Search Results (1,227)

Medicine–food homology (MFH) substances, which possess both medicinal and edible properties, have garnered widespread attention in the global health context of the new era. The MFH industry has experienced explosive growth and has gradually become a key supporting aspect of TCM modernization. However, due to the pollution of the modern environment, the content of pollutants in MFH products has been increasing, raising concerns regarding quality, safety, and efficacy control. Traditional quality-analysis technologies struggle to meet the needs of rapid on-site detection because of their dependence on large instruments and the complexity of operation. This dilemma has propelled advances in sensor technology. With its advantages of high sensitivity, real-time detection, and portability, sensor technology has become a key technical support for quality control and supervision in the field of MFH. In this review, we comprehensively categorize the mainstream sensor types used for analysis in the field of MFH, including intelligent sensors, optics, electrochemistry, biosensors, etc. This review outlines their research status, elaborates on their primary application directions and corresponding core technologies, discusses current challenges (including stability, interference, and cost), and presents future perspectives. Overall, sensor-based technologies offer a promising and scalable solution for the quality control of MFH products, addressing critical challenges such as stability, interference, and cost. With ongoing advances in intelligent sensing, optics, electrochemistry, and biosensing platforms, these methods are poised to play an increasingly vital role in ensuring the safety, efficacy, and quality consistency of MFH products amid growing environmental pressures. Full article

Wastewater-based epidemiology (WBE) has emerged as a powerful approach for population-level monitoring of chemical exposure, health status, and disease transmission by analysing wastewater. Although chromatographic and molecular techniques remain the gold standard in WBE, their high cost, infrastructural demands, and limited suitability for decentralized and real-time monitoring motivate the development of complementary sensing technologies. In this context, optical (bio)sensors, particularly fluorescence-based platforms, have attracted increasing attention due to their high sensitivity, rapid response, and potential for on-site monitoring. This review discusses recent advances in fluorescent optical (bio)sensors for WBE, with a particular focus on carbon dots (CDs), including waste- and biomass-derived CDs produced via green synthesis as well as CDs obtained from commercial chemicals. The applicability of CD-based sensors to wastewater-relevant analytes is evaluated, highlighting current achievements, as well as existing limitations and challenges related to real-sample validation and the translation of these platforms into robust, field-deployable systems for their implementation in sustainable wastewater monitoring and public health surveillance. Full article

Artificial intelligence (AI) has become a transformative tool in the field of molecular biosensing, enabling data-driven optimization in sensor design, signal processing, and real-time monitoring. AI promotes the discovery of biomarkers, the design of high-affinity receptors, and the rational engineering of sensing materials, thereby enhancing sensitivity, specificity, and detection accuracy. In the development of biosensors, AI-assisted strategies have accelerated the identification of novel molecular targets, guided the design of proteins and aptamers with enhanced binding performance, and optimized plasmonic and nanophotonic structures through forward prediction and inverse design frameworks. The integration of artificial intelligence has significantly enhanced the performance of various biosensing platforms, including optical, electrochemical, and microfluidic biosensors. It also enabled automatic feature extraction, noise reduction, dimensionality reduction, and multimodal data fusion, overcoming the challenges posed by complex signals, environmental interference, and device variations. These capabilities are particularly crucial for wearable molecular biosensors, as low signal strength, motion artifacts, and fluctuations in physiological conditions impose strict requirements on robustness and real-time reliability. This review systematically summarizes the latest advancements in AI-assisted molecular biosensors, highlighting representative sensing strategies and algorithms for wearable and real-time monitoring, and discusses the current challenges and future development opportunities of intelligent biosensing technologies. Full article

Polymer-based biosensors have evolved from passive supports into active functional elements that dictate analytical performance in complex real-world samples. This critical review with meta-trend analysis examines 96 original research articles published between 2015 and 2025, evaluating how four polymer classes (conductive polymers, redox-mediator polymers, hydrogels, and molecularly imprinted polymers) address matrix effects in food, beverage, environmental and clinical applications. Electrochemical detection dominates (79% of studies), with conductive polymers enabling low-potential operation that excludes electroactive interference. Hydrogels achieve superior precision (RSD below 3%) in protein-rich matrices through biocompatible microenvironments that preserve enzyme kinetics. Molecularly imprinted polymers provide unmatched stability in harsh environments for trace-level detection of heavy metals and toxins, though delayed response times from slow analyte diffusion persist. Critical evaluation exposes validation deficits: 91% of studies omit limits of quantification, while approximately one-third lack reproducibility (33%) and precision (30%). The multi-matrix challenge, maintaining calibration across different hostile environments, remains the primary barrier to commercial deployment. Advanced architectures, including nanocapsulation, hierarchical nanocomposites, and microneedle-integrated systems, offer pathways to overcome limitations in fouling resistance and operational stability. Full article

