2 infection diagnosis. J Medicalvirology

    Google Scholar 

  36. Eickmann M, Gravemann U, Handke W, Tolksdorf F, Reichenberg S, Müller TH, Seltsam A (2020) Inactivation of three emerging viruses–severe acute respiratory syndrome coronavirus, Crimean-Congo haemorrhagic fever virus and Nipah virus–in platelet concentrates by ultraviolet C light and in plasma by methylene blue plus visible light. Vox Sang 115(3):146–151

    Article  Google Scholar 

  37. Paniz-Mondolfi A, Bryce C, Grimes Z, Gordon RE, Reidy J, Lednicky J, Fowkes M (2020) Central nervous system involvement by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). J Med Virol 92(7):699–702

    Article  Google Scholar 

  38. Acharya et al (2020) An enhanced fitness function to recognize unbalanced human emotions data. Expert Syst Appl 166:114011

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University School of Computer Science & Engineering, Galgotias University, Gautam Budh Nagar, Greater Noida, 201312, Uttar Pradesh, India

    Indu Malik

  2. Department of Computer Science and Engineering, Dronacharya Group of Institution Greater Noida, Greater Noida, Uttar Pradesh, India

    Nipun R. Navadia

  3. Department of Information Technology, ABES Engineering College Ghaziabad, Ghaziabad, Uttar Pradesh, India

    Aatif Jamshed

  4. Department of Computer Science and Engineering, MIET Greater Noida, Greater Noida, Uttar Pradesh, India

    Lalita Verma, Taranjeet Singh & Harshit Bhardwaj

Authors
  1. Indu Malik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nipun R. Navadia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aatif Jamshed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lalita Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Taranjeet Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Harshit Bhardwaj
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Aruna Tiwari

  2. Computer Science and Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Kapil Ahuja

  3. Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Anupam Yadav

  4. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  5. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

  6. School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Malik, I., Navadia, N.R., Jamshed, A., Verma, L., Singh, T., Bhardwaj, H. (2021). Effects of SARS-COV-2 on Blood. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1393. Springer, Singapore. https://doi.org/10.1007/978-981-16-2712-5_8

Download citation

Publish with us

Policies and ethics

Search

Navigation