Targeting the cell cycle machinery for the treatment of cardiovascular disease

Cardiovascular disease represents a major clinical problem affecting a significant proportion of the world's population and remains the main cause of death in the UK. The majority of therapies currently available for the treatment of cardiovascular disease do not cure the problem but merely treat the symptoms. Furthermore, many cardioactive drugs have serious side effects and have narrow therapeutic windows that can limit their usefulness in the clinic. Thus, the development of more selective and highly effective therapeutic strategies that could cure specific cardiovascular diseases would be of enormous benefit both to the patient and to those countries where healthcare systems are responsible for an increasing number of patients. In this review, we discuss the evidence that suggests that targeting the cell cycle machinery in cardiovascular cells provides a novel strategy for the treatment of certain cardiovascular diseases. Those cell cycle molecules that are important for regulating terminal differentiation of cardiac myocytes and whether they can be targeted to reinitiate cell division and myocardial repair will be discussed as will the molecules that control vascular smooth muscle cell (VSMC) and endothelial cell proliferation in disorders such as atherosclerosis and restenosis. The main approaches currently used to target the cell cycle machinery in cardiovascular disease have employed gene therapy techniques. We will overview the different methods and routes of gene delivery to the cardiovascular system and describe possible future drug therapies for these disorders. Although the majority of the published data comes from animal studies, there are several instances where potential therapies have moved into the clinical setting with promising results.

The fatigue and degradation mechanisms of hoisting ropes

Based upon specialised experience of rope mechanics spanning over 20 years, this paper reviews the processes of degradation and fatigue that are relevant to hoisting ropes in mines. The review is brought up to date with an account of the most recent work in this field, which identifies a torsional fatigue process and quantifies the impact of degradation upon the residual service life. A proper understanding of these processes is important in determining how different parameters of hoist design and operation interact to determine rope life. This knowledge is also important in informing decisions relating to rope discard based upon observed condition, as well is identifying the critical features that must be quantified reliably during inspection.

Reprogramming the cell cycle machinery to treat cardiovascular disease

Coronary artery disease is one of the most common heart pathologies. Restriction of blood flow to the heart by atherosclerotic lesions, leading to angina pectoris and myocardial infarction, damages the heart, resulting in impaired cardiac function. Damaged myocardium is replaced by scar tissue since surviving cardiomyocytes are unable to proliferate to replace lost heart tissue. Although narrowing of the coronary arteries can be treated successfully using coronary revascularisation procedures, re-occlusion of the treated vessels remains a significant clinical problem. Cell cycle control mechanisms are key in both the impaired cardiac repair by surviving cardiomyocytes and re-narrowing of treated vessels by maladaptive proliferation of vascular smooth muscle cells. Strategies targeting the cell cycle machinery in the heart and vasculature offer promise both for the improvement of cardiac repair following MI and the prevention of restenosis and bypass graft failure following revascularisation procedures.

Multivariable cluster analysis for high-speed industrial machinery

The overall operation and internal complexity of a particular production machinery can be depicted in terms of clusters of multidimensional points which describe the process states, the value in each point dimension representing a measured variable from the machinery. The paper describes a new cluster analysis technique for use with manufacturing processes, to illustrate how machine behaviour can be categorised and how regions of good and poor machine behaviour can be identified. The cluster algorithm presented is the novel mean-tracking algorithm, capable of locating N-dimensional clusters in a large data space in which a considerable amount of noise is present. Implementation of the algorithm on a real-world high-speed machinery application is described, with clusters being formed from machinery data to indicate machinery error regions and error-free regions. This analysis is seen to provide a promising step ahead in the field of multivariable control of manufacturing systems.