Transplantation of Layer-by-layer Assembled Neural Stem Cells Tethered with Vascular Endothelial Growth Factor Reservoir Promotes Neurogenesis and Angiogenesis after Ischemic Stroke in Mice
Applied materials today(2022)
Third Mil Med Univ
Abstract
Transplantation of neural stem cells (NSCs) is a promising therapeutic strategy for ischemic stroke. However, most of engrafted NSCs hardly survive for a long time, and the majority of transplanted NSCs differentiate into astrocytes around infarct core due to harsh microenvironment after ischemic stroke. Therefore, exploring ap-proaches to foster the regenerative ability of transplanted NSCs in hostile niche is a feasible strategy to reha-bilitate lesions. Here, a feasible method for grafted NSCs modified by layer-by-layer (LbL) assembly (LbL-NSCs) using gelatin and hyaluronic acid (HA) was developed. The results indicated the LbL-NSCs were biocompatible, and held the ability of promoting NSCs differentiation into neurons. Subsequently, vascular endothelial growth factor (VEGF) was laden into gelatin to generate LbL-NSCs tethered with VEGF reservoir [LbL (VEGF)-NSCs], and the VEGF releasing from LbL (VEGF)-NSCs was sustained under pH 7.4 in vitro. Afterward, the results demon-strated transplantation of LbL (VEGF)-NSCs facilitated implanted NSCs survival and differentiation into neurons in distal middle cerebral artery occlusion (dMCAO) mice. Additionally, engraftment of LbL (VEGF)-NSCs rein-forced neurogenesis, angiogenesis, survival of host neurons, and blood brain barrier (BBB) repair, thereafter enhancing functional recovery through reducing the volume of infarct core in dMCAO mice. This investigation provides an appropriate approach for modifying NSCs by LbL assembly using gelatin loaded with VEGF and HA to expedite the rehabilitative ability of engrafted NSCs following ischemic stroke.
MoreTranslated text
Key words
Ischemic stroke,Neural stem cell,VEGF,Angiogenesis,Neurogenesis
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined