SBC2009-204872 CIRCUMFERENTIAL CYCLIC STRAIN IN PATIENTS WITH DESCENDING THORACIC AORTIC ANEURYSMS: IMPLICATIONS FOR ENDOVASCULAR DEVICE DESIGN

INTRODUCTION Endovascular graft (EVG) therapy has emerged as a promising alternative to open surgical repair of thoracic aortic aneurysms. However, the long-term durability of thoracic endovascular repair (TEVAR) remains uncertain due to complications such as incomplete aneurysm exclusion (endoleaks), migration, and stent fracture and collapse. These complications could likely be reduced if the biomechanical environment of the thoracic aorta was better understood. Currently, there are three FDA approved EVGs for treatment of descending thoracic aortic aneurysms (DTAA), but the range of bench-top testing mechanisms for these devices are limited. Despite many advances in medical imaging and analysis techniques, relatively little is known about the wall dynamics of the thoracic aorta, particularly in patients with aneurysms. While one recent study reports diameter change before and after endovascular therap

Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories

Protecting and promoting public health is the mission of the U.S. Food and Drug Administration (FDA). FDA's Center for Devices and Radiological Health (CDRH), which regulates medical devices marketed in the U.S., envisions itself as the world's leader in medical device innovation and regulatory science–the development of new methods, standards, and approaches to assess the safety, efficacy, quality, and performance of medical devices. Traditionally, bench testing, animal studies, and clinical trials have been the main sources of evidence for getting medical devices on the market in the U.S. In recent years, however, computational modeling has become an increasingly powerful tool for evaluating medical devices, complementing bench, animal and clinical methods. Moreover, computational modeling methods are increasingly being used within software platforms, serving as clinical decision support tools, and are being embedded in medical devices. Because of its reach and huge potential, computational modeling has been identified as a priority by CDRH, and indeed by FDA's leadership. Therefore, the Office of Science and Engineering Laboratories (OSEL)—the research arm of CDRH—has committed significant resources to transforming computational modeling from a valuable scientific tool to a valuable regulatory tool, and developing mechanisms to rely more on digital evidence in place of other evidence. This article introduces the role of computational modeling for medical devices, describes OSEL's ongoing research, and overviews how evidence from computational modeling (i.e., digital evidence) has been used in regulatory submissions by industry to CDRH in recent years. It concludes by discussing the potential future role for computational modeling and digital evidence in medical devices

Credible practice of modeling and simulation in healthcare: ten rules from a multidisciplinary perspective

The complexities of modern biomedicine are rapidly increasing. Thus, modeling and simulation have become increasingly important as a strategy to understand and predict the trajectory of pathophysiology, disease genesis, and disease spread in support of clinical and policy decisions. In such cases, inappropriate or ill-placed trust in the model and simulation outcomes may result in negative outcomes, and hence illustrate the need to formalize the execution and communication of modeling and simulation practices. Although verification and validation have been generally accepted as significant components of a model\u27s credibility, they cannot be assumed to equate to a holistic credible practice, which includes activities that can impact comprehension and in-depth examination inherent in the development and reuse of the models. For the past several years, the Committee on Credible Practice of Modeling and Simulation in Healthcare, an interdisciplinary group seeded from a U.S. interagency initiative, has worked to codify best practices. Here, we provide Ten Rules for credible practice of modeling and simulation in healthcare developed from a comparative analysis by the Committee\u27s multidisciplinary membership, followed by a large stakeholder community survey. These rules establish a unified conceptual framework for modeling and simulation design, implementation, evaluation, dissemination and usage across the modeling and simulation life-cycle. While biomedical science and clinical care domains have somewhat different requirements and expectations for credible practice, our study converged on rules that would be useful across a broad swath of model types. In brief, the rules are: (1) Define context clearly. (2) Use contextually appropriate data. (3) Evaluate within context. (4) List limitations explicitly. (5) Use version control. (6) Document appropriately. (7) Disseminate broadly. (8) Get independent reviews. (9) Test competing implementations. (10) Conform to standards. Although some of these are common sense guidelines, we have found that many are often missed or misconstrued, even by seasoned practitioners. Computational models are already widely used in basic science to generate new biomedical knowledge. As they penetrate clinical care and healthcare policy, contributing to personalized and precision medicine, clinical safety will require established guidelines for the credible practice of modeling and simulation in healthcare

Emerging alphaviruses are sensitive to cellular states induced by a novel small-molecule agonist of the STING pathway

