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Weizmann Institute of Science researchers have developed an algorithm to predict the occurrence of infectious diseases, including tuberculosis.
While in certain cases the immune system can kill bacteria and in other cases bacteria can overcome the immune system, there are diseases such as tuberculosis, in which bacteria can sleep for years – sometimes at a later date and sometimes in hibernation.
To test a hypothesis that the future development of disease can be determined within the first 24-48 hours after infection, scientists under the direction of Drs. Roi Avraham from the Department of Biological Regulation of the Institute developed a method for sequencing gene activity developed in the Institute In real encounters between thousands of immune cells and Salmonella bacteria.
Unlike standard laboratory tests, researchers used this method to monitor cell responses to bacteria and identify activation profiles for each cell. The researchers confirmed their hypothesis and identified different reactions and patterns from the first encounters between cells and bacteria as well as their later results.
Building on their single-cell sequencing for Salmonella infections, researchers developed an algorithm based on a known method of unfolding-to extract similar information on individual cell properties from standard blood-sampling datasets.
"The algorithm we have developed can not only define the ensemble of immune cells that participate in the response, but also their level of activity and, therefore, the activity reveal the potential strength of the immune response," Dr. Noa Bossel Ben Moshe, who researched together with Dr. Shelly Hen-Avivi in the group of Avraham headed.
The algorithm was first tested on blood samples from healthy people from the Netherlands. Some samples were infected with salmonella bacteria and the immune response was recorded. While the existing genomic analysis methods revealed no differences between the groups, the algorithm revealed significant differences related to subsequent variations in the ability to kill bacteria.
The researchers then turned to the diagnosis of the tuberculosis outbreak, caused by bacteria that can hide in the body for years.
Researchers found that a UK database of blood tests accompanying patients and carriers over a two-year period enabled the algorithm to be applied to both groups and the subgroup that shifted from the carrier to the onset of the disease during that time Monocytes called immune cells could predict the onset or future course of the disease.
"The algorithm is based on the" first impressions "of immune cells and salmonella, which cause a very different disease than Mycobacterium tuberculosis," said Hen-Avivi.
"Nevertheless, we were able to predict early which of the carriers would develop the active form of the disease."
Although antibiotic resistance represents a major challenge for the treatment of tuberculosis, researchers believe that their algorithm could amplify the disease treatment success.
"If those at risk for an active disease can be identified, when the bacterial load is lower, their chances of recovery are better," said Avraham.
"And the state medical systems in countries where tuberculosis is endemic may be better able to reduce the incidence and incidence of disease while reducing the cost of care." its database on tuberculosis and other pathogens, to refine its algorithm and to develop tools that can be used in the future to predict the development and progression of a range of infectious diseases.
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