Sepsis, a potentially life-threatening complication of an infection, has the highest burden of death and medical expenses in hospitals worldwide. Leukocyte count and CD64 expression on neutrophils (nCD64) are known to correlate strongly with improved sensitivity and specificity of sepsis diagnosis at its onset. A major challenge is the lack of a rapid and accurate point-of-care (PoC) device that can perform these measurements from a minute blood sample. Here, we report a PoC microfluidic biochip to enumerate leukocytes and quantify nCD64 levels from 10 μl of whole blood without any manual processing. Biochip measurements have shown excellent correlation with the results from flow cytometer. In clinical studies, we have used PoC biochip to monitor leukocyte counts and nCD64 levels from patients’ blood at different times of their stay in the hospital. Furthermore, we have shown the biochip’s utility for improved sepsis diagnosis by combining these measurements with electronic medical record (EMR).

Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel biomarker measurements. In this study, we apply machine learning techniques to assess the predictive power of combining multiple biomarker measurements from a single blood sample with electronic medical record data (EMR) for the identification of patients in the early to peak phase of sepsis in a large community hospital setting. Combining biomarkers and EMR data achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved an AUC of 0.75. Furthermore, a single measurement of six biomarkers (IL-6, nCD64, IL-1ra, PCT, MCP1, and G-CSF) yielded the same predictive power as collecting an additional 16 hours of EMR data(AUC of 0.80), suggesting that the biomarkers may be useful for identifying these patients earlier. Ultimately, supervised learning using a subset of biomarker and EMR data as features may be capable of identifying patients in the early to peak phase of sepsis in a diverse population and may provide a tool for more timely identification and intervention.

Through the efforts of many groups, a wide range of fluorescent protein reporters and sensors based on green fluorescent protein and its relatives have been engineered in recent years. Here we explore the incorporation of sensing modalities into de novo designed fluorescence-activating proteins, called mini-fluorescence-activating proteins (mFAPs), that bind and stabilize the fluorescent cis-planar state of the fluorogenic compound DFHBI. We show through further design that the fluorescence intensity and specificity of mFAPs for different chromophores can be tuned, and the fluorescence made sensitive to pH and Ca2+ for real-time fluorescence reporting. Bipartite split mFAPs enable real-time monitoring of protein–protein association and (unlike widely used split GFP reporter systems) are fully reversible, allowing direct readout of association and dissociation events. The relative ease with which sensing modalities can be incorporated and advantages in smaller size and photostability make de novo designed fluorescence-activating proteins attractive candidates for optical sensor engineering.