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Archive for the ‘BioIT: BioInformatics’ Category

The BioPharma Industry’s Unrealized Wealth of Data, by Ben Szekely, Vice President, Cambridge Semantics

Posted in Big Data, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceuticall R&D Informatics, Clinical Genomics, Cancer Informatics, Biomedical Measurement Science, Computational Biology/Systems and Bioinformatics, HealthCare IT, Intelligent Information Systems, Meta-analysis of transcriptome data, tagged on June 21, 2017| Leave a Comment »


The BioPharma Industry’s Unrealized Wealth of Data, by Ben Szekely, Vice President, Cambridge Semantics

Reporter: Aviva Lev-Ari, PhD, RN

 

 

The BioPharma Industry’s Unrealized Wealth of Data

by Ben Szekely, Vice President of Solutions and Pre-sales, Cambridge Semantics

 

Solving the great medical challenges of our time reside within patient data. Clinical trial data, real-world evidence, patient feedback, genetic data, wearables data and adverse event reports contain signals to target medicines at the right patient populations, improve overall safety, and uncover the next blockbuster therapy for unmet medical needs.

However, data sources are large, diverse, multi-structured, messy and highly regulated presenting numerous challenges. As result, extracting value from data are slow to come and require manual work or long-poll dependencies on IT and Data Science teams.

Fortunately, there are new ways being adopted to take better advantage of the ever-growing volumes of patient data.  Called ‘Smart’ Patient Data Lakes (SPDL), these tools create an Enterprise Knowledge Graph built upon foundational and open Semantic Web technology standards, providing rich descriptions of data and flexibility end-to-end.  With the SPDL, biopharma researchers can:

  • Quickly on-board new data without requiring up-front modeling or mapping, ingesting data from any source versus months or weeks of preparation
  • Dynamically map and prepare data at analytics time
  • Horizontally scale in cloud or on-prem infrastructure to 100’s of nodes – allowing billions of facts to be analyzed, queried and explored in real-time   

The world’s BioPharma and research institutions are sitting on a wealth of highly differentiating and life-saving data and should begin to realize its value via Smart Patient Data Lakes (SPDL).

 

 

CONTACT: Nadia Haidar

Global Results Communications ∙ 949-278-7328 ∙ nhaidar@globalresultspr.com

 

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Sperm Analysis by Smart Phone

Posted in Affordable Care Act, Bio Instrumentation in Experimental Life Sciences Research, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceuticall R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Networks, Biomarkers & Medical Diagnostics, Biomedical Measurement Science, Cell Biology, Computational Biology/Systems and Bioinformatics, Health Economics and Outcomes Research, HealthCare IT, Image Processing/Computing, Medical Devices R&D and Inventions, Medical Imaging Technology, Reproductive Biology & Bio Instrumentation, tagged , , , , , on March 29, 2017| 3 Comments »


Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Low sperm count and motility are markers for male infertility, a condition that is actually a neglected health issue worldwide, according to the World Health Organization. Researchers at Harvard Medical School have developed a very low cost device that can attach to a cell phone and provides a quick and easy semen analysis. The device is still under development, but a study of the machine’s capabilities concludes that it is just as accurate as the elaborate high cost computer-assisted semen analysis machines costing tens of thousands of dollars in measuring sperm concentration, sperm motility, total sperm count and total motile cells.

 

The Harvard team isn’t the first to develop an at-home fertility test for men, but they are the first to be able to determine sperm concentration as well as motility. The scientists compared the smart phone sperm tracker to current lab equipment by analyzing the same semen samples side by side. They analyzed over 350 semen samples of both infertile and fertile men. The smart phone system was able to identify abnormal sperm samples with 98 percent accuracy. The results of the study were published in the journal named Science Translational Medicine.

 

The device uses an optical attachment for magnification and a disposable microchip for handling the semen sample. With two lenses that require no manual focusing and an inexpensive battery, it slides onto the smart phone’s camera. Total cost for manufacturing the equipment: $4.45, including $3.59 for the optical attachment and 86 cents for the disposable micro-fluidic chip that contains the semen sample.

 

The software of the app is designed with a simple interface that guides the user through the test with onscreen prompts. After the sample is inserted, the app can photograph it, create a video and report the results in less than five seconds. The test results are stored on the phone so that semen quality can be monitored over time. The device is under consideration for approval from the Food and Drug Administration within the next two years.

 

With this device at home, a man can avoid the embarrassment and stress of providing a sample in a doctor’s clinic. The device could also be useful for men who get vasectomies, who are supposed to return to the urologist for semen analysis twice in the six months after the procedure. Compliance is typically poor, but with this device, a man could perform his own semen analysis at home and email the result to the urologist. This will make sperm analysis available in the privacy of our home and as easy as a home pregnancy test or blood sugar test.

 

The device costs about $5 to make in the lab and can be made available in the market at lower than $50 initially. This low cost could help provide much-needed infertility care in developing or underdeveloped nations, which often lack the resources for currently available diagnostics.

