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Use of 3D Bioprinting for Development of Toxicity Prediction Models

Curator: Stephen J. Williams, PhD

SOT FDA Colloquium on 3D Bioprinted Tissue Models: Tuesday, April 9, 2019

The Society of Toxicology (SOT) and the U.S. Food and Drug Administration (FDA) will hold a workshop on “Alternative Methods for Predictive Safety Testing: 3D Bioprinted Tissue Models” on Tuesday, April 9, at the FDA Center for Food Safety and Applied Nutrition in College Park, Maryland. This workshop is the latest in the series, “SOT FDA Colloquia on Emerging Toxicological Science: Challenges in Food and Ingredient Safety.”

Human 3D bioprinted tissues represent a valuable in vitro approach for chemical, personal care product, cosmetic, and preclinical toxicity/safety testing. Bioprinting of skin, liver, and kidney is already appearing in toxicity testing applications for chemical exposures and disease modeling. The use of 3D bioprinted tissues and organs may provide future alternative approaches for testing that may more closely resemble and simulate intact human tissues to more accurately predict human responses to chemical and drug exposures.

A synopsis of the schedule and related works from the speakers is given below:

 

8:40 AM–9:20 AM Overview and Challenges of Bioprinting
Sharon Presnell, Amnion Foundation, Winston-Salem, NC
9:20 AM–10:00 AM Putting 3D Bioprinting to the Use of Tissue Model Fabrication
Y. Shrike Zhang, Brigham and Women’s Hospital, Harvard Medical School and Harvard-MIT Division of Health Sciences and Technology, Boston, MA
10:00 AM–10:20 AM Break
10:20 AM–11:00 AM Uses of Bioprinted Liver Tissue in Drug Development
Jean-Louis Klein, GlaxoSmithKline, Collegeville, PA
11:00 AM–11:40 AM Biofabrication of 3D Tissue Models for Disease Modeling and Chemical Screening
Marc Ferrer, National Center for Advancing Translational Sciences, NIH, Rockville, MD

Sharon Presnell, Ph.D. President, Amnion Foundation

Dr. Sharon Presnell was most recently the Chief Scientific Officer at Organovo, Inc., and the President of their wholly-owned subsidiary, Samsara Sciences. She received a Ph.D. in Cell & Molecular Pathology from the Medical College of Virginia and completed her undergraduate degree in biology at NC State. In addition to her most recent roles, Presnell has served as the director of cell biology R&D at Becton Dickinson’s corporate research center in RTP, and as the SVP of R&D at Tengion. Her roles have always involved the commercial and clinical translation of basic research and early development in the cell biology space. She serves on the board of the Coulter Foundation at the University of Virginia and is a member of the College of Life Sciences Foundation Board at NC State. In January 2019, Dr. Presnell will begin a new role as President of the Amnion Foundation, a non-profit organization in Winston-Salem.

A few of her relevant publications:

Bioprinted liver provides early insight into the role of Kupffer cells in TGF-β1 and methotrexate-induced fibrogenesis

Integrating Kupffer cells into a 3D bioprinted model of human liver recapitulates fibrotic responses of certain toxicants in a time and context dependent manner.  This work establishes that the presence of Kupffer cells or macrophages are important mediators in fibrotic responses to certain hepatotoxins and both should be incorporated into bioprinted human liver models for toxicology testing.

Bioprinted 3D Primary Liver Tissues Allow Assessment of Organ-Level Response to Clinical Drug Induced Toxicity In Vitro

