Artificial Intelligence In Transfusion Medicine And In Our Mission To “BEAT- COVID”

Authors

  • Jerard Seghatchian

Keywords:

Artificial intelligence, artificial neural network, machine learning theragnostics, coronavirus infection, passive immunotherapy

Abstract

The key objectives of this manuscript are: 1] Demystifying the buzzword of Artificial Intelligence [AI] and comparing neuronal networkings similarity of machines with some mammals; 2] Exploring, from outside sources, the breath of some computerized procedural data generated in laboratory and clinical areas of medicine, by robotic tools in the cutting age of transfusion medicine; 3] Expressing views on the variabilities of covid- associated long lasting microcirculatory disseminated pathogenic autoimmune complications, some leading to development of vasculitis and organ failure in some but can be slowly reverse when patients recover; 4] Focusing on our mission to“beat-covid”, with a proposal for clinical trials, taking into account ways to optimally improve the clinical efficacy of inducing passive immunity through the use of a standardised levels of coronavirus  neutralising antibodies hyperconcentrate recovered timely by a validated affinity column adsorption procedure from coronavirus infected convalescent plasma obtained, by apheresis for plasma exchange therapy [PET];5]Overcoming the potential toxicities of the use of uncontrolled convalescent plasma, until a validated vaccine that covers all variants of coronavirus that appears in the scene and even then to be used as a re-boosting strategy, as the circulatory levels of the coronavirus’ NAB appears to be short about 3 months. Some recommended DDR protocols are proposed to achieve the beat-covid’ objectives, by inducing immunity, in line with personalised precision transfusion where AI and Machine Learning expectedly have major impacts in big data and patterns analyses, for the selection of antiviral drug and others forms of prevention and treatment therapies.

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Published

2020-12-29

Issue

Vol. 7 No. 3 (2020)

Section

Review Articles

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