Red blood cell (RBC) deformability is a critical biophysical property that enables effective passage of RBCs through microvasculature and ensures proper oxygen delivery. Impairment of this property is associated with various pathological conditions, including type 2 diabetes mellitus (T2DM). In this study, we developed an automated microfluidic platform for high-throughput and real-time assessment of RBC deformability under controlled flow conditions. The device features a structured microchannel design and integrated imaging to quantify individual cell deformation responses. Comparative analyses of RBCs from healthy individuals and T2DM patients revealed significant reductions in deformability in the diabetic group. In vivo validation using a diabetic mouse model further confirmed the progressive decline in RBC deformability under chronic hyperglycemia. This microfluidic approach provides a robust and efficient tool for characterizing RBC mechanical properties, offering potential for disease monitoring and clinical diagnostic applications.

Different ginseng species, such as and , have various medicinal and economic values. Industrially and commercially, it is important to differentiate them. We have adopted the DNA hybridization method based on a single-nucleotide polymorphism (SNP) found in the ginseng DNA. The ginseng DNA samples were placed on a nitrocellulose membrane, and hybridization of the target sample with the probes immobilized on the membrane occurred, resulting in red spots for unaided eye visualization. We managed to demonstrate a spot test and then a lateral flow assay. Genomic DNAs were extracted from ginseng root samples and DNA amplification was used to generate the PCR products that flank the SNP site. We conclude that ginseng DNA can be differentiated based on a DNA lateral flow assay, detecting ~3 ng (in 1 μL) of PCR amplicons.

Electrochemiluminescence (ECL) has evolved into a powerful analytical technique due to its ultra-high sensitivity, low background noise, and precise electrochemical control. The development of efficient ECL emitters is central to advancing this technology for practical applications. Covalent organic frameworks (COFs) have recently emerged as promising candidates for constructing high-performance ECL systems. The tunable porosity, ordered π-conjugated structures, and versatile modular functionalities of COFs provide fast massive transport, effective electron transfer, rapid interfacial electrochemical reaction, and enhanced ECL emission performance. This review provides a comprehensive overview of the rational design strategies and structural engineering for COF-based ECL materials at the molecular level. Linkage chemistry, monomer selection (luminophores and π-conjugated non-ECL motifs), precise framework regulation, post-synthetic modification, composite formation, and other ECL enhancement strategies were discussed for developing COF-based ECL emitter. Both the incorporation of aggregation-induced emission and intramolecular charge transfer mechanisms are included to enhance ECL efficiency. Donor-acceptor conjugation, heteroatom element content, isomerism, substitution, and dimensional direction were regarded as effective strategies to regulate the electronic structure and band diagrams for designing high-performance ECL systems. The role of COFs as both active emitters and functional scaffolds for signal amplification is critically examined. Furthermore, their diverse analytical applications across biosensing, food safety, environmental monitoring, and chiral recognition are highlighted. By correlating structural features with ECL performance, this review offers insights into the design principles of next-generation reticular ECL materials and outlines future directions for their practical deployment in sensitive and selective sensing platforms.

The interaction of therapeutic antibodies with Fc receptors is an important property that is actively modified to improve pharmacokinetic profiles and optimize antibody-dependent mechanisms of action. Various modifications of the Fc and hinge regions of antibodies, leading to a change in affinity with various Fc receptors, are widely covered in the literature. However, data on changes in antibody and Fc receptor interactions after antibody binding to the target antigen are poorly covered in the literature. In this work, we demonstrated a change in the affinity of the interaction of antibodies with Fc receptors after binding to the target antigen via the method of biolayer interferometry. An interesting result was a significant weakening of the interaction of FcRn and FcγRIIIa with some of the antibodies when the latter bound to the target antigen, which suggests the importance of this effect for the pharmacokinetic properties and effector mechanisms of action necessary in the treatment of oncological diseases. The sensor-based biolayer interferometry methods presented in this paper allow antibody screening to be performed to detect the effects of the reduced affinity of interactions with Fc receptors, and can be a useful tool in the early development of therapeutic antibodies.

