Biosensors-Basel最新文献

This review addresses the challenges of obtaining high-quality quantitative data in the optical imaging of membrane voltage and calcium dynamics. The paper provides a comprehensive overview and systematization of recent studies that analyze factors limiting signal fidelity and propose strategies to enhance data quality. The primary sources of signal degradation in biological optical imaging, with an emphasis on membrane voltage and calcium imaging, are systematically explored across four major indicator classes: voltage-sensitive dyes (VSDs), genetically encoded voltage indicators (GEVIs), calcium-sensitive dyes (CSDs), and genetically encoded calcium indicators (GECIs). Common mechanisms that compromise data quality are classified into three main categories: fundamental photon shot noise, device-related errors, and sample-related measurement errors. For each class of limitation, its physical or biological origin and characteristic manifestations are described, which are followed by an analysis of available mitigation strategies, including hardware optimization, choice of sensors, sample preparation and experimental design, post-processing and computational correction methods.

This manuscript presents the development of an electrochemical biosensor designed to detect K-562 chronic myeloid leukemia (CML) cells. The biosensor was made of highly oriented pyrolytic graphite (HOPG), functionalized with -OH and -COOH groups by surface etching with strong acids, and subsequently coated with modified titanium dioxide (TiO₂-m). TiO₂-m is TiO₂ modified during its synthesis process using carbon nanotubes functionalized with -OH and -COOH groups. These changes improve the electron transfer kinetics and physicochemical properties of the electrode surface. TiO₂-m improves the sensitivity and selectivity towards leukemic cells. The detection process involved three stages: cell culture, cell adhesion onto the TiO₂-m electrode, and measurement of the electrochemical signal. Fluorescence microscopy and SEM-EDS confirmed cell adhesion and pseudopod formation on the TiO₂-m surface, which is an important finding because K-562 cells are typically nonadherent. Cyclic voltammetry (VC) and differential pulse voltammetry (VDP) demonstrated rapid and sensitive detection of leukemic cells within the concentration range of 6250 to 1,000,000 cells/mL, achieving high reproducibility and strong linearity (R² = 98%) with a detection time of 25 s. The VC and VDP demonstrated rapid and sensitive detection of leukemic cells over a concentration range of 6250 to 1,000,000 cells/mL, achieving adequate reproducibility and stable linearity (R² = 98%), with a detection time of 25 s. These results indicate that the TiO₂-m biosensor is a promising platform for the rapid and efficient electrochemical detection of leukemia cells.

Optical fibers are gaining increasing attention in biomedical applications due to their unique advantages, including flexibility, biocompatibility, immunity to electromagnetic interference, potential for miniaturization, and the ability to perform remote, real-time, and in situ sensing. Label-free optical fiber biosensors represent a promising alternative to conventional cancer diagnostics, offering comparable sensitivity and specificity while enabling real-time detection at ultra-low concentrations without the need for complex labeling procedures. However, the sensing performance of biosensors is fundamentally governed by surface modification. The choice of optimal functionalization strategy is dictated by the sensor type, target biomarker, and detection environment. This review paper presents a comprehensive and expanded overview of various surface functionalization methods specifically designed for cancer biomarker detection using optical fiber biosensors, including silanization, self-assembled monolayers, polymer-based coatings, and different dimensional nanomaterials (0D, 1D, and 2D). Furthermore, the emerging integration of computational methods and machine learning in optimizing functionalized optical sensing has been discussed. To the best of our knowledge, this is the first work that consolidates existing surface modification approaches into a single, cohesive resource, providing valuable insights for researchers developing next-generation fiber optic biosensors for cancer diagnostics. Moreover, the paper points out the current technical challenges and outlines the future perspectives of optical fiber-based biosensors.

The gut-brain axis (GBA) interaction is important for human health and disease prevention. Organ chips are considered a solution for GBA research. Three-dimensional (3D) cultures and microfluidics engineered in an organ chip could improve the scientific knowledge in the GBA interactions field. In this study, a novel organ chip is developed, which achieves multicellular three-dimensional cultivation by utilizing a decellularized matrix. In addition, this paper reports the rapid prototyping process of the GBA microfluidic chip in polydimethylsiloxane (PDMS) using 3D printing interconnecting poly(ethylene/vinyl acetate) (PEVA) microchannel templates. In comparison to the static culture system of the transwell model, the intestinal epithelial barrier (IEB) and blood-brain barrier (BBB) models on our chip demonstrated superior barrier function and the efflux functionality of transporters under appropriate fluidic conditions. Additionally, it is observed that butyrate protected against BBB dysfunction induced by gut-derived lipopolysaccharide (LPS) via enhancing intestinal barrier function. These results demonstrate that this multicellular, three-dimensional cultivation integrated with a fluidic shear stress simulation chip offers a promising tool for gut-brain interaction study to predict therapy of intestinal and neurological disorders.

Early detection of premalignant cervical lesions is essential for improving cervical cancer outcomes; however, current screening methods frequently lack adequate sensitivity and specificity. This research introduces a diagnostic platform that integrates lectin-based biosensors with spectral and multivariate analysis. The biosensors are composed of gold nanoparticles (AuNPs) conjugated with Maackia amurensis (MAA) lectin, which selectively binds to α2,3-linked sialic acid. Validation was performed using cervical cancer cell lines (SiHa, HeLa, C33A), fibroblasts, and cervical scrapes, and specificity was verified by enzymatic removal of sialic acids. Spectral data were obtained using attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and analyzed by principal component analysis (PCA). Application of PCA to the 1600-1350 cm^-1 spectral region, using 99% confidence ellipses, enabled clear differentiation between samples negative and positive for intraepithelial lesions in a double-blind study of 58 patients. The MAA biosensors exhibited high sensitivity and specificity, comparable to established diagnostic methods. These results indicate that the combination of ATR-FTIR spectroscopy, MAA lectin-based biosensors, and chemometric analysis provides a robust and reliable approach for early detection of premalignant cervical lesions, with considerable potential to enhance patient outcomes.

