Search Results (2,093)

Histamine (HIS) in food products can cause poisoning or intolerance reactions, which are usually associated with fish and seafood consumption. Therefore, the detection and quantification of histamine in fish and fish products are important for public health protection. This article provides an overview on the current methodologies and recent advances in histamine detection and quantification applied to fishery products. Among the evaluated studies, chromatographic techniques, especially HPLC, are the most common due to their good performance. However, chromatographic techniques present disadvantages, such as complex sample preparation and use of organic solvents, and attempts have been made to solve them with spectroscopic and enzymatic methodologies, and sensors models. Sensor-based methods (biosensors, electrochemical sensors, optical sensors) allow real-time or near-real-time detection of histamine, making them highly valuable for on-site quality control. The incorporation of selective recognition elements, such as enzymes, aptamers, or molecularly imprinted polymers (MIPs), significantly enhances the selectivity and reduces detection and quantification limits. However, challenges related to stability, interference, and lack of standardisation can limit their ability to fully replace traditional laboratory methods. Future research should therefore focus on developing robust, cost-effective, and standardised detection platforms capable of combining the accuracy of chromatographic methods with the speed and practicality of modern sensors, ultimately strengthening food quality control and reducing the risk of histamine-related intoxications worldwide. Full article

Wearable biosensing is moving beyond single-signal activity tracking toward multimodal, AI-assisted health monitoring that combines biophysical streams with biochemical information from sweat, interstitial fluid, tears, and other accessible biofluids. Recent work has accelerated progress in flexible optical materials, programmable DNA-based sensing architectures, biosafety-aware sweat patches, and edge AI pipelines capable of denoising, calibration, personalization, and low-latency inference. This review synthesizes current advances across general biosensor platforms, vital-sign monitoring, biochemical sweat sensing, motion and biomechanics sensing, and edge AI/data analytics. Particular attention is given to the translational bottlenecks that now dominate the field, including motion artifacts, sensor drift, biofouling, subject-to-subject variability, limited sweat-to-blood equivalence, insufficient external validation, and uneven regulatory readiness. The central argument of this updated review is that the next phase of progress will not be driven by sensitivity alone but by robust multimodal fusion, clinically anchored validation, interoperable data pipelines, and energy-efficient on-device intelligence. By linking materials, electronics, algorithms, and deployment constraints, the review identifies the wearable biosensing strategies most likely to progress from promising laboratory demonstrations to reliable personalized-health tools. Full article

The coronavirus disease (COVID-19) pandemic highlighted critical limitations in ongoing diagnostic and therapeutic strategies, underscoring the urgent need for alternative and complementary approaches. Carbon dots (CDs) represent a novel class of fluorescent carbon-based nanomaterials with increased research interest owing to their enhanced biocompatibility, tunable photoluminescence, aqueous solubility, and rich surface chemistry. This review outlines current approaches in the diagnosis and treatment of COVID-19, along with the main structural, physicochemical, and biological features of CDs. Recent advances in the application of CDs as biosensing and antiviral nanomaterials against SARS-CoV-2 are critically discussed with an emphasis on the influence of synthetic routes, precursor compounds, heteroatom doping, surface functionalization, and purification strategies on their optical behavior and biological performance. CD-based biosensors mainly rely on fluorescence-sensing strategies, exploiting changes in photoluminescence intensity or energy-transfer processes upon recognition of viral targets. Electrochemical approaches are also investigated, highlighting the role of CDs as electrode modifiers to enable sensitive immunodetection in complex biological samples. Apart from diagnosis, the therapeutic potential of CDs is underscored by inhibiting viral entry, replication, disrupting cell membranes, or modulating oxidative stress. Finally, current limitations, challenges, and future perspectives regarding mechanistic understanding, synthesis parameters, biodistribution, and clinical translation are discussed, demonstrating the potential of CDs as novel “shiny weapons” against COVID-19, combining therapeutic and diagnostic functions. Full article

