p-iodophenol-enhanced luminol chemiluminescent assay applied to discrimination between acute lymphoblastic and minimally differentiated acute myeloid (FAB-M0) or acute megakaryoblastic (FAB-M7) leukemias

Introduction: In this report, we propose the application of the p-iodophenol-enhanced luminol chemiluminescent technique to the determination of peroxidase (myeloperoxidase and/or platelet peroxidase) activity in blasts of minimally differentiated acute myeloblastic leukemia (AML-M0) and acute megakaryoblastic leukemia (AML-M7).Methods: the frozen blast cells from 29 patients were thawed and submitted to the optimized protocol.Results: All cases of AML-M7 and AML-M0 exhibited integrated light emission greater than 73 (10(2) mV x s), which was the arbitrary cutoff point set for the discrimination between AML and acute lymphoblastic leukemia (ALL) (mean + 3 x s.d. of ALL samples, n = 10). In addition, five out of seven cases of AML-M0 showed results above the Cutoff point.Conclusion: This highly sensitive enhanced chemiluminescent technique may be applied to discriminate between ALL and AML-M7 or AML-M1 cases, and most AML-M0 cases. It is very simple, cheap and easy to perform compared to other procedures used to measure MPO activity in AML-leukemias including AML-M7 and AML-M0.

Fractal descriptors for discrimination of microscopy images of plant leaves

This study proposes the application of fractal descriptors method to the discrimination of microscopy images of plant leaves. Fractal descriptors have demonstrated to be a powerful discriminative method in image analysis, mainly for the discrimination of natural objects. In fact, these descriptors express the spatial arrangement of pixels inside the texture under different scales and such arrangements are directly related to physical properties inherent to the material depicted in the image. Here, we employ the Bouligand-Minkowski descriptors. These are obtained by the dilation of a surface mapping the gray-level texture. The classification of the microscopy images is performed by the well-known Support Vector Machine (SVM) method and we compare the success rate with other literature texture analysis methods. The proposed method achieved a correctness rate of 89%, while the second best solution, the Co-occurrence descriptors, yielded only 78%. This clear advantage of fractal descriptors demonstrates the potential of such approach in the analysis of the plant microscopy images.