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Cancer remains one of the leading causes of death worldwide, responsible for nearly 10 million deaths in 2020, according to the World Health Organization. A significant challenge in combating this disease is the late detection of cancer, often when abnormal cellular growth has already progressed. To address this, researchers have been exploring innovative diagnostic methods, such as the detection of rare circulating tumor cells (CTCs) in blood samples. These noninvasive markers hold promise for earlier and more effective cancer diagnosis.
However, isolating these rare cells is inherently complex. Conventional methods often require extensive sample preparation, bulky equipment, and significant time, yet still struggle with precision. A groundbreaking solution may now be on the horizon.
In a study published in Physics of Fluids, researchers Afshin Kouhkord and Naser Naserifar from the K. N. Toosi University of Technology in Tehran, Iran, introduced a cutting-edge lab-on-chip platform that leverages standing surface acoustic waves to separate CTCs from red blood cells. This system achieves unprecedented levels of precision and efficiency while significantly reducing energy consumption.
“We combined machine learning algorithms with data-driven modeling and computational data to fine-tune a system for optimal recovery rates and cell separation rates,” said Naserifar. “Our system achieves 100% recovery at optimal conditions.”
This innovative approach employs advanced computational modeling, acoustofluidics, and artificial intelligence to analyze and enhance cell separation. The researchers utilized dualized pressure acoustic fields strategically placed on a lithium niobate substrate. These fields double the impact on target cells, enabling efficient separation with minimal energy use.
The system also generates datasets in real time, tracking cell interaction times and movement patterns. This predictive capability could prove invaluable for understanding tumor cell migration and behavior.
“Our platform provides real-time, energy-efficient, and highly accurate cell separation,” said Kouhkord. “This technology has the potential to improve CTC separation efficiency and open new doors for earlier cancer diagnoses and advancements in personalized medicine.”
The study highlights the promise of microfluidics and applied AI in revolutionizing cancer diagnostics, offering a path toward more accessible and effective early detection methods.
Disclaimer: This article is intended for informational purposes only and does not constitute medical advice. The described technology is still under research and development, and its clinical application requires further validation. For personalized medical advice, consult a healthcare professional.
