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Sideroblastic Anemia and Porphyrias
Published in Harold R. Schumacher, William A. Rock, Sanford A. Stass, Handbook of Hematologic Pathology, 2019
Hematologically, the anemia is usually normochromic and normocytic, but is often macrocytic with mean corpuscular volume exceeding 110 fL. Sometimes, leukopenia and thrombocytopenia occur, signifying a generalized disturbance of the bone marrow. Peripheral blood smears from afflicted individuals demonstrate anisocytosis and poikilocytosis of erythrocytes, giant macrocytes, occasional teardrop poikilocytes, basophilic stippling, and rarely Pappenheimer bodies (Fig. 2, top). Platelets are small or large, and demonstrate multiple vacuoles and either hypergranularity or hypogranularity (Fig. 2, bottom). Neutrophils may be decreased in number. Some display the pseudo-Pelger Huet anomaly of nuclear hyposegmentation (Fig. 3, top). Monocytes can be increased in number (Fig. 3, bottom).
SBA Answers and Explanations
Published in Vivian A. Elwell, Jonathan M. Fishman, Rajat Chowdhury, SBAs for the MRCS Part A, 2018
Vivian A. Elwell, Jonathan M. Fishman, Rajat Chowdhury
Reed–Sternberg cells are diagnostic for Hodgkin’s lymphoma. The Philadelphia chromosome and decreased quantities of leucocytes alkaline phosphatase are commonly observed in chronic myelogenous leukaemia. Auer rods are most often seen in increased numbers in acute myelogenous or myelomonocytic leukaemia. Pappenheimer bodies are abnormal iron granules found inside red blood cells. They are associated with sideroblastic anaemia, haemolytic anaemia, and sickle cell disease.
P
Published in Anton Sebastian, A Dictionary of the History of Medicine, 2018
Pappenheimer Bodies Iron granules found in erythrocytes in peripheral blood in cases of hemolytic anemia. Described by NewYork biochemist, Alwin Max Pappenheimer (1878–1955). He introduced treatment of rickets with cod liver oil (vitamin D) in 1920.
Recent evolutions of machine learning applications in clinical laboratory medicine
Published in Critical Reviews in Clinical Laboratory Sciences, 2021
Sander De Bruyne, Marijn M. Speeckaert, Wim Van Biesen, Joris R. Delanghe
Microscopic examination of stained blood films remains the gold standard for laboratory confirmation of malaria [70]. Nevertheless, microscopic quantification of parasitemia and classification of life cycle stage requires highly trained experts and is rather time-consuming [71]. Furthermore, accuracy is operator-dependent, making standardization complex, and reliability poor [70]. Several ML approaches have been documented in the quantification of parasitemia and discrimination of different species or parasite stages [71–73]. These systems were mainly designed to differentiate between infected and non-infected red blood cells (RBCs). However, Molina et al. [74] recently developed a framework, based on SVM and linear discriminant analysis, that distinguished RBCs infected with malaria from non-infected normal cells or RBCs with other inclusions (i.e. Howell–Jolly bodies, Pappenheimer bodies, basophilic stippling, and overlying platelets) with an excellent accuracy of 97.7%. In another study, Li et al. [75] presented a compact and low-cost automated microscopy platform ($250–$500) in combination with ML to detect Plasmodium falciparum parasites in stained blood smears. The system was able to screen >1.5 million erythrocytes/minute for parasitemia quantification with a simulated diagnostic sensitivity and specificity of more than 90% at a parasitemia level of 50/µL and 100% at a level higher than 150/µL. The low-cost character of the system could be of particular interest to the developing world. Next to the use of stained blood films, several alternative applications have been reported. A simple automatic sensing method using digital in-line holographic microscopy combined with SVM has been proposed to identify unstained malaria-infected RBCs with an accuracy of 97.5% [76]. Heraud et al. [77] demonstrated that infrared (IR) analysis of packed RBCs using a portable spectrometer and a cloud-based SVM system could have the potential to become an accurate POC tool for the diagnosis of malaria in endemic countries. Furthermore, another study [78] also employed IR spectroscopy in combination with supervised ML to screen for malaria parasites in human dried blood spots. The authors found that Logistic Regression appeared to be the best performing model with overall accuracies of 92% for predicting Plasmodium falciparum infections and 85% in predicting mixed infections of Plasmodium falciparum and Plasmodium ovale. At last, De Moraes et al. [79] identified significant differences in the profile of volatile samples, analyzed by gas chromatography–MS, between symptomatic and asymptomatic malaria patients. Predictive models based on ML algorithms were able to identify asymptomatic infections with 100% sensitivity, even when considering low-grade infections not detectable by standard light microscopic examination. Moreover, one of the models showed better performance in the detection of submicroscopic infections compared to the SD Bioline Rapid Diagnostic Test as well as a polymerase chain reaction (PCR)-based method. These results suggest an interesting role of ML and volatile biomarkers in the development of noninvasive and powerful screening methods for the detection of asymptomatic and symptomatic malaria infections under field conditions.