Blood-brain Barrier Disruption in Dementia: Nano-solutions As New Treatment Options
European Journal of Neuroscience(2023)
Edge Hill Univ | Univ Liverpool | UCL
Abstract
Candidate drugs targeting the central nervous system (CNS) demonstrate extremely low clinical success rates, with more than 98% of potential treatments being discontinued due to poor blood-brain barrier (BBB) permeability. Neurological conditions were shown to be the second leading cause of death globally in 2016, with the number of people currently affected by neurological disorders increasing rapidly. This increasing trend, along with an inability to develop BBB permeating drugs, is presenting a major hurdle in the treatment of CNS-related disorders, like dementia. To overcome this, it is necessary to understand the structure and function of the BBB, including the transport of molecules across its interface in both healthy and pathological conditions. The use of CNS drug carriers is rapidly gaining popularity in CNS research due to their ability to target BBB transport systems. Further research and development of drug delivery vehicles could provide essential information that can be used to develop novel treatments for neurological conditions. This review discusses the BBB and its transport systems and evaluates the potential of using nanoparticle-based delivery systems as drug carriers for CNS disease with a focus on dementia.
MoreTranslated text
Key words
Alzheimer's disease,blood-brain barrier,dementia,drug delivery,nanogels,nanoparticles
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
Related Papers
Advanced Science 2024
被引用0
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