 

References:

 

https://www.nytimes.com/2017/03/22/well/live/sperm-counts-via-your-cellphone.html?em_pos=small&emc=edit_hh_20170324&nl=well&nl_art=7&nlid=65713389&ref=headline&te=1&_r=1

 

http://www.npr.org/sections/health-shots/2017/03/22/520837557/a-smartphone-can-accurately-test-sperm-count

 

https://www.ncbi.nlm.nih.gov/pubmed/28330865

 

http://www.sciencealert.com/new-smartphone-microscope-lets-men-check-the-health-of-their-own-sperm

 

https://www.newscientist.com/article/2097618-are-your-sperm-up-to-scratch-phone-microscope-lets-you-check/

 

https://www.dezeen.com/2017/01/19/yo-fertility-kit-men-test-sperm-count-smartphone-design-technology-apps/

 

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2017 Agenda – BioInformatics: Track 6: BioIT World Conference & Expo ’17, May 23-35, 2017, Seaport World Trade Center, Boston, MA

Posted in BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceuticall R&D Informatics, Clinical Genomics, Cancer Informatics, Conference Coverage with Social Media on January 12, 2017| Leave a Comment »


2017 Agenda – BioInformatics: Track 6: BioIT World Conference & Expo ’17, May 23-35, 2017, Seaport World Trade Center, Boston, MA

Reporter: Aviva Lev-Ari, PhD, RN

2017bioit-bit-mini-logo

 

 bioinformatics

http://www.bio-itworldexpo.com/Bio-It_Expo_Content.aspx?id=140955

  #BioIT17

TUESDAY, MAY 23

7:00 am Workshop Registration and Morning Coffee

8:00 – 11:30 Recommended Morning Pre-Conference Workshops*

(W4) Data Visualization to Accelerate Biological Discovery

12:30 – 4:00 pm Recommended Afternoon Pre-Conference Workshops*

(W13) Proteogenomics: Integration of Genomics and Proteomics Data

* Separate registration required.

2:00 – 6:00 Main Conference Registration Open

4:00 PLENARY KEYNOTE SESSION

Click here for detailed information

5:00 – 7:00 Welcome Reception in the Exhibit Hall with Poster Viewing

WEDNESDAY, MAY 24

7:00 am Registration Open and Morning Coffee

8:00 PLENARY KEYNOTE SESSION

Click here for detailed information

9:50 Coffee Break in the Exhibit Hall with Poster Viewing

APPLICATIONS & SOLUTIONS FOR DATA SHARING AND DECISION MAKING

10:50 Chairperson’s Remarks

Kevin Merlo, BioSafety Development Engineer, Dassault Systemes

11:00 Innovative Data Integration Applicable for Therapeutic Protein Development 2.0

Wolfgang Paul, Group Leader and Senior Scientist, Large Molecule Research, Roche

Therapeutic proteins are registered including sequence, structural and functional data and information. Millions of data points are captured during the development of Roche’s innovative therapeutic proteins in data warehouse used by DAMAS (data acquisition, management and analyses system). Fast access and visualization of relevant process and analytical data drive scientific discussion and decision making. Analyzing the stored big data is key towards process development of therapeutic proteins 2.0.

11:30 Informatics – A Silver Bullet for Pharmaceutical Sciences?

William Loging, Ph.D., Associate Professor of Genomics & Head, Production Bioinformatics, Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai

The Pharmaceutical Sciences field is in constant search for the next big innovative push that will increase the success rate of drug programs. The fields of computational chemistry, structural bioinformatics – just to name a few – have changed the way drug researchers look for and identify novel drug candidates. Utilizing more than 15 years of Pharmaceutical experience, and using real world examples of high provide drug projects, this talk will provide practical steps for the merger of informatics and the strategic approaches needed for drug discovery success.

12:00 pm Big Data-Driven Bioinformatics

Frank Lee, Ph.D., Healthcare Life Sciences Industry Leader, Software Defined Infrastructure, IBM Systems, IBM

IBM will discuss the IBM Reference Architecture for Genomics, its new features, and case studies: hybrid cloud with integrated workload and data management for high performance genomics analytics; container technologies for migrating and sharing application and data; and application portal and metadata engine for global access to and searching of distributed resources. A demo of a hybrid cloud-based bioinformatics solution will follow.

12:30 Session Break

12:40 Luncheon Presentation I to be Announced

1:10 Luncheon Presentation II to be Announced

1:40 Session Break

STANDARDS FOR CHEMICAL STRUCTURES

1:50 Chairperson’s Remarks

1:55 PANEL DISCUSSION: Linking and Finding Information Using the IUPAC InChI Standard for Chemical Structures

Steve Heller, Ph.D., Project Director, InChI Trust; Scientific Information Consultant (Moderator)

Evan Bolton, Ph.D., Lead Scientist, National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), and National Institutes of Health (NIH)

Keith T. Taylor, BSc, Ph.D., MRSC, Principal, Ladera Consultancy

Tyler Peryea, Informatics Scientist, National Center for Advancing Translational Sciences (NCATS)

Lawrence Callahan, Ph.D., Chemist, Substance Registration System, Office of Critical Path Programs, Food and Drug Administration (FDA)