We report the first analytical application of albumin nanoparticles loaded with luminescent europium complexes for immunoassay development. These nanoparticles, synthesized via a desolvation method, exhibited a uniform spherical morphology with a hydrodynamic diameter of 263 nm and strong, long-lived luminescence at 615 nm (λex = 360 nm). Surface functionalization with streptavidin enabled specific binding to biotinylated proteins. The nanoparticles were applied as labels in a sandwich time-resolved solid-phase immunoassay for human IgG detection in black 96-well plates. Unlike commercial DELFIA assays, the method eliminates the need for signal enhancement steps, as the nanoparticles intrinsically contain high concentrations of europium complexes. Optimization studies revealed that the sharp emission peaks of europium can compromise assay reproducibility; however, employing surface scanning and increasing measurement replicates per well partially mitigated this effect. Time-resolved detection reduced background by two orders of magnitude and increased signal intensity nearly tenfold in IgG-positive samples. The assay demonstrated minimal cross-reactivity with IgA and IgM (~2%) and enabled IgG detection at serum dilutions up to 1:100,000. Comparative analysis showed strong concordance with commercial immunoassays and no concentration-dependent bias. The primary limitation observed was suboptimal intra-assay reproducibility (CV > 20% in four of six tested sera).

Advances in implantable sensor technologies are revolutionizing the landscape of brain science by enabling chronic, precise, and multimodal interfacing with neural tissues. With the convergence of material science, electronics, and neurobiology, flexible, wireless, bioresorbable, and multimodal sensors are expanding the frontiers of diagnosis, therapy, and brain-machine interfacing. This review presents the latest breakthroughs in implantable neural sensor technologies, emphasizing bio-integration, signal fidelity, and functional adaptability. We highlight innovations such as CMOS-integrated flexible probes, internal ion-gated organic electrochemical transistors (IGTs), multimodal neurotransmitter-electrophysiology sensors, and wireless energy systems. Finally, we discuss the clinical potential, translational challenges, and future directions for brain science and neuroengineering. We further benchmark transduction and analytical performance in physiological media and outline in vivo calibration, antifouling/packaging, and on-node data-efficient processing for long-term stability.

With the aim of developing a new tool to meet the increasing demand for food safety, a whole-cell-based system able to detect the presence of cobalt contamination along the pasta production chain was constructed. This system is based on bacterial cells engineered with a plasmid containing the gene under the control of a promoter sequence, and is able to elicit a fluorescence signal when activated. The promoters of four stress-responsive genes (, , , and ) were used to test their responsiveness to cobalt; the promoter of the gene, coding for a universal stress protein, was chosen. The promoter was activated by cobalt, and the system described was highly sensitive, successfully detecting low concentrations of cobalt within complex food matrices derived from durum wheat seeds when exogenous cobalt was added. In food matrices tested alone, a fluorescence signal was present only in bran and fine bran, confirming that these parts of the wheat seed are the ones in which contaminants accumulate. Conversely, in the other matrices derived from the inner part of grains, no signal was detected. The findings reported contribute to the development a new, effective and sensitive tool for monitoring cobalt contamination, offering a valuable approach to enhance food safety control.

Integrating artificial intelligence (AI) with wearable sensor technologies can revolutionize the monitoring and management of various chronic diseases and acute conditions. AI-integrated wearables are categorized by their underlying sensing techniques, such as electrochemical, colorimetric, chemical, optical, and pressure/stain. AI algorithms enhance the efficacy of wearable sensors by offering personalized, continuous supervision and predictive analysis, assisting in time recognition, and optimizing therapeutic modalities. This manuscript explores the recent advances and developments in AI-powered wearable sensing technologies and their use in the management of chronic diseases, including COVID-19, Diabetes, and Cancer. AI-based wearables for heart rate and heart rate variability, oxygen saturation, respiratory rate, and temperature sensors are reviewed for their potential in managing COVID-19. For Diabetes management, AI-based wearables, including continuous glucose monitoring sensors, AI-driven insulin pumps, and closed-loop systems, are reviewed. The role of AI-based wearables in biomarker tracking and analysis, thermal imaging, and ultrasound device-based sensing for cancer management is reviewed. Ultimately, this report also highlights the current challenges and future directions for developing and deploying AI-integrated wearable sensors with accuracy, scalability, and integration into clinical practice for these critical health conditions.