Abstract: Modeling clinically relevant tissue responses using cell models poses a significant challenge for drug development, in particular for drug induced liver injury (DILI). This is mainly because existing liver models lack longevity and tissue-level complexity which limits their utility in predictive toxicology. In this study, we established and characterized novel bioprinted human liver tissue mimetics comprised of patient-derived hepatocytes and non-parenchymal cells in a defined architecture. Scaffold-free assembly of different cell types in an in vivo-relevant architecture allowed for histologic analysis that revealed distinct intercellular hepatocyte junctions, CD31+ endothelial networks, and desmin positive, smooth muscle actin negative quiescent stellates. Unlike what was seen in 2D hepatocyte cultures, the tissues maintained levels of ATP, Albumin as well as expression and drug-induced enzyme activity of Cytochrome P450s over 4 weeks in culture. To assess the ability of the 3D liver cultures to model tissue-level DILI, dose responses of Trovafloxacin, a drug whose hepatotoxic potential could not be assessed by standard pre-clinical models, were compared to the structurally related non-toxic drug Levofloxacin. Trovafloxacin induced significant, dose-dependent toxicity at clinically relevant doses (≤ 4uM). Interestingly, Trovafloxacin toxicity was observed without lipopolysaccharide stimulation and in the absence of resident macrophages in contrast to earlier reports. Together, these results demonstrate that 3D bioprinted liver tissues can both effectively model DILI and distinguish between highly related compounds with differential profile. Thus, the combination of patient-derived primary cells with bioprinting technology here for the first time demonstrates superior performance in terms of mimicking human drug response in a known target organ at the tissue level.

A great interview with Dr. Presnell and the 3D Models 2017 Symposium is located here:

Please click here for Web based and PDF version of interview

Some highlights of the interview include

  • Exciting advances in field showing we can model complex tissue-level disease-state phenotypes that develop in response to chronic long term injury or exposure
  • Sees the field developing a means to converge both the biology and physiology of tissues, namely modeling the connectivity between tissues such as fluid flow
  • Future work will need to be dedicated to develop comprehensive analytics for 3D tissue analysis. As she states “we are very conditioned to get information in a simple way from biochemical readouts in two dimension, monocellular systems”  however how we address the complexity of various cellular responses in a 3D multicellular environment will be pertinent.
  • Additional challenges include the scalability of such systems and making such system accessible in a larger way
  1. Shrike Zhang, Brigham and Women’s Hospital, Harvard Medical School and Harvard-MIT Division of Health Sciences and Technology

Dr. Zhang currently holds an Assistant Professor position at Harvard Medical School and is an Associate Bioengineer at Brigham and Women’s Hospital. His research interests include organ-on-a-chip, 3D bioprinting, biomaterials, regenerative engineering, biomedical imaging, biosensing, nanomedicine, and developmental biology. His scientific contributions have been recognized by >40 international, national, and regional awards. He has been invited to deliver >70 lectures worldwide, and has served as reviewer for >400 manuscripts for >30 journals. He is serving as Editor-in-Chief for Microphysiological Systems, and Associate Editor for Bio-Design and Manufacturing. He is also on Editorial Board of BioprintingHeliyonBMC Materials, and Essays in Biochemistry, and on Advisory Panel of Nanotechnology.

Some relevant references from Dr. Zhang

Multi-tissue interactions in an integrated three-tissue organ-on-a-chip platform.

Skardal A, Murphy SV, Devarasetty M, Mead I, Kang HW, Seol YJ, Shrike Zhang Y, Shin SR, Zhao L, Aleman J, Hall AR, Shupe TD, Kleensang A, Dokmeci MR, Jin Lee S, Jackson JD, Yoo JJ, Hartung T, Khademhosseini A, Soker S, Bishop CE, Atala A.

Sci Rep. 2017 Aug 18;7(1):8837. doi: 10.1038/s41598-017-08879-x.

 

Reconstruction of Large-scale Defects with a Novel Hybrid Scaffold Made from Poly(L-lactic acid)/Nanohydroxyapatite/Alendronate-loaded Chitosan Microsphere: in vitro and in vivo Studies.

Wu H, Lei P, Liu G, Shrike Zhang Y, Yang J, Zhang L, Xie J, Niu W, Liu H, Ruan J, Hu Y, Zhang C.

Sci Rep. 2017 Mar 23;7(1):359. doi: 10.1038/s41598-017-00506-z.

 

 

A liver-on-a-chip platform with bioprinted hepatic spheroids.

Bhise NS, Manoharan V, Massa S, Tamayol A, Ghaderi M, Miscuglio M, Lang Q, Shrike Zhang Y, Shin SR, Calzone G, Annabi N, Shupe TD, Bishop CE, Atala A, Dokmeci MR, Khademhosseini A.

Biofabrication. 2016 Jan 12;8(1):014101. doi: 10.1088/1758-5090/8/1/014101.

