Search Results (493)

Wearable devices increasingly capture biosignals such as electrocardiograms, photoplethysmograms, inertial signals, and electrodermal activity during daily life, enabling earlier detection and continuous monitoring outside the clinic. Real-time edge machine learning can convert these streams into timely, privacy-preserving inference by placing computation on a wearable (device-only) or a paired phone, with intermittent cloud assist used selectively for dashboards, summarisation, and lifecycle management. Clinical adoption remains uneven because free-living data are noisy, labels are often delayed, and device ecosystems evolve over time. This narrative review organises the literature as an end-to-end deployment pathway: sensing and artefact management, streaming windowing and multimodal alignment, and model families suited to on-device inference. We compare classical feature-based pipelines with learned representations, including compact CNN/TCN and recurrent and efficient attention-based models, and discuss when self-supervised pretraining and distillation are most useful in low-label settings. We then synthesise deployment engineering levers (quantisation, pruning, and distillation) and benchmarking requirements, emphasising runtime constraints that determine feasibility: latency per update, peak RAM, energy per inference, duty cycle, and thermal behaviour. Applications are grouped across cardiovascular monitoring, blood pressure and haemodynamics, sleep and respiration, and movement and stress, with explicit attention to false-alert burden, adherence, and workflow integration. To support translation, we provide a validation ladder and a reliability toolkit covering calibration, uncertainty-aware thresholds and deferral, drift monitoring triggers, and safe update governance. The novelty of this review is a deployment-oriented synthesis that ties modelling choices to edge tiers and resource budgets and provides reusable reporting templates, including an edge-cost card and comparative tables spanning modalities, models, deployment levers, applications, and reliability requirements. Full article

Opportunities abound in microfluidic technologies to impact how we understand extremely complex systems with many constituents which change with time and space. In these technologies, separation science plays a central role towards understanding everything from biology and healthcare to environmental monitoring to the search for life in the Solar system. Separations can amplify the capabilities of detection modalities by isolating targets and/or increasing their concentration while removing background constituents which can interfere with their sensing. In essence, separations increase the amount of information that can be gathered from a sample. The ideal features of next-generation separations capability are present in gradient insulator-based dielectrophoresis (g-iDEP), enabled by the length scale and precision of microfluidics. It acts through electric field interactions with particles, which enables unbiased (label-free) separations since all relevant particles, from atoms to cells, have an accessible response to electricity—either through linear (electrophoresis) or higher-order gradient (dielectrophoresis and related) effects. The technique isolates and concentrates, enabling improved detection function and multidimensional separations. Its foundational theoretical capabilities give it separations power on the order of 1:10⁸, beyond the resolving power of the best mass spectrometers and ultra-high resolution spectroscopies. Experimental evidence is amassing that shows it to be a powerful tool that can resolve tiny differences in cells (antibiotic resistance versus susceptible in unlabeled paired isolates across many species) and differentiate single-point mutations in proteins. Its capabilities are still emerging, and this work aims to quantify the current practice and connect those approaches to the ultimate capabilities of the technique towards quantifying the dynamic range and resolving power of the strategy as a whole. The technique uses two methods of quantifying the electrophysical properties of the target, voltage sweep and spatial methods. The voltage sweep method is lower-resolution and serves as a search mode, while the spatial method is higher-resolution and quantifies the properties over a smaller defined range determined via the sweep method. These quantification methods are examined by collating existing experimental data, performing relevant Monte Carlo simulations, and finite element model calculations. These are summarized to understand the mechanisms currently limiting the technique, facilitate quantitative comparisons with traditional separation science capabilities in terms of resolution and dynamic range, and compare them to the theoretical limits of the strategy. Full article

Haemoglobin (Hb) concentration is a key biomarker for several diseases. Traditional laboratory methods often have limitations due to their time-consuming nature, the need for skilled personnel, or the use of high-cost instrumentation. This work presents a sensing strategy for developing new point-of-care tests (POCTs) for Hb detection via a proof of concept. The proposed sensing approach is implemented using plasmonic plastic optical fiber (POF) sensor chips that integrate an electropolymerized molecularly imprinted polymer (eMIP) film on the plasmonic surface for Hb-selective detection. The developed sensor system demonstrates an ultra-low detection limit of 80 fM in buffer, about five orders of magnitude lower than that of other comparable Hb sensors. Selectivity tests against common interfering proteins, such as bovine serum albumin (BSA) and immunoglobulin G (IgG), confirmed high specificity towards the target analyte. Moreover, the sensor’s performance was tested using a whole-blood sample, yielding results consistent with those of standard haematology analysis. The proposed sensor system, based on simple equipment, provides a quick (about 10 min) and cost-effective (about 10 euros per chip) label-free diagnostic tool for POCTs in real-world scenarios, such as finger-prick sampling, offering a less invasive alternative to traditional laboratory methods, towards devices useful for Internet of Medical Things (IoMT). Full article