Wearable biosensors have swiftly progressed from stiff laboratory prototypes to flexible, skin-like systems capable of ongoing physiological monitoring. Yet, the active and mechanically intense nature of athletic performance reveals the limits of static device designs made solely through traditional 3D printing. This review offers a thorough analysis of the shift from custom 3D-printed platforms to adaptive 4D-printed wearable biosensors that include time-sensitive, stimuli-responsive materials. We carefully investigate how nanomaterial-engineered transducers, including carbon nanomaterials, MXenes, and metallic nanostructures, improve electrochemical sensitivity, signal stability, and mechanical durability in sweat-based and electrophysiological sensing. Additionally, we examine the integration of thermoresponsive polymers, moisture-activated hydrogels, shape-memory materials, and self-healing networks that support autonomous control of skin–sensor contact, microfluidic sweat management, and structural stability under high mechanical strain. Case studies focused on sports monitoring demonstrate how these innovations enable multimodal measurement of mechanical, chemical, and molecular biomarkers in real time. Lastly, we address manufacturing scalability, regulatory issues, and translational challenges necessary to move from proof-of-concept to clinical deployment. By combining nanomaterial-driven electrochemical precision with 4D-printed mechanical adaptability, this review maps a path toward adaptive, self-regulating wearable platforms that sustain analytical accuracy even under extreme physiological demands. Full article

Water pollution caused by pesticides, such as the toxic herbicide Dichlorprop-P (2,4-DP), represents a major global concern. This study reports a novel inhibition-based electrochemical biosensor for the ultra-trace determination of Dichlorprop-P in surface waters, fabricated by modifying a screen-printed carbon electrode with Prussian Blue and tyrosinase. The optimized platform was obtained after 30 drop-casting steps, corresponding to 75 μL Prussian Blue at 8.59 mg/mL, followed by immobilization of 10 μL tyrosinase solution at 50 mg/mL. Fourier-transform infrared spectroscopy and cyclic voltammetry confirmed the successful deposition of the mediator and enzyme. To the best of our knowledge, this is the first tyrosinase-based biosensor proposed for Dichlorprop-P detection. The sensing principle relies on the decrease in the catalytic current generated by tyrosinase-mediated tyrosine oxidation in the presence of the Prussian Blue mediator. The biosensor showed a linear response between 0 and 0.1 μM, with a detection limit of 0.02 μM, a quantification limit of 0.09 μM, and good linearity (R² = 0.9906). Application to Danube River water samples yielded recoveries of 97–105%, supporting its suitability for environmental monitoring. Full article

Food safety systems are increasingly challenged by globalized supply chains, emerging contaminants, and the need for rapid decision-making before contaminated products reach consumers. Although conventional methods remain essential for confirmatory analysis, their dependence on centralized facilities, specialized personnel, and time-intensive workflows limits their suitability for real-time monitoring. Sustainable nanotechnology offers a promising approach to address these limitations by enabling rapid, sensitive, portable, and resource-efficient contaminant detection. This review critically examines recent advances in nano-enabled platforms for detecting foodborne pathogens, toxins, pesticide residues, heavy metals, allergens, and other food-related contaminants. Emphasis is placed on colorimetric, fluorescent, electrochemical, surface-enhanced Raman scattering, and biosensor-based systems employing sustainable nanomaterials, including biopolymer nanoparticles, carbon-based nanostructures, metal and metal oxide nanoparticles, quantum dots, and hybrid nanocomposites. The roles of green synthesis, low-toxicity materials, reduced solvent use, and safe-by-design strategies are evaluated in relation to environmental sustainability and practical implementation. The integration of nanosensors with smart packaging, portable devices, Internet of Things platforms, artificial intelligence, and data-driven risk assessment is also discussed. Key challenges include matrix interference, reproducibility, sensor stability, scalability, regulatory approval, environmental fate, and consumer acceptance. Continued progress will require validated, scalable, and environmentally responsible technologies capable of reliable operation under real-world food system conditions. Full article

Plant secondary metabolites remain indispensable for pharmaceuticals, nutraceuticals, and cosmeceuticals, yet conventional plant culture systems are increasingly limited by inconsistent yields, poor scalability, and inadequate capacity for real-time process monitoring. Microfluidic technologies and organ-on-a-chip (OoC) platforms, originally developed for mammalian biology, are now emerging as powerful tools to overcome these constraints. These systems enable laminar flow, precise gradient generation, single-cell resolution, and biosensor integration, providing unprecedented control over the cellular microenvironment and supporting non-destructive, real-time metabolomic monitoring. While recent reviews have surveyed plant microfluidics broadly covering developmental biology, single-cell phenotyping, and root–microbe interactions, this review provides, to our knowledge, the first synthesis focused specifically on organ-on-a-chip approaches for plant secondary metabolite biosynthesis and real-time metabolomic monitoring. Advances in device fabrication, including PDMS, paper-based, hydrogel, and thermoplastic materials, surface engineering, gradient-based elicitation strategies, and integration of optical, electrochemical, and mass spectrometric detection systems have also been critically examined. Special emphasis is placed on root-on-a-chip, shoot meristem, protoplast, callus, and 3D organoid platforms for studying cell wall mechanics, vacuolar dynamics, cytoskeletal responses, and signalling cascades. However, challenges remain in long-term culture stability and scalability; nonetheless, these technologies offer a roadmap toward programmable ‘plant biosynthetic factories’ to produce high-value natural products. Full article

