Session: 13-04-02: Applications of Micro and Nano Systems in Medicine and Biology II

Paper Number: 112170

112170 - Identifying Sick Cells From High-Resolution Solid-State Micropore Data 

Early detection of diseases such as cancer can drastically improve prognosis and treatment. To this end, solid-state micropores can measure distinct mechanical properties of diseased cells from their translocation behavior – detected as pulses in the data stream – and help diagnose the disease at its early stages. However, the obstacle in such approaches is that the accuracy of the sensor is affected by noise, making the pulse detection task too subjective. This is inefficient especially when the amount of disease-relevant data is only a fraction of the total acquired data. Thus, it is important to intelligently automate the detection process to eliminate the noise and to identify useful patterns toward error-free decision-making in real time.

Solid-state micropores are small orifices in silicon-based membranes that have been used to electrically measure the passage of human cells through these.  For the development of this work, datasets collected from red blood cells (RBCs), white blood cells (WBCs), and cancer cells were used.  These experiments were done with a solid-state micropore of 12 μm diameter.  Normally, the ionic current is measured temporally at a rate of ~2.2 microseconds which give a stable baseline. However, the passage of a cell through the micropore results in a dip in the baseline. These dips (translocation events) are thus registered as pulses and the features of the pulses depict patterns specific to the cell type. The pulse is essentially a sequence of data points falling abruptly downwards and reverting back to the normal baseline leaving behind a valley, with an acceptable base width.

Pattern detection is important in many applications such as mass spectroscopy, ECG, and MRI in order to find useful insights towards diagnosis. With the advent of novel biological applications of solid-state micro and nanoscale devices, the problem of pattern recognition is now coupled with the enormity and resolution of the datasets.  In temporal measurements, it is easy to visually analyze the patterns when the collection time scales are fine-grained and the measured events are plenty in number. However, when the actual pulses constitute only a small fraction of the measured data, automated pattern recognition becomes indispensable.  This paper describes a pattern detection approach based on moving-average filtering, which mitigates the impact of noise.  Moreover, a detection threshold is computed from the mean and standard deviation of the data. The threshold is then used to detect different types of pulses stemming from the healthy and diseased human cells when these translocate through micropores. The extent of smoothing is particular to the data: greater smoothing sufficiently suppresses the noise, however, it deteriorates the pulse shape and vice versa. Additionally, the design approach computes useful features of the detected data and delivers the results for real-time analysis. This can help physicians and scientists to change their strategies of diagnosis by eliminating manual reviews.

 

Presenting Author: SAMIR IQBAL University of Texas Rio Grande Valley

Presenting Author Biography: Dr. Samir Iqbal is a biomedical and electrical engineer and a professor at the University of Texas Rio grande Valley. He earned his Ph.D. in Electrical and Computer Engineering from the Purdue University, West Lafayette, Indiana in 2007.

Dr. Iqbal's research interests focus on developing cutting-edge biosensors and nanotechnology-based platforms for disease diagnosis and treatment. His research has been funded by several agencies, including the National Institutes of Health (NIH) and the National Science Foundation (NSF). He has published over 60 peer-reviewed articles in high-impact scientific journals and has been granted several patents related to biosensors and medical devices.

In addition to his research, Iqbal is also committed to mentoring and educating the next generation of scientists and engineers. He has supervised numerous graduate and undergraduate students and has taught several courses in biomedical engineering and nanotechnology.

Iqbal's contributions to the field of biomedical engineering have been recognized with several awards and honors, including the NSF CAREER Award, and the Best Researcher Award. He is a Fellow of the Royal Society of Chemistry and a member of the National Academy of Inventors.

Authors:

Abdul Hafeez Virginia Tech
Azhar Ilyas New York Institute of Technology
Ali Butt Virginia Tech
SAMIR IQBAL University of Texas Rio Grande Valley