Biosensors-Basel最新文献

Plasmonic nanoparticles (NPs) exhibit exceptional optical and electromagnetic (EM) properties that are, however, confined to their near-field region, limiting effective interactions with non-adsorbed species. Metal-organic frameworks (MOFs), renowned for their high surface area and tunable pores, provide an ideal complement through surface enrichment and subsequent molecular enrichment within their pores. The integration of plasmonic NPs with MOFs into nanohybrids overcomes this spatial constraint. This architectural synergy creates a synergistic effect, yielding properties superior to either component alone. This review summarizes recent advances in NP-MOF nanohybrids, with a focus on synthesis strategies for diverse architectures and their emergent functionalities. We highlight how this synergistic effect enables breakthrough applications in chemical sensing, cancer therapy, and catalysis. Finally, we conclude our discussion and present a critical outlook that explores the challenges and future opportunities in the design and applications of NP-MOF nanohybrids.

Flexible biosensors offer rapid and low-cost diagnostics but are often limited by the mechanical and electrochemical instability of polymer-based designs in biological media. Here, we introduce a metallic flexible microcrack transducer that exploits the intrinsic deformability of superelastic nickel-titanium (NiTi) for label-free impedimetric detection. Mechanical bending of NiTi wires spontaneously generates martensitic-phase microcracks whose metal-gap-metal geometry forms the active transduction sites, where functional interfacial layers and captured analytes modulate the local dielectric environment and govern the impedance response. Our approach imparts a novel dielectric character to the alloy, enabling its unexplored application in the megahertz (MHz) frequency domain (0.01-10 MHz) where native NiTi is merely conductive. Functionalization with Escherichia coli (E. coli)-specific antibodies renders these microdomains biologically active. This effectively transforms the mechanically induced microcracks into tunable impedance elements driven by analyte binding. The γ-bent NiTi sensors achieved stable and quantitative detection of E. coli ATCC 25922 in sterile human urine, with a detection limit of 64 colony forming units (CFU) mL^-1 within 45 min, without redox mediators, external labels, or amplification steps. This work pioneers the use of martensitic microcrack networks, mimicking self-healing behavior in a superelastic alloy as functional transduction elements, defining a new class of metallic flexible biosensors that integrate mechanical robustness, analytical reliability, and scalability for point-of-care biosensing.

Point-of-care testing (POCT) has emerged as a vital diagnostic approach in emergency medicine, primary care, and resource-limited environments because of its convenience, affordability, and capacity to provide immediate results. Here, we present a multifunctional portable nucleic acid detection platform integrating reverse transcription polymerase chain reaction (RT-qPCR) and reverse transcription loop-mediated isothermal amplification (RT-LAMP) within a unified microfluidic device. The system leverages Tesla-valve-based passive flow control to enhance reaction efficiency and operational simplicity. A four-channel optical detection unit allows for multiplex fluorescence quantification (CY5, FAM, VIC, ROX) and has high sensitivity and reproducibility for RT-LAMP. The compact design reduces the overall size by approximately 90% compared with conventional qPCR instruments. For RT-PCR, the system achieves a detection limit of 2.0 copies μL^-1 and improves analytical efficiency by 27%. For RT-LAMP, the detection limit reaches 2.95 copies μL^-1 with a 14% enhancement in analytical efficiency. Compared with commercial qPCR instruments, the device maintains equivalent quantitative accuracy despite significant miniaturization, ensuring reliable performance in decentralized testing. Furthermore, the total RT-LAMP assay time is reduced from more than two hours to 42 min, enabling truly rapid molecular diagnostics. This dual-mode platform offers a flexible, scalable strategy for bridging laboratory-grade molecular assays with real-time POCT applications, supporting early disease detection and epidemic surveillance.

