Consolidation of P2Y12 Testing While Maintaining Quality and Turnaround Time

Objective: To consolidate the test performed at 2 different locations at 1, thereby improving cost effectiveness while maintaining quality and result turnaround time.https://jdc.jefferson.edu/patientsafetyposters/1059/thumbnail.jp

Primary CNS small mature B-cell lymphoma with plasmacytic differentiation presenting as an amyloidoma: a case report and review of literature

Primary central nervous system lymphomas (PCNSL) without systemic involvement are rare and account for only 2-3% of all brain tumors and \u3c1% of all non-Hodgkin’s lymphoma (NHL). Close to 40% of PCNSL are associated with immunosuppression, however, the incidence of primary central nervous system (CNS) lymphomas has shown an increasing trend in immunocompetent patients in recent decades due to better control of HIV and drug-induced immunosuppression [2]. Here, we describe a case of a primary CNS non-Hodgkin’s small mature B-cell lymphoma with plasmacytic differentiation in an immunocompetent individual. A previously healthy 87-year-old Caucasian woman presented to the neurology clinic with complaints of slowly progressing left sided weakness, predominantly in the left arm and leg over the last 6 months. Magnetic resonance imaging of the brain revealed a large, confluent white matter T2-hyperintensity in the right frontal lobe with multifocal nodular enhancement involving the left cerebral hemisphere, cerebellum, and leptomeninges, consistent morphologically with lymphoplasmacytic lymphoma. A bone marrow biopsy showed normal trilineage hematopoiesis with no evidence of lymphoma, myeloma or amyloidosis. Our patient was treated with Rituximab but developed an ischemic infarct of the left frontal white matter. She and her family decided to forego further treatment and switch to hospice care

Clinico-pathological features and PD-1/PD-L1 Expression in Primary Mediastinal Large B Cell Lymphoma

Primary Mediastinal Large B Cell Lymphoma (PMBCL) is a distinct subtype of Diffuse Large B Cell Lymphoma (DLBCL) that has been historically reported to have a worse prognosis than DLBCL. Occasional studies have reported PD-L1 expression in PMBCL, which can emerge as an important target for immune-check point therapy. This study aimed to evaluate clinico-pathological features and characterize the expression of PD-1 and PD-L1 in a single cohort of 15 patients with PMBCL. A total of 15 cases of PMBCL were retrieved from records of the department of Pathology; eleven of these had tissue available for additional immunohistochemistry, specifically, PD-L1 (clone SP142) and PD-1 (clone NAT105). A cut-off of ≥30% was used for PD-1 and PDL-1 expression in tumor cells, and ≥20% for tumor-infiltrating lymphocytes (TILs) and tumor-associated macrophages (TAMs). The median age was 42 years (23-83 years), and 9 of 15 (60%) patients were females. Of the 8 patients with clinical data, three (38%) received aggressive R-EPOCH therapy and responded. Tumor cells showed positive PD-L1 expression in only 1 case (9%), and TAMs showed positive PDL-1 expression in seven cases (64%). None of the cases analyzed showed positive PD-1 expression in TCs, while four cases (36%) showed positive PD-1 expression in TILs

Multiple tumor suppressors regulate a HIF-dependent negative feedback loop via ISGF3 in human clear cell renal cancer.

Whereas VHL inactivation is a primary event in clear cell renal cell carcinoma (ccRCC), the precise mechanism(s) of how this interacts with the secondary mutations in tumor suppressor genes, including PBRM1, KDM5C/JARID1C, SETD2, and/or BAP1, remains unclear. Gene expression analyses reveal that VHL, PBRM1, or KDM5C share a common regulation of interferon response expression signature. Loss of HIF2α, PBRM1, or KDM5C in VHL-/-cells reduces the expression of interferon stimulated gene factor 3 (ISGF3), a transcription factor that regulates the interferon signature. Moreover, loss of SETD2 or BAP1 also reduces the ISGF3 level. Finally, ISGF3 is strongly tumor-suppressive in a xenograft model as its loss significantly enhances tumor growth. Conversely, reactivation of ISGF3 retards tumor growth by PBRM1-deficient ccRCC cells. Thus after VHL inactivation, HIF induces ISGF3, which is reversed by the loss of secondary tumor suppressors, suggesting that this is a key negative feedback loop in ccRCC. © 2018, Liao et al