This session will highlight on-going efforts to strengthen and expand the non-proprietary IUPAC International Chemical Identifier (InChI) standard for chemical structures and its hashed-form, the InChIKey. Information standards are critical to enable effective communication of scientific content. Funding to maintain InChI comes from most major publishers and database providers as well as governmental agencies (NIH, FDA and NIST). The InChI is an open-source, widely adopted standard found in most chemical information containing databases, including those from Chemical Abstracts, Reaxys, ChEMBL, OpenPHACTS, PubChem, DrugBank, PDB, Sigma-Aldrich, and many others, such as internal Pharma corporate databases. InChI is an addition to a database, not a replacement. With the implementation of the ISO identification of medicinal products (IDMP) and the related ISO 11238 standards, adding and having an InChI will allow for an easier, effective, and more complete search for information on a particular drug.

2:55 Sponsored Presentation (Opportunity Available)

3:10 Integrated Informatics for Biologics Discovery

Robert Brown, Ph.D., Vice President, Product Marketing, Dotmatics

3:25 Refreshment Break in the Exhibit Hall with Poster Viewing

MACHINE LEARNING TECHNIQUES AND APPLICATIONS TO PERFORM BIG DATA ANALYTICS ON –OMICS DATA

4:00 Building Disease Networks Using Text Mining and Machine Learning Techniques

Kamal Rawal, Ph.D., Assistant Professor, Biotech and Bioinformatics, Jaypee Institute of Information Technology

Obesity is a global epidemic affecting over 1.5 billion people and is one of the risk factors for several diseases such as type 2 diabetes mellitus and hypertension. We have constructed a comprehensive map of the molecules reported to be implicated in obesity. Using text mining & deep curation strategies combined with omics data, we have explained the therapeutics and side effects of several drugs (i.e., orlistat) at network level.

4:20 Big Data and Systems Biology: From Genome to Phenome (and Everything in Between)

Dan Jacobson, Ph.D., Computational Biologist, Oak Ridge National Laboratory

4:40 Novel Feature Selection Strategies for Enhanced Predictive Modeling and Deep Learning in the Biosciences

Tom Chittenden, Ph.D., D.Phil., Lecturer and Senior Biostatistics and Mathematical Biology Consultant, Harvard Medical School

We have built a robust AI approach that precisely assesses pathogenicity for all genomic missense variants. Coupled with our advanced deepCODE mathematical statistics feature selection strategy for constructing deep learning models, we are able to quantitatively integrate a priori pathway-based biological knowledge with multiple types of high-throughput omics data.

5:00 Network Analysis for Drug Discovery: Benchmarking Results and Best Practices Reported by CBDD Consortium

Marina Bessarabova, Ph.D., Senior Director, Discovery and Translational Science, Life Sciences Professional Services, Clarivate Analytics (Formerly the IP & Science Business of Thomson Reuters)

A large number of advanced approaches to network analysis of -omics data were developed by academia groups in the past 15 years. Adoption of these approaches in drug development requires thorough review of the published approaches, implementation of methods identified as potentially applicable to drug development and benchmarking of the methods with an aim to establish best practices for application of the methods to diseases and mechanism of action understanding, target identification, drug repositioning, patient stratification, biomarker discovery, and drug combination effect prediction. CBDD (Computational Biology Methods for Drug Discovery) is a precompetitive consortium between Novartis, Pfizer, Sanofi, Janssen, Regeneron, UCB, Roche, Takeda, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Merck and Clarivate Analytics (formally Thomson Reuters) focused on adoption of network analysis approaches in drug development: literature review, method implementation and benchmarking. Benchmarking results and best practices for application of network analysis in drug development established by members of the program will be shared during the presentation.

5:30 15th Anniversary Celebration in the Exhibit Hall with Poster Viewing and Best of Show Awards

THURSDAY, MAY 25

7:00 am Registration Open and Morning Coffee

8:00 PLENARY KEYNOTE SESSION & AWARDS PROGRAM

8:05 Benjamin Franklin Awards and Laureate Presentation

8:35 Best Practices Awards Program

8:50 Plenary Keynote

Click here for detailed information

9:45 Coffee Break in the Exhibit Hall and Poster Competition Winners Announced

DATA COMPUTING AND BIOINFORMATICS IN AGRO CHEMICALS AND BIOTECHNOLOGY: CHALLENGES AND OPPORTUNITIES

10:30 Chairperson’s Remarks

Bino John, Ph.D., Computational Biology Group Leader, Dow AgroSciences LLC

10:40 How Biotech and Big Data Are Changing Agro Industry

Bino John, Ph.D., Computational Biology Group Leader, Dow AgroSciences LLC

More than 70% of the increase in food production in the next 50 years is expected to come from technological advances. Indeed, recent advances in genomics and phenomics are beginning to transform the Agro-industry, whereby creating new opportunities for informatics disciplines. While informatics needs in managing, analyzing, and visualizing big data share commonalties between Agro and the biomedical communities, Agro companies face unprecedented challenges in big biological data, generally larger than their peers in the biomedical community.

