Transforming Healthcare Cybersecurity from Reactive to Proactive: Current Status and Future Recommendations.

The recent rise in cybersecurity breaches in healthcare organizations has put patients' privacy at a higher risk of being exposed. Despite this threat and the additional danger posed by such incidents to patients' safety, as well as operational and financial threats to healthcare organizations, very few studies have systematically examined the cybersecurity threats in healthcare. To lay a firm foundation for healthcare organizations and policymakers in better understanding the complexity of the issue of cybersecurity, this study explores the major type of cybersecurity threats for healthcare organizations and explains the roles of the four major players (cyber attackers, cyber defenders, developers, and end-users) in cybersecurity. Finally, the paper discusses a set of recommendations for the policymakers and healthcare organizations to strengthen cybersecurity in their organization.

Comparison of thrombophilic gene mutations among patients experiencing recurrent miscarriage and deep vein thrombosis.

PROBLEM: Inherited thrombophilia has been shown to be a risk factor for cardiovascular disease including deep venous thrombosis as well as reproductive disorders including recurrent pregnancy loss. We have previously reported three out of the 10 thrombophilic mutations studied, plasminogen activator inhibitor-1 (PAI-1) 4G/5G, factor XIII V34L, and homozygous MTHFR C667T, correlated significantly with recurrent pregnancy loss compared with controls. This study was undertaken to compare the frequencies of nine inherited thrombophilias among women with a history of recurrent pregnancy loss with individuals experiencing deep venous thrombosis and fertile controls. METHOD OF STUDY: Six hundred thirty-four participants including 550 women with a history of recurrent pregnancy loss, 43 individuals with deep vein thrombosis and 41 fertile women without a history of recurrent miscarriage. All participants had buccal swabs taken for DNA analyses of nine gene polymorphisms including factor V G1691A, factor V H1299R (R2), factor II Prothrombin G20210A, factor XIII V34L, beta-fibrinogen -455G>A, PAI-1 4G/5G, human platelet antigen 1 a/b (L33P), MTHFR C677T, MTHFR A1298C. Frequencies of thrombophilic gene polymorphisms were compared among the three populations studied. RESULTS: Individuals with a history of DVT had a significantly higher frequency of all of the polymorphisms studied compared with women experiencing a history of recurrent pregnancy loss and the fertile controls. The frequencies of mutations for V34L and PAI-1 4G/5G were significantly increased among women experiencing recurrent pregnancy loss compared with controls. The most prevalent polymorphisms were factor XIII V34L and PAI-1 4G/4G for both individuals with a history of deep vein thrombosis and recurrent pregnancy loss compared with controls. CONCLUSION: Screening for risk factors for inherited thrombophilia with only polymorphisms for factor V von Leiden, factor II prothrombin and MTHFR may be missing the more prevalent identifiers of jeopardy.

Revisiting negative selection algorithms.

This paper reviews the progress of negative selection algorithms, an anomaly/change detection approach in Artificial Immune Systems (AIS). Following its initial model, we try to identify the fundamental characteristics of this family of algorithms and summarize their diversities. There exist various elements in this method, including data representation, coverage estimate, affinity measure, and matching rules, which are discussed for different variations. The various negative selection algorithms are categorized by different criteria as well. The relationship and possible combinations with other AIS or other machine learning methods are discussed. Prospective development and applicability of negative selection algorithms and their influence on related areas are then speculated based on the discussion.