PRIMARY CENTRAL NERVOUS SYSTEM EFFUSION PLASMABLASTIC LYMPHOMA IN IMMUNOCOMPROMISED PATIENT: A RARE PHENOMENON

Primary effusion lymphoma (PEL) is an aggressive neoplasm with a high rate of fatality. PEL cells are known to have morphological diversities, which range from immunoblastic or plasmablastic to anaplastic. Most of these cases are described in immunocompromised as well as immunocompetent patients. Plasmablastic lymphoma remains a diagnostic challenge, especially when encountered with the presentation as PEL. In spite of therapeutic advances, PEL remains an aggressive disease with a high rate of fatality. We describe one case of this extremely rare neoplasm in an immunocompromised patient presenting in the form of primary central nervous system effusion plasmablastic lymphoma. To the best of our knowledge, this is the first case ever been reported in the literature

Correlation of morphologic and cytochemical diagnosis with flowcytometric analysis in acute leukemia

Introduction: The classification of acute leukemias has revolutionized over the years. Immunophenotyping of acute leukemia has gained popularity because of its influence on treatment and prognosis of the disease. The various antigens expressed by the leukemic cells can be assessed by flowcytometry (FCA) and can be used in rendering specific treatment and predicting the outcome of the different types of acute leukemia. Aims: The main aim of this study was to compare the morphologic and cytochemical diagnoses with flowcytometric diagnoses in acute leukemia and to analyze the usefulness of FCA over morphology. Results: In this study we analyzed 50 cases of acute leukemia and found concordance rate as high as 86% between morphologic/cytochemical diagnosis and flowcytometric diagnosis. Of these, complete concordance was seen in 58% of the cases and partial concordance was seen in 22% of the cases. Non-concordance was seen in only 4% of our cases. In remaining 16% of our cases FCA helped in sub classifying the acute leukemia where morphology and cytochemistry had failed to do so. CD19 and 20 were found to be consistent B-cell markers and CD3 was a very specific marker for T-cell leukemia. CD13 and 33 were important myeloid markers and were aided by other secondary panel of markers like CD14, CD117 and CD41. Conclusion: FCA not only helps in confirming morphologic diagnosis in acute leukemia but also helps in assigning specific lineage to the blasts, particularly in acute lymphoid leukemia. Immunophenotyping is of utmorst importance in classifying acute leukemia as it greatly influences the treatment and the prognosis

Machine Learning-Based Detection of Dengue from Blood Smear Images Utilizing Platelet and Lymphocyte Characteristics

Dengue fever, also known as break-bone fever, can be life-threatening. Caused by DENV, an RNA virus from the Flaviviridae family, dengue is currently a globally important public health problem. The clinical methods available for dengue diagnosis require skilled supervision. They are manual, time-consuming, labor-intensive, and not affordable to common people. This paper describes a method that can support clinicians during dengue diagnosis. It is proposed to automate the peripheral blood smear (PBS) examination using Artificial Intelligence (AI) to aid dengue diagnosis. Nowadays, AI, especially Machine Learning (ML), is increasingly being explored for successful analyses in the biomedical field. Digital pathology coupled with AI holds great potential in developing healthcare services. The automation system developed incorporates a blob detection method to detect platelets and thrombocytopenia from the PBS images. The results achieved are clinically acceptable. Moreover, an ML-based technique is proposed to detect dengue from the images of PBS based on the lymphocyte nucleus. Ten features are extracted, including six morphological and four Gray Level Spatial Dependance Matrix (GLSDM) features, out of the lymphocyte nucleus of normal and dengue cases. Features are then subjected to various popular supervised classifiers built using a ten-fold cross-validation policy for automated dengue detection. Among all the classifiers, the best performance was achieved by Support Vector Machine (SVM) and Decision Tree (DT), each with an accuracy of 93.62%. Furthermore, 1000 deep features extracted using pre-trained MobileNetV2 and 177 textural features extracted using Local binary pattern (LBP) from the lymphocyte nucleus are subjected to feature selection. The ReliefF selected 100 most significant features are then fed to the classifiers. The best performance was attained using an SVM classifier with 95.74% accuracy. With the obtained results, it is evident that this proposed approach can efficiently contribute as an adjuvant tool for diagnosing dengue from the digital microscopic images of PBS

Cold Agglutinins associated with Plasmodium falciparum malaria: A case report

Abstract- The combination of anemia in malarial infestations ranges from nutritional deficiency to marrow suppression. An immune hemolytic anemia in Plasmodium falciparum malaria is also a part of this wide-ranging spectrum. But the co-existence of cold agglutinins in a Plasmodium falciparum malaria has only sporadically been reported in literature. We present a case of a patient with falciparum malaria who developed severe anemia and jaundice at the time of presentation. The Coombs ’ test and cold agglutinin test were negative. This case underlines a rare association of cold agglutinins in Plasmodium falciparum malaria. Index Terms- Plasmodium falciparum; cold agglutinins; hemolytic anemia; jaundice

ISSN 2347-954X (Print) Malaria detection by automation: The Manipal experience

Abstract: Diagnosis of malaria has always been a diagnostic challenge in endemic areas. For many centuries microscopy based diagnosis has been a standard method for routine diagnosis of malaria. And still this is a routine diagnostic method used in low endemic areas which allows species identification. In endemic countries like India microscopy based diagnosis of malaria is still used and despite the presence of expert microscopists, laboratory misdiagnosis of malaria is still a problem. This may be due to immense work load, limited resources and manpower. The aim of the study was to confirm utility of the formula developed by Briggs et al and evaluate the feasibility of rapid diagnosis of malaria by using CBC data and malaria factor derived from standard deviation (SD) values of lymphocyte and monocyte by using haematology counter Beckman -Coulter series LH 750 and 755 TM . Three hundred and ten cases and controls were selected from samples sent to clinical lab for evaluation of fever and for routine examination. All cases and controls were scrutinised for malaria factor, thrombocytopenia, monocytosis, mean monocyte volume and pseudoeosinophilia. At cutoff value of 3.4, 97% sensitivity and 89% specificity was obtained. Detection of malaria by automated hematology counters my replace current screening methods for detection of malaria in future, but need of extensive study in different population is need for validation of this method