A Microfluidic Cytometer for Complete Blood Count With a 3.2-Megapixel, 1.1- μm-Pitch Super-Resolution Image Sensor in 65-nm BSI CMOS

Smartphone-Facilitated Mobile Colorimetric Probes for Rapid Monitoring of Chemical Contaminations in Food: Advances and Outlook

Smartphone-derived colorimetric tools have the potential to revolutionize food safety control by enabling citizens to carry out monitoring assays. To realize this, it is of paramount significance to recognize recent study efforts and figure out important technology gaps in terms of food security. Driven by international connectivity and the extensive distribution of smartphones, along with their built-in probes and powerful computing abilities, smartphone-based sensors have shown enormous potential as cost-effective and portable diagnostic scaffolds for point-of-need tests. Meantime, the colorimetric technique is of particular notice because of its benefits of rapidity, simplicity, and high universality. In this study, we tried to outline various colorimetric platforms using smartphone technology, elucidate their principles, and explore their applications in detecting target analytes (pesticide residues, antibiotic residues, metal ions, pathogenic bacteria, toxins, and mycotoxins) considering their sensitivity and multiplexing capability. Challenges and desired future perspectives for cost-effective, accurate, reliable, and multi-functions smartphone-based colorimetric tools have also been debated.

Super-Resolution Electrochemical Impedance Imaging With a 512 × 256 CMOS Sensor Array

Super-resolution imaging is a family of techniques in which multiple lower-resolution images can be merged to produce a single image at higher resolution. While super-resolution is often applied to optical systems, it can also be used with other imaging modalities. Here we demonstrate a 512 × 256 CMOS sensor array for micro-scale super-resolution electrochemical impedance spectroscopy (SR-EIS) imaging. The system is implemented in standard 180 nm CMOS technology with a 10 μm × 10 μm pixel size. The sensor array is designed to measure the mutual capacitance between programmable sets of pixel pairs. Multiple spatially-resolved impedance images can then be computationally combined to generate a super-resolution impedance image. We use finite-element electrostatic simulations to support the proposed measurement approach and discuss straightforward algorithms for super-resolution image reconstruction. We present experimental measurements of sub-cellular permittivity distribution within single green algae cells, showing the sensor's capability to produce microscale impedance images with sub-pixel resolution.

Microfluidic flow cytometry for blood-based biomarker analysis

Flow cytometry has proven its capability for rapid and quantitative analysis of individual cells and the separation of targeted biological samples from others. The emerging microfluidics technology makes it possible to develop portable microfluidic diagnostic devices for point-of-care testing (POCT) applications. Microfluidic flow cytometry (MFCM), where flow cytometry and microfluidics are combined to achieve similar or even superior functionalities on microfluidic chips, provides a powerful single-cell characterisation and sorting tool for various biological samples. In recent years, researchers have made great progress in the development of the MFCM including focusing, detecting, and sorting subsystems, and its unique capabilities have been demonstrated in various biological applications. Moreover, liquid biopsy using blood can provide various physiological and pathological information. Thus, biomarkers from blood are regarded as meaningful circulating transporters of signal molecules or particles and have great potential to be used as non (or minimally)-invasive diagnostic tools. In this review, we summarise the recent progress of the key subsystems for MFCM and its achievements in blood-based biomarker analysis. Finally, foresight is offered to highlight the research challenges faced by MFCM in expanding into blood-based POCT applications, potentially yielding commercialisation opportunities.

A comprehensive review on LED-induced fluorescence in diagnostic pathology

Sensitivity, specificity, mobility, and affordability are important criteria to consider for developing diagnostic instruments in common use. Fluorescence spectroscopy has been demonstrating substantial potential in the clinical diagnosis of diseases and evaluating the underlying causes of pathogenesis. A higher degree of device integration with appropriate sensitivity and reasonable cost would further boost the value of the fluorescence techniques in clinical diagnosis and aid in the reduction of healthcare expenses, which is a key economic concern in emerging markets. Light-emitting diodes (LEDs), which are inexpensive and smaller are attractive alternatives to conventional excitation sources in fluorescence spectroscopy, are gaining a lot of momentum in the development of affordable, compact analytical instruments of clinical relevance. The commercial availability of a broad range of LED wavelengths (255-4600 nm) has opened up new avenues for targeting a wide range of clinically significant molecules (both endogenous and exogenous), thereby diagnosing a range of clinical illnesses. As a result, we have specifically examined the uses of LED-induced fluorescence (LED-IF) in preclinical and clinical evaluations of pathological conditions, considering the present advancements in the field.

