A 21-year-old man presented to the emergency department with generalised weakness, weight loss and decreased appetite for few weeks. He had evidence of severe pancytopenia and haemolysis. His peripheral smear with many schistocytes was suspicious for thrombotic thrombocytopenic purpura (TTP). He was supported with blood transfusions and daily plasmapheresis. His platelet counts worsened despite 4 days of therapy. Bone marrow biopsy was significant for hypercellular bone marrow with megaloblastic changes. Further workup revealed normal ADAMTS13 level, low vitamin B12, positive intrinsic factor antibodies and high methylmalonic acid. Diagnosis of pernicious anaemia was established and he was started on daily treatment with intramuscular vitamin B12 which subsequently improved his symptoms and haematological parameters. This report highlights the importance of checking vitamin B12 level in patients presenting with pancytopenia and TTP-like picture before making a diagnosis of TTP.

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Background: The identification and authentication of Chinese herbal medicines (CHMs) are directly related to their safety and efficacy in clinical treatment. However, the limited number of qualified professionals with expertise fails to meet the demand of the vast CHMs market. To make the CHMs identification more convenient and accurate, this study aimed at assessing the feasibility of the state-of-art automated machine learning (AutoML) technology in CHMs image recognition.Methods: This study presented an experimental AutoML model built on the one-stop Huawei ModelArts platform instead of a handcrafted neural network. A rich and representative dataset of 31,460 images consisting of 315 categories of commonly-used CHMs was built and used for the model creation. Furthermore, the Huawei ModelArts model was compared with a model built on the Baidu EasyDL platform using the same dataset to investigate their ability to recognize CHMs images. Three professionals were also invited to recognize images of 315 categories of CHMs.Results: During the model evaluation, high accuracies of 99.2% and 98.4% were achieved by ModelArts and EasyDL, respectively. In the subsequent held-out tests, the accuracies of ModelArts and EasyDL models were 91.2% and 91.85%, respectively. Both models performed very well individually and no statistically significant difference was found in model performance between these two platforms. However, the model-training time was only approximately 41 minutes on ModelArts platform but 118 minutes on EasyDL. The mean accuracy of the manual recognition for 315 CHMs was 97.46±1.58%.Conclusion: Results revealed that AutoML technology is a fast and simple approach and has great practical potential in the field of CHMs image recognition. Since the Huawei ModelArts platform requires less training time, we recommend it as a priority.