Toward the Development of an Artificial Brain on a Micropatterned and Material-Regulated Biochip by Guiding and Promoting the Differentiation and Neurite Outgrowth of Neural Stem/Progenitor Cells.

ACS Appl Mater Interfaces

Ph.D. Program in Biomedical Engineering, College of Engineering, ‡Graduate Institute of Biochemical and Biomedical Engineering, ∥Graduate Institute of Medical Mechatronics, and ⊥Department of Mechanical Engineering, Chang Gung University, Taoyuan 333, Taiwan.

Published: February 2018


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

An in vitro model mimicking the in vivo environment of the brain must be developed to study neural communication and regeneration and to obtain an understanding of cellular and molecular responses. In this work, a multilayered neural network was successfully constructed on a biochip by guiding and promoting neural stem/progenitor cell differentiation and network formation. The biochip consisted of 3 × 3 arrays of cultured wells connected with channels. Neurospheroids were cultured on polyelectrolyte multilayer (PEM) films in the culture wells. Neurite outgrowth and neural differentiation were guided and promoted by the micropatterns and the PEM films. After 5 days in culture, a 3 × 3 neural network was constructed on the biochip. The function and the connections of the network were evaluated by immunocytochemistry and impedance measurements. Neurons were generated and produced functional and recyclable synaptic vesicles. Moreover, the electrical connections of the neural network were confirmed by measuring the impedance across the neurospheroids. The current work facilitates the development of an artificial brain on a chip for investigations of electrical stimulations and recordings of multilayered neural communication and regeneration.

Download full-text PDF

Source
http://dx.doi.org/10.1021/acsami.7b17863DOI Listing

Publication Analysis

Top Keywords

neural network
12
development artificial
8
artificial brain
8
biochip guiding
8
guiding promoting
8
neurite outgrowth
8
neural
8
outgrowth neural
8
neural stem/progenitor
8
neural communication
8

Similar Publications

Background And Purpose: Socioeconomic determinants of health impact childhood development and adult health outcomes. One key aspect is the physical environment and neighborhood where children live and grow. Emerging evidence suggests that neighborhood deprivation, often measured by the Area Deprivation Index (ADI), may influence neurodevelopment, but longitudinal and multimodal neuroimaging analyses remain limited.

View Article and Find Full Text PDF

Chemically and Electromagnetically dual-enhanced COFs-Au@AgNPs SERS sensor integrated with deep learning for ultrasensitive detection of neonicotinoid pesticides.

Anal Chim Acta

November 2025

Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, China.

Background: With the development of modern agriculture, neonicotinoid pesticides have been widely used due to their high efficiency and strong systemic properties. However, excessive use leads to the accumulation of residues in the food chain, threatening the ecosystem and human health. Pesticide residues are easily accumulated in oilseed crops and become concentrated during the edible oil refining process.

View Article and Find Full Text PDF

Accelerated Patient-specific Non-Cartesian MRI Reconstruction using Implicit Neural Representations.

Int J Radiat Oncol Biol Phys

September 2025

Radiation Oncology, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143. Electronic address:

Purpose: Accelerating MR acquisition is essential for image guided therapeutic applications. Compressed sensing (CS) has been developed to minimize image artifacts in accelerated scans, but the required iterative reconstruction is computationally complex and difficult to generalize. Convolutional neural networks (CNNs)/Transformers-based deep learning (DL) methods emerged as a faster alternative but face challenges in modeling continuous k-space, a problem amplified with non-Cartesian sampling commonly used in accelerated acquisition.

View Article and Find Full Text PDF

Multi-voxel pattern analysis of face and word encoding fMRI in people with temporal lobe epilepsy and healthy individuals.

Epilepsy Behav

September 2025

Department of Clinical and Experimental Epilepsy, University College London, London the United Kingdom of Great Britain and Northern Ireland; MRI Unit, Chalfont Centre for Epilepsy, Bucks, the United Kingdom of Great Britain and Northern Ireland. Electronic address:

Memory functional MRI (fMRI) has been used to explore cognitive processing in people with refractory temporal lobe epilepsy (TLE) to predict memory decline after anterior temporal lobe resection (ATLR). Traditional studies employed univariate analysis (UVA), focusing on isolated voxel activity in mesial temporal regions. By contrast, multivariate pattern analysis (MVPA), examines distributed activity patterns , offering deeper insight into neural networks supporting cognitive functions.

View Article and Find Full Text PDF

Predicting binding affinities of liquid crystal monomers: An activity cliffs-driven multidimensional feature fusion model.

Ecotoxicol Environ Saf

September 2025

Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin Provincial Key Laboratory of Water Resources and Environment, College of New Energy and Environment, Jilin University, Changchun 130012, China.

Liquid crystal monomers (LCMs) have emerged as novel endocrine disrupting chemicals that affect the growth, development, and metabolism of organisms by binding to nuclear hormone receptors (NHRs). However, the studies on the impact of LCMs' molecular features on their binding affinities remain limited. In this study, considering the challenge of activity cliffs in linear quantitative structure-activity relationship modeling, a multidimensional feature fusion model was developed to predict the binding affinities of 1173 LCMs to 15 NHRs.

View Article and Find Full Text PDF