Rapid identification of free thiols is essential in pharmaceutical quality control and food safety. Herein, we demonstrate the sensing concept of “two signals from one event” by exploiting the intrinsic dual functionality of selenium (Se) nanomaterials to construct a signal-on, dual-mode sensing platform for free thiols. The single event is thiol-triggered seeded growth: free thiols interact with L-cysteine-modified Se seeds, driving seed fusion and crystallization into chiral trigonal Se structures. This same event yields two readouts simultaneously: a colorimetric response arising from particle size enlargement (UV-Vis red-shift) and a turn-on circular dichroism (CD) signal from long-range chiral ordering. The method achieves a detection threshold of 10 μmol/L for various free thiols and can be completed within 60 min from seed preparation to signal readout without complex instrumentation. It serves as a general indicator of total free thiols rather than distinguishing individual thiol species. Above the threshold, concentration-dependent color deepening and spectral redshifts provide semi-quantitative estimation of the concentration range. Its practicality was validated by detecting D-penicillamine in commercial tablets. This strategy offers an approach for on-site free thiol screening and highlights the potential of chiral inorganic nanomaterials in multi-mode sensing via chiral transfer and amplification.
Saxitoxin (STX) is a highly potent marine biotoxin, and trace contamination in aquatic environments can pose serious risks to human health. Therefore, reliable detection of low-concentration STX is crucial for water safety monitoring. Here, we developed a robust electrochemical aptasensor based on a gold nanoparticle/chitosan (AuNP/CS) conductive network for STX detection in freshwater samples. The chitosan matrix provides a three-dimensional scaffold for aptamer immobilization, while interconnected AuNPs create efficient electron-transfer pathways across the sensing interface. This integrated architecture improves interfacial conductivity and supports stable target-induced aptamer recognition. The aptasensor exhibits a linear response from 1 to 1000 nM and a limit of detection of 0.74 nM, with an apparent dissociation constant Kd = 70.29 ± 29.2 nM, together with high batch-to-batch consistency and long-term stability, retaining 93% of its initial response after 25 days. In spiked freshwater samples, the aptasensor achieved recoveries ranging from 99.10% to 111.33%, demonstrating the practical potential of this platform for monitoring STX contamination in aquatic environments.
Background: Rapid screening of acetylcholinesterase (AChE) inhibition is essential for monitoring exposure to organophosphate compounds, particularly in field settings where access to laboratory infrastructure is limited. This study aimed to develop a portable paper-based colorimetric biosensor for the semi-quantitative detection of AChE activity in blood samples. Methods: The biosensor was based on a pH-dependent color change adapted from the modified Edson method. The platform was first optimized using experimental models and then evaluated in human capillary blood samples collected under real field conditions. Reference serum cholinesterase activity was determined by a certified clinical laboratory and used for comparison with the colorimetric response of the biosensor. RGB (red, green, and blue) image analysis was performed in a subset of samples to digitally characterize the chromatic response of the device. Results: Samples with preserved AChE activity showed a visible color shift associated with substrate hydrolysis, whereas samples with reduced or inhibited activity maintained darker blue-dominant tones. RGB analysis of human samples revealed significant associations between AChE activity and the R and G channels, supporting the ability of the platform to distinguish between chromatic patterns associated with different ranges of enzymatic activity. Conclusions: The proposed platform demonstrated the feasibility of translating a pH-based AChE assay into a portable paper-based format for semi-quantitative visual and RGB-assisted screening. Its evaluation in human samples demonstrated the feasibility of its use under field conditions as a proof-of-concept. Further studies are needed to optimize its analytical performance and validate its application in larger and more diverse populations.
