RESUMEN
17ß-Estradiol (E2) is a critical sex steroid hormone, which has significant effects on the endocrine systems of both humans and animals. E2 is also believed to play neurotrophic and neuroprotective roles in the brain. Biosensors present a powerful tool to detect E2 because of their small, efficient, and flexible design. Furthermore, Biosensors can quickly and accurately obtain detection results with only a small sampling amount, which greatly meets the detection of the environment, food safety, medicine safety, and human body. This review focuses on previous studies of biosensors for detecting E2 and divides them into non-biometric sensors, enzyme biosensors, antibody biosensors, and aptamer biosensors according to different bioreceptors. The advantages, disadvantages, and design points of various bioreceptors for E2 detection are analyzed and summarized. Additionally, applications of different bioreceptors of E2 detection are presented and highlight the field of environmental monitoring, food and medicine safety, and disease detection in recent years. Finally, the development of E2 detection by biosensor is prospected.
RESUMEN
Microelectrode arrays (MEA) are extensively utilized in encoding studies of retinal ganglion cells (RGCs) due to their capacity for simultaneous recording of neural activity across multiple channels. However, conventional planar MEAs face limitations in studying RGCs due to poor coupling between electrodes and RGCs, resulting in low signal-to-noise ratio (SNR) and limited recording sensitivity. To overcome these challenges, we employed photolithography, electroplating, and other processes to fabricate a 3D MEA based on the planar MEA platform. The 3D MEA exhibited several improvements compared to planar MEA, including lower impedance (8.73 ± 1.66 kΩ) and phase delay (-15.11° ± 1.27°), as well as higher charge storage capacity (CSC = 10.16 ± 0.81 mC/cm2), cathodic charge storage capacity (CSCc = 7.10 ± 0.55 mC/cm2), and SNR (SNR = 8.91 ± 0.57). Leveraging the advanced 3D MEA, we investigated the encoding characteristics of RGCs under multi-modal stimulation. Optical, electrical, and chemical stimulation were applied as sensory inputs, and distinct response patterns and response times of RGCs were detected, as well as variations in rate encoding and temporal encoding. Specifically, electrical stimulation elicited more effective RGC firing, while optical stimulation enhanced RGC synchrony. These findings hold promise for advancing the field of neural encoding.