scholarly journals The technology behind TB DEPOT: a novel public analytics platform integrating tuberculosis clinical, genomic, and radiological data for visual and statistical exploration

Author(s):  
Alyssa Long ◽  
Alexander Glogowski ◽  
Matthew Meppiel ◽  
Lisa De Vito ◽  
Eric Engle ◽  
...  

Abstract Objective Clinical research informatics tools are necessary to support comprehensive studies of infectious diseases. The National Institute of Allergy and Infectious Diseases (NIAID) developed the publicly accessible Tuberculosis Data Exploration Portal (TB DEPOT) to address the complex etiology of tuberculosis (TB). Materials and Methods TB DEPOT displays deidentified patient case data and facilitates analyses across a wide range of clinical, socioeconomic, genomic, and radiological factors. The solution is built using Amazon Web Services cloud-based infrastructure, .NET Core, Angular, Highcharts, R, PLINK, and other custom-developed services. Structured patient data, pathogen genomic variants, and medical images are integrated into the solution to allow seamless filtering across data domains. Results Researchers can use TB DEPOT to query TB patient cases, create and save patient cohorts, and execute comparative statistical analyses on demand. The tool supports user-driven data exploration and fulfills the National Institute of Health’s Findable, Accessible, Interoperable, and Reusable (FAIR) principles. Discussion TB DEPOT is the first tool of its kind in the field of TB research to integrate multidimensional data from TB patient cases. Its scalable and flexible architectural design has accommodated growth in the data, organizations, types of data, feature requests, and usage. Use of client-side technologies over server-side technologies and prioritizing maintenance have been important lessons learned. Future directions are dynamically prioritized and key functionality is shared through an application programming interface. Conclusion This paper describes the platform development methodology, resulting functionality, benefits, and technical considerations of a clinical research informatics application to support increased understanding of TB.

2018 ◽  
Vol 27 (01) ◽  
pp. 177-183 ◽  
Author(s):  
Christel Daniel ◽  
Dipak Kalra ◽  

Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2017. Method: A bibliographic search using a combination of MeSH descriptors and free terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selection of best papers. Results: Among the 741 returned papers published in 2017 in the various areas of CRI, the full review process selected five best papers. The first best paper reports on the implementation of consent management considering patient preferences for the use of de-identified data of electronic health records for research. The second best paper describes an approach using natural language processing to extract symptoms of severe mental illness from clinical text. The authors of the third best paper describe the challenges and lessons learned when leveraging the EHR4CR platform to support patient inclusion in academic studies in the context of an important collaboration between private industry and public health institutions. The fourth best paper describes a method and an interactive tool for case-crossover analyses of electronic medical records for patient safety. The last best paper proposes a new method for bias reduction in association studies using electronic health records data. Conclusions: Research in the CRI field continues to accelerate and to mature, leading to tools and platforms deployed at national or international scales with encouraging results. Beyond securing these new platforms for exploiting large-scale health data, another major challenge is the limitation of biases related to the use of “real-world” data. Controlling these biases is a prerequisite for the development of learning health systems.


1998 ◽  
Vol 4 (S2) ◽  
pp. 1044-1045
Author(s):  
D. N. Howell ◽  
S. E. Miller

Correlative microscopy is employed in a great variety of settings by both diagnostic and investigative pathologists. Combinations of conventional light microscopy (LM), immunohistology, and electron microscopy (EM) are used in a wide range of diagnostic settings, including the analysis of tumors, autoimmune disorders, and infectious diseases. Valuable diagnostic information is also frequently obtained by simultaneous or sequential examination of exfoliated or aspirated cell suspensions (cytopathology) and tissue sections (histopathology) from the same lesion. An even wider range of correlative microscopic methods is employed by pathologists in basic and clinical research. The rationales for using correlative techniques are many and varied, but in most cases fall within a limited number of categories.Pathologists frequently use a second microscopic or preparative technique to improve on the resolution afforded by an initial technique. Electron microscopy is often used to refine the analysis of features initially detected by routine LM or immunohistology.


2021 ◽  
pp. 074873042098732
Author(s):  
N. Kronfeld-Schor ◽  
T. J. Stevenson ◽  
S. Nickbakhsh ◽  
E. S. Schernhammer ◽  
X. C. Dopico ◽  
...  

