No abstract

No abstract

Atypical chronic myeloid leukemia (aCML) is a rare disease that is currently classified under the myelodysplastic (MDS)/myeloproliferative neoplasm (MPN) disease spectrum. MDS/MPN diseases are characterized by the absence of the Philadelphia (Ph) chromosome and the overlap between bone marrow fibrosis and dysplastic features. The Ph chromosome, resulting from BCR-ABL1 translocation, helps to distinguish aCML from chronic myeloid leukemia (CML). The currently reported incidence of aCML is imprecise because aCML is diagnosed primarily based on morphological features and other unspecified laboratory findings, and there is an especially high chance of under-diagnosis of aCML and other MDS/MPN diseases. Recent advances in next-generation sequencing (NGS) have allowed a greater understanding of the nature of aCML, providing better opportunities to achieve higher diagnostic accuracy and for the use of more targeted treatment to achieve better outcomes. Herein, we present a case of a 68-year-old woman who came to our hospital complaining of shortness of breath, fatigue, and weakness, who was found to have significantly increased leukocytosis, hepatosplenomegaly, and was negative for the Ph chromosome. Further investigations with NGS revealed mutations in ASXL1, GATA2, NRAS, and SRSF2 but not CSF3R. In addition to this, peripheral smear and bone marrow aspiration findings were suggestive of aCML based on specific morphological findings. Since the patient was ineligible for a stem cell transplant (SCT), symptomatic treatment was started with cell transfusion; however, the patient continued to have symptomatic anemia that required multiple transfusions. A trial with trametinib, a mitogen-activated protein kinase kinase (MEK) inhibitor, was later started as a targeted therapy based on one of her genetic mutations. Interestingly, the patient's blood counts stabilized, she reported feeling better, and she did not need any blood transfusions for four consecutive months during treatment with trametinib. Unfortunately, our patient later died from sepsis resulting from secondary infections. In light of the significant advancements in NGS, clinicians should always consider utilizing it as a helpful tool to not only establish a rare diagnosis of aCML but also to offer the best available targeted therapy when applicable. This might alleviate the burden associated with the poor prognosis of aCML.

No abstract

As businesses embrace digitization, the Internet of Everything (IoE) begins to take shape and the Cloud continues to empower new innovations for big data-at the heart, Cloud analytic applications gain increasing momentum. Such applications have remarkable benefits for big data processing, making it easy, fast, scalable, and cost-effective; albeit, they pose many security risks. Security breaches causing anomalous activities due to malicious, vulnerable, or misconfigured analytic applications are considered the top security risks to big ''sensitive'' data. The risk is further expanded from the coupling of data analytics with the Cloud. Towards maintaining secure and trustworthy applications, effective anomaly detection and prediction become crucial tasks to be offered by Cloud providers. This paper presents, PredictDeep, a novel security analytics framework for anomaly detection and prediction. The proposed framework leverages log data collected from monitoring systems with graph analytics and deep learning techniques to add intelligence for detecting and predicting known and unknown patterns of security anomalies. It represents the collected data and transforms them into a graph model. The graph model captures the analytical activities as well as their interrelation. In this sense, such a model provides informed insight of the monitored application, understanding its behavior, and revealing anomalous patterns. Different from existing traditional rule-based machine learning and statistics-based approaches, our solution takes the benefits of incorporating not only available node attributes but also graph structure and context information to extract rich features that boost the anomaly classification and prediction. We leverage graph embeddings to represent the nodes and relationships in the graph model as feature vectors to learn and predict anomalies in an inductive way utilizing recent advanced deep graph neural network techniques. This design augments our solution with robustness and computational efficiency. Extensive experiments are conducted over an open-source Hadoop log dataset. The evaluation results demonstrate that PredictDeep is a viable solution and very effective.