The application of intravascular optical coherence tomography (IV-OCT) in neurovascular interventions is constrained by the mechanical inadequacy of conventional catheters in navigating the complex intracranial vasculature. To address this, we modified a coronary IV-OCT catheter, enhancing its mechanical performance for neurovascular applications. The modified catheter featured a 300 mm over-the-microwire segment and a dual-structured shaft (distal 50 mm nonbraided, proximal 250 mm braided) to improve trackability and pushability. We compared the modified and conventional catheters using a benchtop model with a simulated vessel path and an in vivo swine model. Trackability and pushability were quantitatively measured using resistance (N) and advancement distance (mm) in the simulated path. In the animal model, indirect performance metrics included the catheter tension angle (CTA) and pass of catheter (POC) through the fourth curvature of the external carotid artery (ECA). The modified catheter demonstrated superior pushability (172.9 ± 1.96 mm vs. 127.9 ± 2.86 mm, < 0.05) and increased resistance (1.47 ± 0.036 N vs. 0.69 ± 0.032 N, < 0.05). In vivo analysis further showed a significantly greater CTA (115.8 ± 8.5° vs. 77.6 ± 10.3°, < 0.05) and higher POC success rate (83.3% vs. 11.1%, < 0.05). These results indicate that the modified coronary IV-OCT catheter offers enhanced mechanical performance, suggesting its potential for safe and effective use in neurovascular procedures.

In this study, we present a pilot investigation using a single Purple Heart plant (Tradescantia pallida) to explore whether bioelectrical signals for dual-purpose classification tasks: environmental state detection and human emotion recognition. Using an AD8232 ECG sensor at 400 Hz sampling rate, we recorded 3 s bioelectrical signal segments with 1 s overlap, converting them to mel-spectrograms for ResNet18 CNN (Convolutional Neural Network) classification. For lamp on/off detection, we achieved 85.4% accuracy with balanced precision (0.85-0.86) and recall (0.84-0.86) metrics across 2767 spectrogram samples. For human emotion classification, our system achieved optimal performance at 73% accuracy with 1 s lag, distinguishing between happy and sad emotional states across 1619 samples. These results should be viewed as preliminary and exploratory, demonstrating feasibility rather than definitive evidence of plant-based emotion sensing. Replication across plants, days, and experimental sites will be essential to establish robustness. The current study is limited by a single-plant setup, modest sample size, and reliance on human face-tracking labels, which together preclude strong claims about generalizability.

Maintaining an effective facial seal is critical for the performance of tight-fitting industrial respirators used in high-risk sectors such as mining, manufacturing, and construction. Traditional fit verification methods-Qualitative Fit Testing (QLFT) and Quantitative Fit Testing (QNFT)-are limited to periodic assessments and cannot detect fit degradation during active use. This study presents a real-time fit detection system based on embedded breath sensors and machine learning algorithms. A compact sensor module inside the respirator continuously measures pressure, temperature, and humidity, transmitting data via Bluetooth Low Energy (BLE) to a smartphone for on-device inference. This system functions as a multimodal biosensor: intra-mask pressure tracks flow-driven mechanical dynamics, while temperature and humidity capture the thermal-hygrometric signature of exhaled breath. Their cycle-synchronous patterns provide an indirect yet reliable readout of respirator-face sealing in real time. Data were collected from 20 healthy volunteers under fit and misfit conditions using OSHA-standardized procedures, generating over 10,000 labeled breathing cycles. Statistical features extracted from segmented signals were used to train Random Forest, Support Vector Machine (SVM), and XGBoost classifiers. Model development and validation were conducted using variable-size sliding windows depending on the person's breathing cycles, k-fold cross-validation, and leave-one-subject-out (LOSO) evaluation. The best-performing models achieved F1 scores approaching or exceeding 95%. This approach enables continuous, non-invasive fit monitoring and real-time alerts during work shifts. Unlike conventional techniques, the system relies on internal physiological signals rather than external particle measurements, providing a scalable, cost-effective, and field-deployable solution to enhance occupational safety and regulatory compliance.

In the original publication [...].

