バイオセンサーとバイオエレクトロニクス

The maturation of human-induced Pluripotent Stem Cell (iPSC)-derived dopamine neurons holds great promise for disease modeling and drug discovery in neurodegenerative disorders like Parkinson's disease. However, achieving the appropriate maturation state remains a significant challenge. This study explores the application of Organ-chip technology to accelerate the maturation of iPSC-derived dopamine neurons. Organchips provide a microfluidic environment that mimics in vivo conditions, allowing for precise control of biochemical and biophysical cues. By culturing iPSC-derived dopamine neurons within Organ-chips, we observe enhanced maturation, including increased neuronal complexity, functional properties and maturity markers. These findings offer a novel approach to advancing the development of more physiologically relevant in vitro models for neurodegenerative diseases and provide a valuable tool for drug screening and understanding disease mechanisms.

Organoids, spheroids and organs-on-a-chip are innovative three-dimensional cell culture models that mimic the structural and functional complexities of human tissues. They hold great promise in various fields, including drug development, disease modeling and regenerative medicine. To fully harness their potential, it is crucial to monitor and manipulate these 3D models effectively. Biosensors have emerged as powerful tools for probing organoids and organs-on-a-chip, offering real-time insights into cellular responses, metabolic activities and microenvironment dynamics. This paper explores the application of biosensors in studying 3D cell cultures, highlighting their role in advancing our understanding of tissue physiology, drug screening and disease modeling. The integration of biosensors with organoid and organs-on-a-chip technologies opens new avenues for personalized medicine and drug discovery, ultimately paving the way for more accurate and efficient in vitro models.

Bacterial biofilm formation is a complex and dynamic process that plays a crucial role in various industries, including healthcare, biotechnology and environmental science. Real-time monitoring of biofilm formation stages is essential for understanding biofilm development, optimizing prevention and control strategies and ensuring the performance and safety of numerous applications. This paper introduces a novel approach for real-time detection of bacterial biofilm formation stages using Quartz Crystal Microbalance (QCM)-based sensors. By leveraging the QCM's sensitivity to mass changes at the sensor surface, we can accurately and continuously monitor the adhesion, growth and maturation of bacterial biofilms. This method provides valuable insights into the kinetics of biofilm formation, aiding in the development of targeted interventions and enhancing the quality of various processes and products.

Anaerobic reactors play a crucial role in various industrial and environmental applications, such as wastewater treatment and biogas production. To ensure their efficient and uninterrupted operation, condition-based maintenance is essential. This paper presents an AI-enhanced predictive maintenance approach for anaerobic reactors, which leverages artificial intelligence and machine learning techniques to monitor and optimize reactor performance. By analysing real-time sensor data and historical operational information, the proposed system can predict potential issues, schedule maintenance activities and enhance the overall reliability and performance of anaerobic reactors. This research contributes to the sustainability of anaerobic processes and offers a cost-effective solution for ensuring optimal operation.