Nanoneedle arrays provide a promising interface for intracellular delivery, yet scalable control of array geometry and membrane penetration mechanics remains insufficiently understood. Here, we developed a rapid and scalable strategy for fabricating geometry-tunable silicon nanoneedle arrays. One-step SF6/O2 etching produced ordered arrays with center-to-center spacing of 1–5 μm, whereas pseudo-Bosch etching produced high-aspect-ratio (HAR) nanoneedles. Using a microwell-assisted cell-on-probe atomic force microscopy platform, we quantified the first penetration force, penetration probability, and number of penetration events at the single-cell level. For one-step-etching arrays, increasing spacing from 1 to 5 μm reduced the first penetration force from 34.87 ± 2.90 to 4.05 ± 0.30 nN and increased the penetration probability from 0.21 ± 0.03 to 0.87 ± 0.04. A phenomenological inverse-square model captured the force–spacing relationship, supporting an array-level load-sharing mechanism. Under identical vibration-assisted microfluidic conditions, the FITC-dextran-positive fraction increased from 22.8% for 1 μm arrays to 77.1% for 5 μm arrays, whereas 1 μm HAR arrays achieved 28.8%. These results identify array spacing as a key factor governing single-cell penetration and delivery and provide a mechanistic basis for nanoneedle-based biosensor and cell-interface design.
Hydrogen peroxide (H2O2) may be illegally added to milk to delay visible spoilage, producing concentration-dependent changes in its refractive index. This study numerically evaluates the optical response of a B-sil/Al/Al2O3/WS2 surface plasmon resonance (SPR) configuration to these refractive-index variations. The angular SPR response was calculated at λ = 633 nm using the transfer matrix method under TM-polarized illumination. The B-sil/Al/Al2O3/WS2 architecture was established through sequential evaluation of prism material, Al thickness, Al2O3 thickness, and 2D interfacial material, followed by assessment of its response to H2O2-associated refractive-index changes in milk. The final structure consisted of 70 nm Al, 25 nm Al2O3, and 0.80 nm WS2. Across the simulated H2O2 conditions, the resonance angle shifted from 85.65° to 86.11°, while the angular sensitivity ranged from 383.82 to 406.45°/RIU. The best balance among the evaluated metrics was obtained for H2O2-C2, with a sensitivity of 406.45°/RIU, QF of 4.65 RIU−1, FoM of 436.48 RIU−1, LoD of 1.23 × 10−5, and CSF of 436.56. The WS2-containing interface produced calculated angular shifts for small refractive-index variations in the milk sensing medium. Comparison with reported SPR systems for milk-related sensing showed a comparable angular sensitivity range, while QF and detection accuracy were limited by the broad resonance profile. These results characterize the theoretical optical response of the B-sil/Al/Al2O3/WS2 multilayer under H2O2-associated refractive-index changes but do not establish chemical selectivity toward H2O2. Experimental implementation would require an appropriate selective filtering or recognition strategy, together with validation of matrix effects and practical sensing performance.
Nanostructured carbon materials are low-dimensional systems relevant to oncology biosensing, with their utility arising from an electronic structure coupled to defect and edge states, a charge-transfer behavior and optical response that report molecular binding, an interfacial chemistry governing contact with the analyte, and, in selected cases, magnetic properties enabling manipulation and readout. In functional terms, these behaviors trace to specific quantum-relevant features—quantum confinement, edge and defect states, and the resulting size-dependent optical and charge-transfer responses—rather than to a generic quantum-material designation. This critical review treats nanocarbons as engineered biointerfaces whose performance is set by how the carbon surface behaves in biological fluid, how recognition chemistry is anchored, and how the binding event is transduced, with magnetic responsiveness, stability, reproducibility, and fabrication control as decisive constraints. The analysis separates three material classes—non-magnetic nanocarbon sensors, hybrid carbon–magnetic systems, and defect-associated or potentially metal-free magnetic carbons—while grading evidence as direct, adjacent, comparator-derived, or prospective. Directly, graphene, carbon nanotubes, carbon dots, graphene quantum dots, and magnetic carbon hybrids serve in electrochemical, optical and fluorescent, field-effect, and magnetic-assisted formats. Clinical translation, however, remains constrained by biofouling, protein-corona formation, matrix interference, unstable functionalization, batch variability, incomplete standardization, and scarce validation in real samples and patient cohorts. Metal-free or defect-associated magnetic carbons therefore warrant caution, remaining prospective platforms until the preservation of magnetic response, reproducible functionalization, matrix compatibility, safety, and measurable analytical advantage are directly demonstrated.