Type IV pili are widespread and multifunctional filamentous nanomachines that contribute to bacterial motility, adhesion, surface sensing, DNA uptake, biofilm formation, and, in some organisms, extracellular electron transfer. Owing to their small diameter, rapid dynamics, and sensitivity to experimental perturbation, type IV pili have historically been difficult to visualize in their native states. Recent advances in cryo-electron microscopy (cryo-EM), cryo-electron tomography (cryo-ET), fluorescence-based live-cell imaging, and label-free approaches such as interferometric scattering microscopy (iSCAT) have substantially expanded our ability to observe type IV pili across spatial and temporal scales. In this review, we summarize how these visualization strategies have reshaped current understanding of type IV pili, from conserved filament architecture and envelope-spanning assembly machineries to force-dependent behaviors and context-specific physiological functions. We further discuss how imaging-informed knowledge may support translational efforts, including antivirulence intervention, vaccine design, bioelectronic optimization, and microbial engineering, while emphasizing the current limitations of these applications. By integrating structural, dynamic, and functional perspectives, this review aims to provide a coherent framework for future studies of type IV pili in microbiology, infection biology, and biotechnology. Full article

Quartz crystal microbalance (QCM) technology provides a powerful, label-free platform for monitoring molecular interactions in real time with nanogram sensitivity. Recent advances in compact instrumentation have enhanced analytical performance while reducing energy consumption, aligning with the principles of Green Analytical Chemistry. In parallel, the European Union has recommended the replacement of animal-derived antibodies with non-animal alternatives, creating an urgent need for sustainable affinity receptors. In this study, we present an innovative application of polynorepinephrine (PNE)-based molecularly imprinted polymers (MIPs) with a compact QCM sensing. PNE, a bioinspired polymer formed under mild aqueous conditions, offers strong adhesive properties and biocompatibility, enabling robust immobilization of imprinted receptors on gold-coated quartz disks. The resulting PNE-MIP/QCM platform combines the ultrasensitivity of quartz microbalances with the selectivity of molecular imprinting, delivering a reproducible and environmentally responsible affinity sensor. The sensor showed a limit of detection of 11.2 nM and enabled accurate IgG quantification in diluted human serum samples. As a proof of concept, the system was applied to Human Immunoglobulin G (IgG1) detection, demonstrating its potential for sustainable clinical diagnostics. Full article

Thyroxine (T4) is a key hormone regulating metabolic, cardiovascular, and neurodevelopmental processes, yet its clinical quantification still relies on centralized immunoassays that limit rapid or point-of-care monitoring. Here, we present a label-free biosensing platform based on silicon nanowire field-effect transistors (SiNW-FETs) functionalized with a T4-selective DNA aptamer via a 3-Triethoxysilyl propylsuccinic Anhydride (TESPSA)-mediated silanization approach, enabling a streamlined two-step modification for oriented immobilization. The biosensor achieves robust real-time detection of T4 across the physiological concentration range (5–30 pM), with a limit of detection of ~5 pM and a strong linear correlation between drain current and analyte concentration (R² = 0.9931). Specificity is confirmed using non-functionalized devices and estradiol as a non-target control. All measurements were performed in undiluted phosphate-buffered saline, representing a physiologically relevant ionic environment and demonstrating stable sensor performance under realistic buffer conditions. The dose–response behavior follows a Hill model, allowing extraction of binding parameters and confirming that the electrical signal originates from specific aptamer–target interactions. These results demonstrate that aptamer-functionalized SiNW-FETs provide a highly sensitive, selective, and miniaturizable platform for quantitative thyroid hormone monitoring, with strong potential for future point-of-care applications. Full article

