Method and device for analyzing target analyte in sample
A technology of target analytes and analysis methods, which is applied in the field of analyzing target analytes in samples, and can solve problems such as long time
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Embodiment 1
[0398] Example 1: Detection of Gastrointestinal Infection Viruses
[0399] Data set acquisition
[0400] Seegene's Allplex was used to detect Norovirus GI, Norovirus GII, Adenovirus, and Rotavirus, which are the main causes of acute diarrhea TM GI-Virus Assay.
[0401] Using CFX96 TM A real-time polymerase chain reaction detection system (Bio-rad) performs nucleic acid amplification reactions on 30 samples. The nucleic acid amplification reaction was repeatedly performed under the temperature conditions of 95° C. for 10 seconds, 60° C. for 1 minute, and 72° C. for 30 seconds, for a total of 45 cycles. The dataset was acquired from CFX96 using the probe and label oligonucleotide cleavage and extension method (WO2012 / 096523) and MuDT (WO2015 / 147412) technology. The above dataset contains signal values associated with all amplification cycles.
[0402] As shown in Table 1 below, the 30 samples are multiple samples (20 positive samples, 10 negative samples) confirmed to be...
Embodiment 2
[0427] Embodiment 2: the detection (comparative embodiment for embodiment 1) of the gastrointestinal infection virus based on prior art
[0428] Data set acquisition
[0429] Through the same process and acquisition data set as in Example 1.
[0430] Acquisition of the calibrated dataset
[0431] Noise was removed by the Nearest neighbor smoothing algorithm (Winfried Stute et al., Journal of Multivariate Analysis, 34:61 (1990)). A linear regression is performed to the source dataset (raw dataset) where the difference is a cycle above the baseline threshold to obtain the baseline, and the calibrated dataset is obtained by baseline subtraction.
[0432] Utilizes a threshold relative to a cycle threshold to determine the presence or absence of a target nucleic acid molecule
[0433] Positive or negative for each sample is determined by determining whether the calibrated above data set is greater than a threshold associated with a plurality of cycle thresholds. If there is a s...
Embodiment 3
[0443] Example 3: Analysis method of target analyte in sample using normalization method of data set and non-linear matching function
[0444] According to the specific background signal-based normalization method (SBN, Specific Background signal-based Normalization), after the data set is calibrated by the normalization method, according to the method of the present invention, the non-linear matching function is used to analyze whether there is a target analyte in the sample.
[0445] get dataset
[0446] Real-time polymerase chain reactions were performed on the 4 target nucleic acid sequences. Three CFX96 real-time polymerase chain reaction devices (Bio-Rad) were used to simultaneously amplify four target nucleic acid sequences, and for each target nucleic acid sequence, TaqMan probes were used as signal generating units to perform 50 amplification cycles. Using samples containing the same target nucleic acid sequence at the same concentration, 96 reactions were performed un...
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