This study explores the direct growth of boron-doped diamond films on biocompatible silicon nitride (Si₃N₄) ceramic substrates using hot-filament chemical vapor deposition (HFCVD), with a view toward their use in implantable electrochemical biosensors. The focus of this work is the establishment of process–structure–property relationships relevant to biosensor performance, including microstructure, surface chemistry, wettability, and electrical behaviour. The effects of key deposition parameters, namely methane concentration, deposition pressure, and sample holder configuration, were analysed in relation to film microstructure, crystallographic orientation, surface chemistry, wettability, and electrical performance. Under low CH₄/H₂ ratios, microcrystalline diamond films with a pronounced (111) preferential orientation were obtained, enabling improved boron incorporation and electrical resistivity values within the range required for biosensor operation (≈1–10 kΩ). Surface analyses revealed partially hydrogen-terminated diamond layers enriched with oxygen-containing functional groups (C–O and C–O–C), which enhance surface wettability and are suitable for enzyme immobilization. Among the studied conditions, films deposited at 150 mbar and low methane flow displayed the most balanced combination of electrical conductivity, surface wettability, and microstructural stability. Overall, the results highlight the potential of boron-doped CVD diamond grown directly on Si₃N₄ as a robust and biocompatible material platform for future implantable biosensors, particularly for glucose monitoring applications. Full article

Early detection of lung cancer remains challenging due to the extremely low concentrations of disease-specific biomarkers, which limit the development of highly sensitive and reliable point-of-care (PoC) diagnostic devices. Electrochemical biosensors integrating graphene oxide (GO) and gold nanoparticles (AuNPs) have emerged as promising platforms for the rapid, sensitive, and selective detection of lung cancer biomarkers, enabling more timely diagnosis. Biomarkers such as carcinoembryonic antigen (CEA), cytokeratin-19 fragments (CYFRA 21-1), neuron-specific enolase (NSE), and circulating tumour DNA are increasingly investigated for PoC applications since they can be detected in various biological fluids associated with lung cancer. Nanocomposite materials, particularly GO/AuNP hybrids, provide synergistic advantages by combining the large surface area and abundant functional groups of GO for stable immobilization of biorecognition elements with the excellent conductivity and bioconjugation capability of AuNPs that enhance signal transduction. This review critically discusses key biomarker targets for lung cancer, the properties of GO and Au in biosensing, and the role of AuNP/GO nanocomposites in improving biosensor performance. It further examines the application of electrochemical biosensors for lung cancer biomarker detection, highlighting recent developments. Additionally, the review outlines current challenges limiting clinical translation and PoC implementation, provides recommendations to address these barriers, and discusses future perspectives for improving the detection of low-abundance biomarkers for early lung cancer diagnosis. Ultimately, these technologies seem promising for the development of rapid diagnostic tools equivalent to established platforms such as lateral-flow immunoassays. Full article

The coronavirus disease 2019 (COVID-19) pandemic highlighted the need for rapid, accurate, and point-of-care diagnostic technologies, accelerating interest in biosensors as next-generation analytical platforms. However, biosensor performance is governed by a connected sequence of processes, including bioprobe–target recognition, sensor fabrication, structural optimization, and signal interpretation. Because these processes involve multiple interacting variables, conventional empirical approaches often have limitations in efficiently optimizing biosensor performance and interpreting complex analytical signals. Artificial intelligence (AI) and machine learning (ML) provide tools to model these relationships and support prediction-guided biosensor development. This review discusses recent progress in AI-assisted biosensor development in three sequential stages. First, AI-assisted bioprobe design is reviewed, including in silico aptamer discovery, smart-SELEX-based aptamer screening, and peptide receptor design for improving molecular recognition. Second, AI-driven sensor fabrication and structural optimization are discussed, focusing on electrochemical feature extraction, paper-based microfluidic device optimization, and optical biosensor parameter prediction. Third, ML-based signal analysis is examined as a strategy for converting complex electrochemical, colorimetric, and optical responses into quantitative analytical outputs. By organizing these examples as a connected workflow rather than as separate applications, this review highlights how AI can link molecular design, device engineering, and signal interpretation to accelerate the development of next-generation biosensors. Full article

