Biosensors-Basel最新文献

Dynamic monitoring of hemostatic equilibrium is indispensable for clinical safety in high-risk scenarios, while current clinical methods are limited by sample volume, detection speed, and physiological relevance. These shortcomings underscore the demand for novel sensing platforms. Optical biosensors, leveraging label-free detection, rapid response, and multi-level characterization, could serve as a transformative solution for decentralized and point-of-care monitoring. This review systematically summarizes advances in optical coagulation testing, encompassing light transmission aggregometry, laser speckle rheology, optical coherence tomography/elastography, optic-acoustic coupled methods, and fluorescence biosensing. These technologies complementarily capture structural and mechanical and some molecular and cellular dynamics of coagulation, bridging gaps in traditional assays. Despite promising preclinical and clinical correlations, translation barriers persist in lack of standardization of metrics, interference mitigation, and multi-center validation in diverse patient cohorts. Future development of optical biosensing platforms for coagulation testing should focus on modular integration, AI-aided interference correction, and microfluidic miniaturization to realize actionable, real-time coagulation assessment. Optical biosensors hold unparalleled potential to transform hemostatic monitoring from static endpoint testing to dynamic, interpretable evaluation, guiding personalized clinical decisions.

Culturing cells and micro-tissue samples in 3D bio-scaffolding structures is gaining popularity; however, precise control of tissue micro-environment in such systems remains challenging. We describe a family of new hybrid bio-scaffolds with 3D O₂-sensing ability, produced by simple means from readily available bio-scaffolding and O₂-sensing materials. Three different types of phosphorescent O₂-sensing materials-polymeric microparticles (MPs), supramolecular probe MitoXpress and nanoparticulate probes NanO2 and Nano-IR (NPs)-were integrated in Matrigel and agarose scaffolding materials and evaluated. Key working characteristics of such hybrid scaffolds, including heterogeneity, stability, cytotoxicity, optical signals and O₂-sensing properties, ease of fabrication and use, were compared. The results show superiority of the Matrigel hybrids with NanO2 and Nano-IR probes. Demonstration experiments were conducted with HCT116 cells and individual spheroids derived from these cells, culturing them in the Matrigel-NP hybrid scaffolds and monitoring oxygenation and local O₂ gradients on a time-resolved fluorescence plate reader and by phosphorescence lifetime imaging microscopy (PLIM).

Traditional sample preparation for flow cytometry is often labor-intensive, operator-dependent, and reagent-consuming, limiting its suitability for automated and point-of-care biosensing applications. To address these challenges, this study presents a functional modular microfluidic system integrating immunomagnetic beads (IMBs) to enable automated intracellular immunofluorescence (IF) staining. The modular microfluidic platform is enabled by a dynamically actuated three-dimensional magnetic field that couples with IMBs within a microfluidic reaction chamber, requiring only one-dimensional magnet translation to induce effective three-dimensional bead motion. This magnetic-chip cooperative strategy significantly enhances microscale mixing and cell capture, facilitating automated immunostaining of the radiation biomarker in CD4⁺ cells. Finite element simulations were employed to guide magnetic field design by analyzing magnetic force distributions and identifying key parameters, including magnet material, size, spatial arrangement, and chip-magnet distance. Experimental validation using CD4⁺ cell capture confirmed the effectiveness of the magnetic mixing strategy, revealing ∇B·B as the critical design parameter. An N52 NdFeB magnet (6 mm diameter, 10 mm height) positioned within 2.2 mm of the chamber centerline stably retained IMBs at flow rates below 200 µL/min. Under optimized conditions (magnet translation speed of 8 mm/s and a 15 min mixing duration), a maximum cell capture efficiency of 86% was achieved. Subsequent automated γH2AX IF staining demonstrated a strong linear dose-response relationship (R² > 0.9) in mean fluorescence intensity. This study demonstrates a robust and scalable strategy for automating complex IF staining workflows, highlighting the potential of magnetic-field-assisted microfluidic platforms for biosensing applications requiring reliable intracellular biomarker detection.

