Biosensors-Basel最新文献

Oral health is a critical factor in maintaining overall health, and its association with systemic diseases, including cardiovascular disease and diabetes mellitus, has been extensively investigated. Effective plaque removal through proper toothbrushing techniques is fundamental for preventing dental caries and periodontal diseases. Despite standardized guidelines, many individuals fail to adhere to correct brushing techniques, thereby increasing the risk of oral diseases. To address this issue, this study proposes a fine-grained toothbrushing region recognition approach incorporating six machine learning classifiers and two inertial measurement units (IMUs), which are embedded in the toothbrush holder and mounted on the right wrist of the participant, respectively. By analyzing the continuous motion signals, the proposed hierarchical approach is capable of identifying brushing and transition activities and subsequently recognizing specific toothbrushing regions based on the predicted brushing activities. To further improve recognition reliability, post-processing strategies such as contextual smoothing and majority voting are applied. Experimental results demonstrate that random forest achieves the highest recognition accuracy of 96.13%, sensitivity of 96.10%, precision of 95.51%, and F1-score of 95.60%. The results indicate that the proposed approach is both effective and feasible for providing fine-grained toothbrushing region recognition in toothbrushing monitoring.

Smart nucleic acid hydrogels (SNAHs), endowed with stimulus responsiveness, function as programmable molecular switches that can perceive diverse external stimuli and undergo rapid, reversible, and highly specific conformational or performance changes. These dynamic properties have enabled the rational design of biosensors with bionic behaviors, facilitating cascaded "recognition-decision-execution" processes that support advanced biological analysis. Consequently, SNAHs are recognized as a core breakthrough for the next generation of intelligent biosensing units. However, a systematic mapping between SNAH design strategies, specific stimuli, and application fields remains lacking. This review mainly analyzes advances in SNAH-based biosensors over the past five years, proposing flexible and feasible design strategies and key trends in customization. Firstly, we systematically summarize molecular recognition modules involved in the construction of SNAHs, including aptamers, DNAzymes, antibodies, and specific binding peptides. Subsequently, we elaborate on the responses of these modules to external stimuli, so as to further facilitate the signal transduction of signals derived from physical, chemical, and biological sources involving temperature, light, magnetic fields, pH, nucleic acids, proteins, other biomolecules, and pathogens. Additionally, the review outlines the research progress of SNAHs in environmental monitoring, food safety, and medical diagnostics. Finally, we provide an integrated perspective on future opportunities and challenges, highlighting the innovative framework for designing SNAH-based biosensors and offering a practical roadmap for next-generation intelligent sensing applications.

The detection of soluble proteins in biological fluids, as a form of liquid biopsy, is a promising tool for cancer diagnosis and prognosis, as it is less invasive than traditional diagnostic methods. CD44 is one of the most recognized markers of cancer stem cells, a small subset of cells responsible for cancer initiation, progression, and metastasis. Given the importance of CD44 as a cancer biomarker, several review articles explore the diagnostic and therapeutic value of cell-surface CD44. In addition to being a membrane-anchored protein, CD44 is also shed from the cell surface and can be found in various biological fluids. However, the role of soluble CD44 in cancer has not been comprehensively discussed in recent reviews. Measuring soluble CD44 in various biological liquids can provide a practical and valuable tool for cancer diagnosis and treatment monitoring. Therefore, this review comprehensively discusses the role of soluble CD44 as a marker in various cancer types, including serum, saliva, urine, and other fluids. In particular, its role as an early cancer biomarker and as a predictive and prognostic biomarker in several cancers is discussed. This work also provides an overview of a wide range of analytical techniques used to detect soluble CD44. The value of cells expressing CD44 versus soluble CD44 as a biomarker is also compared. The review concludes with a perspective on future directions, emphasizing the shift toward non-invasive analytical methods and the need for standardization of detection, including multiple biomarkers during evaluation, to improve the accuracy of cancer diagnosis.

