Roughly 33 million people worldwide are infected with HIV; disease burden is highest in resource-limited settings. One important diagnostic in HIV disease management is the absolute count of lymphocytes expressing the CD4(+) and CD8(+) receptors. The current diagnostic instruments and procedures require expensive equipment and trained technicians. In response, we have developed microfluidic biochips that count CD4(+) and CD8(+) lymphocytes in whole blood samples, without the need for off-chip sample preparation. The device is based on differential electrical counting and relies on five on-chip modules that, in sequence, chemically lyses erythrocytes, quenches lysis to preserve leukocytes, enumerates cells electrically, depletes the target cells (CD4 or CD8) with antibodies, and enumerates the remaining cells electrically. We demonstrate application of this chip using blood from healthy and HIV-infected subjects. Erythrocyte lysis and quenching durations were optimized to create pure leukocyte populations in less than 1 min. Target cell depletion was accomplished through shear stress-based immunocapture, using antibody-coated microposts to increase the contact surface area and enhance depletion efficiency. With the differential electrical counting method, device-based CD4(+) and CD8(+) T cell counts closely matched control counts obtained from flow cytometry, over a dynamic range of 40 to 1000 cells/μl. By providing accurate cell counts in less than 20 min, from samples obtained from one drop of whole blood, this approach has the potential to be realized as a handheld, battery-powered instrument that would deliver simple HIV diagnostics to patients anywhere in the world, regardless of geography or socioeconomic status.

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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).

This paper describes theory and experiments, taken from biophysics and physiological measurements, to illustrate the technique of signal averaging. In the process, students are introduced to the basic concepts of signal processing, such as digital filtering, Fourier transformation, baseline correction, pink and Gaussian noise, and the cross- and autocorrelation functions. From these computations, the students estimate physically interesting parameters such as the pulse rate and blood flow velocity. They also learn about some of the pitfalls encountered in quantifying the signal and noise components for a meaningful computation of the signal-to-noise ratio.

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.

Complete blood cell counts (CBCs) are one of the most commonly ordered and informative blood tests in hospitals. The results from a CBC, which typically include white blood cell (WBC) counts with differentials, red blood cell (RBC) counts, platelet counts and hemoglobin measurements, can have implications for the diagnosis and screening of hundreds of diseases and treatments. Bulky and expensive hematology analyzers are currently used as a gold standard for acquiring CBCs. For nearly all CBCs performed today, the patient must travel to either a hospital with a large laboratory or to a centralized lab testing facility. There is a tremendous need for an automated, portable point-of-care blood cell counter that could yield results in a matter of minutes from a drop of blood without any trained professionals to operate the instrument. We have developed microfluidic biochips capable of a partial CBC using only a drop of whole blood. Total leukocyte and their 3-part differential count are obtained from 10 μL of blood after on-chip lysing of the RBCs and counting of the leukocytes electrically using microfabricated platinum electrodes. For RBCs and platelets, 1 μL of whole blood is diluted with PBS on-chip and the cells are counted electrically. The total time for measurement is under 20 minutes. We demonstrate a high correlation of blood cell counts compared to results acquired with a commercial hematology analyzer. This technology could potentially have tremendous applications in hospitals at the bedside, private clinics, retail clinics and the developing world.

Enumerating specific cell types from whole blood can be very useful for research and diagnostic purposes—e.g., for counting of cD4 and cD8 t cells in HIV/aIDs diagnostics. We have developed a biosensor based on a differential immunocapture technology to enumerate specific cells in 30 min using 10 µl of blood. this paper provides a comprehensive stepwise protocol to replicate our biosensor for cD4 and cD8 cell counts. the biochip can also be adapted to enumerate other specific cell types such as somatic cells or cells from tissue or liquid biopsies. capture of other specific cells requires immobilization of their corresponding antibodies within the capture chamber. therefore, this protocol is useful for research into areas surrounding immunocapture-based biosensor development. the biosensor production requires 24 h, a one-time cell capture optimization takes 6–9 h, and the final cell counting experiment in a laboratory environment requires 30 min to complete.

Microfluidic devices based on the Coulter principle require a small aperture for cell counting. For applications using such cell counting devices, the volume of the sample also needs to be metered to determine the absolute cell count in a specific volume. Hence, integrated methods to characterize and meter the volume of a fluid are required in these microfluidic devices. Here, we present fluid flow characterization and electrically-based sample metering results of blood through a measurement channel with a cross-section of 15 μm × 15 μm (i.e. the Coulter aperture). Red blood cells in whole blood are lysed and the remaining fluid, consisting of leukocytes, erythrocyte cell lysate and various reagents, is flown at different flow rates through the measurement aperture. The change in impedance across the electrodes embedded in the counting channel shows a linear relationship with the increase in the fluid flow rate. We also show that the fluid volume can be determined by measuring the decrease in pulse width, and increase in number of cells as they pass through the counting channel per unit time.