 

Marc Ferrer, National Center for Advancing Translational Sciences, NIH

Marc Ferrer is a team leader in the NCATS Chemical Genomics Center, which was part of the National Human Genome Research Institute when Ferrer began working there in 2010. He has extensive experience in drug discovery, both in the pharmaceutical industry and academic research. Before joining NIH, he was director of assay development and screening at Merck Research Laboratories. For 10 years at Merck, Ferrer led the development of assays for high-throughput screening of small molecules and small interfering RNA (siRNA) to support programs for lead and target identification across all disease areas.

At NCATS, Ferrer leads the implementation of probe development programs, discovery of drug combinations and development of innovative assay paradigms for more effective drug discovery. He advises collaborators on strategies for discovering small molecule therapeutics, including assays for screening and lead identification and optimization. Ferrer has experience implementing high-throughput screens for a broad range of disease areas with a wide array of assay technologies. He has led and managed highly productive teams by setting clear research strategies and goals and by establishing effective collaborations between scientists from diverse disciplines within industry, academia and technology providers.

Ferrer has a Ph.D. in biological chemistry from the University of Minnesota, Twin Cities, and completed postdoctoral training at Harvard University’s Department of Molecular and Cellular Biology. He received a B.Sc. degree in organic chemistry from the University of Barcelona in Spain.

 

Some relevant references for Dr. Ferrer

Fully 3D Bioprinted Skin Equivalent Constructs with Validated Morphology and Barrier Function.

Derr K, Zou J, Luo K, Song MJ, Sittampalam GS, Zhou C, Michael S, Ferrer M, Derr P.

Tissue Eng Part C Methods. 2019 Apr 22. doi: 10.1089/ten.TEC.2018.0318. [Epub ahead of print]

 

Determination of the Elasticity Modulus of 3D-Printed Octet-Truss Structures for Use in Porous Prosthesis Implants.

Bagheri A, Buj-Corral I, Ferrer M, Pastor MM, Roure F.

Materials (Basel). 2018 Nov 29;11(12). pii: E2420. doi: 10.3390/ma11122420.

 

Mutation Profiles in Glioblastoma 3D Oncospheres Modulate Drug Efficacy.

Wilson KM, Mathews-Griner LA, Williamson T, Guha R, Chen L, Shinn P, McKnight C, Michael S, Klumpp-Thomas C, Binder ZA, Ferrer M, Gallia GL, Thomas CJ, Riggins GJ.

SLAS Technol. 2019 Feb;24(1):28-40. doi: 10.1177/2472630318803749. Epub 2018 Oct 5.

 

A high-throughput imaging and nuclear segmentation analysis protocol for cleared 3D culture models.

Boutin ME, Voss TC, Titus SA, Cruz-Gutierrez K, Michael S, Ferrer M.

Sci Rep. 2018 Jul 24;8(1):11135. doi: 10.1038/s41598-018-29169-0.

A High-Throughput Screening Model of the Tumor Microenvironment for Ovarian Cancer Cell Growth.

Lal-Nag M, McGee L, Guha R, Lengyel E, Kenny HA, Ferrer M.

SLAS Discov. 2017 Jun;22(5):494-506. doi: 10.1177/2472555216687082. Epub 2017 Jan 31.

 

Exploring Drug Dosing Regimens In Vitro Using Real-Time 3D Spheroid Tumor Growth Assays.

Lal-Nag M, McGee L, Titus SA, Brimacombe K, Michael S, Sittampalam G, Ferrer M.

SLAS Discov. 2017 Jun;22(5):537-546. doi: 10.1177/2472555217698818. Epub 2017 Mar 15.

 

RNAi High-Throughput Screening of Single- and Multi-Cell-Type Tumor Spheroids: A Comprehensive Analysis in Two and Three Dimensions.

Fu J, Fernandez D, Ferrer M, Titus SA, Buehler E, Lal-Nag MA.

SLAS Discov. 2017 Jun;22(5):525-536. doi: 10.1177/2472555217696796. Epub 2017 Mar 9.

 

Other Articles on 3D Bioprinting on this Open Access Journal include:

Global Technology Conferences on 3D BioPrinting 2015 – 2016

3D Medical BioPrinting Technology Reporting by Irina Robu, PhD – a forthcoming Article in “Medical 3D BioPrinting – The Revolution in Medicine, Technologies for Patient-centered Medicine: From R&D in Biologics to New Medical Devices”

Bio-Inks and 3D BioPrinting

New Scaffold-Free 3D Bioprinting Method Available to Researchers

Gene Editing for Gene Therapies with 3D BioPrinting

 

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Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare

Curator: Stephen J. Williams, Ph.D.

