Search Results (589)

Capacitive sensors are platforms that enable label-free, real-time detection at low non-perturbing voltages. These sensors do not rely on Faradaic processes, thereby eliminating the need for redox-active species and simplifying system integration for point-of-care diagnostics. However, their sensitivity in high-ionic-strength solutions, such as bodily fluids, is limited due to a reduced Debye length and non-specific interactions. The present review highlights advances in material integration, surface modification, and signal enhancement techniques to mitigate the challenges of deploying capacitive sensors in biofluids (sweat, saliva, blood, serum). This work further expands on the promise of such sensors for advancing liquid biopsies and highlights key technical challenges in translating capacitive systems to clinics. Full article

Electroencephalography (EEG) is a widely used non-invasive method for capturing brain activity, offering valuable insights into cognitive and emotional states relevant to mental health. With the growing complexity and volume of EEG data, machine learning (ML) techniques—particularly deep learning—have become integral in extracting meaningful patterns. While much of the current literature focuses on supervised learning methods that rely on labeled data, unsupervised learning offers an alternative approach capable of discovering hidden structures and novel biomarkers without requiring predefined labels. This systematic review aimed to identify and synthesize recent peer-reviewed research that applied unsupervised or self-supervised learning techniques to EEG data in the context of mental health monitoring, diagnosis, or analysis. A comprehensive search was conducted across six major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, PsycINFO, and Google Scholar, covering literature from January 2018 to March 2025. Following PRISMA guidelines, predefined inclusion and exclusion criteria were applied to screen and assess the relevance and quality of studies. From 512 initial records, 403 unique articles were screened, and 20 underwent full-text review. Ultimately, no studies met all the inclusion criteria. Most were excluded for employing only supervised methods, being review articles, or focusing on non-mental-health applications. The absence of eligible studies highlights a significant gap in current research and emphasizes the need for future empirical work exploring unsupervised techniques in EEG-based mental health applications. Such efforts could pave the way for more scalable, label-free approaches to understanding brain dynamics in psychological conditions. Full article

The escalating threat of infectious diseases necessitates the development of diagnostic technologies that are not only rapid and sensitive but also deployable at the point of care. Electrochemical impedance spectroscopy (EIS) has emerged as a leading technique for the label-free detection of pathogens, offering a unique combination of sensitivity, non-invasiveness, and adaptability. This review provides a comprehensive overview of the design and application of EIS-based biosensors tailored for pathogen detection, focusing on critical components such as biorecognition elements, electrode materials, nanomaterial integration, and surface immobilization strategies. Special emphasis is placed on the mechanisms of signal generation under Faradaic and non-Faradaic modes and how these underpin performance characteristics such as the limit of detection, specificity, and response time. The application spectrum spans bacterial, viral, fungal, and parasitic pathogens, with case studies highlighting detection in complex matrices such as blood, saliva, food, and environmental water. Furthermore, integration with microfluidics and point-of-care systems is explored as a pathway toward real-world deployment. Emerging strategies for multiplexed detection and the utilization of novel nanomaterials underscore the dynamic evolution of the field. Key challenges—including non-specific binding, matrix effects, the inherently low ΔR_ct/decade sensitivity of impedance transduction, and long-term stability—are critically evaluated alongside recent breakthroughs. This synthesis aims to support the future development of robust, scalable, and user-friendly EIS-based pathogen biosensors with the potential to transform diagnostics across healthcare, food safety, and environmental monitoring. Full article

MicroRNAs (miRNAs) are small, non-coding RNAs that regulate gene expression and have emerged as critical biomarkers in various diseases, including cancer. Their stability in bodily fluids and role as oncogenes or tumor suppressors make them attractive targets for non-invasive diagnostics. However, conventional detection methods, such as Northern blotting, RT-PCR, and microarrays, are limited by low sensitivity, lengthy protocols, and limited specificity. Electrochemical biosensors offer a promising alternative, providing high sensitivity, rapid response times, portability, and cost-effectiveness. These biosensors translate miRNA hybridization events into quantifiable electrochemical signals, often leveraging redox-active labels, mediators, or intercalators. Recent advancements in nanomaterials and signal amplification strategies have further enhanced detection capabilities, enabling sensitive, label-free miRNA quantification. This review provides a comprehensive overview of the recent advances in electrochemical biosensing of miRNAs, emphasizing innovative redox-based detection strategies, probe immobilization techniques, and hybridization modalities. The critical challenges and future perspectives in advancing electrochemical miRNA biosensors toward clinical translation and point-of-care diagnostics are discussed. Full article