Behavioral investigation in zebrafish is essential for understanding adaptive responses, where learning represents a key process influenced by external stimuli. The applied stimulus plays a critical role in shaping such responses, therefore making physiologically relevant stimulation strategies important. Hydrodynamic stimuli represent one such modality, providing a natural and non-invasive means of activating mechanosensory responses in aquatic organisms, thereby enabling behavioral manipulation in microfluidic environments. To address this, a microfluidic assay was developed to generate controlled hydrodynamic environments by employing multiple S-shaped magnetic microactuators (SMMAs). Further, motions of these SMMAs were independently controlled to produce spatiotemporally varying vortical flow fields, enabling flow-induced transportation of zebrafish larvae. Flow dynamics were characterized by employing micro-particle image velocimetry (µPIV). Compared to the control condition, transportation time under microactuator-assisted guidance was significantly reduced, with a maximum improvement of 94.3% observed for a representative target zone. Building on this validated transport capability, training-dependent behavioral adaptation was quantified using latency under repeated hydrodynamic-training, where a reduction of 82.7% was achieved. Post-training assessment further demonstrated short-term retention of the acquired behavioral response followed by progressive extinction. These findings demonstrate that the proposed paradigm serves as a foundational behavioral assay leveraging hydrodynamic cues for studying adaptive responses in microfluidics.
A horseradish peroxidase (HRP)-based electrochemical biosensor was developed for melatonin (MLT) detection using a graphene-modified screen-printed electrode (G/SPE). HRP was immobilized at different temperatures (4 °C and 20 °C) and enzyme amounts (10 and 20 µL of 5 mg/mL HRP solution) to optimize the performance of the biosensors. FTIR analysis confirmed successful enzyme immobilization, while differential pulse voltammetry (DPV) showed a clear oxidation peak of MLT at 1.1 V with enhanced current responses for HRP-modified electrodes. The optimal biosensor, prepared at 4 °C with 10 µL enzyme of 5 mg/mL HRP solution, exhibited the best analytical performance, with a linear range of 0.4–3.6 µM, and a detection limit of 1.02 µM. Kinetic studies confirmed strong enzyme-substrate affinity, while the biosensors demonstrated excellent repeatability and a good stability. The method was successfully validated on pharmaceutical products, proving to be a reliable and an accurate tool for MLT quantification in real samples.
Fetal magnetocardiography (fMCG) provides direct non-invasive assessment of fetal cardiac electrophysiology, enabling detailed evaluation of cardiac rhythm, conduction, and repolarization. However, the clinical adoption of conventional fMCG has been limited by its reliance on superconducting quantum interference device (SQUID) systems, which require cryogenic cooling and specialized infrastructure. Optically pumped magnetometers (OPMs) have emerged as a promising cryogen-free alternative with the potential to broaden access to fetal electrophysiological assessment. This review summarizes the technological evolution and early clinical evaluation of OPM-based fMCG through an analysis of original in vivo human studies published up to June 2026. Twelve eligible studies were identified and synthesized narratively. Advances in sensor design, magnetic shielding, acquisition strategies, and signal-processing algorithms have enabled SQUID-comparable signal quality and cardiac interval measurements while substantially reducing cryogenic and infrastructure requirements. OPM-fMCG has demonstrated the potential to assess fetal cardiac time intervals, heart rate variability, fetal movement, and clinically important arrhythmias, including congenital long QT syndrome, atrioventricular block, and supraventricular and ventricular tachyarrhythmias. However, the available evidence remains dominated by small, single-centre studies, with relatively few fetuses affected by clinically significant arrhythmias. Prospective multicenter clinical validation, protocol standardization, independent replication, and regulatory evaluation are therefore required before OPM-fMCG can be integrated into routine diagnostic pathways for pregnancies requiring advanced fetal electrophysiological assessment.