Over the past few decades, Fc fragment-conjugated proteins, such as Protein A, have been extensively utilized across a range of applications, including antibody purification, site-specific immobilization of antibodies, and the development of biosensing platforms. In this study, building upon our group prior research, we designed and screened an affinity DNA functional ligand (A-DNAFL) and experimentally validated its binding affinity ( = 6.59 × 10) toward mouse IgG antibodies, whose binding performance was comparable to that of protein A. Systematic evaluations were performed to assess the binding efficiency under varying pH levels and ionic strength conditions. Optimal antibody immobilization was achieved in PBST-B buffer under physiological pH 7.2-7.4 and containing approximately 154 mM Na and 4 mM K. Two competitive binding assays confirmed that the A-DNAFL binds to the Fc fragment of murine IgG antibody. Furthermore, molecular docking simulations were employed to investigate the interaction mode, revealing key residues involved in binding as well as the contributions of hydrogen bonding and hydrophobic interactions to complex stabilization. Leveraging these insights, A-DNAFL was utilized as a tool for oriented immobilization of antibodies on the sensing interface, enabling the construction of an immunosensor for ricin detection. Following optimization of immobilization parameters, the biosensor exhibited a detection limit of 30.5 ng/mL with the linear regression equation is lg() = 0.329 lg() - 2.027 ( = 9, = 0.938, < 0.001)-representing a 64-fold improvement compared to conventional protein A-based methods. The system demonstrated robust resistance to nonspecific interference. Sensing interface reusability was also evaluated, showing only 8.55% signal reduction after two regeneration cycles, indicating that glycine effectively elutes bound antibodies while preserving sensor activity. In summary, the A-DNAFL presented in this study represents a novel antibody-directed immobilization material that serves as a promising alternative to protein A. It offers several advantages, including high modifiability, low production cost, and a relatively small molecular weight. These features collectively contribute to its broad application potential in biosensing, antibody purification, and other areas of life science research.

The International Union of Pure and Applied Chemistry (IUPAC; https://iupac [...].

(PA) is a significant pathogen of clinical concern that is frequently associated with multidrug resistance, leading to respiratory tract, wound, and hospital-acquired infections. To enable rapid and accurate detection, we developed a fluorescence-based loop-mediated isothermal amplification (LAMP) method, targeting the PA-specific ecfX gene. Among ten primer sets designed, the optimal set (EC2) was identified, and reaction conditions were optimized (Bst polymerase 320 U/mL, Mg 8 mM, dNTP 1.4 mM, inner/outer primer ratio 1:8, 64 °C, 20 min). The assay demonstrated a detection limit that was comparable to a real-time polymerase chain reaction and immunochromatographic assays, but with a markedly reduced turnaround time. No cross-reactivity was observed with non-PA pathogens, and reproducibility tests confirmed high stability. In addition, the reliability of the results was further verified using 60 standard bacterial strains, and the feasibility of the assay was validated with 2 real soil samples and 1 water sample. This LAMP method offers a simple, rapid, and sensitive tool for on-site detection of PA, with potential applications in clinical diagnostics and public health surveillance.

Electrochemiluminescence (ECL) is a chemiluminescence phenomenon triggered by electrochemical reactions at the electrode surface, which has gradually become a high-sensitivity detection technology due to its low background, simple instrumentation, and high sensitivity. Therein, potential-resolved ECL refers to the generation of two or more ECL signals with distinct potentials and wavelengths during an electrochemical process. This unique capability enables simultaneous multi-signal outputs, making potential-resolved ECL particularly promising for self-calibration and multiplexed detection strategies. In this review, we focus on two critical aspects: on the one hand, the advancement of traditional ECL luminophores and potential-resolved ECL systems was reviewed, which were classified, respectively, into three categories to be introduced in detail (inorganic, organic and nanomaterial-based ECL luminophores or potential-resolved ECL of metal-organic complexes, layer-by-layer-modified electrodes, and nanomaterials). On the other hand, we summarized ECL detection strategies based on potential-resolved ECL systems and the application of these protocols in disease biomarker detection, which results in two categories (self-calibration strategies and multi-target strategies) for discussion. In this work, we aim to inspire investigators to explore novel ECL luminophores and design detection strategies with high performance, which could provide strong support for precision medicine, personalized assessment, portable medical devices, and the digital transformation of healthcare.

Functionalized nanochannels are crucial for achieving excellent ion transport properties and enable versatile applications such as ion gating, biosensing, and energy conversion. Conical single nanochannels were fabricated in single-ion-track polyethylene terephthalate (PET) membranes using the ion-track-etching method. Leveraging the high programmability of deoxyribonucleic acid (DNA) strands, a series of DNA molecules were designed to functionalize the outer surface at the tip region (small opening) of the conical PET nanochannels. This approach enabled precise regulation of both spatial charge distribution and steric hindrance on the outer surface, enabling the investigation of ion transport properties under the dominance of outer surface charge effects across ions of different valences. In contrast to the low-valence K, the high-valence cation Ru(NH) exhibited far greater enhancement in ionic current rectification (ICR) within PET films functionalized with DNA of varying charge densities. We used COMSOL simulations to corroborate that higher-valence ions exert more pronounced effects on ion transport in conical nanochannels with greater outer surface charge density. Furthermore, it was confirmed that the tip region plays a critical role in modulating the ion transport properties of conical nanochannels, thereby validating outer surface functionalization as a rational and efficient strategy.