Metamaterials (MMs)-based terahertz (THz) biosensors hold promise for clinical diagnosis, featuring label-free operation, simple, rapid detection, low cost, and multi-cell-type discrimination. However, liquid around cells causes severe interference to sensitive detection. Most existing MMs-based cell biosensors detect dead cells without culture medium (losing original morphology), hindering stable, sensitive multi-cell discrimination. Here, a terahertz biosensor composed of a microcavity and MMs can be used to detect and discriminate multiple cell types within suspension. Its detection mechanism relies on cellular size (radius)/density in suspension, which induces effective permittivity (ε_eff) differences. By designing MMs' split rings with luxuriant gaps, the biosensor achieves a theoretical sensitivity of ~328 GHz/RIU, enabling sensitive responses to suspended cells. It shows a robust, increasing frequency shift (610-660 GHz) over 72 h of cell apoptosis. Moreover, it discriminates nerve cells, glioblastoma (GBM) cells, and their 1:1 mixture with obviously distinct frequency responses (~650, ~630, ~620 GHz), which suggests effective and reliable multi-cell-type recognition. Overall, this study and its measurement method should pave the way for metamaterial-based terahertz biosensors for living cell detection and discrimination, and this technology may inspire further innovations in tumor investigation and treatment.

Organ-on-a-Chip (OOC) platforms are microfluidic systems that recreate key features of human organ physiology in vitro via controlled perfusion. Fluid mechanical stimuli strongly influence cell morphology and function, making this important for cardiovascular OOC applications exposed to pulsatile blood flow. However, many existing OOC devices employ relatively simple chamber geometries and steady inflow assumptions, which may cause non-uniform shear exposure to cells, create stagnant regions with prolonged residence time, and overlook the specific effects of pulsatile perfusion. Here, we used computational fluid dynamics (CFD) to investigate how chamber geometry and inflow conditions shape the near-wall flow environment on a cell culture surface at a matched cycle-averaged volumetric flow rate. Numerical results demonstrated that pillarized chambers markedly reduced relative residence time (RRT) versus the flat chamber, and the small pillar configuration produced the most uniform time-averaged wall shear stress (TAWSS) distribution among the tested designs. Phase-resolved analysis further showed that wall shear stress varies with waveform phase, indicating that steady inflow may not capture features of pulsatile perfusion. These findings provide practical guidance for pillar geometries and perfusion conditions to create more controlled and physiologically relevant microenvironments in OOC platforms, thus improving the reliability of cell experimental readouts.

Accurate malaria diagnosis is essential for effective case management and transmission control; however, the sensitivity, operational requirements, and field applicability of current conventional methods are limited. Hemozoin, an optically and magnetically active crystalline biomarker produced by Plasmodium species, offers a reagent-free target for next-generation diagnostics. This scoping review, following PRISMA-ScR and Joanna Briggs Institute guidance, synthesizes recent advances in hemozoin-based detection technologies and maps the current landscape. Twenty-four studies were reviewed, spanning eight major technology classes: magneto-optical platforms, magnetophoretic microdevices, photoacoustic detection, Raman/SERS spectroscopy, optical and hyperspectral imaging, NMR relaxometry, smartphone-based microscopy, and flow cytometry. Magneto-optical systems-including Hz-MOD, Gazelle™, and RMOD-demonstrated the highest operational readiness, with robust specificity but reduced sensitivity at low parasitemia. Photoacoustic Cytophone studies demonstrated promising sensitivity and noninvasive in vivo detection. Raman/SERS platforms achieved sub-100 infected cell/mL analytical sensitivity but remain laboratory-bound. Microfluidic and smartphone-based tools offer emerging, potentially low-cost alternatives. Across modalities, performance varied by parasite stage, with reduced detection of early ring forms. In conclusion, hemozoin-targeted diagnostics represent a rapidly evolving field with multiple viable translational pathways. While magneto-optical devices are closest to field deployment, further clinical validation, improved low-density detection, and standardized comparison across platforms are needed to support future adoption in malaria-endemic settings.

Due to the complexity of blood glucose dynamics and the high variability of the physiological structure of diabetic patients, implementing a safe and effective insulin dosage control algorithm to keep the blood glucose of diabetic patients within the normal range (70-180 mg/dL) is currently a challenging task in the field of diabetes treatment. Deep reinforcement learning (DRL) has proven its potential in diabetes treatment in previous work, thanks to its strong advantages in solving complex dynamic and uncertain problems. It can address the challenges faced by traditional control algorithms, such as the need for patients to manually estimate carbohydrate intake before meals, the requirement to establish complex dynamic models, and the need for professional prior knowledge. However, reinforcement learning is essentially a highly exploratory trial-and-error learning strategy, which is contrary to the high-safety requirements of clinical practice. Therefore, achieving safer control has always been a major challenge for the clinical application of DRL. This paper addresses this challenge by combining the advantages of DRL and the traditional control algorithm-model predictive control (MPC). Specifically, by using the blood glucose and insulin data generated during the interaction between DRL and patients in the learning process to learn a blood glucose prediction model, the problem of MPC needing to establish a patient's blood glucose dynamic model is solved. Then, MPC is used for forward-looking prediction and simulation of blood glucose, and a safety controller is introduced to avoid unsafe actions, thus restricting DRL control to a safer range. Experiments on the UVA/Padova glucose kinetics simulator approved by the US Food and Drug Administration (FDA) show that the time proportion of adult patients within the healthy blood glucose range under the control of the model proposed in this paper reaches 72.51%, an increase of 2.54% compared with the baseline model, and the proportion of severe hyperglycemia and hypoglycemia events is not increased, taking an important step towards the safe control of blood glucose.

