Bacteria have developed efficient swimming abilities, which can pose significant health risks, particularly in medical settings where catheters are commonly used. These thin tubes, meant to remove fluids from the body, can unfortunately serve as pathways for bacteria to enter and cause urinary tract infections, leading to substantial healthcare costs worldwide. In response, researchers have developed a novel catheter tube that significantly hampers the ability of bacteria to move upstream, effectively reducing the potential for infections without relying on antibiotics or other chemical treatments. This new design, optimized through advanced artificial intelligence (AI), has shown a remarkable 100-fold reduction in the number of bacteria swimming upstream in laboratory experiments.

Fluid inside catheter tubes exhibits what's known as Poiseuille flow, where the fluid moves faster in the center and slows near the walls. Bacteria exploit this by using a unique motion, moving forward along the walls and then back in the middle, to progress through the tube. Researchers at California Institute of Technology (Caltech, Pasadena, CA, USA;) decided to tackle this problem with simple geometries by designing tubes with triangular protrusions, similar to shark fins, lining the tube’s walls. Simulated models demonstrated that these structures effectively redirect bacteria towards the center of the tube where the faster flow sweeps them back downstream. Additionally, the triangles' fin-like curvature creates vortices that disrupt the bacteria's progress. The researchers then set out to verify the design experimentally with the help of additional biology expertise. The team was supported by their previous research into the navigation mechanisms of the nematode Caenorhabditis elegans, a rice grain–sized soil organism commonly studied in research labs, providing them with the tools needed to observe and analyze the movements of microscopic organisms. They utilized 3D printing to create these specially designed catheter tubes and employed high-speed cameras to track bacterial movements. The results were significant, showing a two-order magnitude decrease in the ability of bacteria to swim upstream.

Further simulations were conducted to identify the most effective shape for the triangular obstacles. The team created microfluidic channels, mimicking common catheter tubes, with these optimized triangular designs. Observations of E. coli bacteria moving through these channels closely matched their simulations. To enhance the design further, the team employed advanced AI techniques known as neural operators, drastically reducing the computation time from days to minutes. This AI-optimized model suggested slight modifications to the triangle shapes, boosting their efficacy by an additional 5% in preventing bacteria from swimming upstream. This groundbreaking design represents a significant stride in medical technology, offering a safer and more efficient way to prevent catheter-associated urinary tract infections without the need for antibiotics, marking a significant advancement in patient care and infection control.

"Our journey from theory to simulation, experiment, and, finally, to real-time monitoring within these microfluidic landscapes is a compelling demonstration of how theoretical concepts can be brought to life, offering tangible solutions to real-world challenges," said Tingtao Edmond Zhou, postdoctoral scholar in chemical engineering and a co-first author of the study. 

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