Early detection of colorectal cancer (CRC) is challenging in low-resource settings because access to colonoscopy and centralized laboratory testing is limited. Urine-based metabolite biomarkers offer a non-invasive alternative for CRC triage, but translating a laboratory assay into a point-of-care (PoC) system requires standardized fluid handling, operator-independent timing, field-compatible reagents, and quantitative calibration. Here, we present a low-cost, semi-automated PoC platform integrating a validated, sequential, three-metabolite CRC-biomarker assay with robotic fluid handling, a motorized chromatographic module, an optical reader, Bluetooth electronics, and tablet-guided control. The platform measures urinary diacetylspermine and hippuric acid, with creatinine serving as a normalization reference, and reports absolute quantitative concentrations. Automated dilution, tube positioning, timed incubation, controlled column elution, and software-guided transfer reduce operator-dependent variation. Using pooled urine samples spiked at clinically relevant concentrations, the creatinine assay showed a strong quadratic response (0–50 mM, R2 = 0.998), as did the diacetylspermine assay (0–4 μM, R2 = 0.979), while the hippuric acid assay showed a linear response of R2 = 0.989. The RGB sensor tracked the concentration-dependent trend observed with a laboratory microplate reader (R2 = 0.968–0.999). Reagents reformulated as lyophilized or pre-weighed, vacuum-sealed kits withstood accelerated heat and humidity (45 °C/70% RH) testing and international shipping to the pilot site very well. At approximately USD 490 for the complete instrument (including tablet) and roughly USD 7.65 in consumables per screening test, the platform offers a practical, quantitative, and portable route for decentralized CRC screening.
Continuous physiological monitoring can support pilot-state assessment, but routine cockpit use requires sensing approaches that are both unobtrusive and physiologically reliable. Wearable ECG (wECG) provides stable cardiac recordings through skin-contact electrodes, whereas cockpit-integrated invisible ECG (iECG) can reduce user burden by acquiring signals through instrumented controls. However, iECG depends on intermittent hand contact and may show incomplete ECG morphology even when cardiac timing information is still preserved. This study proposes a window-based framework to assess the quality and task-specific usability of simultaneous wECG and iECG acquired during simulated flight. ECG data were collected in an Airbus A320 simulator from 14 volunteers, including experienced pilots and novices. The framework combines contact availability, R-peak reliability, PQRST morphology, and complementary signal quality indices into graded usability classes for heart rate (HR)-oriented monitoring. The iECG channel remained accessible for most of the analyzed recording time, with 93.8% of windows showing full or partial contact. Several windows classified as low quality by individual SQIs were retained as HR-usable when contact and R-peak timing remained reliable, indicating that single-metric rejection can be overly conservative for external-contact ECG. HR agreement supported the physiological relevance of the proposed classes: concordant wECG–iECG windows showed a mean absolute error (MAE) of 1.1 bpm, compared with 7.1 bpm for discordant windows. Bland–Altman analysis for iECG windows classified as usable for HR estimation showed a small mean bias of 1.4 bpm. These findings indicate that incomplete ECG morphology does not necessarily imply loss of HR usability, and that contact-aware, task-specific classification can preserve useful physiological information from unobtrusive cockpit interfaces.
Monitoring prognostic biomarkers is essential for evaluating cancer treatment efficacy in real time. Here, we present a rapid, proof-of-concept microfluidic assay for cancer biomarker quantification and functional therapy evaluation. The assay requires only 5 µL of sample, eliminates tedious sample manipulation, and uses commercial antibodies to track both biomarker concentration and functional activity. We demonstrated the successful detection of VEGF and EML4-ALK proteins in both lung cancer cell culture supernatant and serum-spiked samples, achieving a detection sensitivity 15 times greater than standard ELISA using the same antibody pairs for the respective antigens. Crucially, we showcase the platform’s distinct applicability to functional assays by monitoring dynamic phosphorylation changes in EML4-ALK following treatment with the tyrosine kinase inhibitor alectinib, successfully capturing a significant, rapid decrease in phosphorylation levels. At this preclinical stage, the platform demonstrates analytical and functional feasibility for highly sensitive and rapid biomarker monitoring, providing a foundation for future validation in patient-derived ALK-positive samples for therapeutic-response assessment.