The detection of particulate matter, particularly pathogenic bacteria, is essential in environmental monitoring, food safety, and clinical diagnostics. Among the various sensing techniques used, cantilever-based sensors offer a promising platform for label-free, real-time detection due to their high sensitivity. Here, we present a coupled cantilever sensor incorporating interdigitated comb-shaped structures to enhance dielectrophoretic (DEP) capture of Escherichia coli in liquid samples. During operation, one cantilever is externally actuated and the other oscillates passively through fluid-mediated coupling. The sensor was experimentally evaluated across a broad concentration range from 10 to 10⁵ cells/mL and the resonant frequency shifts were recorded for both beams. The results showed a strong linear frequency shift across all tested concentrations, without saturation. This demonstrates the sensor’s ability to detect both trace and high bacterial loads without needing recalibration. High frequency shifts of 4863 Hz were recorded for 10⁵ cells/mL and 225 Hz for the lowest concentration of 10 cells/mL, giving a limit of detection of 10 cells/mL. The sensor also showed a higher signal to noise ratio of 265.7 compared to previously reported designs. These findings showed that the enhanced sensor design enables sensitive, linear, and reliable bioparticle detection across a wide range, making it suitable for diverse applications. Full article

Cancer diagnosis requires alternative techniques that allow for early, non-invasive, or minimally invasive identification. Traditional methods, like tissue biopsies, are highly invasive and can be traumatic for patients. Liquid biopsy, a less invasive option, detects cancer biomarkers in body fluids such as blood and urine. However, early-stage cancer often presents low biomarker levels, making sensitivity a challenge for integrating liquid biopsy into early diagnosis. Recent studies revealed that extracellular vesicles (EVs) secreted by cells are apt markers for liquid biopsy. Detecting extracellular vesicles (EVs) for liquid biopsy faces challenges like low sensitivity, EV subtype heterogeneity, and difficulty isolating pure populations. Label-free methods, such as plasmonic biosensors and Raman spectroscopy, offer potential solutions by enabling direct analysis without markers, improving accuracy, and reducing complexity. This review paper discusses current challenges in EV-based liquid biopsy for cancer diagnosis and prognosis. It addresses the effective use of microfluidics and nano-plasmonic approaches to address these challenges. Enhancing label-free EV detection in liquid biopsy could revolutionize early cancer diagnosis by offering non-invasive, cost-effective, and rapid testing. This could improve patient outcomes through personalized treatment and ease the burden on healthcare systems. Full article

Human Activity Recognition (HAR) is crucial for understanding human behaviour through sensor data, with applications in healthcare, smart environments, and surveillance. While traditional HAR often relies on ambient sensors, wearable devices or vision-based systems, these approaches can face limitations in dynamic settings and raise privacy concerns. Device-free HAR systems, utilising Wi-Fi Channel State Information (CSI) to human movements, have emerged as a promising privacy-preserving alternative for next-generation health activity monitoring and smart environments, particularly for multi-user scenarios. However, current research faces challenges such as the need for substantial annotated training data, class imbalance, and poor generalisability in complex, multi-user environments where labelled data is often scarce. This paper addresses these gaps by proposing a hybrid deep learning approach which integrates signal preprocessing, targeted data augmentation, and a customised integration of CNN and Transformer models, designed to address the challenges of multi-user recognition and data scarcity. A random transformation technique to augment real CSI data, followed by hybrid feature extraction involving statistical, spectral, and entropy-based measures to derive suitable representations from temporal sensory input, is employed. Experimental results show that the proposed model outperforms several baselines in single-user and multi-user contexts. Our findings demonstrate that combining real and augmented data significantly improves model generalisation in scenarios with limited labelled data. Full article

