The pathogenesis of tendinopathy. A molecular perspective

There are many publications that discuss the aetiology, diagnosis and treatment of the various forms of tendinopathy, but few are based on conclusive scientific evidence. The pathogenesis of tendinopathy is difficult to study because tendon biopsies are rarely obtained before a tendon has ruptured. There are interesting comparisons with animal tendinopathy, particularly in the equine athlete, although many animal models do not accurately reflect the human condition—the tendon lesions usually heal. However, the application of biochemical and molecular techniques to the study of both animal and human tendinopathy has led to a greater understanding of these common and disabling conditions. This article summarizes current knowledge of the pathogenesis of tendinopathy, with particular emphasis on the molecular pathology of the tendon matrix

Development of an ontology supporting failure analysis of surface safety valves used in Oil & Gas applications

Treball desenvolupat dins el marc del programa 'European Project Semester'.The project describes how to apply Root Cause Analysis (RCA) in the form of a Failure Mode Effect and Criticality Analysis (FMECA) on hydraulically actuated Surface Safety Valves (SSVs) of Xmas trees in oil and gas applications, in order to be able to predict the occurrence of failures and implement preventive measures such as Condition and Performance Monitoring (CPM) to improve the life-span of a valve and decrease maintenance downtime. In the oil and gas industry, valves account for 52% of failures in the system. If these failures happen unexpectedly it can cause a lot of problems. Downtime of the oil well quickly becomes an expensive problem, unscheduled maintenance takes a lot of extra time and the lead-time for replacement parts can be up to 6 months. This is why being able to predict these failures beforehand is something that can bring a lot of benefits to a company. To determine the best course of action to take in order to be able to predict failures, a FMECA report is created. This is an analysis where all possible failures of all components are catalogued and given a Risk Priority Number (RPN), which has three variables: severity, detectability and occurrence. Each of these is given a rating between 0 and 10 and then the variables are multiplied with each other, resulting in the RPN. The components with an RPN above an acceptable risk level are then further investigated to see how to be able to detect them beforehand and how to mitigate the risk that they pose. Applying FMECA to the SSV mean breaking the system down into its components and determining the function, dependency and possible failures. To this end, the SSV is broken up into three sub-systems: the valve, the actuator and the hydraulic system. The hydraulic system is the sub-system of the SSV responsible for containing, transporting and pressurizing of the hydraulic fluid and in turn, the actuator. It also contains all the safety features, such as pressure pilots, and a trip system in case a problem is detected in the oil line. The actuator is, as the name implies, the sub-system which opens and closes the valve. It is made up of a number of parts such as a cylinder, a piston and a spring. These parts are interconnected in a number of ways to allow the actuator to successfully perform its function. The valve is the actual part of the system which interacts with the oil line by opening and closing. Like the actuator, this sub-system is broken down into a number of parts which work together to perform its function. After breaking down and defining each subsystem on a functional level, a model was created using a functional block diagram. Each component also allows for the defining of dependencies and interactions between the different components and a failure diagram for each component. This model integrates the three sub-systems back into one, creating a complete picture of the entire system which can then be used to determine the effects of different failures in components to the rest of the system. With this model completed we created a comprehensive FMECA report and test the different possible CPM solutions to mitigate the largest risks

The proteostasis network and its decline in ageing

Ageing is a major risk factor for the development of many diseases, prominently including neurodegenerative disorders such as Alzheimer disease and Parkinson disease. A hallmark of many age-related diseases is the dysfunction in protein homeostasis (proteostasis), leading to the accumulation of protein aggregates. In healthy cells, a complex proteostasis network, comprising molecular chaperones and proteolytic machineries and their regulators, operates to ensure the maintenance of proteostasis. These factors coordinate protein synthesis with polypeptide folding, the conservation of protein conformation and protein degradation. However, sustaining proteome balance is a challenging task in the face of various external and endogenous stresses that accumulate during ageing. These stresses lead to the decline of proteostasis network capacity and proteome integrity. The resulting accumulation of misfolded and aggregated proteins affects, in particular, postmitotic cell types such as neurons, manifesting in disease. Recent analyses of proteome-wide changes that occur during ageing inform strategies to improve proteostasis. The possibilities of pharmacological augmentation of the capacity of proteostasis networks hold great promise for delaying the onset of age-related pathologies associated with proteome deterioration and for extending healthspan

A review of physics-based models in prognostics: application to gears and bearings of rotating machinery

Health condition monitoring for rotating machinery has been developed for many years due to its potential to reduce the cost of the maintenance operations and increase availability. Covering aspects include sensors, signal processing, health assessment and decision-making. This article focuses on prognostics based on physics-based models. While the majority of the research in health condition monitoring focuses on data-driven techniques, physics-based techniques are particularly important if accuracy is a critical factor and testing is restricted. Moreover, the benefits of both approaches can be combined when data-driven and physics-based techniques are integrated. This article reviews the concept of physics-based models for prognostics. An overview of common failure modes of rotating machinery is provided along with the most relevant degradation mechanisms. The models available to represent these degradation mechanisms and their application for prognostics are discussed. Models that have not been applied to health condition monitoring, for example, wear due to metal–metal contact in hydrodynamic bearings, are also included due to its potential for health condition monitoring. The main contribution of this article is the identification of potential physics-based models for prognostics in rotating machinery