Updated 12/18/2018

In the efforts to reduce healthcare costs, provide increased accessibility of service for patients, and drive biomedical innovations, many healthcare and biotechnology professionals have looked to advances in digital technology to determine the utility of IT to drive and extract greater value from healthcare industry.  Two areas of recent interest have focused how best to use blockchain and artificial intelligence technologies to drive greater efficiencies in our healthcare and biotechnology industries.

More importantly, with the substantial increase in ‘omic data generated both in research as well as in the clinical setting, it has become imperative to develop ways to securely store and disseminate the massive amounts of ‘omic data to various relevant parties (researchers or clinicians), in an efficient manner yet to protect personal privacy and adhere to international regulations.  This is where blockchain technologies may play an important role.

A recent Oncotarget paper by Mamoshina et al. (1) discussed the possibility that next-generation artificial intelligence and blockchain technologies could synergize to accelerate biomedical research and enable patients new tools to control and profit from their personal healthcare data, and assist patients with their healthcare monitoring needs. According to the abstract:

The authors introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship value of the data.  They also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare.  In this system, blockchain and deep learning technologies would provide the secure and transparent distribution of personal data in a healthcare marketplace, and would also be useful to resolve challenges faced by the regulators and return control over personal data including medical records to the individual.

The review discusses:

  1. Recent achievements in next-generation artificial intelligence
  2. Basic concepts of highly distributed storage systems (HDSS) as a preferred method for medical data storage
  3. Open source blockchain Exonium and its application for healthcare marketplace
  4. A blockchain-based platform allowing patients to have control of their data and manage access
  5. How advances in deep learning can improve data quality, especially in an era of big data

Advances in Artificial Intelligence

  • Integrative analysis of the vast amount of health-associated data from a multitude of large scale global projects has proven to be highly problematic (REF 27), as high quality biomedical data is highly complex and of a heterogeneous nature, which necessitates special preprocessing and analysis.
  • Increased computing processing power and algorithm advances have led to significant advances in machine learning, especially machine learning involving Deep Neural Networks (DNNs), which are able to capture high-level dependencies in healthcare data. Some examples of the uses of DNNs are:
  1. Prediction of drug properties(2, 3) and toxicities(4)
  2. Biomarker development (5)
  3. Cancer diagnosis (6)
  4. First FDA approved system based on deep learning Arterys Cardio DL
  • Other promising systems of deep learning include:
    • Generative Adversarial Networks (https://arxiv.org/abs/1406.2661): requires good datasets for extensive training but has been used to determine tumor growth inhibition capabilities of various molecules (7)
    • Recurrent neural Networks (RNN): Originally made for sequence analysis, RNN has proved useful in analyzing text and time-series data, and thus would be very useful for electronic record analysis. Has also been useful in predicting blood glucose levels of Type I diabetic patients using data obtained from continuous glucose monitoring devices (8)
    • Transfer Learning: focused on translating information learned on one domain or larger dataset to another, smaller domain. Meant to reduce the dependence on large training datasets that RNN, GAN, and DNN require.  Biomedical imaging datasets are an example of use of transfer learning.
    • One and Zero-Shot Learning: retains ability to work with restricted datasets like transfer learning. One shot learning aimed to recognize new data points based on a few examples from the training set while zero-shot learning aims to recognize new object without seeing the examples of those instances within the training set.

Highly Distributed Storage Systems (HDSS)

The explosion in data generation has necessitated the development of better systems for data storage and handling. HDSS systems need to be reliable, accessible, scalable, and affordable.  This involves storing data in different nodes and the data stored in these nodes are replicated which makes access rapid. However data consistency and affordability are big challenges.

Blockchain is a distributed database used to maintain a growing list of records, in which records are divided into blocks, locked together by a crytosecurity algorithm(s) to maintain consistency of data.  Each record in the block contains a timestamp and a link to the previous block in the chain.  Blockchain is a distributed ledger of blocks meaning it is owned and shared and accessible to everyone.  This allows a verifiable, secure, and consistent history of a record of events.

