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Diversity and Health Disparity Issues Need to be Addressed for GWAS and Precision Medicine Studies

Curator: Stephen J. Williams, PhD

 

 

From the POLICY FORUM ETHICS AND DIVERSITY Section of Science

Ethics of inclusion: Cultivate trust in precision medicine

 See all authors and affiliations

Science  07 Jun 2019:
Vol. 364, Issue 6444, pp. 941-942
DOI: 10.1126/science.aaw8299

Precision medicine is at a crossroads. Progress toward its central goal, to address persistent health inequities, will depend on enrolling populations in research that have been historically underrepresented, thus eliminating longstanding exclusions from such research (1). Yet the history of ethical violations related to protocols for inclusion in biomedical research, as well as the continued misuse of research results (such as white nationalists looking to genetic ancestry to support claims of racial superiority), continue to engender mistrust among these populations (2). For precision medicine research (PMR) to achieve its goal, all people must believe that there is value in providing information about themselves and their families, and that their participation will translate into equitable distribution of benefits. This requires an ethics of inclusion that considers what constitutes inclusive practices in PMR, what goals and values are being furthered through efforts to enhance diversity, and who participates in adjudicating these questions. The early stages of PMR offer a critical window in which to intervene before research practices and their consequences become locked in (3).

Initiatives such as the All of Us program have set out to collect and analyze health information and biological samples from millions of people (1). At the same time, questions of trust in biomedical research persist. For example, although the recent assertions of white nationalists were eventually denounced by the American Society of Human Genetics (4), the misuse of ancestry testing may have already undermined public trust in genetic research.

There are also infamous failures in research that included historically underrepresented groups, including practices of deceit, as in the Tuskegee Syphilis Study, or the misuse of samples, as with the Havasupai tribe (5). Many people who are being asked to give their data and samples for PMR must not only reconcile such past research abuses, but also weigh future risks of potential misuse of their data.

To help assuage these concerns, ongoing PMR studies should open themselves up to research, conducted by social scientists and ethicists, that examines how their approaches enhance diversity and inclusion. Empirical studies are needed to account for how diversity is conceptualized and how goals of inclusion are operationalized throughout the life course of PMR studies. This is not limited to selection and recruitment of populations but extends to efforts to engage participants and communities, through data collection and measurement, and interpretations and applications of study findings. A commitment to transparency is an important step toward cultivating public trust in PMR’s mission and practices.

From Inclusion to Inclusive

The lack of diverse representation in precision medicine and other biomedical research is a well-known problem. For example, rare genetic variants may be overlooked—or their association with common, complex diseases can be misinterpreted—as a result of sampling bias in genetics research (6). Concentrating research efforts on samples with largely European ancestry has limited the ability of scientists to make generalizable inferences about the relationships among genes, lifestyle, environmental exposures, and disease risks, and thereby threatens the equitable translation of PMR for broad public health benefit (7).

However, recruiting for diverse research participation alone is not enough. As with any push for “diversity,” related questions arise about how to describe, define, measure, compare, and explain inferred similarities and differences among individuals and groups (8). In the face of ambivalence about how to represent population variation, there is ample evidence that researchers resort to using definitions of diversity that are heterogeneous, inconsistent, and sometimes competing (9). Varying approaches are not inherently problematic; depending on the scientific question, some measures may be more theoretically justified than others and, in many cases, a combination of measures can be leveraged to offer greater insight (10). For example, studies have shown that American adults who do not self-identify as white report better mental and physical health if they think others perceive them as white (1112).

The benefit of using multiple measures of race and ancestry also extends to genetic studies. In a study of hypertension in Puerto Rico, not only did classifications based on skin color and socioeconomic status better predict blood pressure than genetic ancestry, the inclusion of these sociocultural measures also revealed an association between a genetic polymorphism and hypertension that was otherwise hidden (13). Thus, practices that allow for a diversity of measurement approaches, when accompanied by a commitment to transparency about the rationales for chosen approaches, are likely to benefit PMR research more than striving for a single gold standard that would apply across all studies. These definitional and measurement issues are not merely semantic. They also are socially consequential to broader perceptions of PMR research and the potential to achieve its goals of inclusion.

Study Practices, Improve Outcomes

Given the uncertainty and complexities of the current, early phase of PMR, the time is ripe for empirical studies that enable assessment and modulation of research practices and scientific priorities in light of their social and ethical implications. Studying ongoing scientific practices in real time can help to anticipate unintended consequences that would limit researchers’ ability to meet diversity recruitment goals, address both social and biological causes of health disparities, and distribute the benefits of PMR equitably. We suggest at least two areas for empirical attention and potential intervention.

First, we need to understand how “upstream” decisions about how to characterize study populations and exposures influence “downstream” research findings of what are deemed causal factors. For example, when precision medicine researchers rely on self-identification with U.S. Census categories to characterize race and ethnicity, this tends to circumscribe their investigation of potential gene-environment interactions that may affect health. The convenience and routine nature of Census categories seemed to lead scientists to infer that the reasons for differences among groups were self-evident and required no additional exploration (9). The ripple effects of initial study design decisions go beyond issues of recruitment to shape other facets of research across the life course of a project, from community engagement and the return of results to the interpretation of study findings for human health.

