T he Adult Treatment Panel III (ATP III) of the National Cholesterol Education Program (NCEP) issued an evidence-based set of guidelines on cholesterol management in 2001. Since the publication of ATP III, five major clinical trials of statin therapy with clinical end points have been published. These trials addressed issues that were not examined in previous clinical trials of cholesterol-lowering therapy. An NCEP working group reviewed the results of these recent trials and assessed their implications for cholesterol management. These clinical trials strongly support the ATP III recommendation that LDL-cholesterol (LDL-C) should be the primary target of lipid-lowering therapy. The trials confirm the benefit of cholesterol-lowering therapy in high-risk patients and support the ATP III treatment goal of LDL-C Ͻ100 mg/dL. In fact, they add to the growing evidence supporting the concept that, for LDL-C in high-risk patients, "the lower, the better" for reducing risk for major cardiovascular events ( Figure). Although recent clinical trials focused on drug therapies for LDL lowering, the NCEP update affirms that therapeutic lifestyle changes (TLC) remain an essential modality in clinical management. TLC has the potential to reduce cardiovascular risk through several mechanisms beyond LDL lowering. Recent clinical trials support the inclusion of patients with diabetes in the high-risk category and confirm the benefits of LDL-lowering therapy in these patients. They further confirm that older persons benefit from therapeutic lowering of LDL-C. The major recommendations for modifications to footnote the ATP III treatment algorithm for LDL lowering are presented in the Table 1 and are summarized in Table 2. In high-risk persons, ATP III established that the recommended LDL-C goal is Ͻ100 mg/dL; when triglycerides are high (Ն200 mg/dL), a secondary goal is a non-HDL-C Ͻ130 mg/dL. According to the update, when risk is very high, an LDL-C goal of Ͻ70 mg/dL is a therapeutic option, ie, a reasonable clinical strategy, based on available clinical trial evidence. This therapeutic option extends also to patients at very high risk who have a baseline LDL-C Ͻ100 mg/dL. For those very high risk patients who have a high triglyceride, a level of non-HDL-C of Ͻ100 mg/dL corresponds to an LDL-C level of Ͻ70 mg/dL. Identifying a very high risk patient depends on clinical judgment. Examples of such patients include those with established cardiovascular disease plus (1) multiple major risk factors (especially diabetes), (2) severe and poorly controlled risk factors (especially continued cigarette smoking), (3) multiple risk factors of the metabolic syndrome (especially high triglyceride Ն200 mg/dL plus non-HDL-C Ն130 mg/dL with low HDL-C [Ͻ40 mg/dL]) and (4) those with acute coronary syndromes. Moreover, when any high-risk patient has high triglyceride or low HDL-C, consideration can be given to combining a fibrate or nicotinic acid with an LDL-lowering drug. See page e149For moderately high-risk persons (2ϩ risk factors and 10-year ris...
In this paper we review the problem of packet loss as it pertains to Network Intrusion Detection, seeking to answer two fundamental research questions which are stepping stones towards building a model that can be used to predict the rate of alert loss based upon the rate of packet loss. The first question deals with how the packet loss rate affects the sensor alert rate, and the second considers how the network traffic composition affects the results of the first question. Potential places where packet loss may occur are examined by dividing the problem into network, host, and sensor based packet loss. We posit theories about how packet loss may present itself and develop the Packet Dropper that induces packet loss into a dataset. Drop rates ranging from 0% to 100% are applied to four different datasets and the resulting abridged datasets are analyzed with Snort to collect alert loss rate. Conclusions are drawn about the importance of the distribution of packet loss and the effect of the network traffic composition.
Cyber resilience must be improved. Improving cyber resilience requires the quantitatively measuring it. However, before cyber resilience can be measured, it must first be scientifically defined. An effort to discover a consensus among researchers as to the scientific definition of resilience, in general, and cyber resilience, specifically, revealed that no such consensus exists. Experts from several disciplines agree that the word resilience is becoming a meaningless buzz word. This paper reviews the literature to establish the current state of the scientific definition of resilience. It briefly surveys the literature to discover what makes a valid scientific definition. It reviews and analyses the historic scientific use of resilience to discover the path from its original meaning to its current diverse and conflicting meanings. These concepts are decomposed using a genus-differentia analysis untangling the various connotations and separating the related but different concepts. Based upon this analysis, a proposal is made that resilience is part of a family of properties under the umbrella of tenacity. This family includes resistance, resilience, persistence, and perseverance. Finally, an initial operational definition of cyber resilience based upon key performance parameters under stress is proposed.
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