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

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

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 protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification

IntroductionArtificial Intelligence (AI) has proven effective in classifying skin cancers using dermoscopy images. In experimental settings, algorithms have outperformed expert dermatologists in classifying melanoma and keratinocyte cancers. However, clinical application is limited when algorithms are presented with ‘untrained’ or out-of-distribution lesion categories, often misclassifying benign lesions as malignant, or misclassifying malignant lesions as benign. Another limitation often raised is the lack of clinical context (e.g., medical history) used as input for the AI decision process. The increasing use of Total Body Photography (TBP) in clinical examinations presents new opportunities for AI to perform holistic analysis of the whole patient, rather than a single lesion. Currently there is a lack of existing literature or standards for image annotation of TBP, or on preserving patient privacy during the machine learning process.MethodsThis protocol describes the methods for the acquisition of patient data, including TBP, medical history, and genetic risk factors, to create a comprehensive dataset for machine learning. 500 patients of various risk profiles will be recruited from two clinical sites (Australia and Spain), to undergo temporal total body imaging, complete surveys on sun behaviors and medical history, and provide a DNA sample. This patient-level metadata is applied to image datasets using DICOM labels. Anonymization and masking methods are applied to preserve patient privacy. A two-step annotation process is followed to label skin images for lesion detection and classification using deep learning models. Skin phenotype characteristics are extracted from images, including innate and facultative skin color, nevi distribution, and UV damage. Several algorithms will be developed relating to skin lesion detection, segmentation and classification, 3D mapping, change detection, and risk profiling. Simultaneously, explainable AI (XAI) methods will be incorporated to foster clinician and patient trust. Additionally, a publicly released dataset of anonymized annotated TBP images will be released for an international challenge to advance the development of new algorithms using this type of data.ConclusionThe anticipated results from this protocol are validated AI-based tools to provide holistic risk assessment for individual lesions, and risk stratification of patients to assist clinicians in monitoring for skin cancer

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

Inverse relationship between nonadherence to original GOLD treatment guidelines and exacerbations of COPD

Hussein D Foda,1,2 Anthony Brehm,1,2 Karen Goldsteen,3 Norman H Edelman2,4 1Division of Pulmonary Critical Care and Sleep Medicine, Department of Medicine, Veterans Affairs Medical Center, Northport, 2Division of Pulmonary Critical Care and Sleep Medicine, Department of Medicine, Stony Brook University Medical Center, Stony Brook, NY, 3MPH Program, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, 4Department of Preventative Medicine and Program in Public Health, Stony Brook University Medical Center, Stony Brook, NY, USA Background: Prescriber disagreement is among the reasons for poor adherence to COPD treatment guidelines; it is yet not clear whether this leads to adverse outcomes. We tested whether undertreatment according to the original Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines led to increased exacerbations.Methods: Records of 878 patients with spirometrically confirmed COPD who were followed from 2005 to 2010 at one Veterans Administration (VA) Medical Center were analyzed. Analysis of variance was performed to assess differences in exacerbation rates between severity groups. Logistic regression analysis was performed to assess the relationship between noncompliance with guidelines and exacerbation rates.Findings: About 19% were appropriately treated by guidelines; 14% overtreated, 44% undertreated, and in 23% treatment did not follow any guideline. Logistic regression revealed a strong inverse relationship between undertreatment and exacerbation rate when severity of obstruction was held constant. Exacerbations per year by GOLD stage were significantly different from each other: mild 0.15, moderate 0.27, severe 0.38, very severe 0.72, and substantially fewer than previously reported.Interpretation: The guidelines were largely not followed. Undertreatment predominated but, contrary to expectations, was associated with fewer exacerbations. Thus, clinicians were likely advancing therapy primarily based upon exacerbation rates as was subsequently recommended in revised GOLD and other more recent guidelines. In retrospect, a substantial lack of prescriber adherence to treatment guidelines may have been a signal that they required re-evaluation. This is likely to be a general principle regarding therapeutic guidelines. The identification of fewer exacerbations in this cohort than has been generally reported probably reflects the comprehensive nature of the VA system, which is more likely to identify relatively asymptomatic (ie, nonexacerbating) COPD patients. Accordingly, these rates may better reflect those in the general population. In addition, the lower rates may reflect the more complete preventive care provided by the VA. Keywords: COPD exacerbations, COPD treatment guidelines, COPD in US Veterans Affairs Medical Center

Advanced Modeling of Peripheral Neuro-Effector Communication and -Plasticity

The peripheral nervous system (PNS) plays crucial roles in physiology and disease. Neuro-effector communication and neuroplasticity of the PNS are poorly studied, since suitable models are lacking. The emergence of human pluripotent stem cells (hPSCs) has great promise to resolve this deficit. hPSC-derived PNS neurons, integrated into organ-on-a-chip systems or organoid cultures, allow co-cultures with cells of the local microenvironment to study neuro-effector interactions and to probe mechanisms underlying neuroplasticity