In New York, a massive team of researchers has used Artificial Intelligence (AI) based algorithms to find similarities in the gene expression data of past pandemic infected individuals, which includes infections like Swine Flu and SARC. Pradipta Ghosh, one of the researchers from the University of California, indicated two telltale signatures. The first is a sum total of 166 genes, which shows the immune reaction of human beings against viral infection. The second set of 20 gene signatures show the severity of the infection in patients. This AI algorithm can predict the need for ventilator support and hospitalization for a patient.
Pradipta Ghosh said, “These viral pandemic-associated signatures tell us how a person’s immune system responds to a viral infection and how severe it might get, and that gives us a map for this and future pandemics.” Also she added that the AI algorithm has been developed with publicly available gene expression data.
The study states that our body releases a protein called cytokine in the blood when infected with a virus. These proteins aid the immune cells to find the infection, but sometimes, our body releases an excess amount of the protein, which leads the immune system to attack its own healthy tissues.
Read More : Microsoft’s Free AI Classroom Series With Certification 14-19 December
This condition in the body, known as the Cytokine storm, is one of the main reasons for some and not all people catching a viral infection, which also includes common flu. The research team found a similar gene expression pattern in each set of data they tested of Covid-19 patients. After a thorough examination of the data, the researchers said the cells lining the lung airway and macrophages, which are also known as white blood cells, are the reason behind the initiation of cytokine storms in the human body.
The researchers claim that this AI algorithm can help doctors in treatment by providing cellular level information of the patient’s condition and also by showing benchmarks to measure improvement in the infected.