Severe Dengue fever can cause Dengue Hemorrhagic Fever (DHF), a life-threatening condition characterized by plasma leakage and hemoconcentration. A hematocrit (Hct) rise of ≥20% is a key indicator for medical intervention, but current monitoring is invasive and intermittent. This study aims to determine the optimal design parameters for a non-invasive optical sensor to continuously monitor hemoconcentration. We developed a high-fidelity Monte Carlo model of light transport in a multi-layered skin model, with the epidermis set to a 5% melanin volume fraction (Fitzpatrick type II/III). To ensure signal reliability, simulations were conducted with a high photon count (1×108 photons), yielding a stochastic (Monte Carlo) signal-to-noise ratio of approximately 36 dB. We simulated diffuse reflectance at four characteristic wavelengths (577 nm, 660 nm, 800 nm-the isosbestic point-, and 940 nm) over source-detector separations of 0.5-8.0 mm. Sensor sensitivity was quantified as the reflectance change for a +25% relative Hct rise (e.g., 42% to 52.5%), mimicking severe hemoconcentration, and its dependence on baseline dermal blood volume fraction (BVF) was investigated. Sensor sensitivity showed a non-linear dependence on BVF, showing a direct correlation with perfusion level, reaching an optimal 6.41% for a robust 5% BVF at 8.0 mm. A dedicated sweep showed that even under low-perfusion shock conditions (1% BVF), the sensor maintains a highly significant sensitivity of 5.71% (also at 8.0 mm), indicating that sensitivity remains high across a physiologically relevant perfusion range. In the analysis, at a robust 5% BVF, the 800 nm wavelength demonstrated superior reliability, with peak sensitivity at 6.41% at 8.0 mm. Visible wavelengths (577 nm and 660 nm) exhibited high theoretical sensitivity, while 940 nm was compromised by water absorption. Based on these findings, a non-invasive optical sensor for hemoconcentration is most effective operating at 800 nm, within the evaluated spectral set, with a source-detector separation of ≥6.0 mm, targeting the deep dermis while minimizing superficial interference. This design provides an optimal balance of tissue penetration, robust sensitivity to Hct changes, and reduced sensitivity to oxygenation-related variability while maintaining signal stability. This work enables the design of a device for continuous monitoring, supporting continuous monitoring of hemoconcentration trends relevant to plasma leakage progression.

Correct focal positioning is essential for microscopy imaging of live moving subjects such as Caenorhabditis elegans. However, many methods can be too slow to perform real-time control to keep the subject in focus. In this work, we propose a convolutional neural network-based method to perform one-shot prediction of the optimal focusing distance, without the need to scan iteratively the optical axis to find the optimal position. A new data augmentation technique is proposed, and its effectiveness is validated through statistical analysis. This technique is shown to improve results without the need for additional data collection. Several architectures are trained in z-stacks of images, using the proposed data augmentation technique, and compared on a validation set. Through this comparison, we find that the ConvNext V2, a novel architecture in this context, outperforms other models proposed in previous works. Furthermore, the impact of the Field of View used for the model's prediction is studied, with the aim of further understanding the influence of spatial resolution and spatial compression on the performance of the model.

Avian influenza (AI), particularly highly pathogenic avian influenza (HPAI), represents a serious and growing threat to global poultry production, international trade, and human health security. Control of AI is complicated by the high evolutionary rate of influenza A viruses, which drives antigenic diversity and ongoing emergence of novel strains. Effective surveillance and disease management therefore depend on timely and accurate diagnostics. While conventional methods-including virus isolation, reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and enzyme-linked immunosorbent assays (ELISAs)-remain effective and widely used, they are limited by long turnaround times, the need for specialized equipment, and reliance on highly trained personnel. In addition, strict state and federal regulatory requirements restrict testing to a limited number of authorized laboratories. Although these regulations are essential for maintaining diagnostic accuracy and quality assurance, they place substantial strain on laboratory capacity during outbreaks and delay actionable results. The need for rapid, on-site decision making has driven interest in alternative diagnostic approaches, including biosensor technologies. A major limitation of current diagnostic strategies is the lack of robust DIVA (Differentiating Infected from Vaccinated Animals) capability. In countries such as the United States, where poultry vaccination against AI is not routinely practiced, the absence of DIVA-compatible diagnostics has hindered adoption of vaccination as a disease management tool, as seropositive birds and products face significant trade restrictions. Biosensor platforms capable of enabling DIVA strategies offer a potential pathway to support vaccination while preserving surveillance integrity. This review examines the current landscape of AI and HPAI diagnostics, emphasizing the limitations of traditional approaches and the opportunities presented by biosensor platforms. We evaluate electrochemical, optical, piezoelectric, and nucleic-acid-based biosensors, with particular attention to biorecognition strategies, performance metrics, field deployability, and applications supporting subtype discrimination, DIVA implementation, and One Health surveillance.