Second, PMR studies are situated within an ecosystem of funding agencies, regulatory bodies, disciplines, and other scholars. This partly explains the use of varied terminology, different conceptual understandings and interpretations of research questions, and heterogeneous goals for inclusion. It also makes it important to explore how expectations related to funding and regulation influence research definitions of diversity and benchmarks for inclusion.

For example, who defines a diverse study population, and how might those definitions vary across different institutional actors? Who determines the metrics that constitute successful inclusion, and why? Within a research consortium, how are expectations for data sharing and harmonization reconciled with individual studies’ goals for recruitment and analysis? In complex research fields that include multiple investigators, organizations, and agendas, how are heterogeneous, perhaps even competing, priorities negotiated? To date, no studies have addressed these questions or investigated how decisions facilitate, or compromise, goals of diversity and inclusion.

The life course of individual studies and the ecosystems in which they reside cannot be easily separated and therefore must be studied in parallel to understand how meanings of diversity are shaped and how goals of inclusion are pursued. Empirically “studying the studies” will also be instrumental in creating mechanisms for transparency about how PMR is conducted and how trade-offs among competing goals are resolved. Establishing open lines of inquiry that study upstream practices may allow researchers to anticipate and address downstream decisions about how results can be interpreted and should be communicated, with a particular eye toward the consequences for communities recruited to augment diversity. Understanding how scientists negotiate the challenges and barriers to achieving diversity that go beyond fulfilling recruitment numbers is a critical step toward promoting meaningful inclusion in PMR.

Transparent Reflection, Cultivation of Trust

Emerging research on public perceptions of PMR suggests that although there is general support, questions of trust loom large. What we learn from studies that examine on-the-ground approaches aimed at enhancing diversity and inclusion, and how the research community reflects and responds with improvements in practices as needed, will play a key role in building a culture of openness that is critical for cultivating public trust.

Cultivating long-term, trusting relationships with participants underrepresented in biomedical research has been linked to a broad range of research practices. Some of these include the willingness of researchers to (i) address the effect of history and experience on marginalized groups’ trust in researchers and clinicians; (ii) engage concerns about potential group harms and risks of stigmatization and discrimination; (iii) develop relationships with participants and communities that are characterized by transparency, clear communication, and mutual commitment; and (iv) integrate participants’ values and expectations of responsible oversight beyond initial informed consent (14). These findings underscore the importance of multidisciplinary teams that include social scientists, ethicists, and policy-makers, who can identify and help to implement practices that respect the histories and concerns of diverse publics.

A commitment to an ethics of inclusion begins with a recognition that risks from the misuse of genetic and biomedical research are unevenly distributed. History makes plain that a multitude of research practices ranging from unnecessarily limited study populations and taken-for-granted data collection procedures to analytic and interpretive missteps can unintentionally bolster claims of racial superiority or inferiority and provoke group harm (15). Sustained commitment to transparency about the goals, limits, and potential uses of research is key to further cultivating trust and building long-term research relationships with populations underrepresented in biomedical studies.

As calls for increasing diversity and inclusion in PMR grow, funding and organizational pathways must be developed that integrate empirical studies of scientific practices and their rationales to determine how goals of inclusion and equity are being addressed and to identify where reform is required. In-depth, multidisciplinary empirical investigations of how diversity is defined, operationalized, and implemented can provide important insights and lessons learned for guiding emerging science, and in so doing, meet our ethical obligations to ensure transparency and meaningful inclusion.

References and Notes

  1. C. P. Jones et al Ethn. Dis. 18496 (2008).
  2. C. C. GravleeA. L. NonC. J. Mulligan
  3. S. A. Kraft et al Am. J. Bioeth. 183 (2018).
  4. A. E. Shields et al Am. Psychol. 6077 (2005).

Bioinformatic Tools for RNASeq: A Curation

Curator: Stephen J. Williams, Ph.D. 

 

Note:  This will be an ongoing curation as new information and tools become available.

RNASeq is a powerful tool for the analysis of the transcriptome profile and has been used to determine the transcriptional changes occurring upon stimuli such as drug treatment or detecting transcript differences between biological sample cohorts such as tumor versus normal tissue.  Unlike its genomic companion, whole genome and whole exome sequencing, which analyzes the primary sequence of the genomic DNA, RNASeq analyzes the mRNA transcripts, thereby more closely resembling the ultimate translated proteome. In addition, RNASeq and transcriptome profiling can determine if splicing variants occur as well as determining the nonexomic sequences, such as miRNA and lncRNA species, all of which have shown pertinence in the etiology of many diseases, including cancer.

However, RNASeq, like other omic technologies, generates enormous big data sets, which requires multiple types of bioinformatic tools in order to correctly analyze the sequence reads, and to visualize and interpret the output data.  This post represents a curation by the RNA-Seq blog of such tools useful for RNASeq studies and lists and reviews published literature using these curated tools.

 

From the RNA-Seq Blog

List of RNA-Seq bioinformatics tools

Posted by: RNA-Seq Blog in Data Analysis, Web Tools September 16, 2015 6,251 Views

from: https://en.wiki2.org/wiki/List_of_RNA-Seq_bioinformatics_tools

A review of some of the literature using some of the aforementioned curated tools are discussed below:

 

A.   Tools Useful for Single Cell RNA-Seq Analysis

 

B.  Tools for RNA-Seq Analysis of the Sliceasome

 

C.  Tools Useful for RNA-Seq read assembly visualization

 

Other articles on RNA and Transcriptomics in this Open Access Journal Include:

NIH to Award Up to $12M to Fund DNA, RNA Sequencing Research: single-cell genomics, sample preparation, transcriptomics and epigenomics, and genome-wide functional analysis.

Single-cell Genomics: Directions in Computational and Systems Biology – Contributions of Prof. Aviv Regev @Broad Institute of MIT and Harvard, Cochair, the Human Cell Atlas Organizing Committee with Sarah Teichmann of the Wellcome Trust Sanger Institute

Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line

Single-cell RNA-seq helps in finding intra-tumoral heterogeneity in pancreatic cancer

First challenge to make use of the new NCI Cloud Pilots – Somatic Mutation Challenge – RNA: Best algorithms for detecting all of the abnormal RNA molecules in a cancer cell

Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis

 


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In addition to providing access to leading Investors, the conference will feature more than 100 pre-screened venture backed, emerging and early stage companies seeking capital, and hardcore networking. 

 

 

Call for TOP INNOVATORS!

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A select group of more than 100 Top Innovators will be chosen to present their breakthrough investment opportunities to an exclusive audience of Venture Capitalists, Corporate Investors, Private Investors, Investment Bankers, and Strategic Partners.

 

Apply to Present / Nominate a company:

For more information or to be considered for one of the Top Innovator slots click here.

 

Seed Pitchfest:

If you are a seed stage company seeking angel funding of less than $1M (and have raised less than $300,000) click here to apply for the Seed stage track.

 

 We look forward to seeing you there. 

 

LPBI & youngStartup Ventures

 

 @@@@@@

Leaders in Pharmaceutical Business Intelligence (LPBI ) Group is pleased to announce its sponsorship and invite you to join us at Venture Summit | West

Special offer:  Register Now & Save $450 off

(Use discount code “LPBI-VIP”)

* Call for Top Innovators Details below

Venture Summit | West

Where Innovation Meets Capital

March 23rd & 24th  2020

Santa Clara Convention Center, Silicon Valley

  

Come meet, interact and network with more than 1,300 VCs, Corporate VCs, angel investors, industry execs and founders of venture backed, emerging and early stage companies at the prestigious Venture Summit | West being held on March 23rd & 24th 2019 at the Santa Clara Convention Center. This year will again feature a dedicated Lifesciences/Healthcare Track.

 

Whether you’re a Lifesciences/Healthcare startup seeking capital and exposure, or an investor seeking new deals, Venture Summit West presented by youngStartup Ventures – is the event of the year you won’t want to miss.

 

A highly productive venture conference, Venture Summit | West is dedicated to showcasing VCs, Corporate VCs and angel investors committed to funding venture backed, emerging and early stage companies.

 

Lineup of over 150 VCs and Angels that spoke and judged at VSW19 includes:

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Voigt, Chairman, North Bay Angels | Jordan Wahbeh, Managing Director, Bay Angels | Timothy Wang, Principal, The Westly Group | Kyoko Watanabe, Managing Director, DEFTA Partners | Patricia Wexler, Co-Founder & Managing Director, Starlight Ventures | James Wu, Senior Associate, M12 | Jonathan Wu, Board Director and Vice President, Sand Hill Angels | Margaret Wu, Investor, Georgian Partners | Veronica Wu, Managing Partner, Hone Capital | Wei Wu, Healthcare Investment Director, BOE Ventures | Matthew Wukasch, Managing Director, Charmides Capital | Wenz Xing, Associate, Bullpen Capital | Sue Xu, Managing Partner , Amino Capital | Lu Zhang, Founder & Managing Partner, Fusion Fund | Jimmy Zhu, Vice President, Citi Ventures | Shelley Zhuang, Founder and Managing Partner, 11.2 Capital | TX Zhuo, Managing Partner, Fika Ventures and many more.

 

 

Special Offer:

LPBI has made special arrangement for our network to receive a special discount of $450 off.

This conference will be attended by the best people in the industry. Please register early to avoid disappointment. 

 

Register Today & Save Click here.   (Use promo code “ LPBI-VIP”)

 

In addition to providing access to leading Investors, the conference will feature more than 100 pre-screened venture backed, emerging and early stage companies seeking capital, and hardcore networking. 

 

Call for TOP INNOVATORS!

Get Noticed > Get Funded > Grow Faster

 

A select group of more than 100 Top Innovators will be chosen to present their breakthrough investment opportunities to an exclusive audience of Venture Capitalists, Corporate Investors, Private Investors, Investment Bankers, and Strategic Partners.

 

  • Apply to Present / Nominate a company:

For more information or to be considered for one of the Top Innovator slots click here.

 

  • Seed Pitchfest:

If you are a seed stage company seeking angel funding of less than $1M (and have raised less than $300,000) click here to apply for the Seed stage track.

 

 We look forward to seeing you there. 

