Automated System for Multiplexing Detection of COVID-19 and Other Respiratory Pathogens.
IEEE J Transl Eng Health Med
; 11: 424-434, 2023.
Article
in En
| MEDLINE
| ID: mdl-37435542
OBJECTIVE: Infectious diseases are global health challenge, impacted the communities worldwide particularly in the midst of COVID-19 pandemic. The need of rapid and accurate automated systems for detecting pathogens of concern has always been critical. Ideally, such systems shall detect a large panel of pathogens simultaneously regardless of well-equipped facilities and highly trained operators, thus realizing on-site diagnosis for frontline healthcare providers and in critical locations such as borders and airports. METHODS & RESULTS: Avalon Automated Multiplex System, AAMST, is developed to automate a series of biochemistry protocols to detect nucleic acid sequences from multiple pathogens in one test. Automated processes include isolation of nucleic acids from unprocessed samples, reverse transcription and two rounds of amplifications. All procedures are carried out in a microfluidic cartridge performed by a desktop analyzer. The system was validated with reference controls and showed good agreement with their laboratory counterparts. In total 63 clinical samples, 13 positives including those from COVID-19 patients and 50 negative cases were detected, consistent with clinical diagnosis using conventional laboratory methods. CONCLUSIONS: The proposed system has demonstrated promising utility. It would benefit the screening and diagnosis of COVID-19 and other infectious diseases in a simple, rapid and accurate fashion. Clinical and Translational Impact Statement- A rapid and multiplex diagnostic system proposed in this work can clinically help to control spread of COVID-19 and other infectious agents as it can provide timely diagnosis, isolation and treatment to patients. Using the system at remoted clinical sites can facilitate early clinical management and surveillance.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Diagnostic_studies
/
Guideline
Limits:
Humans
Language:
En
Journal:
IEEE J Transl Eng Health Med
Year:
2023
Document type:
Article
Country of publication:
United States