Chronic granulomatous disease presenting with small bone osteomyelitis in a young child: A case report.

Chronic granulomatous disease (CGD) is a life threatening inherited disorder with varied clinical presentations often characterized by recurrent bacterial and fungal infections along with widespread granulomatous tissue response. The disease results from phagocytic defects characterized by deficiencies in oxidative burst of neutrophils. Nitroblue tetrazolium reduction test (NBT) and Dihydrorhodamine (DHR) with PMA stimulation by flow cytometry are quick, simple, sensitive and specific laboratory tests that help establish early and reliable diagnosis of CGD with an overall improvement in survival and disease prognosis. We report a case of 2-year old child who presented with small bone osteomyelitis involving bilateral feet and was later diagnosed to have autosomal recessive CGD due to mutation in NCF1 gene.

Flow Cytometric Eosin-5'-Maleimide Test is a Sensitive Screen for Hereditary Spherocytosis.

Hereditary spherocytosis (HS) is a clinically heterogeneous disease characterized by mild to moderate hemolysis resulting from red cell membrane protein defects. Diagnostic tests include hemogram, reticulocyte count and blood smear evaluation, osmotic fragility, cryohemolysis, SDS-PAGE, flow cytometry using eosin-5'-maleimide (EMA) and genetic studies. We evaluated the flow cytometric EMA-binding test and compared it with osmotic fragility in 51 consecutive cases of suspected HS aged between 10 days and 62 years. In addition, 4 cases suspected on blood smears underwent EMA testing alone. The 16 EMA-positive cases who were determined to have HS had overlapping hemoglobin levels and reticulocyte counts with the 35 patients with normal EMA results, highlighting the importance of the flow cytometric test in providing a definitive diagnosis. Flow cytometric EMA binding test was thus a simple and relatively faster method to confirm HS in our experience.

Pilot Study on the Performance of a New System for Image Based Analysis of Peripheral Blood Smears on Normal Samples.

Image analysis based automated systems aiming to automate the manual microscopic review of peripheral blood smears have gained popularity in recent times. In this paper, we evaluate a new blood smear analysis system based on artificial intelligence, Shonit™ by SigTuple Technologies Private Limited. One hundred normal samples with no flags from an automated haematology analyser were taken. Peripheral blood smear slides were prepared using the autostainer integrated with an automated haematology analyser and stained using May-Grunwald-Giemsa stain. These slides were analysed with Shonit™. The metrics for evaluation included (1) accuracy of white blood cell classification for the five normal white blood cell types, and (2) comparison of white blood cell differential count with the automated haematology analyser. In addition, we also explored the possibility of estimating the value of red blood cell and platelet indices via image analysis. Overall white blood cell classification specificity was greater than 97.90% and the precision was greater than 93.90% for all the five white blood cell classes. The correlation of the white blood cell differential count between the automated haematology analyser and Shonit™ was found to be within the known inter cell-counter variability. Shonit™ was found to show promise in terms of its ability to analyse peripheral blood smear images to derive quantifiable metrics useful for clinicians. Future enhancement should include the ability to analyse abnormal blood samples.