Biosensors-Basel最新文献

Aquaculture is a crucial global food production sector that faces challenges in water quality management, food safety, and stress-related health concerns in aquatic species. Cortisol, a key stress biomarker in fish, and Escherichia coli (E. coli) contamination in bivalve mollusks are critical indicators that require sensitive and real-time detection methods. Liquid crystal (LC)-based immunosensors have emerged as a promising solution for detecting biological analytes due to their high sensitivity, rapid response, and label-free optical detection capabilities. Therefore, this study explores the development and application of LC-based immunosensors for the detection of cortisol in artificial and real recirculating aquaculture system (RAS) samples, as well as E. coli in real contaminated water and clam samples during the depuration processes of bivalve mollusks. The biosensors exhibited the capacity to detect cortisol with a response time in seconds and a limit of detection (LOD) of 0.1 ng/mL. Furthermore, they demonstrated specificity to cortisol when tested against different interfering substances, including testosterone, glucose, and cholesterol. Furthermore, it was possible to correlate cortisol concentrations in different filtration stages and track E. coli contamination during depuration. The results confirm the feasibility of LC-based immunosensors as a user-friendly, portable, and efficient diagnostic tool for aquaculture applications.

The integration of flexible electronics and machine learning (ML) algorithms has become a revolutionary force driving the field of intelligent sensing, giving rise to a new generation of intelligent devices and systems. This article provides a systematic review of core technologies and practical applications of ML in flexible electronics. It focuses on analyzing the theoretical frameworks of algorithms such as the Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Reinforcement Learning (RL) in the intelligent processing of sensor signals (IPSS), multimodal feature extraction (MFE), process defect and anomaly detection (PDAD), and data compression and edge computing (DCEC). This study explores the performance advantages of these technologies in optimizing signal analysis accuracy, compensating for interference in high-noise environments, optimizing manufacturing process parameters, etc., and empirically analyzes their potential applications in wearable health monitoring systems, intelligent control of soft robots, performance optimization of self-powered devices, and intelligent perception of epidermal electronic systems.

Magnesium (Mg²⁺) ions require sensitive and selective detection due to their low concentrations and coexistence with similar ions in matrices. This study developed a potentiometric ISE using a new modified polyurethane membrane. The membrane's negative surface charge facilitates selective interaction with Mg²⁺ ion. Optimal performance was obtained at 0.0061% (w/w) κ-carrageenan and 0.0006% (w/w) D2EHPA. The ISE exhibited a near-Nernstian response of 29.49 ± 0.01 mV/decade across a 10^-9-10^-4 M concentration range (R² = 0.992), with a detection limit of 1.25 × 10^-10 M and a response time of 200 s. It remained stable in the pH range 6-8 for one month and demonstrated high selectivity over K⁺, Na⁺, and Ca²⁺ (Kij < 1). The repeatability and reproducibility tests yielded standard deviations of 0.15 and 0.39, while recovery rates confirmed analytical reliability. The water contact angle analysis showed a reduction from ~80° to ~69° after membrane conditioning, indicating increased hydrophilicity and improved interfacial for ion diffusion. FTIR analysis confirmed successful modification by reduced O-H peak intensity, while XRD verified the amorphous structure. SEM revealed a dense top layer with concave morphology, favorable for minimizing leakage and ensuring efficient ion transport within the sensing system.

Background: PTSD diagnosis is challenging. Symptoms overlap with depression and panic attacks. This causes misdiagnosis and delayed treatment. Current methods lack objective biomarkers. This study presents a hybrid AI framework. It combines CNNs and SVMs. The system detects PTSD from ECG signals.

Methods: ECG data from 79 participants were analyzed. Four groups were included. PTSD patients numbered 20. Depression patients numbered 20. Panic attack patients numbered 19. Healthy controls numbered 20. Wavelet transform created scalograms. Three CNN models were tested. AlexNet, GoogLeNet, and ResNet50 were used. Deep features were extracted. SVMs classified the features. Five-fold validation was performed. Statistical tests confirmed significance.

Results: Hybrid models performed robustly. ResNet50 + SVM and AlexNet + SVM achieved statistically equivalent results with accuracies of 97.05% and 97.26%, respectively. AUC reached 1.00 for multi-class tasks. PTSD detection was highly accurate. The system distinguished PTSD from other disorders. Hybrid models beat standalone CNNs. SVM integration improved results significantly.

Conclusions: This is the first ECG-based AI for PTSD diagnosis. The hybrid approach achieves clinical-level accuracy. PTSD is distinguished from depression and panic attacks. Objective biomarkers support psychiatric assessment. Early intervention becomes possible.

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.