Label-free, high-throughput, and 3D morphological analysis of blood cells remains a major challenge in biomedical optics. In this study, we investigate this issue using flow cytometry with digital holographic microscopy (DHM) to enable real-time, label-free imaging of red blood cells (RBCs) and blood coagulation structures (BCSs) without the need for staining or chemical pretreatment. We demonstrate an approach for the automated quantification and statistical characterization of these cells using quantitative phase information reconstructed from digital holograms. Although established image processing techniques such as phase unwrapping and segmentation are used, this study presents, to the best of our knowledge, the first statistical characterization of the 3D morphological features of BCSs. This is particularly useful in analyzing the heterogeneous and complex 3D structures of BCSs, which are difficult to assess using conventional microscopy. The results suggest that this DHM-based flow cytometry system provides a promising platform for non-invasive, real-time morphological evaluation of blood samples and has potential applications in hematological diagnostics and research related to blood coagulation. Full article

The electrospinning technique can produce multifunctional polymeric devices by forming solid fibers from polymer solutions under a high-voltage electric field. Variations such as concentric needles yield core/shell fibers. This study evaluates the effects of applied voltage (12.5–20 kV) and tip-to-collector distance (12.5–20 cm) on the morphology and thermochemical behavior of PLGA/PVA fibers made by coaxial electrospinning compared with casting-produced membranes and monolithic fibers. Optimal coaxial fibers (597 ± 90 nm diameter) were produced at 15 cm/12.5 kV, exhibiting a well-defined core/shell structure (PVA core: ~100 nm; PLGA shell: ~50 nm) confirmed by laser scanning confocal (core solution labeled with fluorescein) and TEM. FTIR and TGA demonstrated nearly complete solvent removal in electrospun samples versus ~10% solvent retention in cast films. XRD analysis indicated that cast films (PLGAff) exhibited minimal crystallinity (Xc ≈ 0.1%), while electrospun PLGA (PLGAe) showed cold crystallization and higher crystallinity (Tcc ≈ 90.6 °C; Xc ≈ 2.45%). DSC detected two different Tg (≈43.2 °C and 52.8 °C) in the coaxial fibers, confirming distinct polymer domains with interfacial interactions. These results establish precise processing/structure relationships for defect-free coaxial fibers and provide fundamental design principles for hybrid systems in controlled drug delivery and tissue engineering applications. Full article

Histopathological staining remains the fibrosis diagnostic gold standard yet suffers from staining artifacts and variability. Nonlinear optical techniques (e.g., spontaneous fluorescence, Second Harmonic Generation) enhance accuracy but struggle with rapid trace-level detection of fibrosis. To address these limitations, a dual-channel nonlinear optical imaging system with excitation wavelengths at 780 nm and 820 nm was developed, enabling simultaneous spontaneous fluorescence and second-harmonic generation imaging through grid localization. This study applies dual-modality nonlinear imaging to achieve label-free, high-resolution visualization of pulmonary and renal fibrosis at the ECM microstructure scale. Through leveraging this system, it is demonstrated that collagen can be rapidly detected via spontaneous fluorescence at 780 nm, whereas the spatial distribution of collagen fibrils is precisely mapped using Second Harmonic Generation at 820 nm. This approach allows for the rapid and sensitive detection of trace fibrosis in a 5-day unilateral ureteral obstruction mouse model. Additionally, we identify that the elastic fibers, which can also be visualized, provide a foundation for staging diagnosis and delivering accurate and quantitative data for pathological studies and analysis. The research findings underscore the potential of this dual-channel nonlinear optical imaging system as a powerful tool for rapid, precise, and noninvasive fibrosis detection and staging. Full article

Two-photon endomicroscopy (2PEM), an endomicroscopic imaging technique based on the two-photon excitation effect, provides several technical benefits, including high spatiotemporal resolution, label-free structural and metabolic imaging, and optical sectioning. These characteristics make it extremely promising for biomedical imaging applications. This paper classifies distal-scanning 2PEMs based on their actuation mechanism (PZT or MEMS) and excitation–collection optical path configuration (common or separate path). Recent representative advancements are reviewed. Furthermore, we introduce its biomedical applications in tissue, organ, and brain imaging with free-behaving mice. Finally, future development directions for distal-scanning 2PEM are discussed. Full article