Data Privacy and Regulatory Issues

The establishment of the Health Insurance Portability and Accountability Act (HIPAA) in 1996 has provided much needed regulatory guidance and framework for clinicians and all concerned parties within the healthcare and health data chain.  The HIPAA act has already provided much needed guidance for the latest technologies impacting healthcare, most notably the use of social media and mobile communications (discussed in this article  Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.).  The advent of blockchain technology in healthcare offers its own unique challenges however HIPAA offers a basis for developing a regulatory framework in this regard.  The special standards regarding electronic data transfer are explained in HIPAA’s Privacy Rule, which regulates how certain entities (covered entities) use and disclose individual identifiable health information (Protected Health Information PHI), and protects the transfer of such information over any medium or electronic data format. However, some of the benefits of blockchain which may revolutionize the healthcare system may be in direct contradiction with HIPAA rules as outlined below:

Issues of Privacy Specific In Use of Blockchain to Distribute Health Data

  • Blockchain was designed as a distributed database, maintained by multiple independent parties, and decentralized
  • Linkage timestamping; although useful in time dependent data, proof that third parties have not been in the process would have to be established including accountability measures
  • Blockchain uses a consensus algorithm even though end users may have their own privacy key
  • Applied cryptography measures and routines are used to decentralize authentication (publicly available)
  • Blockchain users are divided into three main categories: 1) maintainers of blockchain infrastructure, 2) external auditors who store a replica of the blockchain 3) end users or clients and may have access to a relatively small portion of a blockchain but their software may use cryptographic proofs to verify authenticity of data.

 

YouTube video on How #Blockchain Will Transform Healthcare in 25 Years (please click below)

 

 

In Big Data for Better Outcomes, BigData@Heart, DO->IT, EHDN, the EU data Consortia, and yes, even concepts like pay for performance, Richard Bergström has had a hand in their creation. The former Director General of EFPIA, and now the head of health both at SICPA and their joint venture blockchain company Guardtime, Richard is always ahead of the curve. In fact, he’s usually the one who makes the curve in the first place.

 

 

 

Please click on the following link for a podcast on Big Data, Blockchain and Pharma/Healthcare by Richard Bergström:

References

  1. Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., Izumchenko, E., Aliper, A., Romantsov, K., Zhebrak, A., Ogu, I. O., and Zhavoronkov, A. (2018) Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare, Oncotarget 9, 5665-5690.
  2. Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., and Zhavoronkov, A. (2016) Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data, Molecular pharmaceutics 13, 2524-2530.
  3. Wen, M., Zhang, Z., Niu, S., Sha, H., Yang, R., Yun, Y., and Lu, H. (2017) Deep-Learning-Based Drug-Target Interaction Prediction, Journal of proteome research 16, 1401-1409.
  4. Gao, M., Igata, H., Takeuchi, A., Sato, K., and Ikegaya, Y. (2017) Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds, Journal of pharmacological sciences 133, 70-78.
  5. Putin, E., Mamoshina, P., Aliper, A., Korzinkin, M., Moskalev, A., Kolosov, A., Ostrovskiy, A., Cantor, C., Vijg, J., and Zhavoronkov, A. (2016) Deep biomarkers of human aging: Application of deep neural networks to biomarker development, Aging 8, 1021-1033.
  6. Vandenberghe, M. E., Scott, M. L., Scorer, P. W., Soderberg, M., Balcerzak, D., and Barker, C. (2017) Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer, Scientific reports 7, 45938.
  7. Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., and Zhavoronkov, A. (2017) druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico, Molecular pharmaceutics 14, 3098-3104.
  8. Ordonez, F. J., and Roggen, D. (2016) Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition, Sensors (Basel) 16.

Articles from clinicalinformaticsnews.com

Healthcare Organizations Form Synaptic Health Alliance, Explore Blockchain’s Impact On Data Quality

From http://www.clinicalinformaticsnews.com/2018/12/05/healthcare-organizations-form-synaptic-health-alliance-explore-blockchains-impact-on-data-quality.aspx

By Benjamin Ross

December 5, 2018 | The boom of blockchain and distributed ledger technologies have inspired healthcare organizations to test the capabilities of their data. Quest Diagnostics, in partnership with Humana, MultiPlan, and UnitedHealth Group’s Optum and UnitedHealthcare, have launched a pilot program that applies blockchain technology to improve data quality and reduce administrative costs associated with changes to healthcare provider demographic data.