Atom transfer radical polymerization (ATRP) is a leading reversible deactivation radical polymerization method. It has become an emerging technology to synthesize well-defined, tailor-made polymers, promoting the development of advanced materials (e.g., bioconjugates and nanocomposites) with precisely designed and controlled macromolecular architectures. ATRP-produced polymers or polymeric materials have been successfully applied in the fields of drug delivery, tissue engineering, sample separation, environmental monitoring, bioimaging, clinical diagnostics, etc. In this review, we systematically summarize the progress of ATRP-based chemical and biological sensors in different application fields, including ion sensing, small-molecule detection, bioimaging, and signal amplification for biosensors. Finally, we briefly outline the prospects and future directions of ATRP. This review is expected to provide a fundamental and timely understanding of ATRP-based sensors and guide the design of novel materials and methods for sensing applications.

The biophysical characteristics of cellular membranes, particularly their electrical properties in the α-dispersion frequency domain, offer valuable insights into cellular states and are increasingly important for cancer diagnostics through epidermal growth factor receptor (EGFR) expression analysis. However, a critical limitation in these electrical measurements is the confounding effect of morphological changes that inevitably occur during prolonged observation periods. These shape alterations significantly impact measured capacitance values, potentially masking true biological responses to epidermal growth factor (EGF) stimulation that are essential for cancer detection. In this study, we attempted to address this fundamental challenge by developing a deep learning method that establishes a direct computational relationship between cellular morphology and electrical properties. We combined optical trapping technology and capacitance measurements to generate a comprehensive dataset of HeLa cells under two different experimental conditions: (i) DPBS treatment and (ii) EGF stimulation. Our convolutional neural network (CNN) architecture accurately predicts 401-point capacitance spectra (0.1-2 kHz) from binary morphological images at low frequencies (0.1-0.8 kHz, < 10% error rate). This capability allows for the identification and subtraction of morphology-dependent components from measured capacitance changes, effectively isolating true biological responses from morphological artefacts. The model demonstrates remarkable prediction performance across diverse cell morphologies in both experimental conditions, validating the robust relationship between cellular shape and electrical characteristics. Our method significantly improves the precision and reliability of EGFR-based cancer diagnostics by providing a computational framework for a morphology-induced measurement error correction.

Nanotechnology offers powerful new tools to enhance food quality monitoring and safety assurance. In the food industry, nanoscale materials (e.g., metal, metal oxide, carbon, and polymeric nanomaterials) are being integrated into sensory systems to detect spoilage, contamination, and intentional food tampering with unprecedented sensitivity. Nanosensors can rapidly identify foodborne pathogens, toxins, and chemical changes that signal spoilage, overcoming the limitations of conventional assays that are often slow, costly, or require expert operation. These advances translate into improved food safety and extended shelf-life by allowing early intervention (for example, via antimicrobial nano-coatings) to prevent spoilage. This review provides a comprehensive overview of the types of nanomaterials used in food sensory applications and their mechanisms of action. We examine current applications in detecting food spoilage indicators and adulterants, as well as recent innovations in smart packaging and continuous freshness monitoring. The advantages of nanomaterials-including heightened analytical sensitivity, specificity, and the ability to combine sensing with active preservative functions-are highlighted alongside important toxicological and regulatory considerations. Overall, nanomaterials are driving the development of smarter food packaging and sensor systems that promise safer foods, reduced waste, and empowered consumers. However, realizing this potential will require addressing safety concerns and establishing clear regulations to ensure responsible deployment of nano-enabled food sensing technologies. Representative figures of merit include Au/AgNP melamine tests with LOD 0.04-0.07 mg L and minute-scale readout, a smartphone Au@carbon-QD assay with LOD 3.6 nM, FeO/DPV detection of Sudan I at 0.001 µM (linear 0.01-20 µM), and a reusable Au-FeO piezo-electrochemical immunosensor for aflatoxin B1 with LOD 0.07 ng mL (≈15 × reuse), alongside freshness labels that track TVB-N/amine in near-real time and e-nose arrays distinguishing spoilage stages.

In the original publication [...].