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.


Results of QDA analysis of different combinations of RBC parameters
All possible combinations of RBC, HGB, MCV, MCH, MCHC and RDW analyzed via QDA for separating healthy and anemia and separating IDA and TT are as follows:

Physical instrument, and description of the scattering measurement processing flow.
Our as-built prototype is shown in Figure S1. Laser sources (image right) are coupled into single mode fibers, and then combined and directed by a series of mirrors onto the sample (image left), then imaged onto a board-level CCD camera. We note that the instrument as currently built is composed primarily of empty space, and thus future iterations are expected to be substantially smaller, enabling portable testing.
As described in the main text, this system acquires images of the scattered intensity versus angle. These are then analyzed via a custom analysis routine to extract the RBC parameters through Figure S1 -Actual system. comparison with Mie theory. Here we describe the detailed data processing required to extract these parameters.
The purpose of our analysis process is to find the best fit between the theoretical scattering patterns and experimental data. In order to achieve this goal, the first important thing is to setup a database of theoretical Mie scattering patterns for a certain range of sizes for both laser wavelengths.
This database provides the scattering from red blood cells over a wide size range and refractive index range, across two wavelengths. We used in-house MATLAB scripts to generate the theoretical scattering curves based on established Mie theory. In order to make our calculation process more efficient, we need to set an optimal search range and interval for the size. Normal red blood cells have about 5.5 μm as a spherical diameter, so we choose a size range from 3.5μm to 6.5 μm with 1 nm step resolution. As shown in Figure 3E in the main text, each pixel in the recorded image has an associated angle value. The angle range for the theoretical calculations is exactly the angles measured by our experimental system. The refractive index of the spheres is another important parameter for the calculation. As discussed by Friebel and Meinke in 2006 (ref 45 in the main text), the refractive index of blood is linearly related to the MCHC through the following equation: where is the wavelength and concentration dependent refractive index for hemoglobin for the given wavelength , and represents the MCHC. The function n(λ) is the refractive index of water at a given wavelength, and β(λ) is a wavelength-dependent refractive increment tabulated by Friebel and Meinke. For our theoretical database, we calculated theoretical scattering from blood cells with MCHC values in the range from 200 g/L to 400 g/L with 1 g/L steps. Then, we can calculate a 3D matrix (r, ( ), θ) of theoretical Mie scattering patterns for the two wavelengths, resulting in a 3001 ×201 ×570 matrix theoretical Mie scattering data, where r is the sphere radius. The processing flowchart for comparison between experiment and theory is laid out in Figure S2. The theoretical data contained 3001×201 Mie scattering curves based on 3001×201 different size and refractive index pairs which the particle size corresponds to the MCV, and the refractive index to the MCHC, as discussed in Friebel and Mieinke. For each experimental measurement, a subregion of the raw images (see Fig. 3D) is extracted and averaged to form a 1D curve of scattering intensities versus scattering angle that can be fit to theoretical database. By varying the size value and refractive index value, we want to find the best particle size distribution whose theoretical scattering most closely matches the experimental scattering pattern.
To start the fitting process, we first determine the mean cell volume and MCHC by comparing the height and position of the first two peaks of the experimental data.
Step 1 is to find the exact angular position of the first two maxima of the scattering curve. Because of noise and pixilation of our detector, we fit a small region around each maximum to a 5 th order polynomial. The location and maximum intensity of each maximum is then determined for both the experimental data and theoretical data, as shown in Figure S3A. We can compute the ratio of these intensities for the experimental data and for the theoretical database. However, due to experimental discrepancies this method works best with the 405 nm illumination wavelength, and has larger errors with the 655nm wavelength. The reason for this additional error in the 655nm wavelength is simply due to the fact Figure S2 -Fitting process between experiment and theory. Gray elements are input data, blue boxes are analytical operations, and green elements are output red cell parameters.

Figure S3
-Analysis of experimental data: (A) determining the location and peak heights of the first two peaks in the experimental curve; (B) comparing the ratio of the peak heights to the ratios (for single particles) of the entire theoretical database.
that the 655 laser was substantially weaker than the 405 nm laser (5mW vs. 20mW), and of course the scattering cross section of the particles decreases with increasing wavelength. These two factors combine to yield lower SNR for the 655nm laser compared to the 405 nm data (~16@655nm vs ~25@405nm). Further, the 655nm pattern is characterized by a wider fringe spacing with less prominent peaks and troughs. These combine to make it less robust than the 405nm data for the "peak-finding" step of the algorithm. However, the use of a second wavelength helps us in later stages of the algorithm when the sum squared error between experiment and theory is computed.
This is because since the refractive index of the hemoglobin changes vs. wavelength, use of an additional wavelength provides independent information about the best combination of mean size and refractive index compared to one wavelength alone. 655 nm specifically was chosen only due to the availability of low-cost sources at this wavelength due to its use in CD players. In the future we plan to explore other cheaply available wavelengths (eg: 532 nm) to improve the robustness of the system. Following the successful fitting for the 405nm data, we then select every scattering curve in the theoretical database whose ratio (at 405nm) is similar to the experimental data, as shown in Figure S3B. This results in a vector of possible MCHC and MCV pairs. The theoretical data for identified MCHC-MCV pair is fit to the experimental data, with the best fit determining the MCHC and MCV. However, as we can see in the main text, the MCHC is the least accurately determined parameter. This is due to a sample dependent background on the data that corrupts the computation of the intensity ratio. We believe that this background may be due, in part, to scattering by platelets.
As seen in Figure S4, scattering from a hypothetical anemic donor with an MCHC of 31 g/dL, MCV