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Blood counting is one of the most commonly ordered clinical assays, and is often part of the basis for initial diagnosis and screening for disease. While substantial prior research has shown the ability of portable instruments to accurately produce blood counts through image- or flow-based cytometry, these methods require complex sample preparation using either costly commercial imaging chambers or complicated reagents. To address these issues, in this paper we developed a method to prepare trace volumes of whole blood aimed at portable blood counting. The strategy is based on pre-storing dry-form reagents and fabricating a specifically designed cell counter. In order to obtain total cell counts for red blood cells, platelets, and 3-part differentials of white blood cells, two parallel counting chambers with different depths are made from cost- and environmentally friendly materials using soft lithography. As little as 1 μl of whole blood is prepared with pre-stored reagents in centrifuge vials, whereas red blood cells are sphered and white blood cells are stained at the same time. Driven by the capillary force, prepared blood samples enter the hydrophilic chambers automatically. Monolayers of cells are formed when the blood dilution factors and the chamber depths are co-optimized. Combined with our previous custom-built instrument and automated analysis algorithm, the sample preparation strategy allows producing counting results with excellent agreement to a gold-standard clinical hematology instrument. The success of this preparation method may further advance applications of our technology for global use in low-resource settings where central hematology laboratories are not accessible. [ABSTRACT FROM AUTHOR]

Anemia affects more than ¼ of the world's population, mostly concentrated in low-resource areas, and carries serious health risks. Yet current screening methods are inadequate due to their inability to separate iron deficiency anemia (IDA) from genetic anemias such as thalassemia trait (TT), thus preventing targeted supplementation of oral iron. Here we present an accurate approach to diagnose anemia and anemia type using measures of pediatric red cell morphology determined through machine learning applied to optical light scattering measurements. A partial least squares model shows that our system can accurately extract mean cell volume, red cell size heterogeneity, and mean cell hemoglobin concentration with high accuracy. These clinical parameters (or the raw data itself) can be submitted to machine learning algorithms such as quadratic discriminants or support vector machines to classify a patient into healthy, IDA, or TT. A clinical trial conducted on 268 Chinese children, of which 49 had IDA and 24 had TT, shows ＞98% sensitivity and specificity for diagnosing anemia, with 81% sensitivity and 86% specificity for discriminating IDA and TT. The majority of the misdiagnoses are IDA patients with particularly severe anemia, possibly requiring hospital care. Therefore, in a screening paradigm where anyone testing positive for TT is sent to the hospital for gold-standard diagnosis and care, we maximize patient benefit while minimizing use of scarce resources. [ABSTRACT FROM AUTHOR]

We report an accurate method to count red blood a cells, platelets, and white blood cells, as well as to determine hemoglobin in the blood of humans, horses, dogs, cats, and cows. Red and white blood cell counts can also be performed on human body fluids such as cerebrospinal fluid, synovial fluid, and peritoneal fluid. The approach consists of using a compact, custom-built microscope to record large field-of-view, bright-field, and fluorescence images of samples that are stained with a single dye and using automatic algorithms to count blood cells and detect hemoglobin. The total process takes about 15 min, including 5 min for sample preparation, and 10 min for data collection and analysis. The minimum volume of blood needed for the test is 0.5 µL, which allows for minimally invasive sample collection such as using a finger prick rather than a venous draw. Blood counts were compared to gold-standard automated clinical instruments, with excellent agreement between the two methods as determined by a Bland-Altman analysis. Accuracy of counts on body fluids was consistent with hand counting by a trained clinical lab scientist, where our instrument demonstrated an approximately 100-fold lower limit of detection compared to current automated methods. The combination of a compact, custom-built instrument, simple sample collection and preparation, and automated analysis demonstrates that this approach could benefit global health through use in low-resource settings where central hematology laboratories are not accessible. [ABSTRACT FROM AUTHOR]

We monitored time-dependent changes in the Raman spectra of optically trapped red blood cells. By fitting the Raman peaks of individual spectra over time, high-precision time evolutions of peak positions and intensities were obtained. These changes are dependent on the trapping laser power. Characteristic times for these changes were determined for each laser power by fitting the time courses with multi-exponential curves. Raman spectral dynamics showed significant and irreversible changes as a function of trapping duration that we attribute to a combination of photodamage of hemoglobin at short times followed by diffusion of hemoglobin out of the cell at longer times. [ABSTRACT FROM AUTHOR]