Biosensors

The retinal pigment epithelium (RPE) is a monolayer of cells located between retinal photoreceptors and the choroid, playing a critical role in maintaining visual function by protecting the retina and supporting photoreceptor metabolism. Damage to RPE cells can lead to visual disorders, including macular degeneration. Chronic exposure to high-energy blue light has been shown to elevate intracellular reactive oxygen species (ROS) in RPE cells, causing oxidative stress and cellular damage. In this study, a microfluidic platform incorporating a gradient-generating structure was developed to establish controllable and stable gradients of blue light intensity and chemical concentrations. This platform was used to investigate the effects of varying blue light intensities and antioxidant concentrations on oxidative stress in human RPE cells ARPE-19. Cells cultured within the microfluidic channels were exposed to different blue light intensities in combination with chemical treatments. Results demonstrated that ROS production increased with higher blue light intensity, whereas higher antioxidant concentrations effectively reduced ROS accumulation, supporting the ability of these antioxidants to attenuate blue-light-induced intracellular oxidative stress. The present microfluidic device enables simultaneous evaluation of multiple conditions within a single experiment, reducing reagent consumption and enhancing experimental efficiency. This in vitro microfluidic platform integrates chemical and light gradients to assess retinal oxidative damage and antioxidant effects, offering significant potential for ophthalmic drug screening and investigations of retinal protective mechanisms.

Microorganisms are ubiquitous in nature, and microbial activities are closely intertwined with the entire life cycle system and human life. Developing novel technologies for the detection, characterization and manipulation of microorganisms promotes their applications in clinical, environmental and industrial areas. Over the last two decades, terahertz (THz) technology has emerged as a new optical tool for microbiology. The great potential originates from the unique advantages of THz waves including the high sensitivity to water and inter-/intra-molecular motions, the non-invasive and label-free detecting scheme, and their low photon energy. THz waves have been utilized as a stimulus to alter microbial functions or as a sensing approach for quantitative measurement and qualitative differentiation. This review specifically focuses on recent research progress of THz technology applied in the field of microbiology, including two major parts of THz biological effects and the microbial detection applications. At the end of this paper, we summarize the research progress and discuss the challenges currently faced by THz technology in microbiology, along with potential solutions. We also provide a perspective on future development directions. This review aims to build a bridge between THz photonics and microbiology, promoting both fundamental research and application development in this interdisciplinary field.

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and a leading cause of dementia worldwide, characterized by progressive cognitive decline, memory impairment, and functional deterioration. With the rapid growth of the aging population, AD has become a major global health challenge, imposing substantial burdens on patients, families, and healthcare systems. Despite extensive research, early and accurate diagnosis of AD remains challenging due to disease heterogeneity, overlapping clinical manifestations, and the lack of easily accessible, highly sensitive, and specific diagnostic markers. Recent advances in biomedical technologies, including neuroimaging, multi-omics profiling, electronic health records, and digital health tools, have generated large-scale and heterogeneous datasets, providing new opportunities for improving AD diagnosis. However, extracting clinically meaningful information from these complex data sources remains difficult using conventional statistical approaches. Artificial intelligence (AI) has progressively transformed AD diagnosis by evolving from traditional machine learning (ML) approaches based on handcrafted feature engineering to deep learning (DL) models capable of automated representation learning and multimodal information integration. More recently, large language models (LLMs) have further expanded the scope of AI-driven AD diagnosis by enabling contextual understanding of unstructured clinical information, knowledge-guided reasoning, and integration of multimodal biomedical evidence. This transition reflects a shift from feature-based prediction toward more flexible and intelligent diagnostic frameworks. This review synthesizes recent advances in AI-based AD diagnosis, tracing the evolution from traditional ML to DL and LLMs. Particular emphasis is placed on the emerging role of LLMs in extracting disease-related information from speech and clinical narratives, integrating heterogeneous biomedical data sources, and enabling multimodal frameworks for AD assessment.