Bridging the gaps in Alzheimer's disease biomarker research: From multi-omics integration to point-of-care diagnostics, a comprehensive review.

PAM-assembled CRISPR-Cas12a activation-based fluorescent and colorimetric dual-modal biosensor for detecting prostate cancer exosomes.

A graphene-assisted one-dimensional photonic crystal biosensor for oral cancer detection in the mid-infrared region is theoretically proposed and analyzed using the transfer matrix method. The designed structure consists of alternating high- and low-refractive-index dielectric layers surrounding an oral-tissue defect cavity that supports a highly localized defect-mode resonance inside the photonic band gap. Variations in the refractive index of healthy and cancerous oral tissues modify the optical path length of the cavity and induce measurable resonance wavelength shifts, forming the basis of the sensing mechanism. The proposed biosensor exhibits a high refractive-index sensitivity of 1629.82nm/RIU with excellent linearity (R2 = 0.9997). In addition, the structure demonstrates a narrow resonance linewidth with an average full width at half maximum of 17.01nm and a high quality factor of 470.25. The obtained figure of merit, detection accuracy, and limit of detection are 95.81 RIU- 1, 0.0588nm- 1, and 0.0104 RIU, respectively. Reflectance and transmittance analyses confirm the formation of a stable localized defect mode, while electric-field distribution maps reveal strong electromagnetic confinement inside the oral-tissue cavity, leading to enhanced light-matter interaction and improved sensing performance. Furthermore, angular-response analysis, defect-thickness optimization, and fabrication-tolerance evaluation demonstrate the robustness and stability of the proposed design under practical operating conditions. The obtained results indicate that the proposed graphene-assisted photonic crystal biosensor provides a promising platform for highly sensitive and reliable oral cancer detection in the mid-infrared spectral region.

Possessing superior water retention capacity, exceptional biocompatibility, and versatile design flexibility, hydrogels have emerged as a prominent engineering platform for cell and enzyme immobilization. The core advantage of hydrogels lies in the structure-activity relationship between their physicochemical properties-governed by crosslinking mechanisms and fabrication techniques-and the biological performance of the immobilized biocatalysts. This relationship is central to constructing microenvironments that either promote cell proliferation or stabilize enzymatic reactions. This paper systematically reviews this structure-activity paradigm, emphasizing how raw material selection and manufacturing technologies determine hydrogel functionality. We further analyze the critical design criteria for immobilization-oriented hydrogels, which must balance physical performance, support biological activities, and exhibit environmental adaptability. Distinct design requirements for the immobilization of living cells versus enzymes are explicitly compared. Building upon this foundation, the review elaborates on the unique application value of this technology in medical diagnostics, therapeutics, biosensors, industrial biotransformation, and environmental remediation. Finally, we identify core challenges and propose a concrete development roadmap to provide more efficient and stable engineering solutions for related fields.

Silicon photonics has emerged as a promising technology for next-generation biosensors. Its CMOS compatibility, miniaturization, and large-scale fabrication capabilities, combined with high sensitivity, fast response, and inherent label-free detection capabilities, have positioned this technology at the forefront for the deployment of point-of-care (PoC) analytical platforms. However, operating a multiplexed photonic biosensor configuration remains challenging due to limitations such as efficient light coupling to multiple sensing elements, simultaneous signal acquisition, precise microfluidics and biofunctionalization, and optical crosstalk management between sensors. In this study, we introduce an interferometric biosensor that incorporates a new multiplexed nanophotonic bimodal waveguide (BiMW) chip with a compatible microfluidic system and an optimized light-coupling and readout configuration that enables simultaneous, individual detection of up to seven targets in the same sample. All system components have been incorporated into a compact prototype providing an outstanding bulk limit of detection (LOD) of (7 ± 5) × 10-7 RIU. The biosensor capabilities have been validated against a representative clinical scenario, demonstrating effective and sensitive detection of Respiratory Syncytial Virus (RSV) nucleoproteins relevant to infectious disease diagnostics, achieving a simultaneous LOD of 1.2 ± 0.4 ng·mL-1 across all seven sensors.