 

LPBI & youngStartup Ventures

 

LPBI Group – Executive Summary

The Challenge

“Today, there is a digital information explosion in the fields of Life Sciences and Medicine,” said Aviva Lev-Ari, Ph.D., R.N., and founder of Leaders in Pharmaceutical Business Intelligence (LPBI) Group. “To manage that challenge, we are responding to the needs of our multi-faceted audiences in order to guard against information obsolescence through transformational technologies and solutions.”

 

Our Vision:

LPBI Group generates vast scientific content via industry thought leaders through multiple platforms and makes it universally accessible.

 

About LPBI Group

Leaders in Pharmaceutical Business Intelligence Group (“LPBI Group”) is a leading, electronic scientific content-creation venture, offering real-time, original scientific content through advanced platform architecture methodologies since 2012. The company’s commitment is to synthesize, analyze and interpret complex, medical and scientific disease information through electronic publishing venues via the cloud to advance the knowledge and research efforts of the scientific and business community.

LPBI Group was created in 2012 by Dr. Aviva Lev-Ari, Ph.D., R.N. The Group has evolved into three, interrelated domains – an open-access online scientific journal, a series of 16 BioMed e-books and real-time press coverage of BioMed scientific and medical conferences. Through this transformative offering, the Group effectively meets and exceeds the information needs of researchers, scientists and industry pharmaceutical and biotechnology executives by offering real-time and currently available medical and scientific information on any subject as well as cutting-edge biomedical research innovation widely available to the scientific and non-research community. The method of curation includes synthesis, analysis and interpretation of complex medical and scientific areas.

Year over year, there is increased readership of highly valued content, which equates to more than 5,500 scientific articles with over 1.6 million readers in the online scientific journal.

 

Unique, Experienced Team

The Group employs a team of highly experienced individuals in Life Sciences, particularly within the key domains of BioMedicine, Biotechnology, Pharmaco-therapy, Medical and Information Technology, Health Care and the Life Sciences/Medicine connection.

All members of the LPBI Group team have a wealth of expertise and experience in Life Sciences and have been trained to master the methodology of scientific curation used in the articles. Those individuals include senior MDs, MD/PhDs, PharmDs and senior PhDs who expressed interest in medical writing and clinical interpretation of basic research, translational research and interpretation of clinical trials outcomes.

  • Medical Doctors: Dr. Larry H. Bernstein (retired), Dr. Justin Pearlman (Central Maine Medical Center)
  • Doctors of Philosophy: Dr. Justin Pearlman, Dr. Stephen Williams, Dr. Sudipta Saha, Dr. Ritu Saxena, Dr. Tilda Barliya, Dr. Irina Robu, Dr. Dror Nir, Dr. Aviva Lev-Ari
  • Professors in Academia: Prof. Marcus W. Feldman (Stanford University), Prof. Stephen J. Williams (Temple University), Prof. Sudipta Saha (Amity University Noida, Uttar Pradesh, India), Dr. Irina Robu (Windsor, Ontario, Canada), Adam Sonnenberg, PhD(c), Boston University
  • Executives in Biotechnology and Information Technology Industries: Amnon Danzig, MBA, Europe-Asia (business development consultant with specialization in Strategy, Corporate Finance and Human Capital, published author on Value Creation concepts), Rick Mandahl, MBA, Americas (business development consultant in emerging technologies with Biotech, Information Technology focus), Ritu Saxena (Astellas Pharma), Gail S. Thornton, M.A., PhD(c), (Merck & Co., USA, communications consultant, published author on health, wellness, communications strategy)

 

Scientific e-Books Source

The 16 BioMed e-books include topics focused on medical science, diagnostics and therapy. The books’ sustainability is maintained by online continuing updates made to articles in the online scientific journal. Each of the scientific e-books contain an abbreviated electronic table of contents, which consists of ‘live’ links from each article in the e-book to the article in the online scientific journal. Readers of the e-books can click on this link to receive the most, updated “State of Science” for each topic. This unique feature makes the e-books relevant and provides a timely source of knowledge and reference.

Highly qualified content consultants, who are senior MDs, MD/PhDs and Professors, are involved in the production and clinical interpretations of the curations. The topics for curation and the contents of the books are uniquely created and carefully edited. Dr. Aviva Lev-Ari as the editor-in-chief leads this multidisciplinary content compilation using her diverse areas of technical training and experience. It should be noted that all of LPBI’s material could, in principle, be translated into other languages.

 

Journal Ontology

Our intellectual property consists of three classes of Intellectual property assets as described in detail with live links in the following article:

eScientific Publishing a Case in Point: Evolution of Platform Architecture Methodologies and of Intellectual Property Development (Content Creation by Curation) Business Model

https://pharmaceuticalintelligence.com/2019/02/04/escientific-publishing-a-case-in-point-evolution-of-platform-architecture-methodologies-and-of-intellectual-property-development-content-creation-by-curation-business-model/

 

Three Classes of Intellectual Property Assets

These assets include an open-access online scientific journal with curated, current biomedical research; a series of 16 BioMed e-books available via Amazon in five specialties of Medicine: Cardiovascular, Genomic Medicine (a subset of Precision Medicine), Cancer, Immunology and Precision Medicine; and real-time curation of biotech and medical conferences yielding an e-Proceedings at the end of the conference in one click operation.