Not 1 year has passed since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19). Since its emergence, great uncertainty has surrounded the potential for COVID-19 to establish as a seasonally recurrent disease. Many infectious diseases, including endemic human coronaviruses, vary across the year. They show a wide range of seasonal waveforms, timing (phase), and amplitudes, which differ depending on the geographical region. Drivers of such patterns are predominantly studied from an epidemiological perspective with a focus on weather and behavior, but complementary insights emerge from physiological studies of seasonality in animals, including humans. Thus, we take a multidisciplinary approach to integrate knowledge from usually distinct fields. First, we review epidemiological evidence of environmental and behavioral drivers of infectious disease seasonality. Subsequently, we take a chronobiological perspective and discuss within-host changes that may affect susceptibility, morbidity, and mortality from infectious diseases. Based on photoperiodic, circannual, and comparative human data, we not only identify promising future avenues but also highlight the need for further studies in animal models. Our preliminary assessment is that host immune seasonality warrants evaluation alongside weather and human behavior as factors that may contribute to COVID-19 seasonality, and that the relative importance of these drivers requires further investigation. A major challenge to predicting seasonality of infectious diseases are rapid, human-induced changes in the hitherto predictable seasonality of our planet, whose influence we review in a final outlook section. We conclude that a proactive multidisciplinary approach is warranted to predict, mitigate, and prevent seasonal infectious diseases in our complex, changing human-earth system.


Author(s):  
David Callaway ◽  
Jeff Runge ◽  
Lucia Mullen ◽  
Lisa Rentz ◽  
Kevin Staley ◽  
...  

Abstract The United States Centers for Disease Control and Prevention and the World Health Organization broadly categorize mass gathering events as high risk for amplification of coronavirus disease 2019 (COVID-19) spread in a community due to the nature of respiratory diseases and the transmission dynamics. However, various measures and modifications can be put in place to limit or reduce the risk of further spread of COVID-19 for the mass gathering. During this pandemic, the Johns Hopkins University Center for Health Security produced a risk assessment and mitigation tool for decision-makers to assess SARS-CoV-2 transmission risks that may arise as organizations and businesses hold mass gatherings or increase business operations: The JHU Operational Toolkit for Businesses Considering Reopening or Expanding Operations in COVID-19 (Toolkit). This article describes the deployment of a data-informed, risk-reduction strategy that protects local communities, preserves local health-care capacity, and supports democratic processes through the safe execution of the Republican National Convention in Charlotte, North Carolina. The successful use of the Toolkit and the lessons learned from this experience are applicable in a wide range of public health settings, including school reopening, expansion of public services, and even resumption of health-care delivery.


Encyclopedia ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 433-444
Author(s):  
Mario Coccia

Coronavirus disease 2019 (COVID-19) is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which appeared in late 2019, generating a pandemic crisis with high numbers of COVID-19-related infected individuals and deaths in manifold countries worldwide. Lessons learned from COVID-19 can be used to prevent pandemic threats by designing strategies to support different policy responses, not limited to the health system, directed to reduce the risks of the emergence of novel viral agents, the diffusion of infectious diseases and negative impact in society.


2015 ◽  
Vol 14 (4) ◽  
pp. 118-123 ◽  
Author(s):  
Lauren Trees

Purpose – The purpose of this paper is to present enterprise social networking and gamification as two potential tools to help organizations engage Millennial employees in collaboration and learning. Design/methodology/approach – The research provides general descriptions of enterprise social networking and gamification approaches, shares data on adoption of these approaches from APQC’s “2015 Knowledge Management Priorities Data Report” (based on a January 2015 survey of 524 knowledge management professionals) and includes four company examples adapted from APQC’s Connecting People to Content and Transferring and Applying Critical Knowledge best practices studies. The methodology for APQC’s best practices studies involves screening 50 or more organizations with potential best practices in a given research scope area and identifying five or six with proven best practices. APQC then conducts detailed site visits with the selected organizations and publishes case studies based on those site visits. Findings – Enterprise social networking platforms are in place at 50 per cent of organizations, with another 25 per cent planning to implement them by the end of 2015. By providing near-immediate access to information and answers, enterprise social networking helps Millennials learn the ropes at their new workplaces, gives them direct access to more knowledgeable colleagues who can assist and mentor them, and helps them improve their business outcomes by reusing knowledge and lessons learned across projects. Younger workers can also harness the power of social networking to create a sense of belonging and build their reputations in large, dispersed firms, where it is particularly difficult for them to gain visibility. A recent APQC survey indicates that 54 per cent of organizations either currently employ gamification to encourage collaboration or expect to implement it within the next three years. The rush to gamify the enterprise is, at least in part, a reflection of employers’ desire to satisfy Millennials and make them feel connected to a community of co-workers. Although games appeal to a wide range of age groups, Millennials grew up with digital interaction and tend to prefer environments that emphasize teamwork, social learning and frequent feedback – all of which can be delivered through gamification. Originality/value – The value of this paper is to introduce the value of and relationship between enterprise social networking and gamification platforms to human resource (HR) professionals looking to increase engagement and retention rates for Millennial employees.