The collective body, called Synaptic Health Alliance, explores how blockchain can keep only the most current healthcare provider information available in health plan provider directories. The alliance plans to share their progress in the first half of 2019.

Providing consumers looking for care with accurate information when they need it is essential to a high-functioning overall healthcare system, Jason O’Meara, Senior Director of Architecture at Quest Diagnostics, told Clinical Informatics News in an email interview.

“We were intentional about calling ourselves an alliance as it speaks to the shared interest in improving health care through better, collaborative use of an innovative technology,” O’Meara wrote. “Our large collective dataset and national footprints enable us to prove the value of data sharing across company lines, which has been limited in healthcare to date.”

O’Meara said Quest Diagnostics has been investing time and resources the past year or two in understanding blockchain, its ability to drive purpose within the healthcare industry, and how to leverage it for business value.

“Many health care and life science organizations have cast an eye toward blockchain’s potential to inform their digital strategies,” O’Meara said. “We recognize it takes time to learn how to leverage a new technology. We started exploring the technology in early 2017, but we quickly recognized the technology’s value is in its application to business to business use cases: to help transparently share information, automate mutually-beneficial processes and audit interactions.”

Quest began discussing the potential for an alliance with the four other companies a year ago, O’Meara said. Each company shared traits that would allow them to prove the value of data sharing across company lines.

“While we have different perspectives, each member has deep expertise in healthcare technology, a collaborative culture, and desire to continuously improve the patient/customer experience,” said O’Meara. “We also recognize the value of technology in driving efficiencies and quality.”

Following its initial launch in April, Synaptic Health Alliance is deploying a multi-company, multi-site, permissioned blockchain. According to a whitepaper published by Synaptic Health, the choice to use a permissioned blockchain rather than an anonymous one is crucial to the alliance’s success.

“This is a more effective approach, consistent with enterprise blockchains,” an alliance representative wrote. “Each Alliance member has the flexibility to deploy its nodes based on its enterprise requirements. Some members have elected to deploy their nodes within their own data centers, while others are using secured public cloud services such as AWS and Azure. This level of flexibility is key to growing the Alliance blockchain network.”

As the pilot moves forward, O’Meara says the Alliance plans to open ability to other organizations. Earlier this week Aetna and Ascension announced they joined the project.

“I am personally excited by the amount of cross-company collaboration facilitated by this project,” O’Meara says. “We have already learned so much from each other and are using that knowledge to really move the needle on improving healthcare.”

 

US Health And Human Services Looks To Blockchain To Manage Unstructured Data

http://www.clinicalinformaticsnews.com/2018/11/29/us-health-and-human-services-looks-to-blockchain-to-manage-unstructured-data.aspx

By Benjamin Ross

November 29, 2018 | The US Department of Health and Human Services (HHS) is making waves in the blockchain space. The agency’s Division of Acquisition (DA) has developed a new system, called Accelerate, which gives acquisition teams detailed information on pricing, terms, and conditions across HHS in real-time. The department’s Associate Deputy Assistant Secretary for Acquisition, Jose Arrieta, gave a presentation and live demo of the blockchain-enabled system at the Distributed: Health event earlier this month in Nashville, Tennessee.

Accelerate is still in the prototype phase, Arrieta said, with hopes that the new system will be deployed at the end of the fiscal year.

HHS spends around $25 billion a year in contracts, Arrieta said. That’s 100,000 contracts a year with over one million pages of unstructured data managed through 45 different systems. Arrieta and his team wanted to modernize the system.

“But if you’re going to change the way a workforce of 20,000 people do business, you have to think your way through how you’re going to do that,” said Arrieta. “We didn’t disrupt the existing systems: we cannibalized them.”

The cannibalization process resulted in Accelerate. According to Arrieta, the system functions by creating a record of data rather than storing it, leveraging machine learning, artificial intelligence (AI), and robotic process automation (RPA), all through blockchain data.

“We’re using that data record as a mechanism to redesign the way we deliver services through micro-services strategies,” Arrieta said. “Why is that important? Because if you have a single application or data use that interfaces with 55 other applications in your business network, it becomes very expensive to make changes to one of the 55 applications.”

