Biomarkers used for diagnosing pancreatitis and derived from intestinal tract, screening method and application of biomarker
A biomarker and pancreatitis technology, applied in the field of biomarkers for the diagnosis of pancreatitis, can solve the problems of low specificity and unclear pathological basis, and achieve the effect of precise treatment plan and good clinical application prospect
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Embodiment 1
[0039] Example 1 Screening of intestinal microbial flora associated with pancreatitis
[0040] 1. Sample collection
[0041] Fresh, middle and late fecal samples were collected from patients with pancreatitis diagnosed in the hospital and normal subjects, and immediately frozen in a -80°C refrigerator. The sample information is shown in Table 1.
[0042] Table 1 Sample information
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[0045] 2. DNA extraction
[0046] Procedure: Stool sample DNA according to MOBIO DNA Isolation Kit 12888-100 instruction manual for extraction.
[0047] The DNA extraction information is shown in Table 2. After the genomic DNA was extracted, the extracted genomic DNA was detected by 1% agarose gel electrophoresis.
[0048] Table 2 DNA extraction information
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[0051] 3. PCR amplification
[0052] According to the designated sequencing region, synthesize specific primers with barcode.
[0053] In order to ensure the accuracy and reliability of ...
Embodiment 2
[0115] The clinical diagnostic value of embodiment 2 intestinal microbial flora
[0116] 9. Model prediction analysis
[0117] Random Forest (Random Forest) belongs to the machine learning algorithm. It is a classifier containing multiple decision trees. Its classification results are judged on different decision trees according to the attributes of each dimension of the detection sample, and all the judgment results are considered comprehensively. Finally, the final classification is given. For the classification problem, the maximum probability is taken, and the regression analysis takes the probability mean. It can efficiently and quickly select the most important species category (biomarker) for sample classification. Software: R (randomForest package), using random forest, setting 500 decision trees, classification level is species, and sorting by importance. For the sorted species, the importance is increased from large to small to build a classification model and calcu...
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