Biosensors-Basel最新文献

Duck plague virus (DPV), a highly contagious α-herpesvirus in the livestock and poultry environment, poses a significant threat to the healthy growth of ducks, potentially causing substantial economic losses. Effective control of DPV requires the development of specific diagnostic tools. A new fluorescent biosensor (R-C-CHA) was developed to detect virulent strains of DPV. It combined recombinase polymerase amplification (RPA), a CRISPR/Cas12a system, and catalytic hairpin assembly (CHA) for signal enhancement. The RPA primers were specifically designed to target the conserved DPV-CHv UL2 gene region, allowing for the rapid, efficient amplification of the target nucleic acids in isothermal conditions. The CRISPR/Cas12a system was used for sequence-specific recognition, activating its lateral cleavage activity. Furthermore, the CHA cascade reaction was utilized for enzyme-free fluorescent signal amplification. The results showed that the R-C-CHA biosensor completed the detection process in 40 min with a detection limit of 0.02 fg/μL, which was an approximate five-fold improvement compared to traditional RPA-CRISPR/Cas12a biosensors. The R-C-CHA biosensor also demonstrated perfect consistency with clinical detection and polymerase chain reaction (PCR) diagnosis, highlighting its strong potential for rapid detection in livestock and poultry farming settings.

Candida albicans is a major fungal pathogen associated with vulvovaginal candidiasis, and rapid, sensitive detection remains challenging, particularly in amplification-free formats. Here, we report NaPddCas, a microfluidic-free, droplet-based CRISPR/Cas12a detection strategy for qualitative identification of Candida albicans DNA. Unlike conventional bulk CRISPR assays, NaPddCas partitions the reaction mixture into vortex-generated polydisperse droplets, enabling spatial confinement of Cas12a activation events and effective suppression of background fluorescence. This compartmentalization substantially enhances detection sensitivity without nucleic acid amplification or microfluidic devices. Using plasmid and genomic DNA templates, NaPddCas achieved reliable detection at concentrations several orders of magnitude lower than bulk CRISPR/Cas12a reactions. The assay further demonstrated high specificity against non-target bacterial and fungal species and was successfully applied to clinical vaginal secretion samples. Importantly, NaPddCas is designed as a qualitative or semi-qualitative droplet-dependent digital detection method rather than a quantitative digital assay. Owing to its simplicity, sensitivity, and amplification-free workflow, NaPddCas represents a practical approach for laboratory-based screening of Candida albicans infections.

Glycosaminoglycans (GAGs) are highly negatively charged polysaccharides that play essential roles in numerous physiological and pathological processes through their interactions with proteins. These interactions govern cellular signaling, inflammation, coagulation, and recognition. Surface Plasmon Resonance (SPR) has emerged as a key biophysical technique for label-free, real-time characterization of biomolecular interactions, offering insights into binding kinetics, affinity, and specificity. SPR-based approaches to glycosaminoglycan-protein interaction studies offer powerful tools for elucidating the roles of GAGs in a wide range of physiological and pathological processes. In this review, we systematically discuss experimental strategies, data analysis methods, and representative applications of SPR-based glycosaminoglycan-protein interactions. Special attention is given to the challenges associated with GAG heterogeneity and immobilization, as well as recent technological advances that enhance sensitivity and throughput. To our knowledge, this review represents one of the first systematic and up-to-date summaries specifically focused on recent advances in applying SPR to the study of glycosaminoglycan-protein interactions.

Recent years have witnessed significant growth in the development of propyl gallate (PG) sensors. PG can be monitored by various approaches, such as electrochemical and fluorescence methods. The electrochemical approaches have several advantages, such as low cost, a benign fabrication process, and high sensitivity and selectivity. Similarly, the fluorescence method has its own advantages, including low cost, high sensitivity, and fast response. Both methods are promising approaches for the monitoring of PG compared to chromatographic methods. In this mini-review article, we review the progress in the preparation of materials for the determination of PG using electrochemical and fluorescence methods. The fabrication of electrodes and the working principle for PG detection are illustrated. The challenges and future perspectives for PG detection are discussed.

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Time-resolved spectroscopy (TRS) is a promising tool for noninvasive cerebral monitoring in neonates. However, the optimal forehead site for probe placement remains unclear. In this study, we evaluated the effect of probe positioning on TRS-derived optical parameters in neonates. TRS measurements were obtained from the midline and right lateral forehead of 30 neonates (≥36 weeks' corrected gestational age). We compared various parameters between the two probe positions, including optical intensity, attenuation, mean optical path length, scattering coefficient, total hemoglobin (tHb), cerebral oxygen saturation (ScO₂) and cerebral blood volume (CBV). No significant differences were observed in tHb, ScO₂ and CBV between the midline and lateral sites. However, the lateral site showed a significantly lower scattering coefficient and shorter mean path length. Light intensity was increased and attenuation was reduced at the lateral site. Thus, while tHb, ScO₂ and CBV values were consistent between sites, the midline provided more stable scattering and optical path data. These findings suggest that the midline forehead may be a more suitable site for TRS-based neonatal cerebral monitoring.

