A microfluidic biochip enumerates CD4 ⁺ or CD8 ⁺ T lymphocytes in whole blood of healthy and HIV-infected donors.

Viral load measurements are an essential tool for the long-term clinical care of hum an immunodeficiency virus (HIV)-positive individuals. The gold standards in viral load instrumentation, however, are still too limited by their size, cost, and sophisticated operation for these measurements to be ubiquitous in remote settings with poor healthcare infrastructure, including parts of the world that are disproportionately affected by HIV infection. The challenge of developing a point-of-care platform capable of making viral load more accessible has been frequently approached but no solution has yet emerged that meets the practical requirements of low cost, portability, and ease-of-use. In this paper, we perform reverse-transcription loop-mediated isothermal amplification (RT-LAMP) on minimally processed HIV-spiked whole blood samples with a microfluidic and silicon microchip platform, and perform fluorescence measurements with a consumer smartphone. Our integrated assay shows amplification from as few as three viruses in a ~ 60 nL RT-LAMP droplet, corresponding to a whole blood concentration of 670 viruses per µL of whole blood. The technology contains greater power in a digital RT-LAMP approach that could be scaled up for the determination of viral load from a finger prick of blood in the clinical care of HIV-positive individuals. We demonstrate that all aspects of this viral load approach, from a drop of blood to imaging the RT-LAMP reaction, are compatible with lab-on-a-chip components and mobile instrumentation.

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.