Bacteria-Derived Nanovesicle Gene Analysis for Rapid Pathogen Identification
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Solution Overview
Problem
Current methods for diagnosing serious bacterial infectious diseases, such as pneumonia and sepsis, are inefficient, as they can only identify 1% of bacteria through bacterial genomics and require at least 5 days for results, and there is a lack of research on using bacteria-derived nanovesicles for identification.
Innovation Solution
A method involving the extraction of genes from bacteria-derived nanovesicles using PCR with specific primers, followed by thermal treatment to release DNA from the lipid membrane, allowing for the analysis of sequences to predict causative bacteria in clinical samples like urine, blood, or nasal fluid.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bacterial culture methods are used to identify bacteria, then identification can be performed, but the process requires at least 5 days and can only identify 1% of entire bacteria
Solution Approach 1:
The patent extracts genes from bacteria-derived nanovesicles present in clinical samples, separating the genetic material from the intact bacterial cells. This extraction enables direct genetic analysis without requiring bacterial culture, thus resolving the contradiction between identification accuracy and diagnosis time by obtaining bacterial genetic information directly from the clinical sample
Solution Approach 2:
The patent uses bacteria-derived nanovesicles as an intermediary carrier that contains bacterial genetic material. These nanovesicles serve as a mediator between the bacteria and the diagnostic system, allowing genetic analysis without direct bacterial culture. The nanovesicles protect and deliver bacterial DNA/RNA from the clinical sample to the detection system, enabling rapid identification
2Productivity
If antibiotics are used based on clinical experience without bacterial information, then treatment can proceed, but the treatment effectiveness is reduced due to lack of targeted information
Solution Approach 1:
The patent performs preliminary identification of causative bacteria through genetic analysis of nanovesicles before initiating antibiotic treatment. By obtaining bacterial identification information in advance (within hours rather than days), the system enables selection of appropriate antibiotics before treatment begins, improving treatment efficiency and avoiding ineffective empirical therapy
Solution Approach 2:
The patent replaces the mechanical bacterial culture system with a molecular genetic analysis system. Instead of relying on physical bacterial growth and observation, the system uses PCR and sequencing technologies to directly detect bacterial genetic material in nanovesicles, substituting a faster, more information-rich molecular approach for the traditional mechanical culture method
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method enables the identification of bacteria causing serious infections by analyzing sequences in nanovesicles, providing information on causative factors and predicting bacterial infections with increased purity and accuracy, even when directly extracting genes from clinical samples without isolating nanovesicles.
Implementation Method 1
performing a polymerase chain reaction (PCR) on the extracted genes using a pair of primers set forth in SEQ ID NOS: 1 and 2
Implementation Method 2
the bacteria-derived nanovesicles are thermally treated so that DNA in the vesicles exudes out of a lipid membrane
Data Source
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AI summary
The present invention relates to a method for predicting causative factors (bacteria) of serious bacterial infectious diseases in a clinical sample containing bacteria-derived nano-sized extracellular vesicles, that is, nanovesicles, through the analysis of the genes contained in the nanovesicles. According to the present invention, information on bacteria of serious bacterial infection can be provided, a causative factor of the bacterial infection can be predicted, and the resistance of the bacteria to antibiotics can be predicted.