11:00 Offering Outcomes: How Digital Farming Data Is Enabling New Business Models

Tobias Menne, Global Head of Digital Farming, Bayer

11:20 Building the Next-Generation R&D IT Infrastructure for Small Molecule Discovery

Paimun Amini, Chemistry IT Lead, R&D IT, Monsanto Company

Barrett Foat, Ph.D., Data Science Team Lead, Agricultural Productivity Innovations, Monsanto

The Pharma boom in the 90s & 2000s led to the emergence of a rich ecosystem of software companies focused on delivering the IT needs for small molecule discovery. Today, cloud data storage, IoT, and the growth of predictive analytics present new opportunities for the evolution of the R&D pipeline. New technologies allow for integrated software and hardware solutions that optimize productivity while removing the risk of technical debt.

11:40 Sponsored Presentation (Opportunity Available)

12:10 pm Session Break

12:20 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

1:20 Dessert Refreshment Break in the Exhibit Hall with Poster Viewing

LOOKING BEYOND THE GENOME OF THE PATIENT: DATA, ANALYSIS AND TOOLS TO IMPROVE BETTER DISEASE UNDERSTANDING FOR CURRENT TREATMENTS AND DRUG DEVELOPMENT

1:55 Chairperson’s Remarks

Michael N. Liebman, Ph.D., Managing Director, IPQ Analytics, LLC and Strategic Medicine, Inc.

2:00 Distinguishing between Precision Medicine and Accurate Medicine: Application to Heart Failure Patients and Clinical Practice

Michael N. Liebman, Ph.D., IPQ Analytics, LLC and Strategic Medicine, Inc.

Increasingly, patient stratification based on genomic analysis is being considered in disease management. Critically, the need to understand real world medical practice and real world patient complexities extends far beyond the genome of the patient. We have shown examples of this complexity in heart disease and how this impacts development of clinical guidelines, trial design, and development of new patient management approaches.

2:30 CARPEDIEM – Comorbidity and Risk Profiles Evaluation in Diabetes and Heart Morbidities

Sabrina Molinaro, Psy.D., Ph.D., Head, Department of Epidemiology and Health Services, Institute of Clinical Physiology, National Research Council of Italy

Our project uniquely develops a patient record that includes clinical and individual factors (EHR-driven phenotyping) that will be validated through the comparison of existing standards for building new risk algorithms. An understanding of the current limitations and biases of risk profiling in heart disease and diabetes and how an extended, integrated database and automatic rule-based classification system can be used to improve patient management.

3:00 PANEL DISCUSSION: Precision Medicine vs. Accurate Medicine: The Need to Understand Real World Medicine and Real World Patients

Michael N. Liebman, Ph.D., IPQ Analytics, LLC and Strategic Medicine, Inc. (Moderator)

Charles Barr, M.D., MPH, Group Medical Director and Head, Evidence Science and Innovation, Genentech

Hal Wolf, Director, National Leader of Information and Digital Health Strategy, The Chartis Group

4:00 Conference Adjourns

SOURCE

http://www.bio-itworldexpo.com/bioinformatics/

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Dr. Doudna: RNA synthesis capabilities of Synthego’s team represent a significant leap forward for Synthetic Biology

Posted in Big Data, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceuticall R&D Informatics, Clinical Genomics, Cancer Informatics, Computational Biology/Systems and Bioinformatics, CRISPR/Cas9 & Gene Editing, Genome Biology, HealthCare IT, Intelligent Information Systems, mRNA Therapeutics on January 4, 2017| Leave a Comment »


Dr. Doudna: RNA synthesis capabilities of Synthego’s team represent a significant leap forward for Synthetic Biology

Reporter: Aviva Lev-Ari, PhD, RN

 

Synthego Raises $41 Million From Investors, Including a Top Biochemist

By MICHAEL J. de la MERCED JAN. 4, 2017

Jennifer Doudna, a professor at the University of California, Berkeley who helped develop advanced gene-editing techniques, is among investors in Synthego.

Synthego also drew in Dr. Doudna, who had crossed paths with the company’s head of synthetic biology at various industry conferences. According to Mr. Dabrowski, the money from her trust represents the single-biggest check from a non-institutional investor that the start-up has raised.

Synthego’s new funds will help the company take its products to a more global customer base, as well as broaden its offerings. The longer-term goal, Mr. Dabrowski said, is to help fully automate biotech research and take care of much of the laboratory work that scientists currently handle themselves.

The model is cloud technology, where companies rent out powerful remote server farms to handle their computing needs rather than rely on their own hardware.

“We’ll be able to do their full research workflow,” he said. “If you look at how cloud computing developed, it used to be that every company handled their server farm. Now it’s all handled in the cloud.”