Biosensing technologies play a critical role across the healthcare, environmental monitoring, and food safety sectors. The in vivo sensing of biomolecules is challenging due to the non-biocompatibility of nano-microelectrodes. In this regard, lignocellulosic materials will have a significant impact on sensors owing to their outstanding properties. Although lignocellulose lacks conductivity, it can be modified with other metal nanoparticles or conductive polymers to improve its conductivity. By leveraging functionally applied nanomaterials with lignocellulose, promising flexible biosensors can be developed with enhanced sensitivity, selectivity, and versatility. This integration of lignocellulosic materials with nanomaterials enables advanced biosensors with improved performance, facilitated by their high surface area-to-volume ratios and suitability for biomolecule immobilization. Lignocellulosic nanofibrils exhibit thermal stability, absorption in the ultraviolet-visible (UV-vis) region, water stability, and reduced moisture sensitivity and enhance sensor performance. Lignocellulosic materials have emerged as promising substrates for the development of next-generation biosensors. This review explores the suitability of lignocellulose for biosensing applications. Here, we discuss how plant-based materials have been used for biomolecule sensing. Lignocellulose has outstanding mechanical properties, which is why it can be used as a base material and sensing electrode to fabricate brain-on-chip and organ-on-chip devices. Because it is a plant-derived material, it also exhibits microfluidic properties. A cellulose skin-substituted natural polymer shows promise as a substrate for wearable sensors.

Uric acid (UA) is a critical endogenous metabolite that contributes to the regulation of various physiological functions essential for maintaining homeostasis in the human body. Nevertheless, its elevated level causes several diseases that pose serious health issues. Wearable biosensors have gained remarkable interest in personalised healthcare due to their potential for continuous, real-time monitoring of metabolites such as UA in sweat. They offer a non-invasive molecular-level diagnosis of human biomarkers. Nonetheless, rapid sensing and flexibility are the key advantages, making them a superior tool for UA detection. Real-time monitoring of UA in sweat using wearable biosensors is driven toward personalised healthcare management that aims to tackle the potential risk of UA. This review provides a comprehensive overview of the health implications of UA, the importance of wearable biosensors, and the fundamental design and working principles of the advanced wearable UA (bio)sensors. It further discussed hybrid nanocomposites of carbon, MXenes, metal oxides, and polymer-based various types of wearable UA (bio)sensors, including enzyme and non-enzyme-based electrochemical platforms, with a focus on their integration with artificial intelligence (AI) and the Internet of Things (IoT) for smart monitoring and real-time data management. Finally, it highlights the anti-fouling strategy and current challenges for the development of highly sensitive and reliable wearable (bio)sensors for UA detection.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder, often characterized by challenges in social interaction, repetitive behaviors, and restricted interests in work. Early diagnosis is critical for effective intervention, but current methods often rely on subjective behavioral assessments that can delay identification. This underscores the need for reliable biomarkers that can facilitate earlier detection of ASD. Biomarkers are primarily proteins in nature, found in blood, saliva, or other tissues, and have the potential to enhance diagnostic accuracy and speed. They can reveal underlying neurobiological changes associated with ASD, providing objective data to support clinical findings. The search for altered levels of certain amino acids and neurotransmitters and specific genetic biomarkers such as single-nucleotide polymorphisms (SNPs) and copy number variants (CNVs) has gained momentum, driven by the need for more definitive early diagnostic tools. Neuroinflammatory and neuroimaging biomarkers through MRI and fMRI have also shown promise for early detection of ASD. Recent advancements in biosensor technology have significantly improved the prospects for biomarker discovery in ASD. Innovations in nanotechnology and microfluidics have enabled the development of highly sensitive and specific biosensors that can trace minute quantities of biomarkers. These devices allow for non-invasive sampling and real-time monitoring, making early screening more feasible and accessible for young children. This review article mainly focuses on how the integration of these biosensor and biomarker technologies could transform the early detection of ASD, ultimately facilitating timely interventions and improving patients' outcomes.

