Abstract:With the widespread use of electronic health record (EHR), building a secure EHR sharing environment has attracted a lot of attention in both healthcare industry and academic community. Cloud computing paradigm is one of the popular healthIT infrastructure for facilitating EHR sharing and EHR integration. In this paper we discuss important concepts related to EHR sharing and integration in healthcare clouds and analyze the arising security and privacy issues in access and management of EHRs. We describe an EHR security reference model for managing security issues in healthcare clouds, which highlights three important core components in securing an EHR cloud. We illustrate the development of the EHR security reference model through a use-case scenario and describe the corresponding security countermeasures and state of art security techniques that can be applied as basic security guards.

Membership inference attacks seek to infer membership of individual training instances of a model to which an adversary has black-box access through a machine learning-as-a-service API. In providing an in-depth characterization of membership privacy risks against machine learning models, this paper presents a comprehensive study towards demystifying membership inference attacks from two complimentary perspectives. First, we provide a generalized formulation of the development of a black-box membership inference attack model. Second, we characterize the importance of model choice on model vulnerability through a systematic evaluation of a variety of machine learning models and model combinations using multiple datasets. Through formal analysis and empirical evidence from extensive experimentation, we characterize under what conditions a model may be vulnerable to such black-box membership inference attacks. We show that membership inference vulnerability is data-driven and corresponding attack models are largely transferable. Though different model types display different vulnerabilities to membership inference, so do different datasets. Our empirical results additionally show that (1) using the type of target model under attack within the attack model may not increase attack effectiveness and (2) collaborative learning exposes vulnerabilities to membership inference risks when the adversary is a participant. We also discuss countermeasure and mitigation strategies.

Background-Despite increasing appreciation that atherogenesis involves participation of inflammatory cells, information on mediators of communication between different constituents of atherosclerotic plaque remain incomplete. We examined the role of LOX-1, a receptor for oxidized (ox) LDL, in the expression of CD40/CD40L in cultured human coronary artery endothelial cells (HCAECs). Methods and Results-We observed that ox-LDL increased the expression of CD40 and CD40L in a concentration (10 to 80 g/mL)-and time (1 to 24 hours)-dependent manner. These effects of ox-LDL were mediated by activation of LOX-1, because pretreatment of HCAECs with a blocking antibody to LOX-1 (JTX92) prevented the expression of CD40 and CD40L in response to ox-LDL (PϽ0.01). In parallel experiments, HCAECs were incubated with the protein kinase C (PKC) inhibitor bisindolylmaleimide I, and the cells were then exposed to ox-LDL. Both LOX-1 antibody and the PKC inhibitor inhibited PKC activation in response to ox-LDL (PϽ0.01). The PKC inhibitor also blocked the effects of ox-LDL on the expression of CD40 and CD40L (PϽ0.01). In additional experiments, we found that it is the PKC␣, but not PKC␤ and PKC␥, isoform that mediated ox-LDL-induced CD40 and CD40L upregulation. Further experiments showed that upregulation of CD40 mediated induction of proinflammatory genes, because CD40 antibody markedly reduced ox-LDL-induced TNF-␣ generation and P-selectin expression, whereas nonspecific mouse IgG had no effect. Conclusions-These findings indicate that ox-LDL through its receptor LOX-1 triggers the CD40/CD40L signaling pathway that activates the inflammatory reaction in HCAECs. These observations provide novel insight into ox-LDL-mediated inflammation in atherosclerosis.

BackgroundAlthough the problem-based learning (PBL) emerged in 1969 and was soon widely applied internationally, the rapid development in China only occurred in the last 10 years. This study aims to compare the effect of PBL and lecture-based learning (LBL) on student course examination results for introductory Chinese undergraduate medical courses.MethodsRandomized and nonrandomized controlled trial studies on PBL use in Chinese undergraduate medical education were retrieved through PubMed, the Excerpta Medica Database (EMBASE), Chinese National Knowledge Infrastructure (CNKI) and VIP China Science and Technology Journal Database (VIP-CSTJ) with publication dates from 1st January 1966 till 31 August 2014. The pass rate, excellence rate and examination scores of course examination were collected. Methodological quality was evaluated based on the modified Jadad scale. The I-square statistic and Chi-square test of heterogeneity were used to assess the statistical heterogeneity. Overall RRs or SMDs with their 95% CIs were calculated in meta-analysis. Meta-regression and subgroup meta-analyses were also performed based on comparators and other confounding factors. Funnel plots and Egger’s tests were performed to assess degrees of publication bias.ResultsThe meta-analysis included 31studies and 4,699 subjects. Fourteen studies were of high quality with modified Jadad scores of 4 to 6, and 17 studies were of low quality with scores of 1 to 3. Relative to the LBL model, the PBL model yielded higher course examination pass rates [RR = 1.09, 95%CI (1.03, 1.17)], excellence rates [RR = 1.66, 95%CI (1.33, 2.06)] and examination scores [SMD = 0.82, 95%CI (0.63, 1.01)]. The meta-regression results show that course type was the significant confounding factor that caused heterogeneity in the examination-score meta-analysis (t = 0.410, P<0.001). The examination score SMD in “laboratory course” subgroup [SMD = 2.01, 95% CI: (1.50, 2.52)] was higher than that in “theory course” subgroup [SMD = 0.72, 95% CI: (0.56, 0.89)].ConclusionsPBL teaching model application in introductory undergraduate medical courses can increase course examination excellence rates and scores in Chinese medical education system. It is more effective when applied to laboratory courses than to theory-based courses.