Wearable multi-modal body fluid monitoring enables continuous, non-invasive, and context-aware assessment of human physiology. By integrating biochemical and physical information across multiple modalities, wearable systems overcome the limitations of single-marker sensing and provide a more holistic view of dynamic health states. This review offers a system-level overview of recent advances in multi-modal body fluid monitoring, structured into three hierarchical dimensions. We first examine sensing-combination strategies such as multi-marker analysis within single fluids, coupling biochemical signals with bioelectrical, mechanical, or thermal parameters, and emerging multi-fluid acquisition to improve analytical accuracy and physiological relevance. Next, we discuss platform-integration mechanisms based on biochemical, physical, and hybrid sensing principles, along with monolithic and modular architectures enabled by flexible electronics, microfluidics, microneedles, and smart textiles. Finally, the data-processing patterns are analyzed, involving cross-modal calibration, machine learning inference, and multi-level data fusion to enhance data reliability and support personalized and predictive healthcare. Beyond summarizing technical advances, this review establishes a comprehensive framework that moves beyond isolated signal acquisition or simple metric aggregation toward holistic physiological interpretation. It guides the development of next-generation wearable multi-modal body fluid monitoring systems that overcome the challenges of high integration, miniaturization, and personalized medical applications.

Magnetic nanoparticles (MNPs) provide a platform for target detection because of their magnetic responsiveness to alternating magnetic fields (AMFs). We developed a detection method using MNPs modified with tumor-homing peptides (THPs), PL1 and PL3, which selectively bind to protein components enriched in malignant tissues. THP-MNPs were synthesized using maleimide-PEG-NHS linkers and characterized using transmission electron microscopy. Human glioblastoma cancer U87MG and normal tissue-derived HEK293 cells were incubated with THP-MNPs, and the magnetic signals were measured using a high-temperature superconducting quantum interference device (SQUID) magnetometer under an AMF (1.06 kHz). Dark-field microscopy confirmed the preferential binding of THP-MNPs to U87MG cells. In the absence of cells, THP-MNPs exhibited AMF-dependent signal enhancement, which correlated with particle size reduction due to THP release. This increase was completely suppressed in the presence of U87MG cells, indicating a strong THP-mediated interaction. PL3-MNPs exhibited superior discrimination between malignant and non-malignant cells. These results demonstrate that SQUID-based magnetic measurements using THP-MNPs enable rapid and label-free cancer cell detection.

Early diagnosis of Parkinson's disease (PD) is crucial for slowing its progression. Gait analysis is increasingly used to detect early symptoms, with smart insoles emerging as a cost-effective and convenient tool for daily monitoring. However, smart insoles have practical limitations, including durability concerns, limited battery life, and difficulties in minimizing the number of sensors. In this study, we designed a novel deep convolutional neural network model for accurately detecting abnormal gaits in patients with PD using a reduced number of sensors embedded in smart insoles. The proposed convolutional neural network (CNN) model was trained on a gait dataset collected from a total of 29 participants, including 13 healthy individuals, 9 elderly individuals, and 7 patients with Parkinson's disease (PD). Instead of combining plantar pressure data from both feet, the model processes each foot independently through sequential layers to better capture gait asymmetries. The proposed CNN model achieved a classification accuracy of 90.35% using only 8 of the 32 plantar pressure sensors in the smart insole, outperforming a conventional CNN model by approximately 10%. The experimental results demonstrate the potential of our CNN model for effectively detecting abnormal gait patterns in patients with PD while minimizing sensor requirements, enhancing the practicality and efficiency of smart insoles for real-world use.