基于聚合物分析系统流感疫苗裂解效果检测方法的建立及验证
Chinese Journal of Biologicals(2022)
Abstract
目的 建立一种基于聚合物分析系统(Aggregates Sizer)的流感疫苗裂解效果检测方法,并进行方法验证.方法 将流感病毒裂解疫苗原液A(由裂解液制得,病毒颗粒已裂解)和B(由裂解前纯化液制得,病毒颗粒未裂解)按一定比例混合,配制成裂解程度为60%、70%、80%、90%和100%的系列工作液,经Aggregates Sizer检测样本中各粒径病毒颗粒的分布丰度和累积分布丰度,以流感病毒颗粒裂解程度为横坐标,颗粒累积分布丰度为纵坐标建立标准曲线,获得标准曲线方程,计算相关系数(r2).另取60%、80%和 100%裂解程度的工作液作为质量控制(quality control,QC)样本,重复检测3次,准确性以相对误差(relative error,RE)表示,精密性以相对标准偏差(relative standard deviation,RSD)表示.结果 工作液在60%-100%裂解程度范围内与病毒颗粒累积分布丰度呈良好线性关系,标准曲线方程为y=-2 600x+2 610,r2为0.995.不同裂解程度QC样本经3次检测的RE均<2%,RSD均<0.05%.结论 建立了基于Aggregates Sizer系统的流感疫苗裂解效果检测方法,该方法具有良好的准确性和精密性.
More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