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• W2912679745 abstract "Background Meningitis and encephalitis (ME) are central nervous system (CNS) infections mainly caused by bacteria, mycobacteria, fungi, viruses, and parasites that result in high morbidity and mortality. The early, accurate diagnosis of pathogens in the cerebrospinal fluid (CSF) and timely medication are associated with better prognosis. Conventional methods, such as culture, microscopic examination, serological detection, CSF routine analysis, and radiological findings, either are time-consuming or lack sensitivity and specificity. Methods To address these clinical needs, we developed an advanced fragment analysis (AFA)-based assay for the multiplex detection of 22 common ME pathogens, including eight viruses, 11 bacteria, and three fungi. The detection sensitivity of each target was evaluated with a recombinant plasmid. The limits of detection of the 22 pathogens ranged from 15 to 120 copies/reaction. We performed a retrospective study to analyze the pathogens from the CSF specimens of 170 clinically diagnosed ME patients using an AFA-based assay and compared the results with culture (bacteria and fungi), microscopic examination (fungi), polymerase chain reaction (PCR) (Mycobacterium tuberculosis), and Sanger sequencing (virus) results. Results The sensitivity of the AFA assay was 100% for 10 analytes. For Cryptococcus neoformans, the sensitivity was 63.6%. The overall specificity was 98.2%. The turnaround time was reduced to 4-6 hours from the 3-7 days required using conventional methods. Conclusions In conclusion, the AFA-based assay provides a rapid, sensitive, and accurate method for pathogen detection from CSF samples." @default.
• W2912679745 created "2019-02-21" @default.
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• W2912679745 date "2019-01-21" @default.
• W2912679745 modified "2023-10-16" @default.
• W2912679745 title "Development and validation of an advanced fragment analysis‐based assay for the detection of 22 pathogens in the cerebrospinal fluid of patients with meningitis and encephalitis" @default.
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• W2912679745 doi "https://doi.org/10.1002/jcla.22707" @default.
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