Search Results (2,170)

Eye movement classification, particularly the identification of fixations and saccades, plays a vital role in advancing our understanding of neurological functions and cognitive processing. Conventional modalities of data, such as RGB webcams, often face limitations such as motion blur, latency and susceptibility to noise. Neuromorphic Vision Sensors, also known as event cameras (ECs), capture pixel-level changes asynchronously and at a high temporal resolution, making them well suited for detecting the swift transitions inherent to eye movements. However, the resulting data are sparse, which makes them less well suited for use with conventional algorithms. Spiking Neural Networks (SNNs) are gaining attention due to their discrete spatio-temporal spike mechanism ideally suited for sparse data. These networks offer a biologically inspired computational paradigm capable of modeling the temporal dynamics captured by event cameras. This study validates the use of Spiking Neural Networks (SNNs) with event cameras for efficient eye movement classification. We manually annotated the EV-Eye dataset, the largest publicly available event-based eye-tracking benchmark, into sequences of saccades and fixations, and we propose a convolutional SNN architecture operating directly on spike streams. Our model achieves an accuracy of 94% and a precision of 0.92 across annotated data from 10 users. As the first work to apply SNNs to eye movement classification using event data, we benchmark our approach against spiking baselines such as SpikingVGG and SpikingDenseNet, and additionally provide a detailed computational complexity comparison between SNN and ANN counterparts. Our results highlight the efficiency and robustness of SNNs for event-based vision tasks, with over one order of magnitude improvement in computational efficiency, with implications for fast and low-power neurocognitive diagnostic systems. Full article

Aberrant glycosylation is linked to several diseases, making glycoproteins and their glycoforms promising biomarkers. Traditional methods like mass spectrometry offer high sensitivity but are costly, time-consuming, and unsuitable for point-of-care testing. Electrochemical biosensors emerge as an attractive alternative due to their simplicity, affordability, portability, and rapid response. This review focuses on electrochemical strategies developed to assess the glycosylation level of a specific glycoprotein or biological structure rather than merely glycoprotein or cell concentration, as in previous reviews. Approaches include the use of aptamers, boronic acid derivatives, antibodies, and lectins, often combined with nanomaterials for enhanced sensitivity. Applications span the diagnosis/prognosis of several illnesses such as diabetes, congenital disorders of glycosylation, cancer, and neurodegenerative diseases. Innovative designs incorporate microfluidic and paper-based platforms for faster, low-cost analysis, while strategies using dual-signal acquisition or competitive assays improve accuracy. Despite promising sensitivity and selectivity, most sensors require multi-step protocols and lack of validation in clinical samples. Future research should focus on simplifying procedures, integrating microfluidics, and exploring novel capture or detection probes such as metal complexes or metal–organic frameworks. Overall, electrochemical sensors hold significant potential for point-of-care testing, enabling rapid and precise evaluation of glycosylation status, which could drive cell-based biomarker discovery and disease diagnostics. Full article

With the rising demand for maritime surveys of infrastructure, energy resources, and environmental conditions, autonomous robotic fish have emerged as a promising solution with their biomimetic propulsion, agile motion, efficiency, and capacity for underwater inspection, monitoring, data collection, and exploration tasks in complex aquatic environments. Inspired by fish spines, rigid-link fish robots (RLFRs), a category of robotic fish, are widely utilized in robotics research and applications. Their rigid, actuated joints enable them to reproduce the undulatory locomotion and high maneuverability of biological fishes, while the modular nature of rigid links between joints makes them cost-effective and easy to assemble. This review examines and presents recent approaches and advancements in the field of structural design, as well as Reinforcement learning (RL)-enabled controls with sensors and actuators. Existing designs are classified by joint configuration, with key structural, material, fabrication, and propulsion considerations summarized. The review highlights the use of Q-learning, Deep Q-Network (DQN), and Deep Deterministic Policy Gradient (DDPG) algorithms for RLFR controllers, showing their impact on adaptability, motion control, and learning in dynamic hydrodynamic conditions. Technical challenges—including unstructured environments and complex fluid–body interactions—are discussed, along with future directions. This review aims to clarify current progress and identify technological gaps for advancing rigid-link robotic fish. Full article