A thin-film surface plasmon resonance (SPR) sensor is presented using a prism-coupled Kretschmann configuration and an optimized multilayer architecture incorporating black phosphorus (BP) as an ultrathin overlayer. The response is modeled at 633 nm under TM polarization using the transfer-matrix method. Low-concentration sensing conditions in the 1–5 ng/mL range are represented through small effective-refractive-index perturbations of the aqueous sensing medium, providing a preliminary optical framework for evaluating refractive-index response in biosensing-related scenarios. The coupling prism, Au film thickness, and Si₃N₄ spacer thickness are optimized to control resonance depth, linewidth, and angular shift. The optimized CaF₂/Au/Si₃N₄/BP configuration exhibits systematic condition-dependent displacement of the SPR minimum and an evanescent-field distribution that remains strongly localized at the sensing interface while extending into the sensing medium, enabling refractive-index interrogation. High angular sensitivity is obtained at low levels, reaching 517.62°/RIU at 2 ng/mL and 482.82°/RIU at 1 ng/mL, with quality factors above 120 RIU⁻¹ in the same regime. Composite indicators (figure of merit and contrast signal factor) peak at intermediate levels, whereas resonance broadening at higher levels reduces the quality factor and increases the inferred limit of detection, evidencing a sensitivity–resolution trade-off. Benchmarking against reported SPR platforms indicates that BP-assisted interface engineering provides a competitive low-level operating window within a preliminary refractive-index-sensing framework that is relevant to future biosensor design. These results motivate further experimental validation, including BP stabilization, surface biofunctionalization, and practical implementation under liquid-phase sensing conditions. Full article

Background/Objectives: In the area of pharmacology and clinical research, it is necessary to use versatile technologies able to quantify last-line antibiotic molecules with high specificity and sensitivity. This article describes the development of two types of immunosensors based on amperometric and surface plasmon resonance (SPR) measurements and their applicability in the measurement/assessment of therapeutic drug monitoring (TDM) of four last-line antibiotics such as vancomycin, colistin, daptomycin and meropenem in human plasma. In this study, ligand immobilization by preconcentration assays, sensor surface regeneration, determination of sensitivity and correlation of plasma sample quantification results by HPLC were considered. Results: In the case of the electrochemical biosensor the IC50 values obtained were 3.49 μg/L for vancomycin (VAN), 5.44 μg/L for colistin (COL), 0.82 μg/L for meropenem (MER) and 5.10 μg/L for daptomycin (DAP). For the SPRi biosensor the LODs achieved were 19 ng/mL for VAN, 9 μg/L for COL, 12 μg/L for MER and 12.3 μg/L for DAP. Finally, both electrochemical biosensor and the SPRi optical biosensor showed that for the four antibiotics the standard deviations were less than 10% with respect to the HPLC results, with ranges for VAN between ~5–6 µg/mL, for COL ~0.2–0.7 µg/mL, for MER ~4.5–5.5 µg/mL and for DAP ~0.09–0.65 µg/mL. Conclusions: These kinds of biosensors provide a precise and sensitive strategy, together with real-time determination, to quantify last-line antibiotics, with working ranges like those shown by robust techniques such as HPLC and great potential for the clinic. Full article

Patients receiving dialysis treatments suffer from a high rate of systemic comorbid conditions, including cardiovascular disease, mineral and bone disorders, chronic inflammation, amyloidosis, and recurring infections, leading to increased morbidity and mortality rates despite the progress made in the field of renal replacement therapies. The aforementioned conditions result from the continued dysregulation and overproduction of molecular biomarkers, which cannot be adequately monitored by traditional, intermittent laboratory tests. This review critically assesses the newly developed biosensor technologies for the detection of major dialysis biomarkers, including potassium, phosphorus, parathyroid hormone (PTH), β₂-microglobulin, creatinine, and cystatin C, with special emphasis on biosensors based on electrochemistry, optics, impedimetry, nanophotonics, and biological engineering techniques. These recent biosensors have been evaluated based on their analytical performance, the biofluids used in the studies, and their suitability for measuring relevant concentrations of these biomarkers. Special attention is given to biosensors capable of continuous operation or minimally invasive sampling, as well as to newly developed biofluid sampling techniques, including microneedle-, microtube-, and micropillar-based systems, for the long-term monitoring of the biomarkers in the serum of patients receiving dialysis treatments. The biosensing techniques for measuring infection biomarkers have also been discussed, given the high risk of bloodstream and access infections among patients receiving dialysis. The limitations of these biosensors include biofouling, calibration drift, and their integration into the dialysis treatment workflow. Finally, the future prospects of the recent biosensors offer the possibility of the proactive management of the high rate of comorbid conditions in this high-risk population of patients receiving dialysis treatments. Full article

