Figure: The study identified a genetic company in bacteria that, when present, indicates the likelihood of developing antibiotics (photo with the kind permission of the University of Tulane)
Antibiotic resistance is an important threat to world health responsible for more than a million deaths every year. By 2050, the World Health Organization predicts that it could overcome cancer and heart disease as the main cause of death, as bacteria develop new defense against drugs. Resistance occurs when bacteria are exposed to antibiotics that are not effective in killing, emphasizing the importance of choosing the right treatment. In a study published in Natural communicationScientists have discovered a unique genetic company in bacteria that can predict their likelihood of antibiotics development. These findings could enable faster identification of specific treatment procedures that are more effective against these dangerous and resistant drugs.
A study conducted by scientists from the University of Tulane (New Orleans, La, La, USA) and Instututa, Inc. (San Diego, Ca, USA), focuses on Pseudomonas aeruginosaBacteria known for its resistance to several drugs and its frequent role in infections gained in the hospital. This bacterium often occurs in specific DNA repair, a condition that speeds up mutations and increases the likelihood of resistance to antibiotics. In the analysis of bacterial genomes in the search for mutation companies, the team is commonly used in cancer research to monitor genetic changes in tumors, the team identified a distinctive formula associated with these deficiencies that precisely predicted the potential of bacteria to become resistant to antibiotics.
To deteriorate the situation, the studies have found that bacteria can gain resistance to drugs that have not been part of the initial treatment. Fortunately, the same DNA sequencing technology used to detect bacterial “Stop” can also point out the potential targets of treatment. Scientists have managed to identify different paths of resistance and use specific combinations of antibiotics to attack their attack, preventing bacteria to become resistant. Although these findings are still in the early stages, the development of the diagnostic tool could help reduce the wrong use of antibiotics and lead to more accurate treatment of bacterial infections. In the future, information is planning to develop an automatic learning model that can analyze bacterial samples and predict the likelihood of antibiotics resistance.
“At the moment, there is absolutely nothing like and for many patients’ populations it would be a big change. Antibiotic resistance deteriorates year after year,” said Kalene Hall, Doctoral Director, Executive Director and Informate Participation. “I think proper administration of antibiotics and accurate diagnoses is important parts of puzzles.”
Related links:
University of Tulane
Informate, Inc.
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