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Why do traditional PCR tests sometimes fail, but mNGS reveals the truth?
In today's medical world, there are various ways to diagnose infectious diseases, among which the traditional polymerase chain reaction (PCR) test has always been an important tool that doctors and laboratories rely on. However, with the advancement of technology, the emergence of clinical metabolic genomics (mNGS) has gradually shown its advantages over traditional PCR, especially when PCR testing cannot provide clear results in some cases.
mNGS is an advanced genetic sequencing technology that enables comprehensive analysis of microbial and host genetic material from clinical samples without the need for prior knowledge of specific pathogens. Traditional PCR tests often rely on the prior identification of a specific pathogen, which makes diagnosis difficult in cases where the pathogen is unknown.
Compared with PCR, mNGS uses an unbiased analysis method and can identify all potential pathogens by sequencing all genomes in the sample. This feature makes mNGS particularly important when dealing with infections where the cause is uncertain, especially after the patient has experienced multiple negative PCR tests.The sensitivity and specificity of traditional PCR tests are affected by many factors, including the concentration of the pathogen, the quality of the sample, and the presence of inhibitors.
Advantages and application scenarios of mNGS
With mNGS, doctors can get comprehensive information about the cause of an infection in a single test. It can not only quickly identify viruses, bacteria, fungi and parasites, but also detect multiple infections simultaneously. Studies have shown that mNGS is more effective than traditional methods in diagnosing diseases such as meningitis, myelitis, and severe pneumonia.
mNGS testing of CSF samples may reveal pathogens that are missed by traditional testing, which is critical for selecting treatment options.
Limitations of Traditional PCR
Although PCR detection technology has made great progress, it still has its limitations. First, PCR testing requires specific primers, and if the pathogen has not been previously identified, PCR may not detect it. In addition, background noise, sample quality and improper operation may lead to false negative results. This is particularly prominent in two situations: when the Ct value of the pathogen is high and in questionable cases.
mNGS experimental process
The mNGS process includes sample acquisition, RNA/DNA extraction, high-throughput sequencing, and bioinformatics analysis. Every step in this series of processes requires precision and cleanliness to prevent contamination. Especially during the sample extraction and library preparation stages, it is often necessary to focus on eliminating background noise to improve the detection accuracy of pathogen signals.
High-throughput sequencing technology enables a single test to generate a large amount of data, which provides a guarantee for subsequent data analysis and interpretation.
Application of mNGS in antibiotic resistance research
As the problem of antibiotic resistance becomes increasingly serious, the use of mNGS technology to detect genetic variations in microbial resistance has become an important research direction. mNGS can reveal the diversity of drug-resistant genes and help discover new drug resistance mechanisms.
In addition, mNGS has also shown great potential in monitoring epidemics, especially in the early stages of epidemic response, its ability to identify potential pathogens in the first place makes it a cutting-edge tool for public health monitoring.
Challenges and future development of mNGS
Although mNGS is gradually becoming a new diagnostic standard, its clinical penetration is still insufficient and faces challenges such as cost, laboratory validation and data analysis. In the future, more clinical studies are needed to verify its specific application. At the same time, with the continuous improvement of technology and the establishment of relevant standards, mNGS is expected to become a mainstream tool for clinical diagnosis.
In this process, we also need to think about whether traditional diagnostic tools can continue to meet the challenges of new pathogens that may emerge in the future, or whether new technologies such as mNGS should be more widely adopted to reveal the truth?
