Microfluidic Impedance Cytometry (MIC) is an advanced approach for single-cell analysis, harnessing microfluidic technology and impedance-based principles, particularly applicable in cancer diagnostics based on circulating tumor cells (CTCs). However, only relying on one dimensional information from electrical sensing poses challenges when detecting smaller CTCs that exhibit comparable sizes to white blood cells. To address this issue, we propose a microfluidic impedance cytometer featuring custom-designed circuits, electrodes, and a microfluidic chip with constriction channel. This system concurrently extracts both mechanical and electrical properties from processed electrical signals, overcoming the obstacle posed by the intrinsic link between impedance signals and cell sizes. Our system was tested with A549 lung cancer cells, white blood cells, and red blood cells, demonstrating the ability to differentiate cells of similar sizes within the blood sample and accurate cell couting capabilities. This approach shows promise for early cancer detection and monitoring treatment efficacy.

The four papers in this special section focus on the Internet of medical things for healthcare. The current market needs to provide low-cost and convenient biomedical diagnosis as well as proactive health management and smart healthcare service to the elderly people and patientswith chronic illnesses are growing tremendously. Therefore, extensive researches have been dedicated to the development of novel Internet of Medical Things (IoMT) technologies and applications for healthcare engineering, which are leading to a new and promising healthcare strategy transform and a paradigm shift from hospital-centered to patient-centered, and from disease-focus to health-focus. The papers in this section are dedicated to the state-of-the-art IoMT in health engineering related topics, and emphasizes the bioelectronics, biophysics, biochemistry, and microsystems related topics for healthcare engineering.

Nowadays, cardiovascular diseases have become one of the most risks threating human being's life in the world. The early screening and efficient management of cardiovascular diseases are critically important to extend the patients' life. Blood lipid level is a key biochemical factor used to estimate the cardiovascular disease in clinical situation. Triglyceride as one important blood lipid plays an indispensable role in the blood test. On the other hand, smartphone has unprecedentedly large scale users since the last decade, which paves the wide avenue for the dissemination of smartphone-associated medical devices. In this paper, we integrated the smartphone with the Triglyceride (TG) sensory module to monitor the finger pricked whole blood TG at the point of care scale. The miniaturized electrochemical analyzer was immobilized on the main board of the smartphone which enables the smartphone work as the blood TG analyzer incorporating with the disposable electrochemical TG test strip. The blood TG measured by the medical smartphone was compared to the results obtained from the conventional bulky biochemical analyzer with acceptable accuracy, which demonstrated that the proposed medical smartphone is capable of providing a point of care analytical device for blood TC monitoring at the medical level. Its potential in cardiovascular disease prevention and management was believed to be great, since the proposed system is ultracompact, smart, cost effective, reliable, and flexible with medical data acquisition.

Precision medicine requires scalable bioinstrument for a personalized DNA sequencing, which can be label-free, cost-efficient, and high-throughput. This paper mainly presents three kinds of CMOS-based label-free sensors, including: i) a high-sensitivity ion-sensitive field-effect transistor (ISFET) sensor with pH-to-time-to-voltage conversion (pH-TVC); ii) a dual-mode sensor with image and chemical modes for high accuracy; and iii) a THz metamaterial sensor with electrical resonance detection. The developed CMOS multi-modal sensor platform can show a scaled solution for future personalized DNA sequencing.

A dual-mode (chemical/optical) CMOS ion-image sensor is demonstrated towards accurate DNA sequencing by integrating the ion-sensitive field-effect transistor (ISFET) with 4-T CMOS image sensor (CIS) pixel fabricated in standard 0.18μm CIS process. With accurate determination of physical locations for microbeads through contact imaging, local pH for one DNA slice attached on the microbead can be obtained with accurate correlation to improve sequencing accuracy from system perspective. Moreover, towards high-throughput large-arrayed sequencing, pixel-to-pixel ISFET threshold voltage mismatch is reduced by correlated double sampling (CDS) readout that supports both image and pH modes. Measurement results show a readout sensitivity of 103.8mV/pH, a fixed-pattern-noise (FPN) reduction from 4% to 0.3%, and a readout speed of 1200 frames/second (fps).

With the recent advancement in microfluidics based lab-on-a-chip technology, lensless imaging system integrating microfluidic channel with CMOS image sensor has become a promising solution for the system minimization of flow cytometer. The design challenge for such an imaging-based micro-flow cytometer under poor resolution is how to recover cell recognition error under various flow rates. A microfluidic lensless imaging system is developed in this paper using extreme-learning-machine enhanced single-frame super-resolution processing, which can effectively recover the recognition error when increasing flow rate for throughput. As shown in the experiments, with mixed flowing HepG2 and Huh7 cells as inputs, the developed scheme shows that 23% better recognition accuracy can be achieved compared to the one without error recovery. Meanwhile, it also achieves an average of 98.5% resource saving compared to the previous multi-frame super-resolution processing.

This paper proposes a novel algorithm of embedding gray-scale watermark image into an RGB host image based on the Quaternion Fourier Transform (QFT). The R, G, B color components are represented respectively by the three imaginary parts of a pure imaginary quaternion and the gray-scale watermark image is inserted into the real part of the carrier image which is transformed by the QFFT. By using the symmetrical characteristic of the real part, we achieve the insertion of the gray-scale watermark image without lose. Experiment results show that the watermarked image with this method has the biggest imperceptibility with the best PSNR when compared with other gray-scale watermarking schemes.