Biosensors

Alzheimer’s disease (AD) is the most common cause of dementia, affecting 55 million people worldwide. Its characteristics include the accumulation of senile plaques and neurofibrillary tangles. This disease is associated with changes in the concentration of AD biomarkers, such as microRNAs, amyloid peptides, Tau protein, and neurofilament light chains. Due to the fact that neuropathological processes begin decades before the onset of cognitive symptoms, accurate detection of AD biomarkers is crucial for its early diagnosis. The combination of analytical techniques and machine learning methods plays a crucial role in medical innovation. Recently, efforts have been made to develop machine learning-assisted biosensors for AD diagnosis. This article provides an overview of the progress in machine learning-assisted sensing of AD biomarkers in bodily fluids. It mainly includes three parts: machine learning algorithms, machine learning-assisted electrochemical and optical biosensors, and challenges and future perspectives. We believe that this work will contribute to the development of innovative analytical devices based on artificial intelligence for monitoring and managing neurodegenerative diseases.

This study introduces a portable piezoelectric thin-film microbalance platform that combines acoustic signal analysis with deep learning for point-of-care mass detection. The system employs a flexible polyvinylidene fluoride sensor, a smartphone for acoustic signal acquisition, and three deep learning models: convolutional neural network, long short-term memory network, and Transformer. Experimental findings indicate that the Transformer achieves the highest classification accuracy of 99.5%, outperforming the convolutional neural network at 96.9% and the long short-term memory network at 97.3%, attributed to its enhanced capability to capture long-range temporal dependencies. The platform facilitates real-time, label-free detection without the necessity for bulky instrumentation, providing a cost-effective and scalable solution for decentralized diagnostics. This research establishes a foundational framework for intelligent portable micro-mass sensing with significant potential applications in precision medicine, environmental monitoring, and personalized healthcare.

Francisella tularensis, the causative agent of tularemia, is a highly infectious Category A biothreat agent characterized by an exceptionally low infectious dose and diverse transmission routes. Due to the pathogen’s fastidious growth requirements and the high risk of laboratory-acquired infections, traditional cultivation methods are often protracted and hazardous. Consequently, the development of rapid and sensitive diagnostic tools is paramount. This manuscript provides a comprehensive overview of the current landscape of immunoassays, with a specific focus on the evolution from standard laboratory techniques to advanced biosensors. We detail the critical phases of antigen preparation, including high-pressure homogenization and sonication, and the generation of high-affinity polyclonal and monoclonal antibodies. Furthermore, we evaluate the implementation of novel biosensor-like devices, such as electrochemiluminescence and Surface-Enhanced Raman Scattering platforms, designed for point-of-care and field-ready scenarios. By synthesizing recent advancements in nanomaterial-enhanced recognition and microfluidic integration, this review emphasizes the pivotal role of these technologies in achieving early detection and mitigating the impact of both natural outbreaks and potential deliberate misuse of F. tularensis.