SOURCE

Other related articles published in this Open Access Online Scientific Journal include the following:

UPDATED – Status “Interference — Initial memorandum” – CRISPR/Cas9 – The Biotech Patent Fight of the Century: UC, Berkeley and Broad Institute @MIT

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/01/06/status-interference-initial-memorandum-crisprcas9-the-biotech-patent-fight-of-the-century/

 

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Translation of whole human genome sequencing to clinical practice: The Joint Initiative for Metrology in Biology (JIMB) is a collaboration between NIST and Stanford University

Posted in Big Data, Bio Instrumentation in Experimental Life Sciences Research, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceuticall R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Networks, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Biomedical Measurement Science, Cell Biology, Cell Biology, Signaling & Cell Circuits, Chemical Genetics, Clinical Genomics, DNA repair, Gene Regulation, Gene Regulation and Evolution, Gene Therapy & Gene Editing Development, Genetics & Innovations in Treatment, Genome Biology, Genomic Testing: Methodology for Diagnosis, Variation in human protein-coding regions on December 18, 2016| Leave a Comment »


Translation of whole human genome sequencing to clinical practice: The Joint Initiative for Metrology in Biology (JIMB) is a collaboration between the National Institute of Standards & Technology (NIST) and Stanford University.

Reporter: Aviva Lev-Ari, PhD, RN

 

JIMB’s mission is to advance the science of measuring biology (biometrology). JIMB is pursuing fundamental research, standards development, and the translation of products that support confidence in biological measurements and reliable reuse of materials and results. JIMB is particularly focused on measurements and technologies that impact, are related to, or enabled by ongoing advances in and associated with the reading and writing of DNA.

Stanford innovators and industry entrepreneurs have joined forces with the measurement experts from NIST to create a new engine powering the bioeconomy. It’s called JIMB — “Jim Bee” — the Joint Initiative for Metrology in Biology. JIMB unites people, platforms, and projects to underpin standards-based research and innovation in biometrology.

Genome in a Bottle
Authoritative Characterization of
Benchmark Human Genomes

The Genome in a Bottle Consortium is a public-private-academic consortium hosted by NIST to develop the technical infrastructure (reference standards, reference methods, and reference data) to enable translation of whole human genome sequencing to clinical practice. The priority of GIAB is authoritative characterization of human genomes for use in analytical validation and technology development, optimization, and demonstration. In 2015, NIST released the pilot genome Reference Material 8398, which is genomic DNA (NA12878) derived from a large batch of the Coriell cell line GM12878, characterized for high-confidence SNPs, indel, and homozygous reference regions (Zook, et al., Nature Biotechnology 2014).

There are four new GIAB reference materials available.  With the addition of these new reference materials (RMs) to a growing collection of “measuring sticks” for gene sequencing, we can now provide laboratories with even more capability to accurately “map” DNA for genetic testing, medical diagnoses and future customized drug therapies. The new tools feature sequenced genes from individuals in two genetically diverse groups, Asians and Ashkenazic Jews; a father-mother-child trio set from Ashkenazic Jews; and four microbes commonly used in research. For more information click here.  To purchase them, visit:

Data and analyses are publicly available (GIAB GitHub). A description of data generated by GIAB is published here. To standardize best practices for using GIAB genomes for benchmarking, we are working with the Global Alliance for Genomics and Health Benchmarking Team (benchmarking tools).

High-confidence small variant and homozygous reference calls are available for NA12878, the Ashkenazim trio, and the Chinese son with respect to GRCh37.  Preliminary high-confidence calls with respect to GRCh38 are also available for NA12878.   The latest version of these calls is under the latest directory for each genome on the GIAB FTP.

The consortium was initiated in a set of meetings in 2011 and 2012, and the consortium holds open, public workshops in January at Stanford University in Palo Alto, CA and in August/September at NIST in Gaithersburg, MD. Slides from workshops and conferences are available online. The consortium is open and welcomes new participants.

SOURCE
http://jimb.stanford.edu/giab

Stanford innovators and industry entrepreneurs have joined forces with the measurement experts from NIST to create a new engine powering the bioeconomy. It’s called JIMB — “Jim Bee” — the Joint Initiative for Metrology in Biology. JIMB unites people, platforms, and projects to underpin standards-based research and innovation in biometrology.

JIMB World Metrology Day Symposium
May 22, 2017
JIMB WORLD METROLOGY DAY SYMPOSIUM

Stanford University

JIMB’s mission is to motivate standards-based measurement innovation to facilitate translation of basic science and technology development breakthroughs in genomics and synthetic biology.

By advancing biometrology, JIMB will push the boundaries of discovery science, accelerate technology development and dissemination, and generate reusable resources.

 SOURCE

http://jimb.stanford.edu/

VIEW VIDEO

https://player.vimeo.com/video/184956195?wmode=opaque&api=1″,”url”:”https://vimeo.com/184956195″,”width”:640,”height”:360,”providerName”:”Vimeo”,”thumbnailUrl”:”https://i.vimeocdn.com/video/594555038_640.jpg”,”resolvedBy”:”vimeo”}” data-block-type=”32″>

Other related articles published in this Open Access Online Scientific Journal include the following:

“Genome in a Bottle”: NIST’s new metrics for Clinical Human Genome Sequencing

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2012/09/06/genome-in-a-bottle-nists-new-metrics-for-clinical-human-genome-sequencing/

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Etiologies of Cardiovascular Diseases: Epigenetics, Genetics and Genomics: Request for Book Review Writing on Amazon.com

