重组2型脊髓灰质炎病毒样颗粒的制备及免疫原性初步研究
Chinese Journal of Microbiology and Immunology（2021）
Abstract
目的:构建共表达P1和3CD的重组杆状病毒，利用昆虫细胞重组表达2型脊髓灰质炎病毒(poliovirus type 2，PV2)的病毒样颗粒(virus-like particle, VLP)，制备PV2-VLP进行初步免疫原性研究。方法:基于粉纹夜蛾细胞(High 5)密码子偏好性，将PV2的P1基因和3CD基因进行序列优化，并对P1基因进行稳定性突变，构建突变型rBac-PV2-P1s-3CD和野生型rBac-PV2-P1-3CD杆状病毒。Western blot分别检测目的蛋白表达量，离子交换层析纯化PV2-VLP，透射电镜观察VLP高级结构确定组装效率。免疫大鼠评估免疫原性。结果:成功构建了可稳定表达P1s蛋白和3CD蛋白的重组杆状病毒。Western blot结果表明热稳定性突变后VLP产量相比野生型有所提高。电镜观察发现直径约30 nm的三维结构，表明VLP成功组装。动物实验结果表明，重组制备的PV2-VLP具有免疫原性，且能有效刺激产生中和抗体。结论:用昆虫细胞-杆状病毒表达系统可成功制备有效的VLP类疫苗，为PV-VLP疫苗的研制提供参考。
MoreTranslated text
Key words
Poliovirus,Virus-like particles,Insect cells,Recombinant baculovirus
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