Nitric oxide (NO) is a small, highly reactive gaseous signaling molecule that plays diverse and context-dependent roles in microbial physiology and host–microbe interactions. Over the past decade, increasing evidence has revealed the dual nature of NO as both an antimicrobial effector and a signaling mediator involved in microbial stress responses, metabolism, biofilm dynamics, quorum sensing, virulence regulation, and symbiotic interactions. In microbial systems, NO influences adaptation to environmental stress and contributes to mechanisms associated with persistence and antimicrobial resistance. In host organisms, NO functions as a key component of innate immunity while also participating in beneficial interactions involving rhizobia, mycorrhizal fungi, and probiotic microorganisms. Despite its biological significance, accurate detection and quantification of NO remain challenging because of its transient nature, high reactivity, low physiological concentrations, and interference from related reactive oxygen and nitrogen species. Recent advances in biosensing technologies have substantially improved NO detection capabilities through the development of electrochemical, optical, enzyme-based, microfluidic, wearable, and implantable sensing platforms. These innovations are complemented by analytical techniques including electron paramagnetic resonance spectroscopy, mass spectrometry, fluorescence-based imaging, and advanced microscopy, which enhance sensitivity, specificity, and spatiotemporal resolution in complex biological environments. Concurrently, statistical and computational approaches—including sensor calibration models, multivariate analyses, machine learning algorithms, and bioinformatics pipelines—have become increasingly important for extracting biologically meaningful information from NO-related datasets. Unlike previous reviews that primarily focus on either NO biology or sensing technologies, this review integrates current knowledge of NO-mediated microbial physiology and host–microbe interactions with recent developments in biosensor engineering, analytical methodologies, statistical frameworks, and emerging artificial intelligence (AI)-driven data interpretation. We further highlight applications of NO detection in infectious disease diagnostics, antimicrobial screening, probiotic and biofertilizer evaluation, environmental microbiome monitoring, and real-time studies of symbiosis and infection. Finally, future directions including miniaturized sensing platforms, multi-omics integration, AI-assisted analytics, and sensor standardization are discussed. By unifying molecular, analytical, and computational perspectives, this review provides a multidisciplinary framework and roadmap for advancing NO-based research and translational applications across microbial, environmental, and host-associated systems. Full article

Recombinase polymerase amplification (RPA) has become a central amplification strategy for decentralized molecular diagnostics because it operates rapidly at mild temperatures and requires far less thermal control than PCR. Its analytical value increases substantially when paired with microfluidic and paper-based platforms, where sample handling, reagent delivery, amplification, and signal readout can be organized within compact, low-power, and field-compatible formats. This review examines recent progress in microfluidic and paper-based RPA systems across biomedical diagnostics, food safety testing, environmental monitoring, and One Health biosurveillance. Particular attention is given to integrated device architectures, including centrifugal chips, capillary-driven platforms, microfluidic paper-based analysis devices (μPADs), electrochemical biosensors, CRISPR-assisted assays, digital microfluidic systems, and sample-to-answer cartridges. Biomedical applications now span respiratory viruses, reproductive and emerging infections, bacterial and parasitic diseases, pharmacogenomic markers, and cancer-related biomarkers. RPA-enabled platforms are moving steadily into food safety and environmental surveillance, covering pathogen detection, seafood and dairy monitoring, agricultural disease control, antimicrobial-resistance tracking, and airborne pathogen screening. At the same time, the field is shifting toward more intelligent diagnostic formats. Smartphone imaging, artificial intelligence (AI)-assisted interpretation, digital partitioning, cloud connectivity, and automated quality control are increasingly being built into rapid testing workflows, giving these systems greater portability, consistency, and decision-making value. Despite this progress, practical deployment still depends on robust sample preparation, multiplex stability, quantitative reliability, reagent storage, scalable fabrication, and regulatory validation. Continued convergence of RPA chemistry with microfluidics, paper devices, CRISPR recognition, electrochemical readout, and data-assisted interpretation is expected to support more robust and accessible molecular diagnostic workflows. Full article

