Cancer accounted for nearly 10 million deaths in 2020 — almost one in every six deaths globally — according to the World Health Organization. Because the detection of abnormal diseased cellular growth often occurs too late, timely cancer diagnosis remains one of humanity’s most pressing and elusive medical objectives. Recent research has focused on the detection in peripheral blood of rare circulating tumor cells (CTCs), which serve as noninvasive markers that can help inform diagnoses.
It is inherently difficult to separate controllable target cells to examine. Traditional methods typically require elaborate sample preparation, substantial equipment, and large sample volumes — and even then, it is not easy to efficiently separate the cells in question.
In Physics of Fluids, by AIP Publishing, a pair of researchers at the K. N. Toosi University of Technology in Tehran, Iran, proposed a novel system that uses standing surface acoustic waves to separate CTCs from red blood cells with unprecedented precision and efficiency. The platform that Afshin Kouhkord and Naser Naserifar developed integrates advanced computational modeling, experimental analysis, and artificial intelligence algorithms to analyze complex acoustofluidic phenomena.
“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, with significant reductions in energy consumption through precise control of acoustic pressures and flow rates.”
As various ways of enriching particles through microfluidics have emerged, those that employ acoustofluidics are especially promising because they are biocompatible, generate high-force magnitudes at MPa pressure ranges, and produce cell-scale wavelengths.
With their particular method, the researchers included an innovative use of dualized pressure acoustic fields, which doubles the impact on target cells, and strategically located them at critical channel geometry positions on a lithium niobate substrate. By means of acoustic pressure applied within the microchannel, the system design provides for the generation of reliable datasets that illustrate cell interaction times and trajectory patterns, which will help predict tumor cell migration.
“We have produced an advanced, lab-on-chip platform that enables real-time, energy-efficient, and highly accurate cell separation,” said Kouhkord. “The technology promises to improve CTC separation efficiency and open new possibilities for earlier and more effective cancer diagnosis. It also paves the way for microengineering and applied AI in personalized medicine and cancer diagnostics.”
