Red Blood Cell Microparticles Limit Hematoma Growth in Intracerebral Hemorrhage.

BACKGROUND: Spontaneous intracerebral hemorrhage (sICH) is the deadliest stroke subtype with no effective therapies. Limiting hematoma expansion is a promising therapeutic approach. Red blood cell-derived microparticles (RMPs) are novel hemostatic agents. Therefore, we studied the potential of RMPs in limiting hematoma growth and improving outcomes post-sICH. METHODS: sICH was induced in rats by intrastriatal injection of collagenase. RMPs were prepared from human RBCs by high-pressure extrusion. Behavioral and hematoma/lesion volume assessment were done post-sICH. The optimal dose, dosing regimen, and therapeutic time window of RMP therapy required to limit hematoma growth post-sICH were determined. We also evaluated the effect of RMPs on long-term behavioral and histopathologic outcomes post-sICH. RESULTS: RMP treatment limited hematoma growth following sICH. Hematoma volume (mm3) for vehicle- and RMP- (2.66×1010 particles/kg) treated group was 143±8 and 86±4, respectively. The optimal RMP dosing regimen that limits hematoma expansion was identified. RMPs limit hematoma volume when administered up to 4.5-hour post-sICH. Hematoma volume in the 4.5-hour post-sICH RMP treatment group was lower by 24% when compared with the control group. RMP treatment also improved long-term histopathologic and behavioral outcomes post-sICH. CONCLUSIONS: Our results demonstrate that RMP therapy limits hematoma growth and improves outcomes post-sICH in a rodent model. Therefore, RMPs have the potential to limit hematoma growth in sICH patients.

Automated Assessment of Hematoma Volume of Rodents Subjected to Experimental Intracerebral Hemorrhagic Stroke by Bayes Segmentation Approach.

Simulating a clinical condition of intracerebral hemorrhage (ICH) in animals is key to research on the development and testing of diagnostic or treatment strategies for this high-mortality disease. In order to study the mechanism, pathology, and treatment for hemorrhagic stroke, various animal models have been developed. Measurement of hematoma volume is an important assessment parameter to evaluate post-ICH outcomes. However, due to tissue preservation conditions and variables in digitization, quantification of hematoma volume is usually labor intensive and sometimes even subjective. The objective of this study is to develop an automated method that can accurately and efficiently obtain unbiased cerebral hematoma volume. We developed an application (MATLAB program) that can delineate the brain slice from the background and use the Hue information in the Hue/Saturation/Value (HSV) color space to segment the hematoma region. The segmentation threshold of Hue is calculated based on the Bayes classifier theorem so that the minimum error is mathematically ensured and automated processing is enabled. To validate the developed method, we compared the outcomes from the developed method with the hemoglobin content by the spectrophotometric assay method. The results were linearly correlated with statistical significance. The method was also validated by digital phantoms with an error less than 5% compared with the ground truth from the phantoms. Hematoma volumes yielded by the automated processing and those obtained by the operator's manual operation are highly correlated. This automated segmentation approach can be potentially used to quantify hemorrhagic outcomes in rodent stroke models in an unbiased and efficient way.