The development of a GeXP-based multiplex reverse transcription-PCR assay for simultaneous detection of sixteen human respiratory virus types/subtypes.

BACKGROUND: Existing standard non-molecular diagnostic methods such as viral culture and immunofluorescent (DFA) are time-consuming, labor intensive or limited sensitivity. Several multiplex molecular assays are costly. Therefore, there is a need for the development of a rapid and sensitive diagnosis of respiratory viral pathogens. METHODS: A GeXP-based multiplex RT-PCR assay (GeXP assay) was developed to detect simultaneously sixteen different respiratory virus types/subtypes. Seventeen sets of chimeric primers were used to initiate the RT-PCR, and one pair of universal primers was used for the subsequent cycles of the RT-PCR. The specificity of the GeXP assay was examined with positive controls for each virus type/subtype. The sensitivity was evaluated by performing the assay on serial ten-fold dilutions of in vitro-transcribed RNA of all RNA viruses and the plasmids containing the Adv and HBoV target sequence. GeXP assay was further evaluated using 126 clinical specimens and compared with Luminex xTAG RVP Fast assay. RESULTS: The GeXP assay achieved a sensitivity of 20-200 copies for a single virus and 1000 copies when all of the 16 pre-mixed viral targets were present. Analyses of 126 clinical specimens using the GeXP assay demonstrated that GeXP assay and the RVP Fast assay were in complete agreement for 109/126 (88.51%) of the specimens. GeXP assay was more sensitive than the RVP Fast assay for the detection of HRV and PIV3, and slightly less sensitive for the detection of HMPV, Adv, RSVB and HBoV. The whole process of the GeXP assay for the detection of 12 samples was completed within 2.5 hours. CONCLUSIONS: In conclusion, the GeXP assay is a rapid, cost-effective, sensitive, specific and high throughput method for the detection of respiratory virus infections.

Visual detection of human enterovirus 71 subgenotype C4 and Coxsackievirus A16 by reverse transcription loop-mediated isothermal amplification with the hydroxynaphthol blue dye.

A sensitive reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay was developed for rapid visual detection of human enterovirus 71 subgenotype C4 (EV71-C4) and Coxsackievirus A16 (CVA16) infection, respectively. The reaction was performed in one step in a single tube at 65°C for 60 min with the addition of the hydroxynaphthol blue (HNB) dye prior to amplification. The detection limits of the RT-LAMP assay were 0.33 and 1.58 of a 50% tissue culture infective dose (TCID(50)) per reaction based on 10-fold dilutions of a titrated EV71 or CVA16 strain, respectively. No cross-reaction was observed with Coxsackievirus A (CVA) viruses (CVA2, 4, 5, 7, 9, 10, 14, and 24), Coxsackievirus B (CVB) viruses (CVB1,2,3,4, and 5) or ECHO viruses (ECHO3, 6, 11, and 19). The assay was further evaluated with 47 clinical stool specimens diagnosed previously with EV71, CVA16 or other human enterovirus infections. Virus isolates from stool samples were confirmed by virus neutralization testing and sequencing. RT-LAMP with HNB dye was demonstrated to be a sensitive and cost-effective assay for rapid visual detection of human EV71-C4 and CVA16.

Binary particle swarm optimization algorithm with mutation for multiple sequence alignment.

Multiple sequence alignment (MSA) is a fundamental and challenging problem in the analysis of biologic sequence. The MSA problem is hard to be solved directly, for it always results in exponential complexity with the scale of the problem. In this paper, we propose mutation-based binary particle swarm optimization (M-BPSO) for MSA solving. In the proposed M-BPSO algorithm, BPSO algorithm is conducted to provide alignments. Thereafter, mutation operator is performed to move out of local optima and speed up convergence. From simulation results of nucleic acid and amino acid sequences, it is shown that the proposed M-BPSO algorithm has superior performance when compared to other existing algorithms. Furthermore, this algorithm can be used quickly and efficiently for smaller and medium size sequences.