Microwave sensing technology is rapidly advancing and increasingly finding its way into biomedical applications, promising significant improvements for medical care. Concurrently, the rise of artificial intelligence (AI) is enabling significant enhancements in the biomedical domain. Close scrutiny of the recent literature reveals intense activity in both fields, with particularly impactful outcomes deriving from the combined use of advanced microwave techniques and AI for biomedical monitoring. In this review, an up-to-date compilation, from the perspective of the authors, of the most significant works published on these topics in recent years is given, focusing on their integration and current challenges. With the objective of analyzing the current landscape, we survey and compare state-of-the-art biosensors and imaging systems at all healthcare levels, from outpatient contexts to specialized medical equipment and laboratory analysis tools. We also delve into the relevant applications of AI in medicine for processing microwave-derived data. As our core focus, we analyze the synergistic integration of AI in the design of microwave devices and the processing of the acquired data, which have shown notable performances, opening new avenues for compact, affordable, and multi-functional medical devices. We conclude by synthesizing the prevailing technical, algorithmic, and translational challenges that must be addressed to realize this potential.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder marked by persistent memory impairment and complex molecular and cellular pathological changes in the brain. Current treatments, including acetylcholinesterase inhibitors and memantine, only help with symptoms for a short time and do not stop the disease from getting worse. This is mainly because these drugs do not reach the brain well and are quickly removed from the body. The blood-brain barrier (BBB) restricts the entry of most drugs into the central nervous system; therefore, new methods of drug delivery are needed. Nanotechnology-based drug delivery systems (NTDDS) are widely studied as a potential approach to address existing therapeutic limitations. Smart biosensing nanoparticles composed of polymers, lipids, and metals can be engineered to enhance drug stability, improve drug availability, and target specific brain regions. These smart nanoparticles can cross the BBB via receptor-mediated transcytosis and other transport routes, making them a promising option for treating AD. Additionally, multifunctional nanocarriers enable controlled drug release and offer theranostic capabilities, supporting real-time tracking of AD treatment responses to facilitate more precise and personalized interventions. Despite these advantages, challenges related to long-term safety, manufacturing scalability, and regulatory approval remain. This review discusses current AD therapies, drug-delivery strategies, recent advances in nanoparticle platforms, and prospects for translating nanomedicine into effective, disease-modifying treatments for AD.

Synaptic plasticity constitutes a fundamental mechanism of neural systems. Rhythmic activities (e.g., θ and γ oscillations) play a critical role in modulating network plasticity efficiency in biological neural systems. However, the rules governing plasticity and adaptive regulation of in vitro cultured networks under structured electrical stimulation remain insufficiently characterized. To quantitatively investigate these regulatory effects within a highly controlled and low-interference environment, we utilized primary mice hippocampal neurons cultured on multielectrode arrays (MEAs) and executed two dedicated sets of experiments. (1) Spatiotemporal electrical stimulation paradigms, combined with connectivity analysis, revealed pronounced regulation effects of network plasticity. (2) Physiologically inspired rhythmic stimulation (θ: 7.8 Hz, γ: 40 Hz) with varying pulse repetitions was then applied. Although both rhythms induced distinct frequency-dependent plasticity modulation, the disparity between their modulatory effects progressively diminished with increasing stimulation pulse numbers, suggesting an intrinsic adaptive regulatory mechanism. Collectively, our findings characterize the effects of plasticity regulation and reveal the mechanisms underlying adaptive convergence in in vitro neuronal systems. These results advance the understanding of network plasticity, providing a technical foundation for functional shaping and modulation of in vitro neural networks while supporting future explorations into learning-oriented modulation.

Yeast biosensors represent a promising biotechnological innovation for ensuring the safety and quality of fermented beverages such as beer, wine, and kombucha. These biosensors employ genetically engineered yeast strains to detect specific contaminants, spoilage organisms, or hazardous compounds during fermentation or the final product. By integrating synthetic biology tools, researchers have developed yeast strains that can sense and respond to the presence of heavy metals (e.g., lead or arsenic), mycotoxins, ethanol levels, or unwanted microbial metabolites. When a target compound is detected, the biosensor yeast activates a reporter system, such as fluorescence, color change, or electrical signal, providing a rapid, visible, and cost-effective means of monitoring safety parameters. These biosensors offer several advantages: they can operate in real time, are relatively low-cost compared to conventional chemical analysis methods, and can be integrated directly into the fermentation system. Furthermore, as Saccharomyces cerevisiae is generally recognized as safe (GRAS), its use as a sensing platform aligns well with existing practices in beverage production. Yeast biosensors are being investigated for the early detection of contamination by spoilage microbes, such as Brettanomyces and lactic acid bacteria. These contaminants can alter the flavor profile and shorten the product's shelf life. By providing timely feedback, these biosensor systems allow producers to intervene early, thereby reducing waste and enhancing consumer safety. In this work, we review the development and application of yeast-based biosensors as potential safeguards in fermented beverage production, with the overarching goal of contributing to the manufacture of safer and higher-quality products. Nevertheless, despite their substantial conceptual promise and encouraging experimental results, yeast biosensors remain confined mainly to laboratory-scale studies. A clear gap persists between their demonstrated potential and widespread industrial implementation, underscoring the need for further research focused on robustness, scalability, and regulatory integration.