For example, Genomic Medicine, as an emerging medical discipline, is making an impact in the fields of oncology, pharmacology, rare and undiagnosed diseases and infectious disease. This field involves using genomic information about an individual as part of their clinical care (e.g., for diagnostic or therapeutic decision-making) and the health outcomes and policy implications of that clinical use.

The open-access online scientific journal ontology is a relational and hierarchical knowledge base allowing for expansion of the content creation process between the journal and the series of BioMed e-books, but it is not limited to these two areas. For example, one of the research categories, entitled “Interviews with Scientific Leaders,” includes in its growth plan the option of podcasts, an audio media component that will supplement the video media component used extensively in the BioMed e-Series.

The Group’s business is scalable, and the skill set needed for curation of scientific findings for clinical interpretations, as well as in other disciplines can be augmented by appropriate training in the curation methodology to be offered to experts in many fields. In our seven years together, the Group’s writers and editors generated a remarkable synergy among experts, authors and writers, which yields outstanding collaboration in producing these curations. There are many post-doctoral individuals in the biotechnology industry as well as academia who can serve as experts, authors and writers in continuing the Group’s operations, following appropriate onboarding.

Contact us

Aviva Lev-Ari, PhD, RN

Director & Founder

https://lnkd.in/eEyn69r

Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston, NJ, Salt Lake City, Berkeley, Cambridge UK, Maine, New Delhi, Palo Alto, Philadelphia, Toronto

Editor-in-Chief

http://pharmaceuticalintelligence.com

e-Mail: avivalev-ari@alum.berkeley.edu

(M) 617-775-0451

https://cal.berkeley.edu/AvivaLev-Ari,PhD,RN

SkypeID: HarpPlayer83          LinkedIn Profile        Twitter Profile


Artificial Intelligence Innovations in Cardiac Imaging

Reporter: Aviva Lev-Ari, PhD, RN

‘CTA-for-All’ fast-tracks intervention, improves LVO detection in stroke patients

A “CTA-for-All” stroke imaging policy improved large vessel occlusion (LVO) detection, fast-tracked intervention and improved outcomes in a recent study of patients with acute ischemic stroke (AIS), researchers reported in Stroke.

“Combined noncontrast computed tomography (NCCT) and CT angiography (CTA) have been championed as the new minimum standard for initial imaging of disabling stroke,” Mayer, a neurologist at Henry Ford Hospital in Detroit, and co-authors wrote in their paper. “Patient selection criteria that impose arbitrary limits on time from last known well (LKW) or baseline National Institutes of Health Stroke Scale (NIHSS) score may delay CTA and the diagnosis of LVO.”

“These findings suggest that a uniform CTA-for-All imaging policy for stroke patients presenting within 24 hours is feasible and safe, improves LVO detection, speeds intervention and can improve outcomes,” the authors wrote. “The benefit appears to primarily affect patients presenting within six hours of symptom onset.”

SOURCE

https://www.cardiovascularbusiness.com/topics/cardiovascular-imaging/cta-all-fast-tracks-intervention-improves-lvo-detection-stroke?utm_source=newsletter&utm_medium=cvb_cardio_imaging

 

How to integrate AI into the cardiac imaging pipeline

Hsiao said physicians can expect “a little bit of generalization” from neural networks, meaning they’ll work okay on data that they’ve never seen, but they’re not going to produce perfect results the first time around. If a model was trained on 3T MRI data, for example, and someone inputs 1.5T MRI data, it might not be able to analyze that information comprehensively. If some 1.5T data were fed into the model’s training algorithm, though, that could change.

According to Hsiao, all of this knowledge means little without clinical validation. He said he and his colleagues are working to integrate algorithms into the clinical environment such that a radiologist could hit a button and AI could auto-prescribe a set of images. Even better, he said, would be the ability to open up a series and have it auto-prescribe itself.

“That’s where we’re moving next, so you don’t have to hit any buttons at all,” he said.

SOURCE

https://www.cardiovascularbusiness.com/topics/cardiovascular-imaging/how-integrate-ai-cardiac-imaging-pipeline?utm_source=newsletter&utm_medium=cvb_cardio_imaging

 

DiA Imaging, IBM pair to take the subjectivity out of cardiac image analysis

SOURCE

https://www.cardiovascularbusiness.com/topics/cardiovascular-imaging/dia-imaging-ibm-partner-cardiac-image-analysis?utm_source=newsletter&utm_medium=cvb_cardio_imaging

 

FDA clears Ultromics’ AI-based CV image analysis system

Smartphone app accurately finds, identifies CV implants—and fast

According to the study, the finalized model achieved 95% sensitivity and 98% specificity.

Ferrick et al. said that since their training sample size was somewhat small and limited to a single institution, it would be valuable to validate the model externally. Still, their neural network was able to accurately identify CIEDs on chest radiographs and translate that ability into a phone app.

“Rather than the conventional ‘bench-to-bedside’ approach of translational research, we demonstrated the feasibility of ‘big data-to-bedside’ endeavors,” the team said. “This research has the potential to facilitate device identification in urgent scenarios in medical settings with limited resources.”

