Researchers in America have developed an innovative way to overcome the drug -resistant infections that depend on artificial intelligence, and are designed to determine the genetic signs to resist antibiotics in the causes of well -known diseases, such as breakfast bacteria and crushing staphylococcus, which may lead to faster and more effective treatments.
The study was conducted by researchers from the University of Toulin in the United States, published in the “Nature Communications” magazine, and was written by Yurik Alrt.
The researchers used an innovative computer model, which is enhanced by automatic learning algorithms known as the Group Association Model – GAM.
An innovative way and promising results
Unlike traditional diagnostic tools, such as cell transplant tests or some genetic tests, which often face difficulty in determining resistance mechanisms accurately, the technology of the collective correlation model represents a qualitative shift by analyzing the full genetic file of bacteria to determine the genetic mutations responsible for antibiotic resistance.
Dr. Tony Ho, a study researcher and head of the Department of Innovation in Biotechnology at the American University of Witthid and the director of the Toulin Center for Cellular and Molly Diagnosis in the United States, describes this methodology as a way to discover bacteria resistance patterns without prior knowledge of the resistance mechanisms, which makes them more flexible and able to discover genetic changes that are not previously known.
The strength of the collective bonding model lies in its comprehensive analysis of the whole genome serials, allowing scientists to compare bacterial strains of varying resistance patterns.
In this study, the researchers applied the methodology of the collective bonding model on more than 7 thousand strains of dual instincts and nearly 4 thousand strains of staphylococcus, specifying the main mutations associated with the resistance.
They found that the model not only improved the accuracy of the diagnosis, but also reduced the occurrence of wrong positive results, which may lead to inappropriate treatment decisions.
A clearer image
“The current genetic tests may wrongly be classified as bacterial as resisting, affecting the care of patients,” said the study researcher, Julian Saliba, a graduate student at the Toulin University Center for Cellular and Multiple Diagnosis in the United States.
He added: “Our way provides a clearer picture of the mutations that already cause resistance, which reduces the wrong diagnoses and unnecessary changes in treatment.”
This technique allows doctors to predict drug resistance in early stages, allowing them to dispense the appropriate treatment before the infection worsens.
By deepening our understanding of the mechanisms of resistance and facilitating early intervention, this innovative method paves the way for dedicated therapeutic systems, and it is boded with a new era in combating drug -resistant infection.