Advances in Neural Recording and Stimulation Integrated Circuits

In the past few decades, driven by the increasing demands in the biomedical field aiming to cure neurological diseases and improve the quality of daily lives of the patients, researchers began to take advantage of the semiconductor technology to develop miniaturized and power-efficient chips for implantable applications. The emergence of the integrated circuits for neural prosthesis improves the treatment process of epilepsy, hearing loss, retinal damage, and other neurological diseases, which brings benefits to many patients. However, considering the safety and accuracy in the neural prosthesis process, there are many research directions. In the process of chip design, designers need to carefully analyze various parameters, and investigate different design techniques. This article presents the advances in neural recording and stimulation integrated circuits, including (1) a brief introduction of the basics of neural prosthesis circuits and the repair process in the bionic neural link, (2) a systematic introduction of the basic architecture and the latest technology of neural recording and stimulation integrated circuits, (3) a summary of the key issues of neural recording and stimulation integrated circuits, and (4) a discussion about the considerations of neural recording and stimulation circuit architecture selection and a discussion of future trends. The overview would help the designers to understand the latest performances in many aspects and to meet the design requirements better.

Recent progress in smartphone-based techniques for food safety and the detection of heavy metal ions in environmental water

Emerging smartphone-based point-of-care tests (POCTs) are cost-effective, precise, and easy to implement in resource-limited areas. Thus, they are considered a potential alternative to conventional diagnostic testing. This review explores food safety and the detection of metal ions in environmental water based on unprecedented smartphone technology. Specifically, we provide an overview of various methods used for target analyte detection (antibiotics, enzymes, mycotoxins, pathogens, pesticides, small molecules, and metal ions), such as colorimetric, fluorescence, microscopic imaging, and electrochemical methods. This paper performs a comprehensive review of smartphone-based POCTs developed in the last three years (2018-2020) and evaluates their relative advantages and limitations. Moreover, we discuss the imperative role of new technology in the progress of POCTs. Sensor materials (metal nanoparticles, carbon dots, quantum dots, organic substrates, etc.) and detection techniques (paper-based, later flow assay, microfluidic platform, etc.) involved in POCTs based on smartphones, and the challenges faced by these techniques, are addressed.

Human Body-Related Disease Diagnosis Systems Using CMOS Image Sensors: A Systematic Review

According to the Center for Disease Control and Prevention (CDC), the average human life expectancy is 78.8 years. Specifically, 3.2 million deaths are reported yearly due to heart disease, cancer, Alzheimer’s disease, diabetes, and COVID-19. Diagnosing the disease is mandatory in the current way of living to avoid unfortunate deaths and maintain average life expectancy. CMOS image sensor (CIS) became a prominent technology in assisting the monitoring and clinical diagnosis devices to treat diseases in the medical domain. To address the significance of CMOS image ‘sensors’ usage in disease diagnosis systems, this paper focuses on the CIS incorporated disease diagnosis systems related to vital organs of the human body like the heart, lungs, brain, eyes, intestines, bones, skin, blood, and bacteria cells causing diseases. This literature survey’s main objective is to evaluate the ‘systems’ capabilities and highlight the most potent ones with advantages, disadvantages, and accuracy, that are used in disease diagnosis. This systematic review used PRISMA workflow for study selection methodology, and the parameter-based evaluation is performed on disease diagnosis systems related to the human body’s organs. The corresponding CIS models used in systems are mapped organ-wise, and the data collected over the last decade are tabulated.