Continuous monitoring of the Stand-to-Sit (STS) transition serves as a critical indicator of lower-limb frailty in the elderly, for which millimeter-wave radar provides an ideal privacy-preserving, device-free sensing solution. However, robustly distinguishing between safe Controlled Sits (CSs) and dangerous Uncontrolled Descents (UDs) is severely hindered by the prohibitive cost of subjective expert scoring for fine-grained labels, alongside the pervasive “Clever Hans” effect where existing deep models overfit static environmental clutter rather than learning intrinsic human kinematics. To circumvent these bottlenecks, we formulate STS evaluation as a dynamic boundary detection problem and propose SCA-BiMamba, a linear-complexity bidirectional State-Space Model that utilizes actual fall events as extreme kinematic surrogates for UDs. This forces the network to learn a strict physical boundary between CS and physiological failure without subjective grading. Furthermore, we establish a stringent Real-to-Sim diagnostic audit as a core methodological contribution. By projecting models trained on noisy real-world data onto pure-kinematics simulations—incorporating stochastic temporal phase shifts, kinematic overlaps, and unified physiological tremors—we explicitly quantify feature disentanglement. This protocol serves as a formal ‘probing test’ to expose the ‘Clever Hans’ effect, ensuring the model relies on invariant human physics rather than transient environmental artifacts. Extensive experiments demonstrate that SCA-BiMamba achieves highly robust classification on real-world data (averaging 94.2% Macro F1 with 100.0% Uncontrolled Descent Recall), and achieves a highly robust 99.4% ± 1.1% Macro F1 in the simulated zero-shot transfer. We emphasize that this optimal performance reflects the successful abstraction of extreme kinematic boundaries, rather than a flawless resolution of all clinical complexities. Concurrently, it exhibits strict resistance to shortcut learning and sustains robust real-world scalability using merely 20% of the training data, thereby establishing a promising privacy-preserving boundary-based radar motion classification framework for distinguishing controlled sitting from extreme instability surrogates. Full article

Metabolic heterogeneity within the cell microenvironment is a key driver of cancer progression and resistance to therapy. However, current approaches lack the spatial and temporal resolution required to capture its dynamics in living systems. While recent advances in 3D cell culture models and metabolomic profiling have improved our understanding of the tumor niche, their integration with real-time optical sensing remains underdeveloped. Here, we present an integrated platform combining a 3D-printed microreactor culture chamber with Raman spectroscopy to enable non-invasive, spatially resolved metabolic monitoring of living cell cultures. Our microreactor platform generates controlled oxygen and nutrient cues while simultaneously acquiring label-free Raman spectra, revealing extracellular metabolic fingerprints linked to cell catabolism (e.g., glucose and lactate shifts) and acidification. Analysis across four cell lines uncovered temporal evolution as the dominant source of metabolic variance, while spatial heterogeneity along oxygen gradients is a secondary factor. In particular, diffusion-limited regions exhibited localized acidification and accumulation of stress biomarkers—such as the release of nucleotides—features that cannot be detected using conventional bulk assays. By providing a versatile platform for real-time mapping, this work enables the mechanistic dissection of cell adaptation to microenvironmental stress and supports the prediction of metabolic signatures underlying drug response and treatment outcomes. Full article

The rapid and precise detection of biomarkers and pathogens remains a critical challenge in clinical diagnostics. Traditional methodologies are frequently hindered by protracted workflows, complex sample preparation, and reliance on resource-intensive instrumentation. Acoustofluidics—the synergistic integration of acoustics and microfluidics—has emerged as a transformative solution for point-of-care testing (POCT). Bulk acoustic wave (BAW) and surface acoustic wave (SAW) technologies enable the contactless, label-free, and biocompatible manipulation of bioparticles across micro- and nanometer scales. This review critically examines recent advancements in BAW- and SAW-based acoustofluidic biosensors. We elucidate the fundamental principles governing distinct acoustic modes—including Quartz Crystal Microbalance (QCM), film bulk acoustic resonator (FBAR), and Solidly Mounted Resonator (SMR) for BAW and Rayleigh and Love waves for SAW—and evaluate their specific roles in liquid-phase sensing, particle sorting, and cellular focusing. Results show that integrating on-chip sample preparation accelerates diagnostic workflows, reducing assay times to under 10 min. Coupling acoustic manipulation with optical, mass-based, or electrochemical modalities effectively overcomes fundamental diffusion limits, achieving ultrasensitive, multimodal detection. We address translational challenges—acoustothermal heating, biofouling, and scalable integration. Following a discussion of clinical applications in oncology and infectious diseases, we map emerging trajectories, emphasizing AI-driven intelligent microfluidics, modular architectures, and flexible wearable platforms that will ultimately democratize continuous precision diagnostics. Full article