Wearable brain-imaging devices have been developed to meet the growing demand in the healthcare industry for long-term monitoring of brain signals in natural conditions, such as monitoring brain diseases and emotions. However, conventional EEG and fNIRS (functional near-infrared spectroscopy) devices are often expensive, bulky and difficult to operate, making it difficult to monitor patients for long periods in natural conditions. To address these issues, this article proposes a low-cost, portable and multimodal wearable brain signal acquisition scheme. It combines EEG (electroencephalography) and fNIRS to reflect brain activity from different perspectives. In order to make it more wearable, a conductive rubber material is used as the electrode for the EEG. In this study, the corresponding experiments were used to verify the performance of the device. The first is the measurement of internal system noise, which satisfies the data acquisition of EEG and fNIRS at different gain levels. The α-rhythm experiment and the SSVEP (steady-state visual evoked potentials) experiment were used to validate the performance of EEG data acquisition. The performance of the fNIRS was verified by measuring changes in cerebral blood oxygen during breath-hold and breathing. In addition, by decomposing the raw fNIRS data with the VMD (variational mode decomposition) algorithm and performing correlation analysis, heart rate information was separated from the data. The performance of the proposed device was validated in the above experiments, confirming the feasibility of the design for multimodal data acquisition and meeting the requirements for portability and wearability. Furthermore, the proposed device was tested with 31 subjects (15 depressive subjects) to detect depression. Experiments proved the effectiveness of the multimodal signals, which outperformed single modal and surpassed EEG by 8.4% and fNIRS by 23.5%.
Recently, plant-based bioelectronic systems have been explored for environmental sensing applications. However, their intrinsically low electrical conductivity often limits signal sensitivity and measurement reliability. In this work, the electrothermal behavior of living Epipremnum aureum plants incorporating Ag/AgCl nanoparticles supported on nanocellulose was investigated. Electrical and thermal responses were simultaneously measured under controlled environmental conditions using external shunt resistances of 1, 10, 100, and 1000 Ω. Compared with the control without nanoparticle incorporation, the nanoparticle-incorporated plant exhibited stronger electrical responses and distinct electrothermal behavior over the studied temperature range. The measured signals showed nonlinear responses, temporal asymmetry, and resistance-dependent modulation, suggesting changes in charge transport within the plant tissues. Silver-enriched regions and the co-detection of chlorine within the nanoparticle-incorporated plant tissues were identified by environmental scanning electron microscopy and energy-dispersive X-ray spectroscopy. Five machine-learning regression models were trained to estimate temperature using the measured electrothermal voltage signals as predictors. The best-performing model, MLP FitRNet, achieved a mean absolute error of 0.598 °C, a root mean square error of 0.748 °C, and an R2 value of 0.974. These results demonstrate the potential of nanoparticle-incorporated biohybrid plant systems for electrothermal signal analysis and data-driven temperature estimation, while providing a foundation for future intelligent environmental monitoring applications.
Memristive devices have attracted considerable attention as promising candidates for overcoming the energy and data-transfer limitations of conventional computing architectures. In particular, their integration with biosensors offers a pathway toward compact and energy-efficient diagnostic systems. This review examines the development of memristive-device-based biosensors from device-level transduction to system-level integration. At the device level, sensing strategies have evolved from direct sensing toward indirect sensing architectures, improving stability and reusability. At the system level, conventional off-chip implementations have progressively shifted toward fully integrated on-chip implementations. Furthermore, this review highlights the emerging paradigm of in-sensor computing, in which sensing, memory, and computation are co-located within a single physical platform. This approach enables reduced data movement and supports energy-efficient operation for point-of-care applications. Finally, key challenges—including CMOS compatibility, device variability, and reliable multi-threshold sensing operation—are discussed as critical factors for the practical realization of memristive-device-based electrochemical biosensing systems.
