کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6133570 | 1593470 | 2014 | 23 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
New method for the visual detection of human respiratory syncytial virus using reverse transcription loop-mediated amplification
ترجمه فارسی عنوان
روش جدید برای تشخیص بصری از ویروس سونسیتیال تنفسی انسانی با استفاده از تقویت کانال رونویسی معکوس
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
ایمنی شناسی و میکروب شناسی
ویروس شناسی
چکیده انگلیسی
Human respiratory syncytial virus (HRSV) is a seasonal respiratory pathogen that causes respiratory infection in children and the elderly. A new, reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) assay was developed for the rapid (within 1Â h), simultaneous detection of A and B group HRSV. Primers specific for groups A and B were designed to amplify the N and L genes of HRSV, respectively. A fluorescent dye, calcein, was used as an indicator for the endpoint visual detection and/or real-time amplification of HRSV RNA. The detection limit of the new method was 281.17 50% tissue culture infective doses (TCID50)/ml for HRSV A and 1.58 TCID50/ml for HRSV B. To evaluate the validity of this method, a comparison with RT-PCR was performed using 77 nasopharyngeal swabs as samples. Both RT-LAMP and RT-PCR detected HRSV in 38 HRSV samples, yielding a positive rate of 49%. Of the RT-LAMP positive samples, 36 (95%) were also positive by RT-PCR, while two were negative by RT-PCR. Among the 36 RT-LAMP and RT-PCR positive samples, 11 belonged to HRSV group A, while 25 belonged to group B. The results show that the new RT-LAMP is simple, rapid and well suited for HRSV diagnosis, especially in a limited-resource setting.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Virological Methods - Volume 206, 15 September 2014, Pages 84-88
Journal: Journal of Virological Methods - Volume 206, 15 September 2014, Pages 84-88
نویسندگان
Yonglin Mu, Jiawei Zeng, Qianqian Chen, Jia Liu, Lili Wang, Fujia Yao, Meng Cui, Zhixiang He, Chiyu Zhang, Ming Xiao, Ke Lan,