Posted in BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceuticall R&D Informatics, Clinical Genomics, Cancer Informatics, Cardiac and Cardiovascular Surgical Procedures, Cardiovascular Pharmacogenomics, Congenital Heart DIsease, Ecosystems & Industrial Concentration in the Medical Device Sector, Epigenetics and Environmental Factors, Frontiers in Cardiology and Cardiovascular Disorders, Genetic Mutations in Congenital Heart DIsease, Genome Biology, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical on September 11, 2016| Leave a Comment »


cvd-series-a-volume-iii


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

Introduction to Volume Three 

PART 1
Genomics and Medicine

1.1  Genomics and Medicine: The Physician’s View

1.2  Ribozymes and RNA Machines – Work of Jennifer A. Doudna

1.3  Genomics and Medicine: Contributions of Genetics and Genomics to Cardiovascular Disease Diagnoses

1.4 Genomics Orientations for Individualized Medicine, Volume One

1.4.1 CVD Epidemiology, Ethnic subtypes Classification, and Medication Response Variability: Cardiology, Genomics and Individualized Heart Care: Framingham Heart Study (65 y-o study) & Jackson Heart Study (15 y-o study)

1.4.2 What comes after finishing the Euchromatic Sequence of the Human Genome?

1.5  Genomics in Medicine – Establishing a Patient-Centric View of Genomic Data

 

PART 2
Epigenetics – Modifiable Factors Causing Cardiovascular Diseases

2.1 Diseases Etiology

2.1.1 Environmental Contributors Implicated as Causing Cardiovascular Diseases

2.1.2 Diet: Solids, Fluid Intake and Nutraceuticals

2.1.3 Physical Activity and Prevention of Cardiovascular Diseases

2.1.4 Psychological Stress and Mental Health: Risk for Cardiovascular Diseases

2.1.5 Correlation between Cancer and Cardiovascular Diseases

2.1.6 Medical Etiologies for Cardiovascular Diseases: Evidence-based Medicine – Leading DIAGNOSES of Cardiovascular Diseases, Risk Biomarkers and Therapies

2.1.7 Signaling Pathways

2.1.8 Proteomics and Metabolomics

2.1.9 Sleep and Cardiovascular Diseases

2.2 Assessing Cardiovascular Disease with Biomarkers

2.2.1 Issues in Genomics of Cardiovascular Diseases

2.2.2 Endothelium, Angiogenesis, and Disordered Coagulation

2.2.3 Hypertension BioMarkers

2.2.4 Inflammatory, Atherosclerotic and Heart Failure Markers

2.2.5 Myocardial Markers

2.3  Therapeutic Implications: Focus on Ca(2+) signaling, platelets, endothelium

2.3.1 The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets

2.3.2 EMRE in the Mitochondrial Calcium Uniporter Complex

2.3.3 Platelets in Translational Research ­ 2: Discovery of Potential Anti-platelet Targets

2.3.4 The Final Considerations of the Role of Platelets and Platelet Endothelial Reactions in Atherosclerosis and Novel Treatments

2.3.5 Nitric Oxide Synthase Inhibitors (NOS-I)

2.3.6 Resistance to Receptor of Tyrosine Kinase

2.3.7 Oxidized Calcium Calmodulin Kinase and Atrial Fibrillation

2.3.8 Advanced Topics in Sepsis and the Cardiovascular System at its End Stage

2.4 Comorbidity of Diabetes and Aging

2.4.1 Heart and Aging Research in Genomic Epidemiology: 1700 MIs and 2300 coronary heart disease events among about 29 000 eligible patients

2.4.2 Pathophysiological Effects of Diabetes on Ischemic-Cardiovascular Disease and on Chronic Obstructive Pulmonary Disease (COPD)

2.4.3 Risks of Hypoglycemia in Diabetics with Chronic Kidney Disease (CKD)

2.4.4  Mitochondrial Mechanisms of Disease in Diabetes Mellitus

2.4.5 Mitochondria: More than just the “powerhouse of the cell”

2.4.6  Pathophysiology of GLP-1 in Type 2 Diabetes

2.4.7 Developments in the Genomics and Proteomics of Type 2 Diabetes Mellitus and Treatment Targets

2.4.8 CaKMII Inhibition in Obese, Diabetic Mice leads to Lower Blood Glucose Levels

2.4.9 Protein Target for Controlling Diabetes, Fractalkine: Mediator cell-to-cell Adhesion though CX3CR1 Receptor, Released from cells Stimulate Insulin Secretion

2.4.10 Peroxisome proliferator-activated receptor (PPAR-gamma) Receptors Activation: PPARγ transrepression for Angiogenesis in Cardiovascular Disease and PPARγ transactivation for Treatment of Diabetes