Scientific exercise monitoring is significant for injury risk prevention and training outcome promotion. Wearable biosensing technologies have emerged as transformative tools for real-time, in-situ physiological state profiling through dynamic biomarker detection during exercise activities. However, current studies remain suboptimal for practical exercise management due to inherent constraints including single-analyte detection paradigms, limited permeability and breathability, and inadequate thermoregulatory performance. Here, we present a novel wearable composite fabric system engineered for multiplex sweat biomarker monitoring while delivering unprecedented wear comfort. The hierarchical architecture was achieved through strategic integration of interwoven fiber-based sensor arrays with conventional textiles, augmented by bilateral deposition of microbead-enhanced polyvinylidene difluoride (PVDF) and polyacrylonitrile (PAN) electrospun nanofiber membranes. The porous matrix, Janus hierarchical gradient, and microbead-mediated interfacial engineering render this fabric system with excellent breathability of 13 mm/s, water vapor transmission rate of 468.9 g/m2/h, and solar reflectance of 96.2%. Systematic validation revealed the system's capabilities in multiplex biomarker tracking during diverse exercise scenarios, machine learning-powered fatigue assessment, and scientific exercise regimen evaluation. This work establishes a universal framework for developing wearable platforms with reliable sensing functionality and wear comfort, facilitating effective personalized exercise healthcare management.

This scoping review summarizes recent advances in biosensor technologies for the early, non-invasive detection of oral cancer, with a focus on salivary biomarkers and point-of-care platforms. Five databases (PubMed, Scopus, Web of Science, ProQuest, and EBSCO) were searched for studies published from 2019 to 2025. After duplicate removal and screening, nine eligible original studies were charted. The included platforms were primarily electrochemical, optical, and transistor-based biosensors targeting biomarkers such as interleukin-8 (IL-8), cytokeratin fragment 21.1 (Cyfra 21.1), cancerous inhibitor of PP2A (CIP2A/P90), and high-risk HPV genotypes. Reported limits of detection were frequently in the femtomolar range, although reporting of assay time, sample volume, and clinical diagnostic accuracy was inconsistent. Overall, biosensors show strong analytical potential for oral cancer screening; however, translation to routine clinical use will require standardized analytical validation, careful control of pre-analytical saliva variables, and well-designed multicenter clinical studies reporting sensitivity and specificity against appropriate reference standards.

Wearable biosensing technologies are advancing sports performance monitoring by enabling the continuous and real-time measurement of physiological and biochemical parameters. Among non-invasive biofluids, sweat has become the most widely studied medium due to its easy accessibility during physical activity and its presence of multiple relevant biomarkers. This review critically examines the recent developments in sweat-based wearable biosensing technologies and identifies the key challenges that hinder their transition from laboratory prototypes to practical sports-monitoring systems. The discussion includes a brief introduction to sweat generation, the important biomarkers present in sweat, and their significance in sports health monitoring. Various electrochemical sensing platforms designed for sweat analysis are reviewed, with an emphasis on their structural designs and operational mechanisms. Major application areas, including lactate monitoring for fatigue detection, electrolyte sensing for hydration assessment, and cortisol measurement for stress evaluation, are discussed. This review also highlights the important challenges, including sensor calibration, motion-related artifacts, variability in sweat composition among individuals, and long-term operational stability. Emerging approaches, including multimodal sensing, machine-learning-assisted data interpretation, nanomaterial-enabled sensors, and closed-loop feedback systems, are also discussed as potential solutions to improve the reliability and real-world applicability of sweat-based wearable biosensors for sports performance monitoring.

