A perspective on magnetic microfluidics: Towards an intelligent future

Single-Cell RNA Sequencing in Organ and Cell Transplantation

Single-cell RNA sequencing is a high-throughput novel method that provides transcriptional profiling of individual cells within biological samples. This method typically uses microfluidics systems to uncover the complex intercellular communication networks and biological pathways buried within highly heterogeneous cell populations in tissues. One important application of this technology sits in the fields of organ and stem cell transplantation, where complications such as graft rejection and other post-transplantation life-threatening issues may occur. In this review, we first focus on research in which single-cell RNA sequencing is used to study the transcriptional profile of transplanted tissues. This technology enables the analysis of the donor and recipient cells and identifies cell types and states associated with transplant complications and pathologies. We also review the use of single-cell RNA sequencing in stem cell implantation. This method enables studying the heterogeneity of normal and pathological stem cells and the heterogeneity in cell populations. With their remarkably rapid pace, the single-cell RNA sequencing methodologies will potentially result in breakthroughs in clinical transplantation in the coming years.

Image-based real-time feedback control of magnetic digital microfluidics by artificial intelligence-empowered rapid object detector for automated in vitro diagnostics

In vitro diagnostics (IVD) plays a critical role in healthcare and public health management. Magnetic digital microfluidics (MDM) perform IVD assays by manipulating droplets on an open substrate with magnetic particles. Automated IVD based on MDM could reduce the risk of accidental exposure to contagious pathogens among healthcare workers. However, it remains challenging to create a fully automated IVD platform based on the MDM technology because of a lack of effective feedback control system to ensure the successful execution of various droplet operations required for IVD. In this work, an artificial intelligence (AI)-empowered MDM platform with image-based real-time feedback control is presented. The AI is trained to recognize droplets and magnetic particles, measure their size, and determine their location and relationship in real time; it shows the ability to rectify failed droplet operations based on the feedback information, a function that is unattainable by conventional MDM platforms, thereby ensuring that the entire IVD process is not interrupted due to the failure of liquid handling. We demonstrate fundamental droplet operations, which include droplet transport, particle extraction, droplet merging and droplet mixing, on the MDM platform and show how the AI rectify failed droplet operations by acting upon the feedback information. Protein quantification and antibiotic resistance detection are performed on this AI-empowered MDM platform, and the results obtained agree well with the benchmarks. We envision that this AI-based feedback approach will be widely adopted not only by MDM but also by other types of digital microfluidic platforms to offer precise and error-free droplet operations for a wide range of automated IVD applications.

Microfluidic systems for particle capture and release: A review

Microfluidic technology has emerged as a promising tool in various applications, including biosensing, disease diagnosis, and environmental monitoring. One of the notable features of microfluidic devices is their ability to selectively capture and release specific cells, biomolecules, bacteria, and particles. Compared to traditional bulk analysis instruments, microfluidic capture-and-release platforms offer several advantages, such as contactless operation, label-free detection, high accuracy, good sensitivity, and minimal reagent requirements. However, despite significant efforts dedicated to developing innovative capture mechanisms in the past, the release and recovery efficiency of trapped particles have often been overlooked. Many previous studies have focused primarily on particle capture techniques and their efficiency, disregarding the crucial role of successful particle release for subsequent analysis. In reality, the ability to effectively release trapped particles is particularly essential to ensure ongoing, high-throughput analysis. To address this gap, this review aims to highlight the importance of both capture and release mechanisms in microfluidic systems and assess their effectiveness. The methods are classified into two categories: those based on physical principles and those using biochemical approaches. Furthermore, the review offers a comprehensive summary of recent applications of microfluidic platforms specifically designed for particle capture and release. It outlines the designs and performance of these devices, highlighting their advantages and limitations in various target applications and purposes. Finally, the review concludes with discussions on the current challenges faced in the field and presents potential future directions.

Microsystem Advances through Integration with Artificial Intelligence

Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.

Magnetically Actuated Hydrogel Stamping-Assisted Cellular Mechanical Analyzer for Stored Blood Quality Detection

Cellular mechanical property analysis reflecting the physiological and pathological states of cells plays a crucial role in assessing the quality of stored blood. However, its complex equipment needs, operation difficulty, and clogging issues hinder automated and rapid biomechanical testing. Here, we propose a promising biosensor assisted by magnetically actuated hydrogel stamping to fulfill it. The flexible magnetic actuator triggers the collective deformation of multiple cells in the light-cured hydrogel, and it allows for on-demand bioforce stimulation with the advantages of portability, cost-effectiveness, and simplicity of operation. The magnetically manipulated cell deformation processes are captured by the integrated miniaturized optical imaging system, and the cellular mechanical property parameters are extracted from the captured images for real-time analysis and intelligent sensing. In this work, 30 clinical blood samples with different storage durations (<14 days and >14 days) were tested. A deviation of 3.3% in the differentiation of blood storage durations by this system compared to physician annotation demonstrated its feasibility. This system should broaden the application of cellular mechanical assays in diverse clinical settings.

Construction of a desirable hyperbolic microfluidic chip for ultrasensitive determination of PCT based on chemiluminescence

Since procalcitonin (PCT) is a specific inflammation indicator of severe bacterial inflammation and fungal infection, it is of great significance to construct a sensitive and rapid microfluidic chip to detect PCT in clinical application. The design of micromixers using a lab-on-a-chip (LOC) device is the premise to realizing the adequate mixing of analytical samples and reagents and is an important measure to improve the accuracy and efficiency of determination. In this research study, we investigate the mixing characteristics of hyperbolic micromixers and explore the effects of different hyperbolic curvatures, different Reynolds numbers (Re) and different channel widths on the mixing performance of the micromixers. Then, an optimal micromixer was integrated into a microfluidic chip to fabricate a desirable hyperbolic microfluidic chip (DHMC) for the sensitive determination of inflammation marker PCT with a limit of detection (LOD) as low as 0.17 ng mL^-1via a chemiluminescence signal, which can be used as a promising real-time platform for early clinical diagnosis.

Analysis of Von Kármán Swirling Flows Due to a Porous Rotating Disk Electrode

The study of Von Kármán swirling flow is a subject of active interest due to its applications in a wide range of fields, including biofuel manufacturing, rotating heat exchangers, rotating disc reactors, liquid metal pumping engines, food processing, electric power generating systems, designs of multi-pore distributors, and many others. This paper focusses on investigating Von Kármán swirling flows of viscous incompressible fluid due to a rotating disk electrode. The model is based on a system of four coupled second-order non-linear differential equations. The purpose of the present communication is to derive analytical expressions of velocity components by solving the non-linear equations using the homotopy analysis method. Combined effects of the slip λ and porosity γ parameters are studied in detail. If either parameter is increased, all velocity components are reduced, as both have the same effect on the mean velocity profiles. The porosity parameter γ increases the moment coefficient at the disk surface, which monotonically decreases with the slip parameter λ. The analytical results are also compared with numerical solutions, which are in satisfactory agreement. Furthermore, the effects of porosity and slip parameters on velocity profiles are discussed.