Antibiotic-resistant bacteria absorb the global crisis, put public health at risk, and complicate treatment for former infectious diseases.
As antibiotic resistance expands, medical procedures such as organ transplantation and cancer therapy face an increased risk.
In a groundbreaking study conducted by researchers at the University of Joterburg Chalmers University of Technology, and the Fraunhofer Chalmers Centre (AI) of Artificial Intelligence (AI) is being used to predict and track antibiotic resistance with unprecedented accuracy.
Increased threat to antibiotic resistance
Antibiotic-resistant bacteria are a growing global concern and pose a major threat to public health.
As bacteria evolve to resist antibiotics, infections that were once easily curable, such as pneumonia and blood poisoning, become life-threatening.
The World Health Organization (WHO) has identified antibiotic resistance as one of the most pressing challenges in modern medicine, complicating medical procedures such as organ transplantation and cancer treatment.
The key driver behind this rapid spread is the ability of bacteria to exchange genes, including those that make them resistant.
This genetic introduction allows resistance to spread rapidly between bacterial populations, making infections more difficult to control and treat.
AI models predict antibiotic resistance
This study utilized AI to better understand and predict the development of antibiotic resistance.
Scientists have trained AI models on vast quantities of genetic data to help identify patterns of bacteria evolution.
By analyzing approximately one million bacterial genomes, this model reveals important insights into how and where resistance genes are transferred.
Where and how resistance spreads
This study highlights that antibiotic resistance manifests in two major environments: wastewater treatment plants and the human body.
These positions act as hot spots where bacteria-carrying resistance genes frequently interact and often interact in the presence of antibiotics, accelerating gene transfer.
Another important finding is that resistance is likely to spread among genetically similar bacteria. When bacteria acquire new genes, they must consume energy to integrate and use them.
However, if new genes come from similar bacterial strains, the adaptation process is more efficient, reducing energy costs and increasing the likelihood of widening resistance.
The role of AI in the fight against resistance
To verify the effectiveness of the AI model, researchers tested it with bacteria with known resistance transfer.
This model successfully predicts gene metastasis in four of the five cases, demonstrating its potential as a powerful tool for tracking and predicting antibiotic resistance.
In the future, AI-driven models may be useful for real applications. For example, they were able to enhance molecular diagnostics and enable healthcare providers to detect new antibiotic-resistant strains more effectively.
Additionally, AI can play a role in environmental monitoring and can help track resistance gene movements in wastewater treatment plants and other high-risk regions.
Furthermore, improving AI models and training them on larger datasets will allow researchers to improve accuracy and uncover deeper insights into bacterial evolution.
This data-driven approach could lead to better treatment strategies, improved infection control measurements, and strong defense against the increased threat of antibiotic-resistant bacteria.
The fight against antibiotic resistance is not over, but AI-powered insights have given scientists valuable tools to predict, monitor and ultimately suppress its spread.
By continuing to improve these models, the health and scientific community can work towards a future where antibiotic resistance is better managed and public health is protected on a global scale.
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