To address the clinical urgency of simultaneously monitoring multiple biomarkers in chronic wound infections, this study presents the innovative development of an electrochemical sensor based on a MWCNTs/MXene/PVA composite hydrogel. A dual-channel conductive network is constructed via the electrostatic self-assembly of the two-dimensional material MXene and multi-walled carbon nanotubes (MWCNTs). This strategy not only enhances the charge transfer efficiency but also effectively suppresses the aggregation of MWCNTs and exposes the electrocatalytic active sites. Additionally, the thermal annealing process is incorporated to facilitate the ordered arrangement of polyvinyl alcohol (PVA) nanocrystalline domains, strengthening the hydrogen bond-mediated interfacial adhesion and resolving the issues of hydrogel swelling and delamination. The detection limit (LOD) of the optimized sensor (MWCNTs_0.6/MXene_0.4/PVA) for pyocyanin (PCN) within complex biological matrices, including phosphate-buffered saline (PBS), Luria–Bertani (LB) broth, and saliva, was decreased to a range of 0.84~0.98 μM. Leveraging the disparities in the characteristic oxidation potentials (ΔE > 0.3 V) of PCN, uric acid (UA), and histamine (HA) in simulated wound exudate (SWE), the multi-component synchronous detection functionality of the non-specific sensor was expanded for the first time. This study offers a high-precision and multi-parameter integrated approach for point-of-care testing (POCT) of wound infections. Full article

Background: Tau protein is a nonspecific marker of neurodegeneration, and its phosphorylated form, ptau-181, is specifically associated with Alzheimer’s disease (AD). Calculating the ratio of the phosphorylated form to total tau protein can help distinguish AD from other tauopathies or neurodegeneration, as well as reduce the impact of individual differences in total tau protein levels. This also allows one to monitor and compare the dynamics of changes within the same patient. Methods: Two SPRi biosensors were constructed, sensitive to the proteins described (total tau and ptau-181) for plasma determinations. The use of these biosensors requires prior sensor validation, during which specific parameters of the analytical method are established. Tests of the optimal concentration of the receptor layer in which particular antibodies were immobilized showed that the optimal concentration for total tau protein determinations was 1000 ng/mL. For ptau-181, it was 90 ng/mL. Biosensor layer formation was confirmed by analysis over a wide angle range, which enabled the generation of SPR curves. The dynamic range of the sensors is 1–50 pg/mL for total tau and 1–100 pg/mL for ptau-181. The limits of detection are 0.18 pg/mL and 0.037 pg/mL, respectively. Low standard deviation (SD) and coefficient of variation (CV) values indicate the good precision and accuracy of the results obtained using the SPRi biosensors. Specificity testing confirmed that no interferents influenced the assay. The method is therefore suitable for analyzing biological materials, such as blood plasma. Results: Proteins were thus measured in the blood plasma of AD patients and controls. Statistical analysis revealed significant differences in the concentrations of tau and ptau-181 protein between the two groups. The calculated ptau/total tau ratio for both sample groups also demonstrated high statistical significance. Conclusions: This suggests that a high ratio may be characteristic of AD. However, more extensive analysis is needed to obtain cutoff values. The ROC curves indicate that both biosensors have good diagnostic utility, with lower specificity for total tau. Full article

Quartz Crystal Microbalance with Dissipation monitoring (QCM-D) systems are widely used for the real-time analysis of mass changes and viscoelastic properties in biological samples, enabling applications such as biomolecular interaction studies, biosensing, and fluid characterization. However, their accessibility has been limited by high acquisition costs. To address this limitation, a low-cost, open-source QCM-D system was developed. Unlike other affordable, open-hardware alternatives, this system is specifically optimized for potential biomedical applications by integrating active thermal control to preserve the physical properties of the samples and dissipation monitoring to characterize their viscoelastic behavior. A 10 MHz quartz crystal with a sensor module and a control and acquisition unit were integrated. The full system was built at a total cost below USD 500. Performance validation showed a temperature stability of ±0.13 °C, a frequency stability of ±2 Hz in air, and a limit of detection (LOD) of 0.46% polyethylene glycol (PEG), thereby enabling stable, reproducible measurements and the sensitive detection of small mass and interfacial changes in low-concentration samples. These results demonstrate that key QCM-D sensing capabilities can be achieved at a fraction of the cost, providing an accessible and reliable platform for potential biomedical research. Full article

