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Objective Semaphorin 3A (Sema3A) plays a regulatory role in immune responses. The aim of this study was to evaluate Sema3A levels in patients with systemic sclerosis (SSc), especially in major vascular involvements such as digital ulcer (DU), scleroderma renal crisis (SRC), pulmonary arterial hypertension (PAH), and to compare Sema3A level with SSc disease activity. Methods In SSc patients, patients with DU, SRC, or PAH were grouped as major vascular involvements and those without as nonvascular, and Sema3A levels were compared between the groups and with a healthy control group. The Sema3A levels and acute phase reactants in SSc patients, as well as their association with the Valentini disease activity index and modified Rodnan skin score, were evaluated. Results The Sema3A values (mean ± SD) were 57.60 ± 19.81 ng/mL in the control group (n = 31), 44.32 ± 5.87 ng/mL in patients with major vascular involvement SSc (n = 21), and 49.96 ± 14.00 ng/mL in the nonvascular SSc group (n = 35). When all SSc patients were examined as a single group, the mean Sema3A value was significantly lower than controls (P = .016). The SSc with major vascular involvement group had significantly lower Sema3A levels than SSc with nonmajor vascular involvement group (P = .04). No correlation was found between Sema3A, acute phase reactants, and disease activity scores. Also, no relationship was observed between Sema3A levels and diffuse (48.36 ± 11.47 ng/mL) or limited (47.43 ± 12.38 ng/mL) SSc types (P = .775). Conclusion Our study suggests that Sema3A may play a significant role in the pathogenesis of vasculopathy and can be used as a biomarker in SSc patients with vascular complications such as DU and PAH.

Container technology provides a lightweight operating system level virtual hosting environment. Its emergence profoundly changes the development and deployment paradigms of multi-tier distributed applications. However, due to the incomplete implementation of system resource isolation mechanisms in the Linux kernel, some security concerns still exist for multiple containers sharing an operating system kernel on a multi-tenancy container-based cloud service. In this paper, we first present the information leakage channels we discovered that are accessible within containers. Such channels expose a spectrum of system-wide host information to containers without proper resource partitioning. By exploiting such leaked host information, it becomes much easier for malicious adversaries (acting as tenants in a container cloud) to launch attacks that might impact the reliability of cloud services. We demonstrate that the information leakage channels could be exploited to infer private data, detect and verify co-residence, build covert channels, and launch more advanced cloud-based attacks. We discuss the root causes of the containers’ information leakage and propose a two-stage defense approach. As demonstrated in the evaluation, our defense is effective and incurs trivial performance overhead.

Thrombotic Thrombocytopenic Purpura is a syndrome of microangiopathic hemolytic anemia accompanied by thrombocytopenia, neurological disorders, renal failure and fever. Acute pancreatitis is a rare cause of Thrombotic Thrombocytopenic Purpura and this manifestation, at the same time, is a rare complication of acute pancreatitis. Thrombotic Thrombocytopenic Purpura is induced in acute pancreatitis by poorly understood mechanism, which involves multiple pathways apart from only ADAMTS-13 deficiency. Here, we analyze the case of a 47-year-old female who presented with an acute pancreatitis. She was diagnosed with Thrombotic Thrombocytopenic Purpura and an acute pancreatitis at the same time, with thrombocytopenia and peripheral smear findings at presentation. Therefore, Thrombotic Thrombocytopenic Purpura secondary to the pancreatitis was considered in this case. In this work, we have discussed details of our case and the different mechanisms involved in pathogenesis of Thrombotic Thrombocytopenic Purpura in acute pancreatitis and their outcome with prompt management.

Many modern entity recognition systems, including the current state-of-the-art de-identification systems, are based on bidirectional long short-term memory (biLSTM) units augmented by a conditional random field (CRF) sequence optimizer. These systems process the input sentence by sentence. This approach prevents the systems from capturing dependencies over sentence boundaries and makes accurate sentence boundary detection a prerequisite. Since sentence boundary detection can be problematic especially in clinical reports, where dependencies and co-references across sentence boundaries are abundant, these systems have clear limitations. In this study, we built a new system on the framework of one of the current state-of-the-art de-identification systems, NeuroNER, to overcome these limitations. This new system incorporates context embeddings through forward and backward n -grams without using sentence boundaries. Our context-enhanced de-identification (CEDI) system captures dependencies over sentence boundaries and bypasses the sentence boundary detection problem altogether. We enhanced this system with deep affix features and an attention mechanism to capture the pertinent parts of the input. The CEDI system outperforms NeuroNER on the 2006 i2b2 de-identification challenge dataset, the 2014 i2b2 shared task de-identification dataset, and the 2016 CEGS N-GRID de-identification dataset ( p < 0.01 ). All datasets comprise narrative clinical reports in English but contain different note types varying from discharge summaries to psychiatric notes. Enhancing CEDI with deep affix features and the attention mechanism further increased performance.

De-identification of protected health information is an essential method for protecting patient privacy. Most institutes require de-identification of patient data prior to conducting scientific studies; therefore, it is important for clinical scientists to be cognizant of all modes of de-identification and all services provided by their de-identification tools. In this article, we discuss eight different modes of de-identification that yield de-identified data at different levels of quality. Most of these modes can be used in combination to achieve the best performance.