We report a freeze-dried ready-to-use yeast thyroid screen (YTS), preserving the general dose-response characteristics of the freshly prepared counterpart. This field-deployable method reduces the assay time of the overall procedure from several days to 5 h with no requirement for sterile conditions, thus fulfilling key requirements for on-site implementation in a biosensor array. The effects of cell density and concentration of the cryoprotectant trehalose on median effective concentrations (EC50), limit of detection (LOD) and biosensor induction (IF) were determined and monitored over a storage period of 5 months. In addition, the impact of these parameters was monitored on the biosensor survival rate during freeze-drying and the subsequent storage process. Throughout the 5-month study, the freeze-dried recombinant yeast assay retained comparable dose-response characteristics to those of the freshly prepared counterpart, displaying median values of EC50 in the range of 350 nM to 550 nM and LODs in the range of 20 nM to 45 nM of the reference compound thyroxine (T4). Long-term stabilization is demonstrated using spiked (T4, 2 µM) river water and extracted wastewater effluent. After 5 months of storage, the T4-equivalent activities were 96 ± 38% and 112 ± 15% for river water and wastewater, respectively. In summary, we have successfully demonstrated a proof of principle of a field-deployable yeast thyroid screen (YTS) by using freeze-dried cells and trehalose as a cryoprotectant to achieve storability for up to 5 months at 4 °C.
Localized surface plasmon resonance biosensors are promising devices for label-free detection of live cells and biomolecules. However, typical plasmonic sensors have limited surface area, planar electromagnetic fields, and poor compatibility with three-dimensional (3D) interactions with cells or biomolecules. In this study, a 3D plasmonic sensor around microposts was developed using reversal nanoimprint lithography for highly sensitive cell and DNA detection. Au nanopillars were conformally integrated onto the bottom, sidewall, and top of microposts, forming additional sensing surface area along the sidewall of microposts for plasmonic sensing. The 3D plasmonic sensors exhibited tunable resonance peaks and refractive index (RI) sensitivities by varying the micropost height. The highest sensitivity of 1306 nm per RI unit was obtained from the sensor with 10 μm-tall microposts at a resonance wavelength of 1315 nm, which was significantly higher than that of typical planar plasmonic sensors. The platform was applied to live MC3T3-E1 cell detection, showing a resonance peak shift of 71 ± 11.6 nm at a cell concentration of 106 cells/mL with a cell concentration ranging from 102 to 106 cells/mL. In addition, DNA hybridization detection was demonstrated over a concentration range of 10-15-10-7 M complementary target DNA, with a resonance shift of 68 ± 2.5 nm observed at 10-7 M target DNA concentration. The 3D plasmonic sensor provides a scalable device for additional plasmonic biointerfaces with enhanced analyte accessibility and light-matter interactions. This platform offers high-sensitivity biosensing involving live cells, nucleic acids, and other biological targets.
Recent statistics reported by the World Health Organization and the International Agency for Research on Cancer indicate that the global incidence of bladder cancer has continued to increase in recent years, particularly in industrialized countries. Therefore, the timely diagnosis of early-stage bladder cancer is of great clinical importance for improving patient prognosis and treatment outcomes. In this context, computational optical sensing frameworks that integrate Spectrum-Aided Visual Enhancer (SAVE) technology with cystoscopy have attracted significant attention to overcome the limitations of conventional visual data interpretation. In this study, an AI-driven optical biosensing framework was evaluated using 1372 white-light cystoscopy (WLC) images of bladder cancer (RGB-WLC) collected in collaboration with Chung Shan Medical University Hospital. Hyperspectral conversion technology was applied to extract precise spectral information from the white-light images. Subsequently, dimensionality reduction was performed based on the characteristic wavelengths of narrow-band imaging cystoscopy at 415 nm and 540 nm to generate hyperspectral reconstructed narrow-band images. The images were categorized into Ta stage (Ta), above T1 stage (Above T1), and four additional classes. The dataset was divided into training and testing sets to establish both a standard white-light cystoscopy model (RGB-WLC) and an advanced hyperspectral biosensing model utilizing the YOLOv8 architecture for enhanced pattern recognition. Model performance was evaluated using sensitivity, F1-score, and overall accuracy. The standard RGB-WLC model achieved an accuracy of 0.852, whereas the SAVE-based biosensing model achieved an accuracy of 0.948, representing an improvement of approximately 11.27%. The results demonstrate that combining algorithmic hyperspectral reconstruction with deep learning architectures effectively addresses the challenges of clinical data interpretation and significantly enhances the detection and staging performance of bladder cancer imaging.