The type I interferon (IFN) system represents an essential innate immune response that renders cells resistant to virus growth via the molecular actions of IFNinduced effector proteins. IFN-mediated cellular states inhibit growth of numerous and diverse virus types, including those of known pathogenicity as well as potentially emerging agents. As such, targeted pharmacologic activation of the IFN response may represent a novel therapeutic strategy to prevent infection or spread of clinically impactful viruses. In light of this, we employed a high-throughput screen to identify small molecules capable of permeating the cell and of activating IFN-dependent signaling processes. Here we report the identification and characterization of N-(methylcarbamoyl)-2-([5-(4- methylphenyl)-1,3,4-oxadiazol-2-yl]sulfanyl)-2-phenylacetamide (referred to as C11), a novel compound capable of inducing IFN secretion from human cells. Using reverse geneticsbased loss-of-function assays, we show that C11 activates the type I IFN response in a manner that requires the adaptor protein STING but not the alternative adaptors MAVS and TRIF. Importantly, treatment of cells with C11 generated a cellular state that potently blocked replication of multiple emerging alphavirus types, including chikungunya, Ross River, Venezuelan equine encephalitis, Mayaro, and O'nyong-nyong viruses. The antiviral effects of C11 were subsequently abrogated in cells lacking STING or the type I IFN receptor, indicating that they are mediated, at least predominantly, by way of STING-mediated IFN secretion and subsequent autocrine/paracrine signaling. This work also allowed characterization of differential antiviral roles of innate immune signaling adaptors and IFN-mediated responses and identified MAVS as being crucial to cellular resistance to alphavirus infection

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine

Physiological closed-loop controlled medical devices automatically adjust therapy delivered to a patient to adjust a measured physiological variable. In critical care scenarios, these types of devices could automate, for example, fluid resuscitation, drug delivery, mechanical ventilation, and/or anesthesia and sedation. Evidence from simulations using computational models of physiological systems can play a crucial role in the development of physiological closed-loop controlled devices; but the utility of this evidence will depend on the credibility of the computational model used. Computational models of physiological systems can be complex with numerous non-linearities, time-varying properties, and unknown parameters, which leads to challenges in model assessment. Given the wide range of potential uses of computational patient models in the design and evaluation of physiological closed-loop controlled systems, and the varying risks associated with the diverse uses, the specific model as well as the necessary evidence to make a model credible for a use case may vary. In this review, we examine the various uses of computational patient models in the design and evaluation of critical care physiological closed-loop controlled systems (e.g., hemodynamic stability, mechanical ventilation, anesthetic delivery) as well as the types of evidence (e.g., verification, validation, and uncertainty quantification activities) presented to support the model for that use. We then examine and discuss how a credibility assessment framework (American Society of Mechanical Engineers Verification and Validation Subcommittee, V&V 40 Verification and Validation in Computational Modeling of Medical Devices) for medical devices can be applied to computational patient models used to test physiological closed-loop controlled systems

An invisible workforce: the neglected role of cleaners in patient safety on maternity units.

Hospital cleaning has been shown to impact on rates of healthcare-associated infections (HCAIs) and good environmental hygiene is critical to quality care, yet those tasked with the role of ensuring a safe and clean environment often go unrecognised as members of the healthcare workforce. Sepsis is a leading cause of maternal and newborn death, a significant proportion of these cases are estimated to be due to HCAIs. Deliveries in health institutions have now reached 75% globally, and in low and middle income countries the corresponding increased pressure on facilities  has impacted both quality of care provided and quality of the birth environment in terms of infection prevention and control (IPC) and HCAIs. The paper discusses the neglected role of health facility cleaners, providing evidence from the literature and from needs assessments conducted by The Soapbox Collaborative and partners in Bangladesh, India, The Gambia and Zanzibar. While not the primary focus of the assessments, common themes emerged consistently pointing to institutional neglect of cleaning and cleaners. The paper argues that low status within facilities, wider societal marginalisation, lack of training, and poor pay and working conditions contribute to the lack of prioritisation placed on health facility environmental hygiene. With increased international attention focused towards health facility water, sanitation and hygiene and a growing focus on IPC, now is the time to address the neglect of this frontline healthcare workforce. We propose that provision of and improved training can enable the recognition of the valuable role cleaning staff play, as well as equipping these staff with the tools required to perform their job to the highest standard. In addition to training, wider systems changes are necessary to establish improvements in environmental hygiene and the role of cleaning staff, including addressing resource availability, supportive supervision, and an increased emphasis on preventative healthcare

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN