Performance Assessment of the Devyser High-Throughput Sequencing–Based Assay for Chimerism Monitoring in Patients after Allogeneic Hematopoietic Stem Cell Transplantation
Journal of Molecular Diagnostics（2021）
AZ Sint Jan Brugge Oostende AV
Abstract
Chimerism analysis is widely used to aid in the clinical management of patients after allogeneic hematopoietic stem cell transplantation. Many laboratories currently use assays based on PCR followed by capillary electrophoresis, with a limit of quantification of 1% to 5%. Assays with a lower limit of quantification could allow for earlier relapse detection, resulting in improved patient care. This study investigated the analytical, clinical, technical, and practical performance of the Devyser next-generation sequencing chimerism assay, a commercial high-throughput sequencing-based assay for chimerism analysis. Performance of this assay was compared with that of the Promega PowerPlex 16 HS assay, a commercial capillary electrophoresis-based assay. A limit of quantification of 0.1% was achievable with the Devyser assay. The repeatability, reproducibility, trueness, and linearity of the Devyser assay were acceptable. The Devyser assay showed potential for earlier relapse detection compared with the Promega assay. Conclusive analysis was not possible for 3% of donor-recipient pairs with the Devyser assay due to an insufficient number of informative markers; this factor was not an issue for the Promega assay. Further improvements in assay design or data analysis may allow the assay's applicability to be extended to all donor-recipient pairs studied. Technical performance criteria for chimerism analysis by high-throughput sequencing were suggested and evaluated. Both assays were found to be practical for use in a clinical diagnostics laboratory.
MoreTranslated text
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