Phoenix Auditory Neurons As 3R Cell Model for High Throughput Screening of Neurogenic Compounds
Hearing Research(2021)SCI 1区SCI 2区SCI 3区
Univ Geneva | Med Univ Innsbruck | Translational Hearing Research
Abstract
Auditory neurons connect the sensory hair cells from the inner ear to the brainstem. These bipolar neurons are relevant targets for pharmacological intervention aiming at protecting or improving the hearing function in various forms of sensorineural hearing loss. In the research laboratory, neurotrophic compounds are commonly used to improve survival and to promote regeneration of auditory neurons. One important roadblock delaying eventual clinical applications of these strategies in humans is the lack of powerful in vitro models allowing high throughput screening of otoprotective and regenerative compounds. The recently discovered auditory neuroprogenitors (ANPGs) derived from the A/J mouse with an unprecedented capacity to self-renew and to provide mature auditory neurons offer the possibility to overcome this bottleneck. In the present study, we further characterized the new phoenix ANPGs model and compared it to the current gold-standard spiral ganglion organotypic explant (SGE) model to assay neurite outgrowth, neurite length and glutamate-induced Ca2+ response in response to neurotrophin-3 (NT-3) and brain derived neurotrophic factor (BDNF) treatment. Whereas both, SGEs and phoenix ANPGs exhibited a robust and sensitive response to neurotrophins, the phoenix ANPGs offer a considerable range of advantages including high throughput suitability, lower experimental variability, single cell resolution and an important reduction of animal numbers. The phoenix ANPGs in vitro model therefore provides a robust high-throughput platform to screen for otoprotective and regenerative neurotrophic compounds in line with 3R principles and is of interest for the field of auditory neuroscience.
MoreTranslated text
Key words
Cochlea,Auditory neurons,Spiral ganglion explants,Neurotrophins,Auditory neuron regeneration,Neuronal outgrowth
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
1991
被引用909 | 浏览
1995
被引用88 | 浏览
2007
被引用363 | 浏览
2004
被引用29 | 浏览
2015
被引用62 | 浏览
2012
被引用54 | 浏览
2009
被引用1629 | 浏览
2016
被引用23 | 浏览
2018
被引用314 | 浏览
2017
被引用8 | 浏览
2018
被引用12 | 浏览
2019
被引用50 | 浏览
2020
被引用17 | 浏览
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
GPU is busy, summary generation fails
Rerequest