Cardiovascular disease (CVD), the leading cause of death worldwide, has been viewed as one of the major problems for wealthy and industrialized nations for decades, and the need for rapid detection and timely diagnosis has the utmost importance. Cardiac troponin I (cTnI) is a promising biomarker for early diagnosis of acute myocardial infarction (AMI). Hence, the development of immunoassay based biosensor for cTnI is necessary. Over the past decades, there have been extensive researches regarding cTnI detection, including colorimetric, fluorescence, paramagnetic, electrochemical, and surface plasmon resonance. However, conventional laboratory methods are time-consuming and require expensive and bulky equipment. In light of this, the need for point of care testing becomes more crucial. Here, we use a programmable microcontroller unit (MCU) to operate the device. A digital-to-analog converter (DAC) is used to deliver a modulating signal to LEDs, and then the modulated light excites the samples in the microfluidic reaction wells. The signals from the sample and control group are obtained by two photodetectors individually. They will be amplified and demodulated through the lock-in amplifier and digitized by analog-to-digital converters (ADC) to the MCU. And the collected data will be presented on the device and uploaded synchronically to the smartphone via Bluetooth. The whole processing time is less than 5 minutes. Next, we use the microfluidic platform to simplify complicated laboratory procedures. In our study, we focus on using cTnI to detect the samples in the human serum or blood. In order to solve low efficacy caused by the non-specific binding, we used Zwitterionic carboxybetaine disulfide (CB) as a self-assembled monolayer in the experimental design. The use of self-assembled monolayer can not only decrease non-specific binding problem but also shorten the analysis time.
Oral cavity cancer is one of the most common malignancies. Development of immunoassay based biosensor for interleukin-8 (IL-8) protein is required. The miniaturization of the device is also necessary in order to provide ready-touse portable diagnostic tools (point of care diagnostic) for clinical uses. In the current study, a compact, portable and reliable biomarker detecting and analyzing system is presented. The light absorption analysis was performed by a low-cost and portable optical sensor device. We used a programmable microcontroller unit (MCU) to operate the device. A digitalto-analog converter (DAC) was used to deliver a modulating signal to LEDs, and then the modulated light excited the samples in the microfluidic detection chamber. The signals from the sample and control group were obtained by two photodetectors individually. The photodetectors were amplified and demodulated through the lock-in amplifier, and digitized by analog-to-digital converters (ADC) to the MCU. After that, the analyzed data was uploaded to the smartphone via Bluetooth. The device demonstrates a good measurement accuracy and shorter detection time compared to conventional methods.
Human blood analysis has provided rich information in rapid clinical diagnosis. Different from conventional blood cell counting method which is environment-dependent and costly, this study proposes an advanced blood cells imaging method at micron-scale to reduce the size of the equipment and decrease the total cost of testing. This approach applies the deep learning method and a convolutional neural network in reconstructing object images from the diffraction patterns. The holographic image is extracted by the convolution layer and the feature classification of the hidden layer rapidly identifies each diffraction pattern of the holographic image. The mean IoU for masks generated from the hologram is 0.876. Consequently, this deep learning approach is significantly more preferable to conventional calculation. It, thus, provides a portable, compact and cost-effective contrast-enhanced microholography system for clinical diagnosis.
Complete Blood Count (CBC) is a routine blood test program that is often prescribed by physicians. It analyzes the values of various blood cells and can be used for detection of related diseases and infections. Flow cytometry has been one of the widely used technique to count, characterize and classify blood cells in the previous studies. However, the technique suffers from time-consuming procedure for analyzing the target cell and the bulky design of the detection platform. Hence, we propose a high-throughput lensless holographic microscopy for rapid blood analysis and imaging in large field-of-view (FOV) of 30 mm2. The holographic technique can simultaneously record the amplitude and phase of the diffraction pattern, which are different due to cell sizes and materials for further cell counting. Then the original cell images are digitally reconstructed by scalar diffraction theory and inverse Fourier transform to identify red blood cells, white blood cells and platelets. The lens-free holographic platform has compact size that makes it easy-to-handle. The lens-free design is able to analyze the blood samples on a chip by using the spatial coherence light (LED) emanating from a pinhole, and imaging on the CMOS sensor with a spatial resolution of 1.67 μm. The application of a CBC can diagnose anemia, infections, and other disorders. We believe that the lensless on-chip holographic platform will be a cost-effective tool for point-of-care cytometry.