Odor detection underpins food safety, environmental monitoring, medical diagnostics, and many more fields. Current artificial sensors developed for odor detection struggle with complex mixtures, while non-invasive recordings lack reliable single-trial fidelity. To develop a general system for odor detection, in this study we present preliminary work where we test two hypotheses: (i) that spectral features of local field potentials (LFPs) are sufficient for robust single-trial odor detection and (ii) that signals from the olfactory bulb alone are adequate. To test these hypotheses, we propose an ensemble of complementary one-dimensional convolutional networks (ResCNN and AttentionCNN) that decodes the presence of odor from multichannel olfactory bulb LFPs. Tested on 2349 trials from seven awake mice, our final ensemble model supports both hypotheses, achieving a mean accuracy of 86.2%, an F1-score of 85.3%, and an AUC of 0.942, substantially outperforming previous benchmarks. The t-SNE visualization confirms that our framework captures biologically significant signatures. These findings establish the feasibility of robust single-trial detection of odor presence from extracellular LFPs and demonstrate the potential of deep learning models to provide deeper understanding of olfactory representations. Full article

The exceptional olfactory capabilities of trained detection dogs demonstrate high potential for identifying infectious diseases. However, safe and standardized canine training requires specific chemical targets rather than infectious biological samples. This study presents an analytical proof-of-concept combining untargeted metabolomics and machine learning (ML) to decode the specific odor profile of SARS-CoV-2 infection. Using headspace solid-phase microextraction gas chromatography coupled with time-of-flight mass spectrometry (HS-SPME-GC/MS-ToF), axillary sweat samples from 76 individuals (SARS-CoV-2 positive and negative) were analyzed. Data preprocessing and dimensionality reduction were performed to feed a Partial Least Squares-Discriminant Analysis (PLS-DA) model. The optimized model achieved an overall accuracy of 79%, with a specificity of 89% and sensitivity of 70% in external validation, identifying a specific panel of Volatile Organic Compounds (VOCs) as discriminant biomarkers. The optimized model achieved robust classification performance, effectively distinguishing infected individuals from healthy controls based solely on their volatilome. Six VOCs were found to be consistently presented in COVID-19-positive individuals. These compounds were proposed as candidate odor signatures for constructing artificial training aids to standardize and accelerate the training of detection dogs. This study establishes a framework where machine learning-driven metabolomic profiling directly informs biological sensor training, offering a novel synergy between ML and biological intelligence in disease detection. This study establishes a scalable computational framework to translate biological samples into chemical data, providing the scientific basis for designing safe, synthetic K9 training aids for future infectious disease outbreaks without the biosafety risks associated with handling live pathogens. Full article

Rapid detection of β-lactamase activity is becoming increasingly important as β-lactam resistance spreads at an alarming rate and conventional diagnostics often require several hours to deliver actionable results. Over the past decade, methods based on luminescence, bioluminescence, chemiluminescence, and fluorescence have become powerful tools for the functional assessment of resistance resulting from β-lactamase activity. These approaches provide highly sensitive, activity-based readouts, often within minutes, and frequently rely on simple optical instrumentation. In this review, we summarize recent developments in luminescent probe design between 2015 and 2025, with emphasis on reaction mechanisms, analytical performance, and the ability of these systems to discriminate between different β-lactamases, including narrow-spectrum enzymes, AmpC, ESBL, and carbapenemases. We also discuss their applications in bacterial cultures, clinical isolates, complex biological matrices and, in some cases, in vivo models. While luminescent assays are not yet positioned to replace standard susceptibility testing, they offer a practical and increasingly robust complement to culture-based and molecular methods. The emerging trends highlighted here, such as self-immobilizing fluorogenic probes, chemiluminescent relay systems, nanomaterial-based sensors and AI-assisted mobile platforms, suggest that luminescent β-lactamase detection could play a meaningful role in future rapid diagnostics and resistance surveillance. Full article