Biosensors have evolved significantly since their invention in the mid-twentieth century. From a simple electrochemical device to the current inclusion of AI, these sophisticated tools are capable of label-free, real-time multiplex detection. To make these sensing systems even more powerful, the incorporation of DNA origami has allowed this technology to become extremely precise, recognisable, and programmable to a range of molecules. This paper systematically summarises the incorporation of DNA origami with biosensors such as fluorescence, surface-enhanced Raman spectroscopy (SERS), surface plasmon resonance (SPR), and electrochemical sensors as well as approaches that are used to design DNA origami nanostructures. These tools allow a range of targets to be detected, ranging from small molecules to larger biological species. Collectively, these studies demonstrate that DNA origami-based biosensors provide high sensitivity; precise spatial control; and rapid, modular detection capabilities. Furthermore, their versatility enables applications across a diverse range of sectors. However, key challenges including limited reproducibility, structural instability, photobleaching, and non-specific binding continue to hinder their widespread adoption. This review proposes future directions aimed at overcoming key limitations, including enhancing biocompatibility and structural stability, to support the development of more advanced and clinical point-of-care-applicable biosensors. Full article

Rapid urbanization and the rapid accumulation of multi-source and multi-temporal Earth observation data are creating an increasing demand for remote sensing models that can flexibly support fine-grained monitoring beyond fixed label taxonomies. Open-vocabulary remote sensing image semantic segmentation (OVRSIS) aims to segment text-specified categories beyond a fixed label space with vision–language foundation models. However, dense remote sensing scenes make pixel–text matching highly vulnerable to semantic confusion and misalignment, owing to extreme scale variation, thin structures, repetitive textures, and prompt sensitivity. To address these challenges, we propose UGDMoE, an uncertainty-guided mixture-of-experts framework for OVRSIS. First, we design a domain-specific MoE decoder with three geometrically specialized experts—for slender structures, mid-scale objects, and large-region context—routed by the alignment-risk cue $U_0$. Second, we introduce a lightweight prompt–response estimation strategy that quantifies prediction dispersion across semantically equivalent prompts to derive $U_0$ in an annotation-free manner. Third, we develop prompt ensemble-based likelihood calibration (PELC), which takes the shared alignment-risk cue $U_0$ as input to calibrate prompt-specific logits before refinement. Finally, we design a lightweight uncertainty-aware structure refinement module that, guided by $U_0$, selectively fuses early visual features with segmentation logits to restore boundary continuity and connectivity of thin structures. We conduct extensive experiments on eight OVRSIS benchmarks under cross-dataset evaluation protocols. Trained on DLRSD, it achieves 46.97 m-mIoU and 63.31 m-mACC, surpassing the strongest baseline by 0.76 and 0.62 points; trained on iSAID, it reaches 37.47 m-mIoU and 58.52 m-mACC, improving over the strongest competitor by 0.71 and 0.61 points. UGDMoE consistently achieves state-of-the-art performance and remains robust under training-source changes. Full article

Heavy metal mercury is one of the most significant and toxic environmental contaminants. Its inorganic form (Hg²⁺) and organic form (organic mercury, OrHg) can cause irreversible harm to human health and the ecological environment, and the latter is particularly prone to bioaccumulation and bioamplification in the food chain. Therefore, there is an urgent need for a rapid, reliable and specific detection of Hg²⁺ and OrHg to evaluate the potential risk for human health. Here, a novel label-free fluorescent sensing platform based on ssDNA aptamer (A_A-T7)-templated AuNCs was established for sensitive recognition and specific detection of Hg²⁺ and OrHg. In the presence of OrHg, the fluorescence of pure A_A-T7-templated AuNCs was visibly enhanced through forming Ag/AuNCs based on Ag⁰-doped AIEE effect. However, they were obviously quenched because of generating non-fluorescent Au/Ag/Hg ANPs via metallophilic interactions among Au³⁺, Ag⁺, and Hg²⁺ (5d¹⁰-4d¹⁰-5d¹⁰) when only Hg²⁺ existed. This fluorescent sensing platform could detect as low as 20.0 nM (4.0 ng Hg/g) and has a good linear detection range, with target concentrations ranging from 0.25 μM to 2.00 μM, recoveries of 98.0–108.0%, and RSD ≤ 5.0%. Low-toxic A_A-T7-templated AuNCs could be used for cytotoxicity analysis and intracellular fluorescent imaging. The method has been successfully applied to the determination of Hg²⁺ and OrHg in tap water, seawater and dried golden pomfret fish muscle samples, demonstrating promising prospects for the assay of mercury species in environmental samples and aquatic products to ensure human health and food safety. Full article

