UC San Diego

Sepsis is a life-threatening condition that results from a severe immune response to a bloodstream infection. Identifying sepsis-causing organisms rapidly and accurately within a clinically relevant time-frame remains a significant challenge. To properly identify sepsis-causing pathogens, the ideal diagnostic should be: (a) rapid, (b) broad-based, (c) capable of polymicrobial detection, (d) highly sensitive and specific, (e) minimally invasive, (f) easily integrated into clinical workflow, (g) able to detect antibiotic resistance determinants, (h) and able to identify new and unknown pathogens. The current gold standard for pathogen detection is blood culture which has limited sensitivity and detection capabilities and requires a significant amount of time. Other technologies have been developed to address some of the ideal sepsis criteria; although many meet several of the criteria for the ideal sepsis diagnostic, none have successfully fulfilled them all. We have developed a novel device that meets each of these criteria; it can identify sepsis-causing bacteria at a clinically relevant load for neonatal sepsis within four hours through an integrated system which incorporates universal PCR amplification, High Resolution Melt across a 20,000 well microfluidic chip and a machine learning algorithm. The bacterial DNA is separated by digitization across the picoliter-sized wells and the 16S gene is targeted and amplified using universal primers. This device fingerprints each well simultaneously and compares them to a library of characterized curves by utilizing a machine learning algorithm. This technology could be a valuable clinical addition as an ideal sepsis diagnostic.