Background/Objectives: Periodontitis is a chronic inflammatory disease with marked inter-individual heterogeneity and well-established links to cardiometabolic and other systemic conditions. Conventional clinical diagnostics remain indispensable. However, they provide limited real-time insight into molecular activity and host-response biology. This review aimed to synthesize recent advances in point-of-care diagnostics and emerging molecular biomarkers relevant to periodontal disease and its systemic associations. Methods: We performed a state-of-the-art narrative review of literature published between 2018 and 2026. The focus was on point-of-care biosensing technologies and molecular biomarkers assessed in oral and related biological matrices. These included saliva, gingival crevicular fluid, blood, and dental plaque. Evidence was prioritized based on analytical performance, clinical validity, and translational readiness. Results: Substantial progress has been made in multiplex optical and electrochemical point-of-care platforms. These include microfluidic systems and early intraoral wearable sensors. Such technologies enable quantification of host-response proteins, including MMP-8, cytokines, and chemokines. In parallel, omics-derived biomarkers are emerging as clinically informative adjuncts for diagnosis and monitoring. MicroRNAs, cell-free DNA, extracellular vesicle–derived signals, proteomic profiles, and microbiome classifiers demonstrate promising discrimination. They also provide mechanistic links to systemic inflammation. Clinical translation remains limited by study heterogeneity, spectrum bias, and insufficient external validation. Conclusions: Near-term clinical value lies in adjunctive risk stratification and longitudinal disease monitoring. Replacement of conventional periodontal examination is not currently justified. Meaningful clinical and public-health impact will require standardized disease definitions. Harmonized sampling and reporting protocols are essential. Multicenter validation across comorbidity strata is needed. Regulatory-grade evidence must be generated for in vitro diagnostics and artificial intelligence software classified as medical devices. Full article

Microfluidics has emerged as a powerful platform for the analysis of minute sample volumes, driving its widespread adoption in biosensing applications. Optical imaging and electrochemical sensing are two typical integration strategies, each offering distinct advantages. The optical methods provide detailed spatial mapping of chemical processes, while electrochemical techniques enable selective detection that is unhindered by optical scattering from impurities. Here, we introduce a novel optical imaging–electrochemical sensor for integrated microfluidic analysis. This approach employs an electrochemical workstation to modulate optical signals, enabling the simultaneous acquisition of decoupled optical images and electrochemical readings. Consequently, it delivers complementary information, revealing both the spatial distribution of analytes and their intrinsic electrochemical properties. We detail the system design and imaging principle, demonstrate its utility through the analysis of noble metal nanoparticles, which are commonly used for signal amplification in biosensors, and finally apply it to monitor biological processes on live cells. We believe this integrated methodology will develop into a powerful tool for operando analysis in microfluidics, significantly expanding its application in the biosensing of complex biological fluids. Full article

Polymers and their composites have introduced significant advancements in engineering and technology. The primary advantages of polymeric materials include their lightweight nature, ease of manufacturing, anti-corrosion properties, reduced power consumption during assembly and integration, as well as enhanced stiffness, durability, and fatigue resistance. Polymer coatings with conductive polymers allow efficient charge transfer and make electrodes more flexible, helping them better match the mechanical properties of soft tissues. In addition, polymer coatings can protect electrodes from corrosion, reduce biofouling, and provide sites for attaching biomolecules, making them essential for reliable and long-term bioelectrode and biosensor performance. Polymer coatings for electrochemical bioelectrodes play a crucial role in enhancing sensor performance and stability in biological environments as they improve the interaction between electronic devices and biological tissues. These coatings enhance biocompatibility by reducing inflammation and tissue damage while also lowering electrode impedance to improve signal quality. The present review focuses on the most recent developments in polymer coatings for electrochemical biosensors and respective applications. The manuscript provides an overview of polymer materials, emerging strategies, coating approaches, and the resulting enhancements in bioelectrochemical applications. Full article