Privacy-preserving data imputation

In this paper, we investigate privacy-preserving data imputation on distributed databases. We present a privacypreserving protocol for filling in missing values using a lazy decision tree imputation algorithm for data that is horizontally partitioned between two parties. The participants of the protocol learn only the imputed values; the computed decision tree is not learned by either party. 1

Data privacy in knowledge discovery

This thesis addresses data privacy in various stages of extracting knowledge embedded in databases. Advances in computer networking and database technologies have enabled the collection and storage of vast quantities of data. Legal and ethical considerations might require measures to protect an individual's privacy in any use or release of the data. In this thesis, we address the problem of preserving privacy in the two following cases: (1) in distributed knowledge discovery; (2) in situations where the output of a data mining algorithm could itself breach privacy. We present results in two different models, namely secure multiparty computation (SMC) and differential privacy. The first part of the thesis presents privacy preserving protocols in the SMC model. Secure multiparty computation involves the collaborative computation of functions based on inputs from multiple parties. The privacy goal is to ensure that all parties receive only the final output without any party learning anything beyond what can be inferred from the output. Within this framework we address the problem of preserving privacy in the preprocessing and the data mining stages of knowledge discovery in databases. For the preprocessing stage, we present private protocols for the imputation of missing data in a dataset that is shared between two parties. For the data mining stage, we introduce the notion of arbitrarily partitioned data that generalizes both horizontally and vertically partitioned data. We present a privacy-preserving protocol for k-means clustering of arbitrarily partitioned data. We also develop a new simple k-clustering algorithm that was designed to be converted into a communication-efficient protocol for private clustering. The second part of the thesis deals with privacy in situations where the output of a data mining algorithm could itself breach privacy. In this setting, we present private inference control protocols in the SMC model for On-line Analytical Processing systems. In the differential privacymodel, the goal is to provide access to a statistical database while preserving the privacy of every individual in the database, irrespective of any auxiliary information that may be available to the database client. Under this privacy model, we present a practical privacy preserving decision tree classifier using random decision trees.Ph.D.Includes abstractVitaIncludes bibliographical referencesby Geetha Jagannatha

Private Inference Control for Aggregate Database Queries

Data security is a critical issue for many organizations. Sensitive data must be protected from both inside and outside attackers. Access control policies and related mechanisms have been used for several decades to prevent unauthorized users from accidentally or deliberately extracting sensitive information. However, access control mechanisms alone cannot ensure the security of a database. An authorized user might invoke a sequence of queries, each of which is under his privileges, but whose results can be combined to infer some additional information about the data. Various “inference control ” methods have been developed in the past to prevent users from inferring sensitive information through a sequence of queries. Private inference control provides privacy properties to both database owners and users making queries. It protects the database owner by limiting access to the data according to a specified inference control policy, but also to protect the user by preventing the database owner from learning anything about the user’s queries. In this paper, we study private inference control for aggregate queries, such as those provided by statistical databases or modern database languages, to a database in a way that satisfies both privacy requirements and inference control requirements. For each query, the client learns the value of the function for that query if and only if the query passes a specified inference control rule. The server learns nothing about the queries, and the client learns nothing other than the query output for passing queries. We present general protocols for aggregate queries with private inference control. We also present more efficient protocols for two important types of aggregate query: SUM and COUNT.

Privacy-preserving imputation of missing data

Handling missing data is a critical step to ensuring good results in data mining. Like most data mining algorithms, existing privacy-preserving data mining algo-rithms assume data is complete. In order to maintain privacy in the data mining process while cleaning data, privacy-preserving methods of data cleaning will be required. In this paper, we address the problem of privacy-preserving data imputa-tion of missing data. Specifically, we present a privacy-preserving protocol for filling in missing values using a lazy decision tree imputation algorithm for data that is horizontally partitioned between two parties. The participants of the protocol learn only the imputed values; the computed decision tree is not learned by either party