We report a highly sensitive label-free optical biosensor based on nematic liquid crystals, for the detection of proteins. The principles of biosensing are based on the change in the liquid crystal alignment induced by biomolecules adsorbed on the cell inner surface, which can be easily detected with a polarizing optical microscope. Although this approach is well-known, we propose here an experimental strategy that allows us to reach a detection limit of the order of 10⁻¹³ g/mL, orders of magnitude higher than the one reported in the literature for similar biosensors. Furthermore, our method leads to assessing a well-defined, specific dependence of protein concentration on cell birefringence, for rapid quantitative biosensing. The proposed biosensor can additionally be used for the detection of antibodies. Full article

Sensitive quantitative detection of breast cancer gene synthetic sequences is crucial for related biosensing research. To address the limitations of traditional sensors for detecting ultra-low concentrations, this study developed a novel fiber-optic biosensor by combining nanomaterial sensitization with nanoparticle signal amplification strategies. A fiber optic sensor based on single-mode fiber-thin-core fiber-multimode fiber-single-mode fiber structure was fabricated and functionalized with black phosphorus (BP) nano-interface. The Au@cDNA complex was prepared by covalently immobilizing sulfhydryl-modified complementary DNA (cDNA) on the surface of gold nanoparticles (AuNPs). The complex specifically hybridized with the probe DNA (pDNA) immobilized on the surface of the sensor. The experimental results show that this sensor has a sensitivity of 0.793 nm/lgM and a detection limit of 20.27 fM in the concentration range of 100 fM to 100 nM. Specifically, the BP-functionalized sensor exhibits superior dynamic range, higher sensitivity, and lower detection limits for detecting Au@cDNA. The synergistic effect of interfacial sensitization by BP and signal amplification by AuNPs significantly enhances detection performance, providing a promising platform for ultra-sensitive biosensing applications. Full article

Alzheimer’s disease (AD) is the most common cause of dementia, affecting 55 million people worldwide. Its characteristics include the accumulation of senile plaques and neurofibrillary tangles. This disease is associated with changes in the concentration of AD biomarkers, such as microRNAs, amyloid peptides, Tau protein, and neurofilament light chains. Due to the fact that neuropathological processes begin decades before the onset of cognitive symptoms, accurate detection of AD biomarkers is crucial for its early diagnosis. The combination of analytical techniques and machine learning methods plays a crucial role in medical innovation. Recently, efforts have been made to develop machine learning-assisted biosensors for AD diagnosis. This article provides an overview of the progress in machine learning-assisted sensing of AD biomarkers in bodily fluids. It mainly includes three parts: machine learning algorithms, machine learning-assisted electrochemical and optical biosensors, and challenges and future perspectives. We believe that this work will contribute to the development of innovative analytical devices based on artificial intelligence for monitoring and managing neurodegenerative diseases. Full article

Organ-on-a-chip (OoC) technologies, also termed microphysiological systems (MPSs), integrate microfluidics, engineered biomaterials, human-derived cells, and on-chip biosensing to model human physiology in microscale devices that deliver quantitative, time-resolved readouts. This review surveys the 2010–2025 literature, emphasizing how sensing, standardized sampling, and analytics enable clinical concordance and fit-for-purpose regulatory use. We synthesize advances in (i) materials, fabrication, and microfluidic design; (ii) organ- and disease-focused case studies; and (iii) translational benchmarks that align chip outputs with clinical pharmacokinetics, toxicology, and biomarker datasets. Across organ systems, platforms increasingly incorporate vascularization, immune components, and organoid hybrids, paired with real-time measurements of barrier integrity, metabolism, electrophysiology, and secreted biomarkers using impedance (TEER), electrochemical, and optical modalities. Representative benchmarking studies report cardiac OoCs achieving AUROC ≥ 0.85 for torsadogenic risk classification, and renal chips improving prediction of transporter-mediated clearance relative to conventional in vitro assays. We summarize validation approaches and regulatory developments relevant to new approach methodologies, including the FDA Modernization Act 2.0, and discuss how AI and multi-omics can automate signal and image analysis, harmonize cross-platform datasets, and support digital-twin workflows that couple OoC measurements to in silico models. Overall, biosensor-enabled OoCs are progressing toward quantitatively benchmarked platforms for safety pharmacology, ADME/PK–PD, and precision medicine. Full article

