Human pluripotent stem cells for the modelling and treatment of respiratory diseases

Respiratory diseases are among the leading causes of morbidity and mortality worldwide, representing a major unmet medical need. New chemical entities rarely make it into the clinic to treat respiratory diseases, which is partially due to a lack of adequate predictive disease models and the limited availability of human lung tissues to model respiratory disease. Human pluripotent stem cells (hPSCs) may help fill this gap by serving as a scalable human in vitro model. In addition, human in vitro models of rare genetic mutations can be generated using hPSCs. hPSC-derived epithelial cells and organoids have already shown great potential for the understanding of disease mechanisms, for finding new potential targets by using high-throughput screening platforms, and for personalised treatments. These potentials can also be applied to other hPSC-derived lung cell types in the future. In this review, we will discuss how hPSCs have brought, and may continue to bring, major changes to the field of respiratory diseases by understanding the molecular mechanisms of the pathology and by finding efficient therapeutics

Cyber-physical systems design for runtime trustworthiness maintenance supported by tools

The trustworthiness of cyber-physical systems is a critical factor for establishing wide-spread adoption of these systems. Hence, especially the behavior of safety-critical software components needs to be monitored and managed during system operation. Runtime trustworthiness maintenance should be planned and prepared in early requirements and design phases. This involves the identification of threats that may occur and affect user’s trust at runtime, as well as related controls that can be executed to mitigate the threats. Furthermore, observable and measureable system quality properties have to be identified as indicators of threats, and interfaces for reporting these properties as well as for executing controls have to be designed and implemented. This paper presents a process model for preparing and designing systems for runtime trustworthiness maintenance, which is supported by several tools that facilitate the tasks to be performed by requirements engineers and system designer

A tool for monitoring and maintaining system trustworthiness at runtime

Trustworthiness of software systems is a key factor in their acceptance and effectiveness. This is especially the case for cyber-physical systems, where incorrect or even sub-optimal functioning of the system may have detrimental effects. In addition to designing systems with trustworthiness in mind, monitoring and maintaining trustworthiness at runtime is critical to identify issues that could negatively affect a system's trustworthiness. In this paper, we present a fully operational tool for system trustworthiness maintenance, covering a comprehensive set of quality attributes. It automatically detects, and in some cases mitigates, trustworthiness threatening events. The use of such a tool can enable complex software systems to support runtime adaptation and self-healing, thus reducing the overall upkeep cost and complexity

Rationality versus reality: the challenges of evidence-based decision making for health policy makers

<p>Abstract</p> <p>Background</p> <p>Current healthcare systems have extended the evidence-based medicine (EBM) approach to health policy and delivery decisions, such as access-to-care, healthcare funding and health program continuance, through attempts to integrate valid and reliable evidence into the decision making process. These policy decisions have major impacts on society and have high personal and financial costs associated with those decisions. Decision models such as these function under a shared assumption of rational choice and utility maximization in the decision-making process.</p> <p>Discussion</p> <p>We contend that health policy decision makers are generally unable to attain the basic goals of evidence-based decision making (EBDM) and evidence-based policy making (EBPM) because humans make decisions with their naturally limited, faulty, and biased decision-making processes. A cognitive information processing framework is presented to support this argument, and subtle cognitive processing mechanisms are introduced to support the focal thesis: health policy makers' decisions are influenced by the subjective manner in which they individually process decision-relevant information rather than on the objective merits of the evidence alone. As such, subsequent health policy decisions do not necessarily achieve the goals of evidence-based policy making, such as maximizing health outcomes for society based on valid and reliable research evidence.</p> <p>Summary</p> <p>In this era of increasing adoption of evidence-based healthcare models, the rational choice, utility maximizing assumptions in EBDM and EBPM, must be critically evaluated to ensure effective and high-quality health policy decisions. The cognitive information processing framework presented here will aid health policy decision makers by identifying how their decisions might be subtly influenced by non-rational factors. In this paper, we identify some of the biases and potential intervention points and provide some initial suggestions about how the EBDM/EBPM process can be improved.</p

Environmentalism, pre-environmentalism, and public policy

In the last decade, thousands of new grassroots groups have formed to oppose environmental pollution on the basis that it endangers their health. These groups have revitalized the environmental movement and enlarged its membership well beyond the middle class. Scientists, however, have been unable to corroborate these groups' claims that exposure to pollutants has caused their diseases. For policy analysts this situation appears to pose a choice between democracy and science. It needn't. Instead of evaluating the grassroots groups from the perspective of science, it is possible to evaluate science from the perspective of environmentalism. This paper argues that environmental epidemiology reflects ‘pre-environmentalist’ assumptions about nature and that new ideas about nature advanced by the environmental movement could change the way scientists collect and interpret data.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45449/1/11077_2005_Article_BF01006494.pd