This study aims to develop an innovative electrochemical genosensor based on activated biochar (ABC) derived from the biomass of the seaweed Sargassum spp. The synthesis process begins with the pyrolysis of Sargassum spp. at 500 °C to obtain biochar (BC), which is chemically activated with nitric acid (HNO₃). The physicochemical properties of the resulting material, such as morphology and surface area, were characterized using techniques including scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and the Brunauer-Emmett-Teller (BET) method for surface area. BET results showed an increase in surface area from 22.9367 ± 0.0879 m²/g (BC) to 159.2915 ± 2.2641 m²/g (ABC). For the development of the genosensor, a hydrolyzed collagen gel matrix enriched with ABC is created. This nanostructured, biocompatible mixture is used to immobilize a DNA probe on a graphite electrode, employing the large surface area of ABC and the formation of a functional HC-based coating. The system's viability was evaluated by cyclic voltammetry (CV), which showed changes in the maximum anodic peak current (Ipa) during fabrication: 27.78 ± 1.87 μA for the bare electrode, 35.25 ± 1.24 μA for ABC 30%, and 39.25 ± 1.84 μA for HC + ABC 30%. After ssDNA immobilization and hybridization to dsDNA, Ipa decreased to 28.81 ± 1.565 μA and 23.10 ± 1.25 μA, respectively. Finally, hematoxylin (Hx) was used as an intercalating indicator from hybridization, reducing the maximum anodic peak current to 15.51 ± 1.13 μA, consistent with additional interfacial limitations associated with dsDNA formation. Overall, the developed system demonstrates a sustainable, promising platform for molecular diagnostics in electrochemical DNA biosensor development.

Early diagnosis of Alzheimer's disease represents a critical clinical challenge, and the high-sensitive biomarkers measurement holds great potential for enabling early identification and intervention. This study proposes an electrochemical immunosensing strategy based on inkjet printing for the quantitative detection of Tau-441. Conductive patterns were formed by inkjet printing, followed by surface functionalization with gold nanoparticles to immobilize highly specific anti-Tau-441. This process created a stable and high affinity immunorecognition interface that enhances electron transfer and signal amplification. Furthermore, we developed and integrated a low-power portable detection platform to achieve a rapid detection process encompassing sample loading, signal acquisition, and on-device readout. The method shows a linear response from 50 fg/mL to 10 ng/mL and a limit of detection of 16 fg/mL (S/N = 3), with high specificity and good reproducibility. By combining scalable inkjet fabrication with a self-contained handheld reader, this method shortens the path from sample to result and offers a practical route for on-site screening and early intervention in Alzheimer's disease.

Over the past decade, clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins, originally identified as adaptive immune systems in bacteria and archaea that defend against invading nucleic acids, have revolutionized biological research [...].

Lysophosphatidic acid (LPA) is a cell-signaling lipid that has been proposed as an early-stage biomarker for ovarian cancer (OC). Diagnosing OC in Stage I is critical to improving patient outcomes, increasing the survival rate from 30% (when diagnosed in late stages of the disease) to over 90%. This significant improvement is due to the success of early interventions; however, current diagnostic methods are not as effective at early-stage detection, with only 15% of cases diagnosed in Stage I and over 70% diagnosed in Stage III or IV. There is a strong need for LPA detection that is sensitive, specific, rapid, low-cost, and automated to truly validate its effectiveness as a diagnostic characteristic for OC. We report the preliminary development and characterization of one such biosensor, which makes use of the advantages of magnetic particles and chemiluminescence for quick, sensitive detection of LPA. The sensor has proven to be viable, with a positive response to LPA concentration, a measurement time of 5 s after incubation, and an LOD of 3.5 nM.