Magnetosomes are organelle-like structures within magnetotactic bacteria that store iron biominerals in membrane-bound vesicles. In bacteria, formation of these structures is highly regulated by approximately 30 genes, which are conserved throughout different species. To compartmentalize iron in mammalian cells and provide gene-based contrast for magnetic resonance imaging, we introduced key magnetosome proteins. The expression of essential magnetosome genes mamI and mamL as fluorescent fusion proteins in a human melanoma cell line confirmed their co-localization and interaction. Here, we investigate the expression of two more essential magnetosome genes, mamB and mamE, using confocal microscopy to describe fluorescent fusion protein expression patterns and analyze the observed intracellular mobility. Custom software was developed to characterize fluorescent particle trajectories. In mammalian cells, essential magnetosome proteins display different diffusive behaviours. However, all magnetosome proteins travelled at similar velocities when interacting with mammalian mobile elements, suggesting that MamL, MamL + MamI, MamB, and MamE interact with similar molecular motor proteins. These results confirm that localization and interaction of essential magnetosome proteins are feasible within the mammalian intracellular compartment.

Total joint arthroplasty is among the most common surgical procedures performed worldwide, with frequency increasing due to demographic changes. Accelerating the diagnostic process using new techniques is crucial for effective therapy. This pilot study aims to test such innovative technology in the context of periprosthetic joint infection (PJI) using Scattered Light Integrating Collector (SLIC) technology. While we wish to evaluate whether SLIC can be used to reliably detect the status of infection within human tissue samples in the future, our current research focused on building its foundation by evaluating steps of sample preparation that allow for heightened growth depiction. It is, to our knowledge, the first study concerning the usage of solid human tissue samples using the SLIC device. Adult patients presenting with native or periprosthetic joint infections were included in this prospective study. Biopsies were obtained using sequential sampling, and bacterial density was optimized through titration series. Cryopreservation and agents influencing coagulation were investigated. Our study demonstrates that simple pretreatment could aid in detecting pathogen growth in infected tissue samples. Findings showed a clear advantage for no addition of agents affecting coagulation. Additionally, our protocols proved reliable after prolonged cryopreservation at -20 °C for up to 8 weeks, showing no significant difference compared to primary testing. AUC comparison showed comparable results for sample storage at -80 °C for up to 8 weeks. Similar outcomes were seen for samples ranging from 25 µL to 300 µL, with biological replicates displaying higher thresholds for larger volumes without significant differences. This study introduces a simple and quick diagnostic tool for detecting bacterial growth using tissue biopsies and develops an SOP for further research with this innovative technique. The suggested SOP enables SLIC to hint at an underlying bacterial infection within 5 h using joint tissue, offering a possible novel approach in diagnosing periprosthetic joint infections and septic arthritis. While not yet designed to compare sensitivity to other culture methods, it provides a solid basis for further clinical research.

Widespread and simple detection of diseases and disfunctions in the body is crucial for reliable and prompt diagnostics, efficient use of healthcare resources, and improved quality of life. The presence of a large number of metabolic products in saliva, the relationship between their levels in saliva and blood, the diagnostic value of many of these compounds, and the advantages of noninvasive sampling drive interest in oral fluid as a biomatrix. This review summarizes established oral fluid biomarkers, as well as potential salivary indicators for remote health monitoring and noninvasive point-of-care diagnostics. Recent advances in the search for new solutions for sensitive and high-throughput immunodetection of biomarkers in oral fluid are discussed, along with strategies for overcoming the analytical and technical challenges associated with the salivary matrix testing. Another focus of the current review is optical and electrochemical immunosensors with an emphasis on lateral flow immunoassays for point-of-care testing due to their speed, simplicity and cost-effectiveness. Finally, future directions are discussed that may enable non-invasive monitoring of endocrine, infectious, immune, neurodegenerative diseases and other human conditions using immunoassay platforms, paving the way for personalized and accessible healthcare.

Gas sensors provide versatile opportunities for detecting volatile organic compounds (VOCs) such as acetone, methanol, ethanol, propanol, isoprene, and aldehydes in exhaled breath (EB) associated with COVID-19 respiratory infections. These VOCs provide valuable information about metabolic markers linked with COVID-19. They have opened opportunities to develop sensors for COVID-19 screening based on breath analysis. These sensors have the potential to provide the rapid detection of viruses in healthcare settings. RT-PCR, as a conventionally adopted diagnostic method, has a detection limit around 10-100 RNA copies/mL, with an accuracy of around 95%. Gas sensors have demonstrated VOC detection limits at the ppm level in COVID-19 EB and have displayed a sensitivity and specificity of 98.2% and 74.3%, respectively. Multiple gas sensors combined with machine learning algorithms have the potential to enhance the specificity of VOC detection. In addition to having an accuracy similar to that of the PCR method, the VOC-based diagnosis of COVID-19 offers unique advantages in terms of non-invasive and rapid detection. This review provides an overview of state-of-the-art gas sensors developed for COVID-19 detection. Despite there being significant developments in this field, there are certain challenges that still need to be addressed-these include the impact of environmental factors, the specificity of detection, the sensing range, and precision limitations, leading to accuracy issues. Despite these existing challenges, the integration of gas sensors with machine learning methods can enhance the accuracy of the detection of COVID-19. Future research directions are proposed to validate and standardize the application of gas sensors for COVID-19 in clinical settings.