White blood cell detection and classification using Euler’s Jenks optimized multinomial logistic neural networks

Due to the wide acceptance of White Blood Cells (WBCs) in disease diagnosis, detection and classification of WBC are hot topic. Existing methodologies have some drawbacks such as significant degree of error, higher accuracy, time bound and higher misclassification rate. A WBCs detection and classification called, Jenks Optimized Logistic Convolutional Neural Network (JO-LCNN) method has proposed. Initally, Eulers Principal Axis is used as a convolution model to obtain a rotation invariant form of image by differentiating the background and RBCs, then eliminating them which leaves only the WBCs. By eliminating the wanton features, inherent features are detected contributing to minimum misclassification rate. According to above, Jenks Optimization function is used as a pooling model to obtain feature map for lower resolution. Therefore JO-LCNN is used for removing tiny objects in image and complete nuclei. Finally, Multinomial Logistic classifier is used to classify five types of classes by means of loss function and updating weight according to the loss function, therefore classifying with higher accuracy rate. Using LISC database for WBCs with different parameters as classification accuracy, false positive rate and time complexity are performed. Result shows that JO-LCNN, efficiently improves accuracy with less time, misclassification rate than the state-of-art methods.

A Real-Time Coprime Line Scan Super-Resolution System for Ultra-Fast Microscopy

A fundamental technical challenge for ultra-fast cell microscopy is the tradeoff between imaging throughput and resolution. In addition to throughput, real-time applications such as image-based cell sorting further requires ultra-low imaging latency to facilitate rapid decision making on a single-cell level. Using a novel coprime line scan sampling scheme, a real-time low-latency hardware super-resolution system for ultra-fast time-stretch microscopy is presented. The proposed scheme utilizes analog-to-digital converter with a carefully tuned sampling pattern (shifted sampling grid) to enable super-resolution image reconstruction using line scan input from an optical front-end. A fully pipelined FPGA-based system is built to efficiently handle the real-time high-resolution image reconstruction process with the input subpixel samples while achieving minimal output latency. The proposed super-resolution sampling and reconstruction scheme is parametrizable and is readily applicable to different line scan imaging systems. In our experiments, an imaging latency of 0.29 μs has been achieved based on a pixel-stream throughput of 4.123 giga pixels per second, which translates into imaging throughput of approximately 120000 cells per second.

Achieving Super-Resolution With Redundant Sensing

OBJECTIVE

A new technique is presented to enhance the precision of the analog-to-digital (AD) and digital-to-analog (DA) conversion, which are fundamental operations of many biomedical information processing systems. In practice, the precision of these operations is always bounded, first by the random mismatch error occurred during system implementation, and subsequently by the intrinsic quantization error determined by the system architecture itself.

METHODS

Here, we derive a new mathematical interpretation of the previously proposed redundant sensing architecture that not only suppresses mismatch error but also allows achieving an effective resolution exceeding the system's intrinsic resolution, i.e., super-resolution (SR). SR is enabled by an endogenous property of redundant structures regarded as "code diffusion" where the references' value spreads into the neighbor sample space as a result of mismatch error.

RESULTS

Using Monte Carlo methods, we show a profound theoretical increase of 8-9 b effective resolution or 256-512× enhancement of precision on a 10-b device at 95% sample space.

CONCLUSION

The proposed SR mechanism can be applied to substantially improve the precision of various AD and DA conversion processes beyond the system resource constraints.

SIGNIFICANCE

The concept opens the possibility for a wide range of applications in low-power fully integrated sensors and devices where the cost-accuracy tradeoff is inevitable. As a proof-of-concept demonstration, we point out an example where the proposed technique can be used to enhance the precision of an implantable neurostimulator design.

CMOS Enabled Microfluidic Systems for Healthcare Based Applications

With the increased global population, it is more important than ever to expand accessibility to affordable personalized healthcare. In this context, a seamless integration of microfluidic technology for bioanalysis and drug delivery and complementary metal oxide semiconductor (CMOS) technology enabled data-management circuitry is critical. Therefore, here, the fundamentals, integration aspects, and applications of CMOS-enabled microfluidic systems for affordable personalized healthcare systems are presented. Critical components, like sensors, actuators, and their fabrication and packaging, are discussed and reviewed in detail. With the emergence of the Internet-of-Things and the upcoming Internet-of-Everything for a people-process-data-device connected world, now is the time to take CMOS-enabled microfluidics technology to as many people as possible. There is enormous potential for microfluidic technologies in affordable healthcare for everyone, and CMOS technology will play a major role in making that happen.

Smartphone-based analytical biosensors

With the rapid development, mass production, and pervasive distribution of smartphones in recent years, they have provided people with portable, cost-effective, and easy-to-operate platforms to build analytical biosensors for point-of-care (POC) applications and mobile health.