Machine learning hunts down the cause of paralysis

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A machine learning algorithm is actually working to hunt down the cause for paralysis in an illness that affects the immune system. In a recent outbreak of a polio-like disease, Artificial Intelligence (AI) is actually being used to examine the immune system to determine the main cause for this mysterious illness.

Flaccid Myelitis or AFM is an emerging disease that causes limb weakness and paralysis symptoms. The condition resembles the same symptoms as polio, and although it is quite rare, it’s continuing to grow in cases across the United States. Over 134 cases of AFM were treated in the United States this year, and the sickness develops quite quickly leaving many of the people who are diagnosed with the illness in a difficult spot for recovery.

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Researchers were unable to develop any pathogen within the spinal fluid to help sick children. Identifying the culprit and the main cause of the disease involved a series of diagnostics including how the immune system responded to various pathogens. A series of response diagnosis tests were completed to lead to new treatments of the disease. The main problem with the outbreak in identifying the causes that the target was extremely elusive within the immune system.

The current outbreaks of the disease started first and October of 2014. There have been diagnoses almost every year since and the only way to determine the cause of the outbreak comes from blood samples.

Response diagnostics were almost impossible to determine even when examined by an immunologist. It wasn’t until cogitation all system of AI learning was developed by Stanford University in California. The simulation shows in real time what the immune system is seeing and can prompt responses based off of the integration of the disease.

In the past, testing was mostly carried out on mystery illnesses and how they affected RNA and DNA. Searching through various tissues in the blood was like trying to find a needle in a haystack and often involve a full sequence of over 23,000 human genes to identify the cause.

Using machine learning, rather than sequencing the entire genome, it is possible for scientists to identify various genes which are relevant to AFM cases only. Testing the specific genes rapidly speed up the process of identifying and detecting the virus.

Although this AI testing is not considered as the standard test for identifying the virus, AFM researchers are expecting to eventually mirror their results with testing on someone’s immune system which is fighting the virus. When results can be confirmed between the AI simulation and proper test subjects it will be possible to consider this a standardized test for the disease in the future.

This could serve as a promising solution for identifying the root cause of AFM and preventing paralysis before it has a chance to take hold. In a disease that is rapid to progress, completing an early diagnosis and working to discover a cause early on could help to prevent the more severe symptoms of the disease.


Source: Some information used in this article is from https://www.nature.com/articles/d41586-018-07631-3?fbclid=IwAR0w54xO48hl5Vmx6ypJ2LxUnKehuY0rewRvxRdWAFW5yLEAyfIyFKSJF9E

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