A UV‐C LED‐based Unit for Continuous Decontamination of the Sheath Fluid in a Flow‐cytometric Cell Sorter
ENGINEERING IN LIFE SCIENCES（2022）
Deutsch Rheuma Forschungszentrumein Inst Leibniz
Abstract
Aseptic cell sorting is challenging, especially when a flow-cytometric cell sorter is not operated in a sterile environment. The sheath fluid system of a cell sorter may be contaminated with germs such as bacteria, yeasts, viruses, or fungi. Thus, a regular chemical cleaning procedure is required to prepare a sorter for aseptic cell sorting by flushing the fluidic system. However, this procedure is time consuming, and most importantly, the researcher can never be sure that the cleaning process was successful. Here we present a method in which the sheath fluid of a cell sorter was decontaminated by irradiation with UV-C light using a flow-through principle. Using this principle, we were able to achieve a 5 log reduction of bacteria in the sheath fluid.
MoreTranslated text
Key words
aseptic sorting,flow-cytometric cell sorting,UV-C LEDs
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