Surface acoustic wave (SAW)-based microfluidics has emerged as a promising technology for precisely manipulating particles and cells at the micro- and nanoscales. Acoustofluidic devices offer advantages such as low energy consumption, high throughput, and label-free operation, making them suitable for particle manipulation tasks including pumping, mixing, sorting, and separation. In this review, we provide an overview and discussion of recent advancements in SAW-based microfluidic devices for micro- and nanoparticle manipulation. Through a thorough investigation of the literature, we explore interdigitated transducer designs, materials, fabrication techniques, microfluidic channel properties, and SAW operational modes of acoustofluidic devices. SAW-based actuators are mainly based on lithium niobate piezoelectric transducers, with a plethora of wavelengths, microfluidic dimensions, and transducer configurations, applied for different fluid manipulation methods: mixing, sorting, and separation. We observed the accuracy of particle sorting across different size ranges and discussed different alternative device configurations to enhance sensitivity. Additionally, the collected data show the successful implementation of SAW devices in real-world applications in medical diagnostics and environmental monitoring. By critically analyzing different approaches, we identified common trends, challenges, and potential areas for improvement in SAW-based microfluidics. Furthermore, we discuss the current state-of-the-art and opportunities for further research and development in this field.

Diabetes mellitus is a prevalent chronic autoimmune disease with a high impact on global health, affecting millions of adults and resulting in significant morbidity and mortality. Achieving optimal blood glucose levels is crucial for diabetes management to prevent acute and long-term complications. Carbohydrate counting (CC) is widely used by patients with type 1 diabetes to adjust prandial insulin bolus doses based on estimated carbohydrate content, contributing to better glycemic control and improved quality of life. However, accurately estimating the carbohydrate content of meals remains challenging for patients, leading to errors in bolus insulin dosing. This review explores the current limitations and challenges in CC accuracy and emphasizes the importance of personalized educational programs to enhance patients’ abilities in carbohydrate estimation. Existing tools for assessing patient learning outcomes in CC are discussed, highlighting the need for individualized approaches tailored to each patient’s needs. A comprehensive review of the relevant literature was conducted to identify educational programs and assessment tools dedicated to training diabetes patients on carbohydrate counting. The research aims to provide insights into the benefits and limitations of existing tools and identifies future research directions to advance personalized CC training approaches. By adopting a personalized approach to CC education and assessment, healthcare professionals can empower patients to achieve better glycemic control and improve diabetes management. Moreover, this review identifies potential avenues for future research, paving the way for advancements in personalized CC training and assessment approaches and further enhancing diabetes management strategies.

For patients with Type 1 diabetes mellitus (T1DM), accurate carbohydrate counting (CC) is essential for successful blood glucose regulation. Unfortunately, mistakes are common and may lead to an incorrect dosage of prandial insulin. In this work, we aim to demonstrate that each person has their own limits for CC errors, which can be computed using patient-specific data. To validate the proposed method, we tested it using several scenarios to investigate the effect of different CC errors on postprandial blood glucose. Virtual subjects from the T1DM Simulator were used in a clinical trial involving 450 meals over 90 days, all following the same daily meal plan but with different intervals for CC errors near, below, and above the limit computed for each patient. The results show that CC errors within personalized limits led to acceptable postprandial glycemic fluctuations. In contrast, experiments where 50% and 97.5% of the meals present a CC error outside the computed safe interval revealed a pronounced degradation of the time in range. Given these results, we consider the proposed method for obtaining personalized limits for CC errors an excellent starting point for an initial assessment of patients’ capabilities in CC and to provide appropriate ongoing education.