Development of algorithms helps predict the risk of tuberculosis

Israeli scientists have developed an algorithm based on data from the initial exposure of a bacterium to the human body's immune system, helping to predict tuberculosis.

Israeli scientists have developed an algorithm based on data from the initial exposure of a bacterium to the human body's immune system, helping to predict tuberculosis.

According to a study conducted by the Weizmann Institute of Science, published in the journal Nature Communications on July 22, Israeli scientists have developed an algorithm that can predict the risk of tuberculosis or Infectious diseases in humans.

Picture 1 of Development of algorithms helps predict the risk of tuberculosis
TB patient treated at a hospital in Kanpur, India.(Source: AFP / VNA).

The study is based on an examination of the initial exposure of a bacterium to the human body's immune system, which can lead to an outbreak of a disease soon after, or even more. years later.

First, scientists tested true contacts between immune cells and bacteria, which used blood samples containing immune cells and salmonella bacteria.

Next, they developed an algorithm based on the data from this contact, which not only stated the composition of the cells but also their activation and reactivity.

In the specific application of the diagnosis of the onset of tuberculosis, scientific research has shown that the activity level of immune cells monocytes can be used to predict the onset or course of pathology. usually caused by "dormant" bacteria in the body for many years.