Image: Researchers used this chip to analyze microbes present in whole blood samples (Photo courtesy of UC San Diego)
Globally, an alarming proportion of one in five deaths is attributed to sepsis-related complications, with children accounting for 41% of these deaths. Common practice involves administering antibiotics to patients with sepsis while awaiting blood culture results, which may contribute to antibiotic resistance. Ineffective treatment of sepsis can be harmful, as up to 30% of patients receive the wrong treatment, further increasing their risk of death. The critical nature of early and accurate diagnosis in cases of sepsis is underlined by the fact that the risk of mortality increases by 4% every hour that the infection is not adequately identified or treated. The new testing technique now offers faster and more accurate detection of pathogens in blood samples compared to traditional blood cultures, which are the standard in the diagnosis of infections.
The new method, called digital DNA fusion analysis, was developed by researchers at UC San Diego (La Jolla, CA, USA) and is capable of providing results in less than six hours. This means significant improvement within the typical 15 hours to several days that culture methods require, depending on the pathogen involved. The process uses universal, high-resolution digital DNA melting, which involves heating the DNA until it separates. Each DNA sequence reveals a unique signature during the fusion process. By imaging and analyzing this process, machine learning algorithms can discern DNA types in samples and identify pathogens. This method not only outperforms blood cultures in terms of speed, but also has a significantly lower risk of generating false positives compared to other emerging DNA detection technologies such as next-generation sequencing.
The research began with one milliliter of blood from each of the 17 patients in the preliminary clinical trial. These samples were taken at the same time as blood cultures from infants and young children. The researchers refined the DNA isolation process and machine learning techniques to minimize or eliminate the interference of human DNA as opposed to pathogenic DNA in the samples. They improved a machine learning algorithm to accurately distinguish between pathogen melting curves and background noise. This algorithm correlates the observed curves with a database of known DNA melting curves. In addition, it can identify curves produced by organisms not in this database, which is particularly useful for detecting rare or emerging pathogens in a sample.
The results of this method were not only consistent with those obtained from blood cultures of the same samples, but also produced no false positive results. This is in contrast to other nucleic acid amplification-based tests and next-generation DNA sequencing databases, which tend to amplify all DNA present, leading to false positives. Contamination from various sources such as the environment, tubes, reagents, and skin can often present problems in the interpretation of test results. This new method detected pathogens between 7.5 hours and approximately 3 days faster than conventional blood cultures. Moreover, it provides more than just a binary positive or negative result; quantifies the extent of pathogen presence in samples. Future plans include conducting a larger clinical trial and extending the methodology to adult patients.
“This is the first time this method has been tested on whole blood from patients with suspected sepsis. Therefore, this study is a more realistic look at how this technology might work in real-world clinical scenarios,” said Stephanie Fraley, a professor at UC San Diego. “We want to give doctors the ability to treat their patients not based on aggregate data, but based on accurate and precise individual data, enabling truly personalized medicine.”
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