Escherichia coli (E. coli) is a microorganism commonly found in water and food matrices, and its rapid and accurate detection is crucial for maintaining public health and ensuring food safety. However, traditional molecularly imprinted polymer (MIP) sensors often face challenges such as tedious template removal and prolonged sensing times. This study develops a label-free bacterial molecularly imprinted sensor that utilizes the synergistic effect of polypyrrole (PPy) and multi-walled carbon nanotubes (MWCNTs) to achieve highly sensitive detection of E. coli. Based on the large specific surface area and superior conductivity of MWCNTs, as well as the favorable electrochemical polymerization properties of PPy, a PPy/MWCNTs composite film was fabricated via a one-step electropolymerization process. The prepared sensor exhibited excellent kinetic characteristics, with a template removal time of only 15 min, and could be regenerated and used for subsequent detection within 30 min. Under optimized conditions, the biosensor showed a satisfactory linear response over the concentration range of 10²–10⁸ CFU/mL, with a low detection limit of 65 CFU/mL (3σ/S). Furthermore, recovery experiments conducted in tap water and lemon juice samples yielded satisfactory recoveries ranging from 87.1% to 114.8%, demonstrating the reliability and practical applicability of the proposed sensor for bacterial detection in real samples. This sensor offers advantages such as simple preparation, low material cost, and high sensitivity, providing a reliable and practical analytical platform for the rapid and reliable detection of bacteria. Full article

Biosensors based on electrolyte-gated organic field-effect transistors (EGOFETs) have attracted considerable attention due to their advantages, including low cost, inherent signal amplification, and low-voltage operation. A critical step influencing sensing performance is the integration of specific receptors onto the device surface. Among various strategies, the covalent immobilization of biorecognition elements onto gold surfaces via thiol chemistry is one of the most widely used approaches. In this study, we report the optimization of a mixed self-assembled monolayer (SAM) composed of 11-mercaptoundecanoic acid (11-MUA) and 3-mercaptopropionic acid (3-MPA) for label-free detection of human IgG using EGOFETs. The quality of the SAM was systematically modulated by varying the total concentration from 10 to 400 mM and characterized using X-ray Photoelectron Spectroscopy (XPS), Electrochemical Impedance Spectroscopy (EIS), Cyclic Voltammetry (CV), and Atomic Force Microscopy (AFM). The results revealed that a concentration of 50 mM yielded a densely packed and well-ordered monolayer. After covalent immobilization of anti-IgG antibodies via 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride/N-hydroxysuccinimide (EDC/NHS) chemistry and subsequent blocking with ethanolamine and bovine serum albumin (BSA), the functionalized gate electrodes were integrated into poly(3-hexylthiophene) (P3HT)-based EGOFETs. Electrical measurements demonstrated that EGOFET biosensors functionalized with the 50 mM SAM achieved optimal sensing performance. The devices exhibited a highly linear response (R² = 0.998) over a wide concentration range from 1 fM to 10 nM, with a LOD of 2.82 fM, and showed excellent selectivity against non-target immunoglobulins A and M (IgA and IgM). This SAM concentration optimization strategy provides a versatile approach for engineering high-performance EGOFET biosensors, with potential applicability to a broad range of disease biomarkers. Full article

Field-effect transistor (FET) biosensors using graphene have become one of the most promising biosensing platforms for the early diagnosis of diseases with features such as high sensitivity, label-free detection and application compatibility with point-of-care systems. Herein, we critically discuss recent advances in graphene FET (GFET) biosensor development toward clinically relevant biomarkers associated with representative diseases including cancer, neurodegenerative disease, infectious disease, and inflammatory conditions. Recent progress was reviewed to evaluate GFET architectures, surface functionalization methods, and detection quality. The biomarkers explored were clusterin in Alzheimer’s disease, thrombin in coagulopathy, estrogen receptor α (ER-α) in breast cancer, Carcinoembryonic antigen in lung cancer, microRNAs for malignant tumors, exosomes derived from HepG2 for the hepatocellular carcinoma (HCC) cell line, interleukin-6 (IL-6) for chronic obstructive pulmonary disease (COPD), Polyclonal antibodies and antigens (P24) for HIV and prostate-specific antigen for prostate cancer. The developed devices demonstrate ultralow detection limits at femtomolar to attomolar concentrations with the aid of designed antibodies, aptamers and nanomaterials. Herein, this review presents the sensing mechanisms and biomedical application of various GFET platforms, focusing on their emerging potential as next-generation platforms for rapid, non-invasive and point-of-care diagnostics. Full article