Microfluidic technology offers a transformative approach for biomedical diagnostics by enabling miniaturization and automation of analytical procedures. Its integration into Point-of-Care Testing (POCT) holds promise for rapid, decentralized, and accessible diagnostics. However, translating laboratory microfluidics into practical POCT devices faces key challenges, including the handling of complex clinical samples, integration of multiple functions on a single chip, ensuring reliable analytical performance in clinical deployment scenarios, and meeting regulatory requirements. This review explores the evolution of microfluidic technology and the unique requirements for POCT, while highlighting the main challenges in translating laboratory systems into practical applications. Representative case studies and recent technological advances are presented to illustrate progress and solutions, and future directions are discussed with a focus on strategies to bridge the gap between laboratory research and effective, real-world POCT deployment.

Ultrasound can penetrate centimetres of soft tissue, focus energy with millimetre precision, and operate safely under real-time image guidance. Leveraging these advantages, sonogenetics combines therapeutic ultrasound with genetic, cellular, and molecular engineering to create remotely programmable living systems. While widely applied in neuronal modulation, this review highlights recent progress in precision medicine, focusing on immunoengineering and genome engineering, illustrating how sonogenetics is moving beyond neuromodulation to enable precise control of immune responses and targeted genetic modifications. We introduce the fundamental principles of sonogenetics and key ultrasound-responsive biological actuators, including heat-shock promoters, thermosensitive and mechanosensitive ion channels, gas vesicles, microbubbles, and acoustically responsive nanoparticles. These convert acoustic signals into biological responses and are integrated into synthetic genetic circuits to control cell behaviour with high spatial and temporal precision. We then overview immunoengineering and genome engineering, covering cellular therapies such as CAR-T and engineered bacteria, synthetic materials, and CRISPR-based genome and transcriptome editors. This context supports recent applications, including ultrasound-guided immune modulation, remote control of CAR-T cells, tumour microenvironment reprogramming, targeted genome editing, and epigenetic regulation in vivo. We also discuss the emerging role of artificial intelligence in optimizing sonogenetic designs and outline translational challenges, including actuator safety and immunogenicity, ultrasound penetration limits, targeting accuracy, and regulatory pathways for device-biologic combinations. Key priorities include closed-loop dosimetry, scalable vector delivery, and actuator optimization. In summary, sonogenetics provides a programmable, non-invasive toolkit for controlling cellular functions, opening opportunities in basic research, diagnostics, and next-generation therapeutics.

The shortened radio frequency wavelength in high field MRI makes it challenging to create a uniform excitation pattern over a large field of view, or to achieve satisfactory transmission efficiency at a local area. Transmit arrays are one tool that can be used to create a desired excitation pattern. To be effective, it is important to be able to control the current amplitude and phase at the array elements. The control of the current may get complicated by the coil coupling in many applications. Various methods have been proposed to achieve current control, either in the presence of coupling, or by effectively decouple the array elements. These methods are applied in different subsystems in the RF transmission chain: coil; coil-amplifier interface; amplifier, etc. In this review paper, we provide an overview of the various approaches and aspects of transmit current control and decoupling.

Cardiac digital twins (CDTs) are personalized virtual representations used to understand complex cardiac mechanisms. A critical component of CDT development is solving the ECG inverse problem, which enables the reconstruction of cardiac sources and the estimation of patient-specific electrophysiology (EP) parameters from surface ECG data. Despite challenges from complex cardiac anatomy, noisy ECG data, and the ill-posed nature of the inverse problem, recent advances in computational methods have greatly improved the accuracy and efficiency of ECG inverse inference, strengthening the fidelity of CDTs. This paper aims to provide a comprehensive review of the methods for solving ECG inverse problems, their validation strategies, their clinical applications, and their future perspectives. For the methodologies, we broadly classify state-of-the-art approaches into two categories: deterministic and probabilistic methods, including both conventional and deep learning-based techniques. Integrating physics laws with deep learning models holds promise, but challenges such as capturing dynamic electrophysiology accurately, accessing accurate domain knowledge, and quantifying prediction uncertainty persist. Integrating models into clinical workflows while ensuring interpretability and usability for healthcare professionals is essential. Overcoming these challenges will drive further research in CDTs.

Advancements in wearable sensor technologies and the digitization of medical records have contributed to the unprecedented ubiquity of biomedical time series data. Data-driven models have tremendous potential to assist clinical diagnosis and improve patient care by improving long-term monitoring capabilities, facilitating early disease detection and intervention, as well as promoting personalized healthcare delivery. However, accessing extensively labeled datasets to train data-hungry deep learning models encounters many barriers, such as long-tail distribution of rare diseases, cost of annotation, privacy and security concerns, data-sharing regulations, and ethical considerations. An emerging approach to overcome the scarcity of labeled data is to augment AI methods with human-like capabilities to leverage past experiences to learn new tasks with limited examples, called few-shot learning. This survey provides a comprehensive review and comparison of few-shot learning methods for biomedical time series applications. The clinical benefits and limitations of such methods are discussed in relation to traditional data-driven approaches. This paper aims to provide insights into the current landscape of few-shot learning for biomedical time series and its implications for future research and applications.

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Radiofrequency ablation (RFA) combined with saline infusion into tissue is a promising technique to ablate larger tumours. Nevertheless, the application of saline-infused RFA remains at clinical trials due to the contradictory findings as a result of the inconsistencies in experimental procedures. These inconsistencies not only magnify the number of factors to consider during the treatment, but also obscure the understanding of the role of saline in enlarging the coagulation zone. Consequently, this can result in major complications, which includes unwanted thermal damages to adjacent tissues and also incomplete ablation of the tumour. This review aims to identify the key factors of saline responsible for enlarging the coagulation zone during saline-infused RFA, and provide a proper understanding on their effects that is supported with findings from computational studies to ensure a safe and reliable cancer treatment.