SOURCE

https://www.cardiovascularbusiness.com/topics/cardiovascular-imaging/smartphone-app-accurately-finds-identifies-cv-implants?utm_source=newsletter&utm_medium=cvb_cardio_imaging

Machine learning cuts cardiac MRI analysis from minutes to seconds

“Cardiovascular MRI offers unparalleled image quality for assessing heart structure and function; however, current manual analysis remains basic and outdated,” Manisty said in a statement. “Automated machine learning techniques offer the potential to change this and radically improve efficiency, and we look forward to further research that could validate its superiority to human analysis.”

It’s estimated that around 150,000 cardiac MRIs are performed in the U.K. each year, she said, and based on that number, her team thinks using AI to read scans could mean saving 54 clinician-days per year at every health center in the country.

“Our dataset of patients with a range of heart diseases who received scans enabled us to demonstrate that the greatest sources of measurement error arise from human factors,” Manisty said. “This indicates that automated techniques are at least as good as humans, with the potential soon to be ‘superhuman’—transforming clinical and research measurement precision.

SOURCE

https://www.cardiovascularbusiness.com/topics/cardiovascular-imaging/machine-learning-speeds-cardiac-mri-analysis?utm_source=newsletter&utm_medium=cvb_cardio_imaging

 

General SOURCE

From: Cardiovascular Business <news@mail.cardiovascularbusiness.com>

Reply-To: Cardiovascular Business <news@mail.cardiovascularbusiness.com>

Date: Tuesday, December 17, 2019 at 9:31 AM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: Cardiovascular Imaging | December 2019


Genetic Testing in CVD and Precision Medicine

Reporter: Aviva Lev-Ari, PhD, RN

 

See


Series A: e-Books on Cardiovascular Diseases
 

Series A Content Consultant: Justin D Pearlman, MD, PhD, FACC

VOLUME THREE

Etiologies of Cardiovascular Diseases:

Epigenetics, Genetics and Genomics

http://www.amazon.com/dp/B018PNHJ84

by  

Larry H Bernstein, MD, FCAP, Senior Editor, Author and Curator

and

Aviva Lev-Ari, PhD, RN, Editor and Curator

Genetic Testing in CVD and Precision Medicine

Based on

. 2018 Apr; 3(2): 313–326.
Published online 2018 May 30. doi: 10.1016/j.jacbts.2018.01.003
PMCID: PMC6059349
PMID: 30062216

Cardiovascular Precision Medicine in the Genomics Era

Alexandra M. Dainis, BSa and Euan A. Ashley, BSc, MB ChB, DPhila,b,c,

 

In 2010, we introduced an approach to the evaluation of a personal genome in a clinical context . A patient with a family history of coronary artery disease (CAD) and sudden death was evaluated by a cardiac clinical team in conjunction with whole genome sequencing and interpretation. The genomic analysis revealed an increased genetic risk for myocardial infarction and type 2 diabetes. In addition, a pharmacogenomics analysis was performed to assess how the genetics of the patient might influence response to certain drugs, including lipid-lowering therapies and warfarin . This clinical assessment, which focused heavily on cardiovascular risk, suggested that whole genome sequencing might provide clinically relevant information for patients.

A 2011 joint statement from the Heart Rhythm Society and the European Heart Rhythm association recommended genetic testing as a class I indication for patients with a number of channelopathies and cardiomyopathies, including long QT syndrome (LQTS), arrhythmogenic right ventricular cardiomyopathy, familial dilated cardiomyopathy (DCM), and hypertrophic cardiomyopathy (HCM) . Similarly, a statement from the American Heart Association and the American College of Cardiology recommended genetic testing for HCM, DCM, and thoracic aortic aneurysms to facilitate familial cascade screening and deduce causative mutations .

The diagnostic power of genetic testing is significant across the spectrum of CVDs, ranging from cardiomyopathies to life-threatening arrhythmias . In the clinic, genetic testing can:

  • 1.

    clarify disease diagnoses: genetic testing can help to clarify the diagnosis of diseases that cause similar clinical presentation (e.g., cardiac hypertrophy could be TTR amyloidosis, Fabry disease, or sarcomeric HCM);

  • 2.

    facilitate cascade screening: genetic testing can help to identify relatives at risk for CVD before disease symptoms manifest if a disease-associated variant is found in a proband and then screened for in relatives;

  • 3.

    direct more precise therapy: genetic testing can help physicians choose appropriate treatments and plan appropriate timing of those treatments. For example, inherited connective tissue disease due to variants in ACTA2MYH11, or TGFBR2 might prompt consideration of surgical intervention at a smaller aortic aneurysm diameter ; and

  • 4.

    identify patients for targeted therapies: targeted medical therapies, including antibody-based therapeutics, gene editing, and silencing technologies, are available or under development for several genetic diseases, including LQTS, Duchenne muscular dystrophy (DMD), TTR cardiac amyloidosis , and Fabry disease .