The integration of artificial intelligence (AI) and micro/nanorobotics is fundamentally reshaping biosensing by enabling autonomous, adaptive, and high-resolution biological analysis. These miniaturized robotic systems fabricated using advanced techniques such as photolithography, soft lithography, nanoimprinting, 3D printing, and self-assembly can navigate complex biological environments to perform targeted sensing, diagnostics, and therapeutic delivery. AI-driven algorithms, mainly those in machine learning (ML) and deep learning (DL), act as the brains of the operation, allowing for sophisticated modeling, genuine real-time control, and complex signal interpretation. This review focuses recent advances in the design, fabrication, and functional integration of AI-enabled micro/nanorobots for biomedical sensing. Applications that demonstrate their potential range from quick point-of-care diagnostics and in vivo biosensing to next-generation organ-on-chip systems and truly personalized medicine. We also discuss key challenges in scalability, energy autonomy, data standardization, and closed-loop control. Collectively, these advancements are paving the way for intelligent, responsive, and clinically transformative biosensing systems.

Biosensing technology serves as a cornerstone in biomedical diagnostics, environmental monitoring, personalized medicine, and wearable devices, playing an indispensable role in precise detection and real-time monitoring. Compared with traditional sensing platforms, functional nanomaterials-by virtue of their ultra-large specific surface area, exceptional optoelectronic properties, and superior catalytic activity-significantly enhance the sensitivity, selectivity, and response speed of biosensors. This has enabled ultrasensitive, rapid, and even in situ detection of disease biomarkers, pollutants, and pathogens. This review summarizes recent advances in five key categories of functional nanomaterials-metallic, semiconductor, carbon-based, two-dimensional, and stimulus-responsive materials-for advanced biosensing applications. It elucidates the structure-property relationships governing sensing performance, such as the surface plasmon resonance of gold nanoparticles and the high carrier mobility of graphene, and analyzes the core mechanisms behind optical sensing, electrochemical sensing, and emerging multimodal sensing strategies. With a focus on medical diagnostics, wearable health monitoring, and environmental and food safety surveillance, the review highlights the application value of functional nanomaterials across diverse scenarios. Current research is progressively moving beyond single-performance optimization toward intelligent design, multifunctional integration, and real-world deployment, though challenges related to industrial application remain. Finally, the review outlines existing issues in the development of functional nanomaterial-based biosensors and offers perspectives on the integration of nanomaterials with cutting-edge technologies and the construction of novel sensing systems. This work aims to provide insights for the rational design of functional nanomaterials and the cross-disciplinary translation of biosensing technologies.

Chronic wounds constitute a major global public health challenge, characterized by a high risk of infection, prolonged healing times, and frequent recurrence. Conventional wound assessment methods, which primarily rely on visual clinical inspection and laboratory-based analyses, are limited by inherent subjectivity, delayed feedback, and a lack of capacity for real-time monitoring of the dynamic biochemical changes at the wound site. Significantly, recent advancements in flexible electronics, nanomaterials, and energy harvesting technologies have boosted the rapid development of wearable electrochemical biosensors. These devices have emerged as a transformative platform for the continuous, non-invasive analysis of critical biomarkers within the wound microenvironment, including pH, temperature, inflammatory cytokines, metabolites, and pathogen-derived molecules. This review critically examines the latest progress in wearable electrochemical biosensors for wound monitoring and management. Key discussions include (1) the special requirements for sensor design, targeting the chronic wound's pathological characteristics; (2) cutting-edge development in self-powered systems, multimodal sensor integration, closed-loop theranostics, and artificial intelligence (AI)-assisted decision-making; and (3) a critical appraisal of challenges in accuracy, stability, biocompatibility, energy management, and clinical translation. Finally, the review explores future trends, such as biodegradable sensors, multi-parameter fusion algorithms, and remote intelligent management systems, with the aim of establishing a foundational framework and providing technical guidance for developing next-generation intelligent wound care solutions.