Microbial marker related to norovirus infectious diarrhea and application thereof
A virus infection and microbiological technology, applied in the direction of microbiology, microbiological-based methods, microbiological determination/testing, etc., to achieve good diagnostic efficiency, high specificity, and high sensitivity
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0053] Example 1 Detection of Microflora Associated with Norovirus Infectious Diarrhea
[0054] 1. Collection of samples
[0055] The research objects collected in this study include: 35 cases of children with norovirus infectious diarrhea (norovirus infection group, NV group), 25 cases of healthy children (healthy control group, NOR group), excluding antibiotics, probiotics, After taking Chinese herbal medicine and other substances that may affect the structure of the intestinal flora, a total of 31 stool samples from children with norovirus infectious diarrhea and 25 stool samples from healthy children were collected. The stool samples were stored at -80°C for DNA extraction, sequencing and bioinformatic analysis.
[0056] 2. 16S rRNA sequencing
[0057] (1) Extraction of DNA
[0058] After the genomic DNA was extracted, the extracted genomic DNA was detected by 1% agarose gel electrophoresis.
[0059] (2) PCR amplification
[0060] Synthesize specific primers with barc...
Embodiment 2
[0117] Example 2 evaluates and analyzes the diagnostic efficacy of microbial markers
[0118] 1. Model prediction analysis and detection of microbial marker diagnostic efficacy
[0119]Random forest (Random Forest) is used for analysis. The random forest belongs to the machine learning algorithm and is a classifier containing multiple decision trees. Its classification results are based on the attributes of each dimension of the detection sample on different decision trees Make a judgment, and give the final classification after comprehensively considering all the judgment results. 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 specie...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com