Microfluidic-Based Novel Optical Quantification of Red Blood Cell Concentration in Blood Flow.
Yudong WangBharath Babu NunnaNiladri TalukderEon Soo LeePublished in: Bioengineering (Basel, Switzerland) (2022)
The optical quantification of hematocrit (volumetric percentage of red blood cells) in blood flow in microfluidic systems provides enormous help in designing microfluidic biosensing platforms with enhanced sensitivity. Although several existing methods, such as centrifugation, complete blood cell count, etc., have been developed to measure the hematocrit of the blood at the sample preparation stage, these methods are impractical to measure the hematocrit in dynamic microfluidic blood flow cases. An easy-to-access optical method has emerged as a hematocrit quantification technique to address this limitation, especially for the microfluidic-based biosensing platform. A novel optical quantification method is demonstrated in this study, which can measure the hematocrit of the blood flow at a targeted location in a microchannel at any given instant. The images of the blood flow were shot using a high-speed camera through an inverted transmission microscope at various light source intensities, and the grayscale of the images was measured using an image processing code. By measuring the average grayscale of the images of blood flow at different luminous exposures, a relationship between hematocrit and grayscale has been developed. The quantification of the hematocrit in the microfluidic system can be instant and easy with this method. The innovative proposed technique has been evaluated with porcine blood samples with hematocrit ranging from 5% to 70%, flowing through 1000 µm wide and 100 µm deep microchannels. The experimental results obtained strongly supported the proposed optical technique of hematocrit measurement in microfluidic systems.
Keyphrases
- blood flow
- high speed
- single cell
- high throughput
- red blood cell
- circulating tumor cells
- label free
- high resolution
- atomic force microscopy
- deep learning
- convolutional neural network
- optical coherence tomography
- stem cells
- mesenchymal stem cells
- air pollution
- mass spectrometry
- cancer therapy
- tandem mass spectrometry
- single molecule