Synthetic microbial community (SynCom) biosensors are emerging from the convergence of whole-cell biosensing, synthetic ecology, and computational design. Conventional whole-cell biosensors (WCBs) use a single microbial chassis to convert analyte recognition into optical, electrochemical, gaseous, or growth-linked outputs. This compact architecture supports low-cost and field-oriented detection, but it can be limited by cellular burden, narrow dynamic range, environmental interference, and difficulty in interpreting multicomponent signals. Natural microbial consortia provide an ecological template in which sensing, transformation, stress tolerance, and response are distributed across interacting populations. SynCom biosensors seek to translate this logic into engineered platforms with defined members, assigned functional roles, designed communication, and interpretable readouts. This review traces the transition from WCBs to natural consortia and engineered multicellular biosensors, emphasizing functional partitioning, signal routing, community control, and artificial intelligence (AI)-assisted design. AI is discussed as a practical tool for narrowing design space, predicting interactions, decoding complex biosignals, and supporting adaptive operation. Key challenges remain in community stability, orthogonal communication, data quality, biosafety, standardization, and real-sample validation. Future progress will depend on parsimonious community design, reliable containment, quantitative validation, and computational workflows that connect community composition with sensing performance. Full article

Seasonal epidemics and high variability of influenza viruses pose a severe threat to global public health security. Neuraminidase, a key functional enzyme in the life cycle of influenza viruses, represents an important target for anti-influenza drug development. Given the continuous emergence of drug-resistant strains against first-line clinical neuraminidase inhibitors (NAIs) such as oseltamivir, there is an urgent need to develop novel, broad-spectrum, and resistance-overcoming NAIs. Natural products, characterized by structural diversity and a wide range of biological activities, provide abundant resources for the discovery of new NAIs. Recent advances in computer-aided drug design, intelligent analytical platforms, and modern screening technologies have accelerated the identification of natural product-derived NAIs. In particular, biosensor-based strategies, including electrochemical, fluorescence, bioluminescence, and surface-enhanced Raman scattering biosensors, have demonstrated significant advantages in sensitivity, selectivity, rapid response, and high-throughput screening. In combination with computational methods and experimental approaches such as affinity ultrafiltration and activity-guided separation, these technologies have promoted the development of intelligent, precise, and multimodal screening platforms. Looking forward, the integration of biosensor-based high-throughput screening platforms with artificial intelligence algorithms is expected to drive the next generation of natural product screening platforms and facilitate the efficient discovery and clinical translation of novel NAIs. This paper systematically reviews the research progress of screening strategies for natural product-derived NAIs; introduces representative natural active NAIs, including phenols, terpenoids, and alkaloids; and prospects future development directions, aiming to provide a scientific reference for the efficient discovery of NAIs from natural products. Full article

Asthma is a prevalent, long-term inflammatory airway condition that is difficult to diagnose and treat because there is no single reliable diagnostic test. Misdiagnosis is therefore common, with rates as high as 73% in juvenile groups and up to 35% in adult populations. This ultimately exacerbates their illness by postponing therapy for some people and administering needless medication to others. Although well-known biomarkers such as blood eosinophils and fractional exhaled nitric oxide, as well as conventional diagnostic techniques such as spirometry, have improved clinical assessment, they are nevertheless constrained in many healthcare settings by limited availability, high cost, and inconsistent use. Furthermore, these indicators primarily reflect type-2 inflammation and are less useful for non-type-2 asthma, highlighting the need for more comprehensive, readily accessible diagnostic techniques. Identifying novel biomarkers of oxidative stress, metabolic alterations, and airway inflammation, including volatile organic compounds and redox-related chemicals, has been the focus of recent studies. These biomarkers offer opportunities for improved disease phenotyping and non-invasive detection. Simultaneously, advances in biosensor technology have enabled highly sensitive platforms to rapidly detect these biomarkers at low concentrations. In particular, optical biosensors are becoming more and more popular due to their ability to do real-time detection without the need for labels and their ease of miniaturization for point-of-care devices. This work summarizes traditional diagnostic tools alongside existing information on asthma phenotypes and clinically important biomarkers, and discusses advanced biosensors ranging from electrochemical to optical systems, including recent developments in nanomaterial-enhanced optical biosensing techniques. The importance of artificial intelligence and smartphone-integrated hardware is also covered, along with the main challenges that need to be overcome for these technologies to become useful clinical tools for asthma diagnosis and monitoring. Full article