Medication non-adherence is a prevalent, complex problem. Failure to follow medication schedules may lead to major health complications, including death. Proper medication adherence is thus required in order to gain the greatest possible drug benefit during a patient’s treatment. Interventions have been proven to improve medication adherence if deviations are detected. This review focuses on recent advances in the field of technology-based medication adherence approaches and pays particular attention to their technical monitoring aspects. The taxonomy space of this review spans multiple techniques including sensor systems, proximity sensing, vision systems, and combinations of these. As each technique has unique advantages and limitations, this work describes their trade-offs in accuracy, energy consumption, acceptability and user’s comfort, and user authentication.

In recent years, the field of micro- and nanochannel fabrication has seen significant advancements driven by the need for precision in biomedical, environmental, and industrial applications. This review provides a comprehensive analysis of emerging fabrication technologies, including photolithography, soft lithography, 3D printing, electron-beam lithography (EBL), wet/dry etching, injection molding, focused ion beam (FIB) milling, laser micromachining, and micro-milling. Each of these methods offers unique advantages in terms of scalability, precision, and cost-effectiveness, enabling the creation of highly customized micro- and nanochannel structures. Challenges related to scalability, resolution, and the high cost of traditional techniques are addressed through innovations such as deep reactive ion etching (DRIE) and multipass micro-milling. This paper also explores the application potential of these technologies in areas such as lab-on-a-chip devices, biomedical diagnostics, and energy-efficient cooling systems. With continued research and technological refinement, these methods are poised to significantly impact the future of microfluidic and nanofluidic systems.

We present a novel method to rapidly assess drug efficacy in targeted cancer therapy, where antineoplastic agents are conjugated to antibodies targeting surface markers on tumor cells. We have fabricated and characterized a device capable of rapidly assessing tumor cell sensitivity to drugs using multifrequency impedance spectroscopy in combination with supervised machine learning for enhanced classification accuracy. Currently commercially available devices for the automated analysis of cell viability are based on staining, which fundamentally limits the subsequent characterization of these cells as well as downstream molecular analysis. Our approach requires as little as 20 μL of volume and avoids staining allowing for further downstream molecular analysis. To the best of our knowledge, this manuscript presents the first comprehensive attempt to using high-dimensional data and supervised machine learning, particularly phase change spectra obtained from multi-frequency impedance cytometry as features for the support vector machine classifier, to assess viability of cells without staining or labelling.

We present a portable non-invasive approach for measuring indicators of inflammation and oxidative stress in the respiratory tract by quantifying a biomarker in exhaled breath condensate (EBC). We discuss the fabrication and characterization of a miniaturized electrochemical sensor for detecting nitrite content in EBC using reduced graphene oxide. The nitrite content in EBC has been demonstrated to be a promising biomarker of inflammation in the respiratory tract, particularly in asthma. We utilized the unique properties of reduced graphene oxide (rGO); specifically, the material is resilient to corrosion while exhibiting rapid electron transfer with electrolytes, thus allowing for highly sensitive electrochemical detection with minimal fouling. Our rGO sensor was housed in an electrochemical cell fabricated from polydimethyl siloxane (PDMS), which was necessary to analyze small EBC sample volumes. The sensor is capable of detecting nitrite at a low over-potential of 0.7 V with respect to an Ag/AgCl reference electrode. We characterized the performance of the sensors using standard nitrite/buffer solutions, nitrite spiked into EBC, and clinical EBC samples. The sensor demonstrated a sensitivity of 0.21 μA μM⁻¹ cm⁻² in the range of 20–100 μM and of 0.1 μA μM⁻¹ cm⁻² in the range of 100–1000 μM nitrite concentration and exhibited a low detection limit of 830 nM in the EBC matrix. To benchmark our platform, we tested our sensors using seven pre-characterized clinical EBC samples with concentrations ranging between 0.14 and 6.5 μM. This enzyme-free and label-free method of detecting biomarkers in EBC can pave the way for the development of portable breath analyzers for diagnosing and managing changes in respiratory inflammation and disease.

Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.

The surge in demand for 3D MOSFETs, such as FinFETs, driven by recent technological advances, is explored in this review. FinFETs, positioned as promising alternatives to bulk CMOS, exhibit favorable electrostatic characteristics and offer power/performance benefits, scalability, and control over short-channel effects. Simulations provide insights into functionality and leakage, addressing off-current issues common in narrow band-gap materials within a CMOS-compatible process. Multiple structures have been introduced for FinFETs. Moreover, some studies on the fabrication of FinFETs using different materials have been discussed. Despite their potential, challenges like corner effects, quantum effects, width quantization, layout dependencies, and parasitics have been acknowledged. In the post-planar CMOS landscape, FinFETs show potential for scalability in nanoscale CMOS, which leads to novel structures for them. Finally, recent developments in FinFET-based sensors are discussed. In a general view, this comprehensive review delves into the intricacies of FinFET fabrication, exploring historical development, classifications, and cutting-edge ideas for the used materials and FinFET application, i.e., sensing.