REFERENCES

7. Ashley E.A., Butte A.J., Wheeler M.T. Clinical assessment incorporating a personal genome. Lancet. 2010;375:1525–1535. [PMC free article] [PubMed[]
8. Ackerman M.J., Priori S.G., Willems S. HRS/EHRA expert consensus statement on the state of genetic testing for the channelopathies and cardiomyopathies: this document was developed as a partnership between the Heart Rhythm Society (HRS) and the European Heart Rhythm Association (EHRA) Europace. 2011;13:1077–1109. [PubMed[]
9. Gersh B.J., Maron B.J., Bonow R.O. 2011 ACCF/AHA guideline for the diagnosis and treatment of hypertrophic cardiomyopathy: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2011;58:2703–2738. [PubMed[]
10. Harper A.R., Parikh V.N., Goldfeder R.L., Caleshu C., Ashley E.A. Delivering clinical grade sequencing and genetic test interpretation for cardiovascular medicine. Circ Cardiovasc Genet. 2017;10(2) [PubMed[]
11. Walsh R., Thomson K.L., Ware J.S. Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples. Genet Med. 2017;19:192–203. [PMC free article] [PubMed[]
12. Sturm A.C., Hershberger R.E. Genetic testing in cardiovascular medicine: current landscape and future horizons. Curr Opin Cardiol. 2013;28:317–325. [PubMed[]
13. Caleshu C., Ashley E. Genetic testing for cardiovascular conditions predisposing to sudden death. In: Wilson M.G., Drezner J., editors. IOC Manual of Sports Cardiology. Wiley & Sons, Ltd; Hoboken, NJ: 2016. pp. 175–186. []
14. Benson M.D., Dasgupta N.R., Rissing S.M., Smith J., Feigenbaum H. Safety and efficacy of a TTR specific antisense oligonucleotide in patients with transthyretin amyloid cardiomyopathy. Amyloid. 2017;24:217–223. [PubMed[]
15. Parikh V.N., Ashley E.A. Next-generation sequencing in cardiovascular disease: present clinical applications and the horizon of precision medicine. Circulation. 2017;135:406–409. [PMC free article] [PubMed[]

SOURCE

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6059349/


AI Acquisitions by Big Tech Firms Are Happening at a Blistering Pace: 2019 Recent Data by CBI Insights

Reporter: Stephen J. Williams, Ph.D.

Recent report from CBI Insights shows the rapid pace at which the biggest tech firms (Google, Apple, Microsoft, Facebook, and Amazon) are acquiring artificial intelligence (AI) startups, potentially confounding the AI talent shortage that exists.

The link to the report and free download is given here at https://www.cbinsights.com/research/top-acquirers-ai-startups-ma-timeline/

Part of the report:

TECH GIANTS LEAD IN AI ACQUISITIONS

The usual suspects are leading the race for AI: tech giants like Facebook, Amazon, Microsoft, Google, & Apple (FAMGA) have all been aggressively acquiring AI startups in the last decade.

Among the FAMGA companies, Apple leads the way, making 20 total AI acquisitions since 2010. It is followed by Google (the frontrunner from 2012 to 2016) with 14 acquisitions and Microsoft with 10.

Apple’s AI acquisition spree, which has helped it overtake Google in recent years, was essential to the development of new iPhone features. For example, FaceID, the technology that allows users to unlock their iPhone X just by looking at it, stems from Apple’s M&A moves in chips and computer vision, including the acquisition of AI company RealFace.

In fact, many of FAMGA’s prominent products and services came out of acquisitions of AI companies — such as Apple’s Siri, or Google’s contributions to healthcare through DeepMind.

That said, tech giants are far from the only companies snatching up AI startups.

Since 2010, there have been 635 AI acquisitions, as companies aim to build out their AI capabilities and capture sought-after talent (as of 8/31/2019).

The pace of these acquisitions has also been increasing. AI acquisitions saw a more than 6x uptick from 2013 to 2018, including last year’s record of 166 AI acquisitions — up 38% year-over-year.

In 2019, there have already been 140+ acquisitions (as of August), putting the year on track to beat the 2018 record at the current run rate.

Part of this increase in the pace of AI acquisitions can be attributed to a growing diversity in acquirers. Where once AI was the exclusive territory of major tech companies, today, smaller AI startups are becoming acquisition targets for traditional insurance, retail, and healthcare incumbents.

For example, in February 2018, Roche Holding acquired New York-based cancer startup Flatiron Health for $1.9B — one of the largest M&A deals in artificial intelligence. This year, Nike acquired AI-powered inventory management startup Celect, Uber acquired computer vision company Mighty AI, and McDonald’s acquired personalization platform Dynamic Yield.

Despite the increased number of acquirers, however, tech giants are still leading the charge. Acquisitive tech giants have emerged as powerful global corporations with a competitive advantage in artificial intelligence, and startups have played a pivotal role in helping these companies scale their AI initiatives.

Apple, Google, Microsoft, Facebook, Intel, and Amazon are the most active acquirers of AI startups, each acquiring 7+ companies.

To read more on recent Acquisitions in the AI space please see the following articles on this Open Access Online Journal

Diversification and Acquisitions, 2001 – 2015: Trail known as “Google Acquisitions” – Understanding Alphabet’s Acquisitions: A Sector-By-Sector Analysis

Clarivate Analytics expanded IP data leadership by new acquisition of the leading provider of intellectual property case law and analytics Darts-ip

2019 Biotechnology Sector and Artificial Intelligence in Healthcare

Forbes Opinion: 13 Industries Soon To Be Revolutionized By Artificial Intelligence

Artificial Intelligence and Cardiovascular Disease

Multiple Barriers Identified Which May Hamper Use of Artificial Intelligence in the Clinical Setting

Top 12 Artificial Intelligence Innovations Disrupting Healthcare by 2020

The launch of SCAI – Interview with Gérard Biau, director of the Sorbonne Center for Artificial Intelligence (SCAI).