Nitroxides are stable organic free radicals with a wide range of applications. They have found applications in chemistry, biochemistry, biophysics, molecular biology, and biomedicine as EPR/NMR imaging techniques. As spin labels and probes, they are used in electron paramagnetic resonance (EPR) spectroscopy in the study of proteins, lipids, nucleic acids, and enzymes, as well as for measuring oxygen concentration in cells and cellular organelles, as well as tissues and intracellular pH. Their unique redox properties have allowed them to be used as exogenous antioxidants. In this review, we have discussed the chemical properties of nitroxides and their antioxidant properties. Furthermore, we have considered their use as radioprotectors and protective agents in ischemia/reperfusion in vivo and in vitro. We also presented other applications of nitroxides in protecting cells and tissues from oxidative stress and in protein studies and discussed their use in EPR/MRI. Full article

Deterministic lateral displacement (DLD) has emerged as a powerful microfluidic technique for label-free particle separation with high resolution. Although recent innovations in pillar geometry have broadened its biomedical applications, the fundamental mechanisms dictating flow behavior and separation efficiency remain not fully understood. In this study, we conducted dissipative particle dynamics simulations to systematically investigate the separation of rigid spherical particles and red blood cells (RBCs) in DLD arrays with inverse L-shaped pillars. The simulations established a predictive formula for the critical separation size in such devices and demonstrated that inverse L-shaped pillars enabled a reduced critical separation size compared with conventional circular pillars. Additionally, we revealed that the inverse L-shaped pillars could act as deformability sensors, promoting localized RBC deformation near their protrusions and inducing stiffness-dependent bifurcation in cell trajectories, which enables effective sorting based on cell deformability. These findings advance the mechanistic understanding of inverse L-shaped DLD arrays and provide valuable design principles for their potential applications. Full article

Dielectrophoresis (DEP) has been widely employed in microfluidic platforms for particle or cell manipulation in biomedical science applications due to its accurate, fast, label-free, and low-cost diagnostic technique. However, the application of the DEP technique towards protein manipulation has yet to be extensively explored due to the challenges of the complexity of protein itself, such as its complex morphologies, extremely minuscule particle size, inherent electrical properties, and temperature sensitivity, which make it relatively more challenging. Furthermore, given that protein DEP investigation requires entering the micro- to nano-scale level of DEP configuration, various challenging factors such as electrohydrodynamic effects, electrolysis, joule heating, and electrothermal force that emerge will make it more difficult in realizing protein DEP investigation. This review study has discussed the fundamental theory of DEP and considerations toward protein DEP manipulation. In particular, it focused on the DEP theoretical principle towards protein, protein DEP application challenges, microfluidic platform considerations, medium considerations, and a critically reviewed list of protein bioparticles that have been investigated were all highlighted. Full article

Large language models (LLMs) have revolutionized natural language processing across diverse domains, yet they also raise critical fairness and ethical concerns, particularly regarding gender bias. In this study, we conduct a systematic, mathematically grounded investigation of gender bias in four leading LLMs—GPT-4o, Gemini 1.5 Pro, Sonnet 3.5, and LLaMA 3.1:8b—by evaluating the gender distributions produced when generating “perfect personas” for a wide range of occupational roles spanning healthcare, engineering, and professional services. Leveraging standardized prompts, controlled experimental settings, and repeated trials, our methodology quantifies bias against an ideal uniform distribution using rigorous statistical measures and information-theoretic metrics. Our results reveal marked discrepancies: GPT-4o exhibits pronounced occupational gender segregation, disproportionately linking healthcare roles to female identities while assigning male labels to engineering and physically demanding positions. In contrast, Gemini 1.5 Pro, Sonnet 3.5, and LLaMA 3.1:8b predominantly favor female assignments, albeit with less job-specific precision. These findings demonstrate how architectural decisions, training data composition, and token embedding strategies critically influence gender representation. The study underscores the urgent need for inclusive datasets, advanced bias-mitigation techniques, and continuous model audits to develop AI systems that are not only free from stereotype perpetuation but actively promote equitable and representative information processing. Full article