Screening of nutritional and genetic anemias using elastic light scattering.

Anemia affects more than » 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.

Performance of a cost-effective and automated blood counting system for resource-limited settings operated by trained and untrained users.

Current flow-based blood counting devices require expensive and centralized medical infrastructure and are not appropriate for field use. In this article we report a streamlined, easy-to-use method to count red blood cells (RBC), white blood cells (WBC), platelets (PLT) and 3-part WBC differential through a cost-effective and automated image-based blood counting system. The approach consists of using a compact, custom-built microscope with large field-of-view to record bright-field and fluorescence images of samples that are diluted with a single, stable reagent mixture and counted using automatic algorithms. Sample collection utilizes volume-controlled capillary tubes, which are then dropped into a premixed, shelf-stable solution to stain and dilute in a single step. Sample measurement and analysis are fully automated, requiring no input from the user. Cost of the system is minimized through the use of custom-designed motorized components. We compare the performance of our system, as operated by trained and untrained users, to the clinical gold standard on 120 adult blood samples, demonstrating agreement within Clinical Laboratory Improvement Amendments guidelines, with no statistical difference in performance among different operator groups. The system's cost-effectiveness, automation and performance indicate that it can be successfully translated for use in low-resource settings where central hematology laboratories are not accessible.

A new red cell index and portable RBC analyzer for screening of iron deficiency and Thalassemia minor in a Chinese population.

Anemia is a widespread public health problem with 1/4 ~1/3 of the world's population being affected. In Southeast Asia, Thalassemia trait (TT) and iron deficiency anemia (IDA) are the two most common anemia types and can have a serious impact on quality of life. IDA patients can be treated with iron supplementation, yet TT patients have diminished capacity to process iron. Therefore, distinguishing between types of anemia is essential for effective diagnosis and treatment. Here, we present two advances towards low-cost screening for anemia. First: a new red-cell-based index, Joint Indicator A, to discriminate between IDA, TT, and healthy children in a Chinese population. We collected retrospective data from 384 Chinese children and used discriminant function analysis to determine the best analytic function to separate healthy and diseased groups, achieving 94% sensitivity and 90% specificity, significantly higher than reported indices. This result is achieved using only three red cell parameters: mean cell volume (MCV), red cell distribution width (RDW) and mean cell hemoglobin concentration (MCHC). Our second advance: the development of a low cost, portable red cell analyzer to measure these parameters. Taken together, these two results may help pave the way for widespread screening for nutritional and genetic anemias.

Raman Plus X: Biomedical Applications of Multimodal Raman Spectroscopy.

Raman spectroscopy is a label-free method of obtaining detailed chemical information about samples. Its compatibility with living tissue makes it an attractive choice for biomedical analysis, yet its translation from a research tool to a clinical tool has been slow, hampered by fundamental Raman scattering issues such as long integration times and limited penetration depth. In this review we detail the how combining Raman spectroscopy with other techniques yields multimodal instruments that can help to surmount the translational barriers faced by Raman alone. We review Raman combined with several optical and non-optical methods, including fluorescence, elastic scattering, OCT, phase imaging, and mass spectrometry. In each section we highlight the power of each combination along with a brief history and presentation of representative results. Finally, we conclude with a perspective detailing both benefits and challenges for multimodal Raman measurements, and give thoughts on future directions in the field.