In this article, we present a highly sensitive surface plasmon resonance (SPR) sensor to detect the cancer biomarker Carcinoembryonic antigen (CEA). The sensor is designed using the Kretschmann configuration and incorporates an FK51A prism along with layered materials such as silver (Ag), silver arsenic sulfide (Ag3AsS3), and gallium sulfide (GaS) and selected (2D) materials like black phosphorus and Graphene. These 2D materials are chosen for their excellent optical properties and their ability to increase electric field confinement in sensing interfaces. The simulation results show that the proposed multilayer sensor provides considerable improvements in the sensing performances including high sensitivity, sharp resonance curves, and increased figure of merit (FoM). Results confirms that the proposed sensor with WS2 is gained a maximum sensitivity of 350.61°/RIU and an extraordinary quality factor (QF) of 106.650 RIU-1. Compared to existing sensors, this sensor shows better results with a simple construction process. The use of customized 2D materials also improves signal-to-nosie ratio (SNR) of 1.57 and combined sensitivity factor (CSF) of 68.20, which is highly suitable for detecting the initial stage CEA. This task indicates that integrating 2D materials in SPR platforms can greatly extend biosensor technologies for cancer diagnosis.

Integrative simulation and spectroscopy illuminates L-asparaginase stability on nanocomposite surface for biosensing.

Exploring a novel bifunctional nanomaterial with excellent photoelectrochemical and photocatalytic performance is crucial for developing multifunctional sensors with high sensitivity and reliability. In this work, BiOI hollow microspheres with a unique layered structure were successfully synthesized. Their excellent electron transport capabilities and abundant catalytic sites endow them with both highly efficient photoelectrochemical (PEC) performance and photoresponsive oxidase activity. On the basis of these properties, we have innovatively developed, for the first time, a dual-mode biosensor for the detection of Staphylococcus aureus (S. aureus). During the detection process, utilizing an exonuclease III (Exo III)-mediated target-amplification strategy, we achieved the efficient enrichment of the probe molecule Au@DA on the BiOI surface. Under illumination, BiOI exhibits outstanding oxidase-like catalytic activity, capable of efficiently catalyzing the oxidation of dopamine (DA) to polydopamine (PDA). Concurrently, the Z-type heterojunction formed between BiOI and PDA significantly enhances the photocurrent response, increasing its intensity by 2 orders of magnitude. Furthermore, the generated PDA possesses excellent photothermal conversion properties and can be further utilized for temperature signal detection, thereby establishing a dual-signal output system with photocurrent and temperature signals. This effectively avoids the false-positive issues that may arise from single-signal detection and significantly enhances detection reliability. This study not only provides new insights into the design of PEC sensors enhanced by photoelectrocatalytic synergy but also advances the development of PEC biosensing technology through functional integration and signal diversification strategies, offering significant application prospects in the field of environmental monitoring.

Vibrio alginolyticus is a prevalent aquatic pathogen that poses significant risks to public health. Thus, the development of rapid and highly sensitive detection methods is imperative. In this study, we developed a novel aptasensor leveraging the CRISPR/Cas13a system. By integrating a triple-amplification strategy comprising "aptamer competition recognition-T7 transcription amplification-Cas13a trans-cleavage", this platform enables efficient and specific detection of V. alginolyticus. The biosensor employs a V. alginolyticus-specific aptamer (Apt) as the recognition element. In the presence of the target bacterium, Apt binds to a surface membrane protein, resulting in the release of a blocking strand (Block). This triggers a conformational change in a hairpin probe (HP), thereby exposing the T7 promoter sequence. Subsequently, T7 RNA polymerase initiates an isothermal transcription reaction, producing abundant RNA products. These RNAs activate the trans-cleavage activity of Cas13a, which cleaves a fluorescent reporter probe to generate a quantifiable signal. This method eliminates the need for nucleic acid extraction and sophisticated instrumentation. It achieves a detection limit as low as 2 CFU mL-1, and demonstrates high specificity by effectively distinguishing closely related species (e.g., Vibrio parahaemolyticus). When applied to simulated seawater and seafood samples, the recovery rates ranged from 94.61% to 106.56%, indicating robust anti-interference capacity and reproducibility. This work establishes a highly sensitive and specific biosensing technology for the on-site rapid detection of aquatic pathogens, offering promising applications in environmental monitoring and food safety.