2.4.11 CABG or PCI: Patients with Diabetes – CABG Rein Supreme

2.4.12 Reversal of Cardiac Mitochondrial Dysfunction

2.4.13  BARI 2D Trial Outcomes

2.4.14 Overview of new strategy for treatment of T2DM: SGLT2 inhibiting oral antidiabetic agents

2.5 Drug Toxicity and Cardiovascular Diseases

2.5.1 Predicting Drug Toxicity for Acute Cardiac Events

2.5.2 Cardiotoxicity and Cardiomyopathy Related to Drugs Adverse Effects

2.5.3 Decoding myocardial Ca2+ signals across multiple spatial scales: A role for sensitivity analysis

2.5.4. Leveraging Mathematical Models to Understand Population Variability in Response to Cardiac Drugs: Eric Sobie, PhD

2.5.5 Exploiting mathematical models to illuminate electrophysiological variability between individuals.

2.5.6 Clinical Effects and Cardiac Complications of Recreational Drug Use: Blood pressure changes, Myocardial ischemia and infarction, Aortic dissection, Valvular damage, and Endocarditis, Cardiomyopathy, Pulmonary edema and Pulmonary hypertension, Arrhythmias, Pneumothorax and Pneumopericardium

 

2.6 Male and Female Hormonal Replacement Therapy: The Benefits and the Deleterious Effects on Cardiovascular Diseases

2.6.1  Testosterone Therapy for Idiopathic Hypogonadotrophic Hypogonadism has Beneficial and Deleterious Effects on Cardiovascular Risk Factors

2.6.2 Heart Risks and Hormones (HRT) in Menopause: Contradiction or Clarification?

2.6.3 Calcium Dependent NOS Induction by Sex Hormones: Estrogen

2.6.4 Role of Progesterone in Breast Cancer Progression

PART 3
Determinants of Cardiovascular Diseases Genetics, Heredity and Genomics Discoveries

Introduction

3.1 Why cancer cells contain abnormal numbers of chromosomes (Aneuploidy)

3.1.1 Aneuploidy and Carcinogenesis

3.2 Functional Characterization of Cardiovascular Genomics: Disease Case Studies @ 2013 ASHG

3.3 Leading DIAGNOSES of Cardiovascular Diseases covered in Circulation: Cardiovascular Genetics, 3/2010 – 3/2013

3.3.1: Heredity of Cardiovascular Disorders

3.3.2: Myocardial Damage

3.3.3: Hypertention and Atherosclerosis

3.3.4: Ethnic Variation in Cardiac Structure and Systolic Function

3.3.5: Aging: Heart and Genetics

3.3.6: Genetics of Heart Rhythm

3.3.7: Hyperlipidemia, Hyper Cholesterolemia, Metabolic Syndrome

3.3.8: Stroke and Ischemic Stroke

3.3.9: Genetics and Vascular Pathologies and Platelet Aggregation, Cardiac Troponin T in Serum

3.3.10: Genomics and Valvular Disease

3.4  Commentary on Biomarkers for Genetics and Genomics of Cardiovascular Disease

PART 4
Individualized Medicine Guided by Genetics and Genomics Discoveries

4.1 Preventive Medicine: Cardiovascular Diseases

4.1.1 Personal Genomics for Preventive Cardiology Randomized Trial Design and Challenges

4.2 Gene-Therapy for Cardiovascular Diseases

4.2.1 Genetic Basis of Cardiomyopathy

4.3 Congenital Heart Disease/Defects

4.4 Cardiac Repair: Regenerative Medicine

4.4.1 A Powerful Tool For Repairing Damaged Hearts

4.4.2 Modified RNA Induces Vascular Regeneration After a Heart

4.5 Pharmacogenomics for Cardiovascular Diseases

4.5.1 Blood Pressure Response to Antihypertensives: Hypertension Susceptibility Loci Study

4.5.2 Statin-Induced Low-Density Lipoprotein Cholesterol Reduction: Genetic Determinants in the Response to Rosuvastatin

4.5.3 SNPs in apoE are found to influence statin response significantly. Less frequent variants in PCSK9 and smaller effect sizes in SNPs in HMGCR

4.5.4 Voltage-Gated Calcium Channel and Pharmacogenetic Association with Adverse Cardiovascular Outcomes: Hypertension Treatment with Verapamil SR (CCB) vs Atenolol (BB) or Trandolapril (ACE)

4.5.5 Response to Rosuvastatin in Patients With Acute Myocardial Infarction: Hepatic Metabolism and Transporter Gene Variants Effect

4.5.6 Helping Physicians identify Gene-Drug Interactions for Treatment Decisions: New ‘CLIPMERGE’ program – Personalized Medicine @ The Mount Sinai Medical Center

4.5.7 Is Pharmacogenetic-based Dosing of Warfarin Superior for Anticoagulation Control?

Summary & Epilogue to Volume Three

 

 

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Previously undiscerned value of hs-troponin

Posted in Artificial Intelligence, Big Data, BioIT: BioInformatics, Biomedical Measurement Science, Cardiomyopathy, Clinical & Translational, Computational Biology/Systems and Bioinformatics, Congestive Heart Failure, Curation, Diagnostics and Lab Tests, Disease Biology, FDA Regulatory Affairs, FDA, CE Mark & Global Regulatory Affairs: process management and strategic planning - GCP, GLP, ISO 14155, Frontiers in Cardiology and Cardiovascular Disorders, Heart Failure (HF), tagged , , , on June 18, 2016| Leave a Comment »


Previously undiscerned value of hs-troponin

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

LPBI

 

Troponin Rise Predicts CHD, HF, Mortality in Healthy People: ARIC Analysis

Veronica Hackethal, MD

Increases in levels of cardiac troponin T by high-sensitivity assay (hs-cTnT) over time are associated with later risk of death, coronary heart disease (CHD), and especially heart failure in apparently healthy middle-aged people, according to a report published June 8, 2016 in JAMA Cardiology[1].