2015 ◽  
Vol 7 (3) ◽  
Author(s):  
Rudolf Urbanics ◽  
Péter Bedőcs ◽  
János Szebeni

AbstractPigs provide a sensitive and quantitative animal model of complement (C) activation-related pseudoallergy (CARPA) caused by liposomes and a wide range of nanoparticulate drugs or drug nanocarriers (nanomedicines). The tetrad of symptoms (hemodynamic, hematological, laboratory and skin changes) that arise within minutes after i.v. injection of reactogenic nanomedicines (RNMs) are highly reproducible among different pigs but the presence, direction and relative severity of symptoms are very different with different RNMs and their administration schedule. Bolus administration of RNMs usually trigger pulmonary hypertension with or without various degrees of systemic hyper- or hypotension, tachy-or bradycardia, arrhythmia, blood cell and inflammatory mediator changes and skin rash. These reactions can be rapid or protracted, and fully tachyphylactic, semi-tachyphylactic or non-tachyphylactic. Slow infusion usually diminishes the reactions and/or entail delayed, protracted and less severe hemodynamic and other changes. The goal of this review is to present some technical details of the porcine CARPA model, point out its constant and variable parameters, show examples of different reactions, highlight the unique features and capabilities of the model and evaluate its utility in preclinical safety assessment. The information obtained in this model enables the understanding of the complex pathomechanism of CARPA involving simultaneous anaphylatoxin and inflammatory mediator actions at multiple sites in different organs.


Author(s):  
Walter Leal Filho ◽  
Abul Al-Amin ◽  
Gustavo Nagy ◽  
Ulisses Azeiteiro ◽  
Laura Wiesböck ◽  
...  

There are various climate risks that are caused or influenced by climate change. They are known to have a wide range of physical, economic, environmental and social impacts. Apart from damages to the physical environment, many climate risks (climate variability, extreme events and climate-related hazards) are associated with a variety of impacts on human well-being, health, and life-supporting systems. These vary from boosting the proliferation of vectors of diseases (e.g., mosquitos), to mental problems triggered by damage to properties and infrastructure. There is a great variety of literature about the strong links between climate change and health, while there is relatively less literature that specifically examines the health impacts of climate risks and extreme events. This paper is an attempt to address this knowledge gap, by compiling eight examples from a set of industrialised and developing countries, where such interactions are described. The policy implications of these phenomena and the lessons learned from the examples provided are summarised. Some suggestions as to how to avert the potential and real health impacts of climate risks are made, hence assisting efforts to adapt to a problem whose impacts affect millions of people around the world. All the examples studied show some degree of vulnerability to climate risks regardless of their socioeconomic status and need to increase resilience against extreme events.


Author(s):  
Michael Plotnikov ◽  
John Collura

Rapid proliferation of small, unmanned aircraft systems (UAS) promises to revolutionize traditional methods used to carry out civil engineering surveys and analyses and conduct physical infrastructure inspections. One of the most promising areas of implementation of innovative UAS technology includes the integration of UAS into current state Department of Transportation (DOT) bridge inspections. While regular bridge inspections are paramount for road user safety, many traditional inspection methods and procedures are cumbersome, expensive, and time consuming; present significant hazards to both the traveling public and the inspection personnel; and are disruptive to normal operations of the transportation facilities. The results of recent studies indicate that UAS can serve as a useful tool in many highway bridge inspection procedures, while significantly reducing costs and time and improving safety. The major factors that affect the success of integrating UAS into the bridge inspection process relate to selection of the proper types of UAS platforms and avionics, data collection sensors and processing software, as well as conduct of task-specific pilot training. The paper provides an examination of current standard bridge inspection procedures and protocols currently carried out by state DOTs; an evaluation of state DOT experiences with the integration of UAS technology into bridge inspections; and an assessment of the issues and challenges associated with this technology. It is expected that this paper will be of interest to a wide range of stakeholders representing state and federal governments, academia, and industry.


2022 ◽  
Vol 54 (7) ◽  
pp. 1-38
Author(s):  
Lynda Tamine ◽  
Lorraine Goeuriot

The explosive growth and widespread accessibility of medical information on the Internet have led to a surge of research activity in a wide range of scientific communities including health informatics and information retrieval (IR). One of the common concerns of this research, across these disciplines, is how to design either clinical decision support systems or medical search engines capable of providing adequate support for both novices (e.g., patients and their next-of-kin) and experts (e.g., physicians, clinicians) tackling complex tasks (e.g., search for diagnosis, search for a treatment). However, despite the significant multi-disciplinary research advances, current medical search systems exhibit low levels of performance. This survey provides an overview of the state of the art in the disciplines of IR and health informatics, and bridging these disciplines shows how semantic search techniques can facilitate medical IR. First,we will give a broad picture of semantic search and medical IR and then highlight the major scientific challenges. Second, focusing on the semantic gap challenge, we will discuss representative state-of-the-art work related to feature-based as well as semantic-based representation and matching models that support medical search systems. In addition to seminal works, we will present recent works that rely on research advancements in deep learning. Third, we make a thorough cross-model analysis and provide some findings and lessons learned. Finally, we discuss some open issues and possible promising directions for future research trends.


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