Wearable biosensors generate continuous physiological data in smart Internet of Disease (IoD) environments. These data can support early disease detection and remote patient monitoring. However, wearable data are often noisy, sensitive, and distributed across different devices. This paper proposes a multimodal neuro-symbolic model to overcome these limitations and incorporates it into a secure Edge-Fog-Cloud framework for anomaly detection in smart healthcare applications. The proposed system integrates the semantic analysis of clinical text using Bio-ClinicalBERT with temporal numerical data using an LSTM-based model, creating a unified neuro-symbolic artificial intelligence (AI) pipeline. Initial data processing is performed at the Edge, whereas inference is carried out at distributed Fog nodes for low-latency anomaly detection. Model training is handled in the Cloud, and privacy-preserving federated learning (FL) is supported through Homomorphic Encryption (HomEnc) to facilitate collaborative model training without sharing raw patient data. A sharded Tangle ledger is also used, with transactions broadcast by the Fog nodes and validated in the Cloud to create tamper-evident transaction logs. Furthermore, Honey Encryption (HoneyEnc) is integrated into the Fog layer to enhance security against brute-force attacks. Experimental results show that the proposed framework achieved 99.22% accuracy and a 99.31% F1-score on the held-out test set, with bootstrap 95% confidence intervals of 98.96-99.47% for accuracy and 99.08-99.53% for the F1-score. It also reduced detection latency from 185 ms in the baseline setting to approximately 50 ms in the Fog-inference setting. The blockchain layer achieved approximately 500 Transactions Per Second (TPS), while higher throughput was observed under increased transaction load and shard parallelism. Because the evaluation is based on synthetic multimodal EHR-like data and controlled simulations, the reported findings should be interpreted as proof-of-concept internal validation rather than evidence of deployment-ready clinical generalizability; external validation using real wearable biosensor data, hospital IoMT streams, or public clinical datasets such as MIMIC-III/MIMIC-IV is required before clinical deployment. These results highlight the potential of the proposed system for secure data processing and trustworthy anomaly detection in smart healthcare environments.
Listeria monocytogenes (LM), one of the most virulent foodborne pathogens, poses a serious threat to public health due to its strong environmental adaptability and high pathogenicity. Rapid, sensitive, and real-time detection of LM is of great importance. Chemiresistive gas sensors have attracted enormous attention in LM detection owing to their advantages of low cost, simple structure, fast response, and easy miniaturization, which can achieve indirect detection of LM by recognizing its specific metabolic volatile organic compounds. This review summarizes the recent progress in chemiresistive gas sensors for the detection of LM metabolites. First, the metabolic characteristics of LM and the typical volatile organic compound (3-hydroxy-2-butanone) as its characteristic biomarker are introduced. Then, the performance and sensing mechanisms of different types of chemiresistive gas sensors for LM metabolite detection are summarized and elaborated systematically. The application of chemiresistive gas sensors for the detection of actual samples and the progress in the design of related detection devices are introduced. Finally, the current challenges faced by chemiresistive gas sensors in LM metabolite detection and their future development prospects are discussed. This review provides a comprehensive reference for the research and practical application of chemiresistive gas sensors in Listeria monocytogenes detection.