 


In Data Science, A Pioneer Practitioner’s Portfolio of Algorithm-based Decision Support Systems for Operations Management in Several Industrial Verticals: Analytics Designer, Aviva Lev-Ari, PhD, RN

An overview of Data Science as a discipline is presented in

Data Science & Analytics: What do Data Scientists Do in 2020 and a Pioneer Practitioner’s Portfolio of Algorithm-based Decision Support Systems for Operations Management in Several Industrial Verticals

 

On this landscape about IT, The Internet, Analytics, Statistics, Big Data, Data Science and Artificial Intelligence, I am to tell stories on my own pioneering work in data science, Algorithm-based decision support systems design for different organizations in several sectors of the US economy:

Images on 12/7/2019

  • Startups:
  1. TimeØ Group – The leader in Digital Marketplaces Design
  2. Concept Five Technologies, Inc. – Commercialization of DoD funded technologies
  3. MDSS, Inc. – SAAS in Analytical Services
  4. LPBI Group – Pharmaceutical & Media
  • Top Tier Management Consulting: SRI International, Monitor Group;
  • OEM: Amdahl Corporation;
  • Top 6th System Integrator: Perot System Corporation;
  • FFRDC: MITRE Corporation.
  • Publishing industry: was Director of Research at McGraw-Hill/CTB.
  • Northeastern University, Researcher on Cardiovascular Pharmacotherapy at Bouve College of Health Sciences (Independent research guided by Professor of Pharmacology)

Type of institutions:

  • For-Profit corporations: Amdahl Corp, PSC, McGraw-Hill
  • For-Profit Top Tier Consulting: Monitor Company, Now Deloitte
  • Not-for-Profit Top Tier Consulting: SRI International
  • FFRDC: MITRE
  • Pharmaceutical & Media Start up in eScientific Publishing: LPBI Group:
  1. Developers of Curation methodology for e-Articles [N = 5,700],
  2. Developers of electronic Table of Contents for e-Books in Medicine [N = 16, https://lnkd.in/ekWGNqA] and
  3. Developers of Methodologies for real time press coverage and production of e-Proceedings of Biotech Conferences [N = 70].

 

Autobiographical Annotations: Tribute to My Professors

 

Pioneering implementations of analytics to business decision making: contributions to domain knowledge conceptualization, research design, methodology development, data modeling and statistical data analysis: Aviva Lev-Ari, UCB, PhD’83; HUJI MA’76

https://pharmaceuticalintelligence.com/2018/05/28/pioneering-implementations-of-analytics-to-business-decision-making-contributions-to-domain-knowledge-conceptualization-research-design-methodology-development-data-modeling-and-statistical-data-a/

Recollections of Years at UC, Berkeley, Part 1 and Part 2

  • Recollections: Part 1 – My days at Berkeley, 9/1978 – 12/1983 – About my doctoral advisor, Allan Pred, other professors and other peers

https://pharmaceuticalintelligence.com/2018/03/15/recollections-my-days-at-berkeley-9-1978-12-1983-about-my-doctoral-advisor-allan-pred-other-professors-and-other-peer/

  • Recollections: Part 2 – “While Rolling” is preceded by “While Enrolling” Autobiographical Alumna Recollections of Berkeley – Aviva Lev-Ari, PhD’83

https://pharmaceuticalintelligence.com/2018/05/24/recollections-part-2-while-rolling-is-preceded-by-while-enrolling-autobiographical-alumna-recollections-of-berkeley-aviva-lev-ari-phd83/

Accomplishments

The Digital Age Gave Rise to New Definitions – New Benchmarks were born on the World Wide Web for the Intangible Asset of Firm’s Reputation: Pay a Premium for buying e-Reputation

For @AVIVA1950, Founder, LPBI Group @pharma_BI: Twitter Analytics [Engagement Rate, Link Clicks, Retweets, Likes, Replies] & Tweet Highlights [Tweets, Impressions, Profile Visits, Mentions, New Followers] https://analytics.twitter.com/user/AVIVA1950/tweets

Thriving at the Survival Calls during Careers in the Digital Age – An AGE like no Other, also known as, DIGITAL

Professional Self Re-Invention: From Academia to Industry – Opportunities for PhDs in the Business Sector of the Economy

Reflections on a Four-phase Career: Aviva Lev-Ari, PhD, RNMarch 2018

Was prepared for publication in American Friends of the Hebrew University (AFHU), May 2018 Newsletter, Hebrew University’s HUJI Alumni Spotlight Section.

Aviva Lev-Ari’s profile was up on 5/3/2018 on AFHU website under the Alumni Spotlight at https://www.afhu.org/

On 5/11/2018, Excerpts were Published in AFHU e-news.

https://us10.campaign-archive.com/?u=5c25136c60d4dfc4d3bb36eee&id=757c5c3aae&e=d09d2b8d72

https://www.afhu.org/2018/05/03/aviva-lev-ari/