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A fully automated and label-free sample-to-answer white blood cell (WBC) cytometry platform for rapid immune state monitoring is demonstrated. The platform integrates (1) a WBC separation process using the multidimensional double spiral (MDDS) device and (2) an imaging process where images of the separated WBCs are captured and analyzed. Using the deep-learning-based image processing technique, we analyzed the captured bright-field images to classify the WBCs into their subtypes. Furthermore, in addition to cell classification, we can detect activation-induced morphological changes in WBCs for functional immune assessment, which could allow the early detection of various diseases. The integrated platform operates in a rapid (30 min), fully automated, and label-free manner. The platform could provide a promising solution to future point-of-care WBC diagnostics applications.

Cell separation has consistently been a pivotal technology of sample preparation in biomedical research. Compared with conventional bulky cell separation technologies applied in the clinic, cell separation based on microfluidics can accurately manipulate the displacement of liquid or cells at the microscale, which has great potential in point-of-care testing (POCT) applications due to small device size, low cost, low sample consumption, and high operating accuracy. Among various microfluidic cell separation technologies, inertial microfluidics has attracted great attention due to its simple structure and high throughput. In recent years, many researchers have explored the principles and applications of inertial microfluidics and developed different channel structures, including straight channels, curved channels, and multistage channels. However, the recently developed multistage channels have not been discussed and classified in detail compared with more widely discussed straight and curved channels. Therefore, in this review, a comprehensive and detailed review of recent progress in the multistage channel is presented. According to the channel structure, the inertial microfluidic separation technology is divided into (i) straight channel, (ii) curved channel, (iii) composite channel, and (iv) integrated device. The structural development of straight and curved channels is discussed in detail. And based on straight and curved channels, the multistage cell separation structures are reviewed, with a special focus on a variety of latest structures and related innovations of composite and integrated channels. Finally, the future prospects for the existing challenges in the development of inertial microfluidic cell separation technology are presented.

With the ubiquitous deployment of smartphones worldwide recently, biomedical diagnostic devices based on smartphones are potential to be widely used for smart health management on the Internet of Medical Things (IoMT) applications under point-of-care (PoC) settings. Therefore, this paper introduces one smartphone-based blood lipid data acquisition dongle to monitor blood lipid level, including total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C), using only a fingertip blood drop for cardiovascular disease management. During detection, a thin photochemical test strip that consists of LEDs and detectors is plugged into the compact dongle, where changed color intensities will indicate the lipid determinant concentration from the measured reflection coefficient of the chromogenic product. Such smartphone-based photochemical blood lipid data acquisition dongle was validated by comparing with a clinical analyzer, and reached a correlation coefficient over 0.903 and averaged coefficient of variation (CV)% of 4.575% for blood lipid measurement of 115 patients. The photochemical dongle is, therefore, promising for the future chronic disease management in the IoMT.

White blood cells (WBCs) play a significant role in the human immune system, and the content of various subtypes of WBCs is usually maintained within a certain range in the human body, while deviant levels are important warning signs for diseases. Hence, the detection and classification of WBCs is an essential diagnostic technique. However, traditional WBC classification technologies based on image processing usually need to segment the collected target cell images from the background. This preprocessing operation not only increases the workload but also heavily affects the classification quality and efficiency. Therefore, we proposed one high-efficiency object detection technology that combines the segmentation and recognition of targets into one step to realize the detection and classification of WBCs in an image at the same time. Two state-of-the-art object detection models, Faster RCNN and Yolov4, were employed and comparatively studied to classify neutrophils, eosinophils, monocytes, and lymphocytes on a balanced and enhanced Blood Cell Count Dataset (BCCD). Our experimental results showed that the Faster RCNN and Yolov4 based deep transfer learning models achieved classification accuracy rates of 96.25% and 95.75%, respectively. For the one-stage model, Yolov4, while ensuring more than 95% accuracy, its detection speed could reach 60 FPS, which showed better performance compared with the two-stage model, Faster RCNN. The high-efficiency object detection network that does not require cell presegmentation can remove the difficulty of image preprocessing and greatly improve the efficiency of the entire classification task, which provides a potential solution for future real-time point-of-care diagnostic systems.

The lensless on-chip microscope is an emerging technology in the recent decade that can realize the imaging and analysis of biological samples with a wide field-of-view without huge optical devices and any lenses. Because of its small size, low cost, and being easy to hold and operate, it can be used as an alternative tool for large microscopes in resource-poor or remote areas, which is of great significance for the diagnosis, treatment, and prevention of diseases. To improve the low-resolution characteristics of the existing lensless shadow imaging systems and to meet the high-resolution needs of point-of-care testing, here, we propose a high-precision on-chip microscope based on in-line holographic technology. We demonstrated the ability of the iterative phase recovery algorithm to recover sample information and evaluated it with image quality evaluation algorithms with or without reference. The results showed that the resolution of the holographic image after iterative phase recovery is 1.41 times that of traditional shadow imaging. Moreover, we used machine learning tools to identify and count the mixed samples of mouse ascites tumor cells and micro-particles that were iterative phase recovered. The results showed that the on-chip cell counter had high-precision counting characteristics as compared with manual counting of the microscope reference image. Therefore, the proposed high-precision lensless microscope on a chip based on in-line holographic imaging provides one promising solution for future point-of-care testing (POCT).