Gallic acid (GA) modulates the flavor, aging, and fermentationdegree of traditional Chinese tea. Herein, hierarchically porous FeNi-CNF (PFeNi-CNF) nanozymes were fabricated via electrospinning and carbonization for GA detection, utilizing differential thermal decomposition kinetics of carbon precursors. The hierarchical porous structure optimizes mass transfer and facilitates active site formation, which endows PFeNi-CNF with 3.22-fold peroxidase-like activity compared to FeNi-CNF (calculated based on the absorbance values), as well as outstanding long-term stability and cycling durability. A highly selective and sensitive GA colorimetric sensor was developed with an LOD of 0.02 μM and a linear range of 0.05-3 μM. Machine learning model-assisted tea sample analysis (based on R, G, B, H, S, V values) was performed (highest accuracy of 99%), providing a fast, accurate, and convenient GA sensing platform for detection, recognition, and prediction of biosensing and food technology.

The benefits of intrinsic sensitivity, speed, cost, and simplicity remain to be robust dynamic forces toward the progress of electrochemical (bio) sensors. In this comprehensive review, the breakthrough ultrasensitive electrochemical sensing performances of emerging efficient sensors for the calendar year up to 2025 have been explored. The goal of this effort is to provide progress in electrode engineering and patterns for electrochemical signal amplification used in sensors. Also, we reviewed enzyme-based biosensors, immunosensors, genosensors, cytosensors, and small molecules. The novel strategies are crucial for the progress of electrochemical sensors, which paves a path for future research. This review provides a roadmap for the advancement of metal-based sensing materials with various techniques. However, we express regret to authors of key publications that were inadvertently left out. Only the original and review articles published in available journals were considered, whereas patents, book chapters, or book serials were omitted for this review.

Bioinspired nanofluidic iontronics is emerging as a pivotal technology for next-generation biosensing and neuromorphic computing. Although constrained by nanoscale perturbations and fabrication heterogeneity, artificial intelligence (AI) effectively mitigates inherent signal and manufacturing bottlenecks, driving a paradigm shift toward bidirectional empowerment. In this Mini-Review, we summarize recent advances in the convergence of AI and iontronics, focusing on the core scientific conflict between algorithmic robustness and nanoscale physical stochasticity. We analyze how this interaction reshapes three key dimensions of the research workflow: (1) AI-enabled characterization for feature extraction and mechanistic analysis amidst noise; (2) AI-driven fabrication via surrogate models and inverse structural design to bridge the design-to-fabrication gap; and (3) AI-expanded applications in intelligent biosensing and physical neuromorphic computing. Finally, we propose a synergistic roadmap: managing physical imperfections via manufacturing-aware models while shifting AI from merely circumventing errors to architectures that actively exploit intrinsic stochasticity.

Biosensors for the detection of arthropod-borne veterinary viruses: A comprehensive review.

This article presents a theoretical investigation of a highly sensitive one-dimensional photonic crystal (1D-PC) biosensor for early malaria detection using the Transfer Matrix Method (TMM). The proposed structure, Air/(PbS/SiO2)4/defect/(PbS/SiO2)4/substrate, is optimized by adjusting the defect layer thickness and incidence angle to enhance defect-mode resonance characteristics. The design enables strong resonant peaks and significant wavelength shifts at 1550 nm. The sensor operates over a refractive index range of 1.371-1.402 RIU, corresponding to different malaria infection stages. A maximum sensitivity of 977.74 nm/RIU and a quality factor of 93.8 × 103 are achieved at a defect thickness of 1.2da. Increasing the thickness to 1.4da improves sensitivity to 1163.22 nm/RIU with a quality factor of 82.57 × 103. The design also exhibits a low detection limit of ~10-6 RIU and a high figure of merit, indicating strong potential for rapid, accurate, and cost-effective malaria diagnosis.