The novel findings, based on a cohort of >8000 participants from the Atherosclerosis Risk in Communities (ARIC) study followed up to 16 years, are the first to show “an association between temporal hs-cTnT change and incident CHD events” in asymptomatic middle-aged adults,” write the authors, led by Dr John W McEvoy (Johns Hopkins University School of Medicine, Baltimore, MD).

Individuals with the greatest troponin increases over time had the highest risk for poor cardiac outcomes. The strongest association was for risk of heart failure, which reached almost 800% for those with the sharpest hs-cTnT rises.

Intriguingly, those in whom troponin levels fell at least 50% had a reduced mortality risk and may have had a slightly decreased risk of later HF or CHD.

“Serial testing over time with high-sensitivity cardiac troponins provided additional prognostic information over and above the usual clinical risk factors, [natriuretic peptide] levels, and a single troponin measurement. Two measurements appear better than one when it comes to informing risk for future coronary heart disease, heart failure, and death,” McEvoy told heartwire from Medscape.

He cautioned, though, that the conclusion is based on observational data and would need to be confirmed in clinical trials. Moreover, high-sensitivity cardiac troponin assays are widely used in Europe but are not approved in the US.

An important next step after this study, according to an accompanying editorial from Dr James Januzzi (Massachusetts General Hospital, Boston, MA), would be to evaluate whether the combination of hs-troponin and natriuretic peptides improves predictive value in this population[2].

“To the extent prevention is ultimately the holy grail for defeating the global pandemic of CHD, stroke, and HF, the main reason to do a biomarker study such as this would be to set the stage for a biomarker-guided strategy to improve the medical care for those patients at highest risk, as has been recently done with [natriuretic peptides],” he wrote.

The ARIC prospective cohort study entered and followed 8838 participants (mean age 56, 59% female, 21.4% black) in North Carolina, Mississippi, Minneapolis, and Maryland from January 1990 to December 2011. At baseline, participants had no clinical signs of CHD or heart failure.

Levels of hs-cTnT, obtained 6 years apart, were categorized as undetectable (<0.005 ng/mL), detectable (≥0.005 ng/mL to <0.014 ng/mL), and elevated (>0.014 ng/mL).

Troponin increases from <0.005 ng/mL to 0.005 ng/mL or higher independently predicted development of CHD (HR 1.41; 95% CI 1.16–1.63), HF (HR 1.96; 95% CI 1.62–2.37), and death (HR 1.50; 95% CI 1.31–1.72), compared with undetectable levels at both measurements.

Hazard ratios were adjusted for age, sex, race, body-mass index, C-reactive protein, smoking status, alcohol-intake history, systolic blood pressure, current antihypertensive therapy, diabetes, serum lipid and cholesterol levels, lipid-modifying therapy, estimated glomerular filtration rate, and left ventricular hypertrophy.

Subjects with >50% increase in hs-cTnT had a significantly increased risk of CHD (HR 1.28; 95% CI 1.09–1.52), HF (HR 1.60; 95% CI 1.35–1.91), and death (HR 1.39; 95% CI 1.22–1.59).

Risks for those end points fell somewhat for those with a >50% decrease in hs-cTnT (CHD: HR 0.47; 95% CI 0.22–1.03; HF: HR 0.49 95% CI 0.23–1.01; death: HR 0.57 95% CI 0.33–0.99).

Among participants with an adjudicated HF hospitalization, the group writes, associations of hs-cTnT changes with outcomes were of similar magnitude for those with HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF).

Few biomarkers have been linked to increased risk for HFpEF, and few effective therapies exist for it. That may be due to problems identifying and enrolling patients with HFpEF in clinical trials, Dr McEvoy pointed out.

“We think the increased troponin over time reflects progressive myocardial injury or progressive myocardial damage,” Dr McEvoy said. “This is a window into future risk, particularly with respect to heart failure but other outcomes as well. It may suggest high-sensitivity troponins as a marker of myocardial health and help guide interventions targeting the myocardium.”

Moreover, he said, “We think that high-sensitivity troponin may also be a useful biomarker along with [natriuretic peptides] for emerging trials of HFpEF therapy.”

But whether hs-troponin has the potential for use as a screening tool is a question for future studies, according to McEvoy.

In his editorial, Januzzi pointed out several implications of the study, including the possibility for lowering cardiac risk in those with measurable hs-troponin, and that HF may be the most obvious outcome to target. Also, optimizing treatment and using cardioprotective therapies may reduce risk linked to increases in hs-troponin. Finally, long-term, large clinical trials on this issue will require a multidisciplinary team effort from various sectors.

“What is needed now are efforts toward developing strategies to upwardly bend the survival curves of those with a biomarker signature of risk, leveraging the knowledge gained from studies such as the report by McEvoy et al to improve public health,” he concluded.

 

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