The detailed characterization of antigen-specific serum antibodies is hindered by the lack of efficient, gentle isolation methods. In this context, standard column affinity chromatography, although a powerful purification tool, presents practical challenges, including high antigen consumption and elution conditions that risk inducing antibody polyreactivity, while conventional acidic elution often compromises antibody integrity. This study introduces a novel microscale method for isolating specific immunoglobulins using anionic detergents as mild eluents. We employed antigen-functionalized hydrogel microarrays and magnetic beads as micro-immunosorbents. Among the tested detergents, sodium lauroyl glutamate (SLG) was optimal, achieving up to 78.3% recovery of functional antibodies. The optimized protocol, including recovery via G25-Sephadex gel filtration, effectively isolated specific antibodies from complex serum, retaining 58.5-85.3% of their functional bioactivity. Multiplex immunoassays confirmed the high specificity of the isolated antibodies and the lack of detergent-induced polyreactivity. The method was successfully adapted to isolate both specific antibodies (virus, dietary, and autoimmune) and total IgG, demonstrating versatility across platforms. This work establishes a robust, efficient, and gentle workflow for obtaining high-purity, bioactive antibodies, enabling their subsequent in-depth analysis for research applications.

Protein modifications, particularly post-translational modifications (PTMs) such as phosphorylation and glycosylation, are fundamental mechanisms regulating cellular activity and disease pathogenesis, with their detection emerging as a promising frontier for advanced diagnostics. This review systematically examines the integration of engineered protein modifications with biosensing technologies to enhance analytical performance and diagnostic accuracy. Through critical analysis of current methodologies, we highlight how strategic manipulation of PTMs improves biosensor sensitivity and specificity in applications ranging from early disease detection to environmental monitoring. The analysis identifies significant advancements in detection platforms while acknowledging persistent challenges in real-world integration and standardization. We conclude that optimizing protein modification-based sensing strategies represents a crucial pathway for developing robust, clinically translatable diagnostic tools, and propose focused research directions to address existing technical barriers and accelerate practical implementation.

Neuroplasticity-based active movement opens an avenue for functional recovery in post-stroke patients. Active rehabilitation techniques have attracted wide attention based on their abilities to enhance patient involvement, facilitate precise personalized intervention, and provide comprehensive treatment via cross-domain approaches. Emerging evidence suggests that active rehabilitation methods can respond to patients' motor intentions in real-time and significantly increase motivation and engagement, leading to efficient utilization of critical recovery windows and better rehabilitation outcomes. In this review, we focus on the physiological basis of active rehabilitation, including mechanisms of neuroplasticity, and discuss recent advances in intent detection and feedback devices. We also examine treatment options for different stages of stroke recovery, providing a comprehensive reference for engineers to design optimized rehabilitation techniques and for clinicians to select appropriate rehabilitation protocols. These developments create new opportunities to improve the lives of stroke patients and offer greater hope for their recovery.

A scalable and facile fabrication strategy is presented for developing a flexible, nanostructured, non-enzymatic electrochemical sensor for lactate detection based on copper-modified laser-induced graphene (CuNPs/LIG). A one-step electrodeposition process was employed to uniformly decorate the porous LIG framework with copper nanostructures, offering a cost-effective and reproducible approach for constructing enzyme-free sensing platforms. Scanning electron microscopy and energy-dispersive X-ray spectroscopy confirmed dense Cu nanostructure loading and efficient interfacial integration across the conductive LIG surface. The resulting CuNPs/LIG electrode exhibited excellent electrocatalytic performance, achieving a sensitivity of 8.56 μA µM^-1 cm^-2 with a low detection limit of 42.65 μM and a linear response toward lactate concentrations ranging from 100 to 1100 μM in artificial saliva under physiological conditions. The sensor maintained high selectivity in the presence of physiologically relevant interferents. Practical applicability was demonstrated through recovery studies, where recovery rates exceeding 104% showcase the sensor's analytical reliability in complex biological matrices. Overall, this work establishes a robust, sensitive, and cost-efficient Cu-nanostructured LIG sensing platform, offering strong potential for non-invasive lactate monitoring in real-world biomedical and wearable applications.

Quantifying microbial growth with high temporal resolution remains essential yet challenging due to limitations of optical, manual, and biochemical methods. Here, we introduce an AI-enhanced electrochemical impedance spectroscopy platform for real-time, label-free monitoring of Saccharomyces cerevisiae growth. Broadband impedance measurements (1 Hz-100 kHz) were collected from yeast cultures across log-phase development. Engineered features-derived from impedance magnitude and phase-captured dielectric and conductive shifts associated with cell proliferation, membrane polarization, and ionic redistribution. A Gaussian Process Regression model trained on these features predicted optical density (OD600) with high precision (RMSE = 0.79 min; R² = 0.9996; r = 0.9998), and achieved 100% classification accuracy when discretized into 15-min growth intervals. The system operated with sub-millisecond latency and minimal memory footprint, enabling embedded deployment. Benchmarking against conventional methods revealed superior throughput, automation potential, and independence from labeling or turbidity-based optics. This AI-driven platform forms the core of a real-time digital twin for yeast culture monitoring, capable of predictive tracking and adaptive control. By fusing electrochemical biosensing with machine learning, our method offers a scalable and robust solution for intelligent fermentation and bioprocess optimization.