Wine alcoholic fermentation occurs in a dynamic biochemical environment where interactions between the vessel and the product can cause inorganic and organic species to migrate into the fermenting must or wine. At low pH and with rising ethanol levels, fermentation tanks made of stainless steel, concrete or cementitious materials, ceramics, or polymers exhibit material-specific behaviors that may promote the release of toxic trace elements or alter technologically important ions. These changes can affect yeast physiology, fermentation kinetics, and matrix stability, directly impacting wine safety and quality. They may also influence the evolution of key fermentation metabolites and phenolic constituents, thereby affecting process performance, color development, oxidative stability, and other quality-related attributes. This review synthesizes current evidence on migration mechanisms and examines how vessel composition shapes the chemical and microbiological profile of fermentation. It also critically evaluates biosensor technologies—covering both biorecognition elements and signal-transduction strategies—and assesses the transition from laboratory prototypes to in situ or at-line implementations capable of detecting both migration-related events and process-relevant compositional changes with operational value for HACCP-based control. Electrochemical, optical, bienzymatic, and nanozyme-enabled platforms are discussed in terms of selectivity, matrix compatibility, and long-term functional stability under polyphenol and protein interference, CO₂ variability, fouling and biofouling, and calibration drift. Particular attention is given to analytes associated with vessel-derived migrants and to biosensor targets related to fermentation metabolites and phenolic indicators, which support dynamic process monitoring and quality-focused decision making. Considering regulatory compliance requirements across the EU, US, and Asia, we propose a practical pathway for integrating biosensors into HACCP monitoring by treating vessel–product interactions as critical control points, while laboratory reference methods remain essential for verification and compliance documentation. Full article

In this study, an enzyme-free electrochemical sensor based on zinc oxide (ZnO) nanorods synthesized by the thermal decomposition of zinc acetate is presented. The suggested approach ensures simplicity, environmental friendliness, and scalability of the process without the use of an autoclave or high pressure. The morphology and structure of the samples are studied using SEM, TEM, XRD, Raman, FTIR, XPS, PL, and UV-Vis spectroscopy. It is found that heat treatment at 450 °C increases the degree of crystallinity, increases the size of crystallites, and reduces the concentration of surface defects, which leads to improved optical and electrochemical characteristics of the material. Beyond conventional sensitivity metrics, our study demonstrates that the selective detection of ascorbic acid (AA) and uric acid (UA) can be achieved by controlling the applied potential on a single ZnO electrode, an approach that leverages differences in redox energetics and surface interaction dynamics rather than complex surface functionalization. It is shown in this work that the synthesized ZnO samples subjected to heat treatment in air at 450 °C exhibit high sensitivity to ascorbic acid (9951.87 μA·mM⁻¹·cm⁻²; LoD = 1.11 μM) at a potential of 0.2 V and to uric acid (5762.48 μA·mM⁻¹·cm⁻²; LoD = 1.71 μM) in a phosphate buffer solution (pH 7) at a potential of 0.4 V with a linear range of 3 mM, offering a way to create simplified multicomponent electrochemical biosensors based on potential-controlled selectivity. Full article

In recent years, metal phthalocyanine (MPc)-based sensors have garnered significant interest for applications in environmental monitoring, biomedical diagnostics, and industrial process control, owing to their efficient and cost-effective sensing capabilities. In contrast to conventional inorganic materials, MPcs are a class of small-molecule materials characterized by a stable, π-conjugated macrocyclic framework with a tunable central metal ion. This structural architecture imparts unique physicochemical properties, including high chemical stability, excellent redox activity, structural versatility, considerable dielectric constant and electrical conductivity, along with pronounced optical absorption and excellent environmental stability. By incorporating different metal ions into the macrocyclic core, their functional characteristics can be precisely modulated to achieve high sensitivity and selectivity toward various gas, ion, or biomolecule targets. Leveraging these advantages, MPcs have been extensively utilized in diverse sensing platforms, such as photoelectric, gas, and biosensors. This review outlines recent advances in MPc-based sensor research and provides perspectives on their future development trends. Full article

Viruses suddenly emerging from obscurity or anonymity affect our quality of life and increase incidence rate and mortality. A typical example is the global coronavirus disease 2019 (COVID-19) pandemic. Although severe acute respiratory syndrome coronavirus 2, known as the pathogen of COVID-19 has been significantly eliminated, its monitoring is still crucial, as the infectious disease may break out again. Therefore, it is necessary to develop simple and effective tools for monitoring COVID-19 and other diseases. Here, we summarize the progress of machine-learning-based biosensors in the monitoring and management of COVID-19. This article mainly includes three sections: machine learning algorithms, machine-learning-assisted biosensors, and challenges and future perspectives. We believe that this work is valuable for developing artificial-intelligence-based innovative analytical devices for healthcare monitoring and management of COVID-19 and other infectious diseases. Full article