Biomolecules play pivotal roles in cellular signaling, metabolic regulation and the maintenance of physiological homeostasis in the human body, and their dysregulation is closely associated with the onset and progression of various human diseases. Consequently, the development of highly sensitive, selective, and stable detection platforms for these molecules is of significant value for drug discovery, pharmaceutical quality control, pharmacodynamic studies, and personalized medicine. In recent years, electrochemical biosensors, particularly those integrated with functional nanomaterials and biorecognition elements, have emerged as powerful analytical platforms in pharmaceutics and biomedical analysis, owing to their high sensitivity, exquisite selectivity, rapid response, simple operation, low cost and suitability for real-time or in situ monitoring in complex biological systems. This review summarizes recent progress in the electrochemical detection of representative biomolecules, including dopamine, glucose, uric acid, hydrogen peroxide, lactate, glutathione and cholesterol. By systematically summarizing and analyzing existing sensing strategies and nanomaterial-based sensor designs, this review aims to provide new insights for the interdisciplinary integration of pharmaceutics, nanomedicine, and electrochemical biosensing, and to promote the translational application of these sensing technologies in drug analysis, quality assessment, and clinical diagnostics. Full article

This study aimed to establish the concurrent validity and agreement of the K-power (KINVENT Biomecanique, Montpellier, France) hybrid sensor system that combines Ultra-Wideband and Inertial Measurement Unit measures against criterion timing gates for recording 20-m sprint performance in adolescent athletes. Fifteen trained adolescent track and field sprinters (age: 15.2 ± 2.4 years) performed two maximal 20-m sprints. Sprint times were simultaneously recorded using timing gates and the K-power sensor. Validity and agreement were assessed using paired-samples t-tests, Intraclass Correlation Coefficients (ICCs), Coefficient of Variation (CV), and Bland–Altman analysis. Sensitivity was determined by comparing the Typical Error (TE) to the Smallest Worthwhile Change (SWC). No significant systematic bias was observed between the devices (p > 0.05). The K-power sensor demonstrated excellent absolute agreement (ICC = 0.96, [95% CI = 0.94–0.98) and a low relative error (CV = 1.07%). The device displayed high sensitivity, with a TE (0.034 s) smaller than SWC (0.040 s). In conclusion, the K-power sensor is a valid and reliable instrument for measuring 20-m sprint times, being a practical alternative to timing gates. While the system is sensitive (TE < SWC), the Minimal Detectable Change of 0.094 s likely reflects the inherent biological variability of adolescent mechanics; thus, coaches should view changes exceeding 0.09 s as meaningful for individual athletes. Full article

Neuromuscular diseases are biologically diverse, clinically heterogeneous, and often difficult to diagnose and treat, highlighting the need for computational tools that can help resolve overlapping phenotypes and support timely, mechanism-informed interventions. This narrative review synthesizes recent advances in artificial intelligence (AI) and machine learning applied to neuromuscular diseases across diagnosis, outcome modeling, biomarker development, and therapeutics. AI-based approaches may assist clinical and genetic diagnosis from phenotypic data; however, early phenotype-driven tools have seen limited clinician adoption due to modest accuracy, usability challenges, and poor workflow integration. Electrophysiological studies remain central to diagnosing neuromuscular diseases, and AI shows promise for accurate classification of electrophysiological signals. Predictive models for disease outcome and progression—particularly in amyotrophic lateral sclerosis—are under active investigation, but most remain at an early stage of development and are not yet ready for routine clinical use. Digital biomarkers derived from imaging, gait, voice, and wearable sensors are emerging, with MRI-based quantification of muscle fat replacement representing the most mature and widely accepted application to date. Efforts to apply AI to therapeutic discovery, including drug repurposing and optimization of gene-based therapies, are ongoing but have thus far yielded limited clinical translation. Persistent barriers to broader adoption include disease rarity, data scarcity, heterogeneous acquisition protocols, inconsistent terminology, limited external validation, insufficient model explainability, and lack of seamless integration into clinical workflows. Addressing these challenges is essential to moving AI tools from the laboratory into clinical practice. We conclude with a practical checklist of considerations intended to guide the development and adoption of AI tools in neuromuscular disease care. Full article