Accelerate distributes the data to the workforce, making it available to them one business process at a time.

“We’re building those business processes without disrupting the existing systems,” said Arrieta, and that’s key. “We’re not shutting off those systems. We’re using human-centered design sessions to rebuild value exchange off of that data.”

The first application for the system, Arrieta said, can be compared to department stores price-matching their online competitors.

It takes the HHS close to a month to collect the amalgamation of data from existing system, whether that be terms and conditions that drive certain price points, or software licenses.

“The micro-service we built actually analyzes that data, and provides that information to you within one second,” said Arrieta. “This is distributed to the workforce, to the 5,000 people that do the contracting, to the 15,000 people that actually run the programs at [HHS].”

This simple micro-service is replicated on every node related to HHS’s internal workforce. If somebody wants to change the algorithm to fit their needs, they can do that in a distributed manner.

Arrieta hopes to use Accelerate to save researchers money at the point of purchase. The program uses blockchain to simplify the process of acquisition.

“How many of you work with the federal government?” Arrieta asked the audience. “Do you get sick of reentering the same information over and over again? Every single business opportunity you apply for, you have to resubmit your financial information. You constantly have to check for validation and verification, constantly have to resubmit capabilities.”

Wouldn’t it be better to have historical notes available for each transaction? said Arrieta. This would allow clinical researchers to be able to focus on “the things they’re really good at,” instead of red tape.

“If we had the top cancer researcher in the world, would you really want her spending her time learning about federal regulations as to how to spend money, or do you want her trying to solve cancer?” Arrieta said. “What we’re doing is providing that data to the individual in a distributed manner so they can read the information of historical purchases that support activity, and they can focus on the objectives and risks they see as it relates to their programming and their objectives.”

Blockchain also creates transparency among researchers, Arrieta said, which says creates an “uncomfortable reality” in the fact that they have to make a decision regarding data, fundamentally changing value exchange.

“The beauty of our business model is internal investment,” Arrieta said. For instance, the HHS could take all the sepsis data that exists in their system, put it into a distributed ledger, and share it with an external source.

“Maybe that could fuel partnership,” Arrieta said. “I can make data available to researchers in the field in real-time so they can actually test their hypothesis, test their intuition, and test their imagination as it relates to solving real-world problems.”

 

Shivom is creating a genomic data hub to elongate human life with AI

From VentureBeat.com
Blockchain-based genomic data hub platform Shivom recently reached its $35 million hard cap within 15 seconds of opening its main token sale. Shivom received funding from a number of crypto VC funds, including Collinstar, Lateral, and Ironside.

The goal is to create the world’s largest store of genomic data while offering an open web marketplace for patients, data donors, and providers — such as pharmaceutical companies, research organizations, governments, patient-support groups, and insurance companies.

“Disrupting the whole of the health care system as we know it has to be the most exciting use of such large DNA datasets,” Shivom CEO Henry Ines told me. “We’ll be able to stratify patients for better clinical trials, which will help to advance research in precision medicine. This means we will have the ability to make a specific drug for a specific patient based on their DNA markers. And what with the cost of DNA sequencing getting cheaper by the minute, we’ll also be able to sequence individuals sooner, so young children or even newborn babies could be sequenced from birth and treated right away.”

While there are many solutions examining DNA data to explain heritage, intellectual capabilities, health, and fitness, the potential of genomic data has largely yet to be unlocked. A few companies hold the monopoly on genomic data and make sizeable profits from selling it to third parties, usually without sharing the earnings with the data donor. Donors are also not informed if and when their information is shared, nor do they have any guarantee that their data is secure from hackers.

Shivom wants to change that by creating a decentralized platform that will break these monopolies, democratizing the processes of sharing and utilizing the data.

“Overall, large DNA datasets will have the potential to aid in the understanding, prevention, diagnosis, and treatment of every disease known to mankind, and could create a future where no diseases exist, or those that do can be cured very easily and quickly,” Ines said. “Imagine that, a world where people do not get sick or are already aware of what future diseases they could fall prey to and so can easily prevent them.”

Shivom’s use of blockchain technology and smart contracts ensures that all genomic data shared on the platform will remain anonymous and secure, while its OmiX token incentivizes users to share their data for monetary gain.

Rise in Population Genomics: Local Government in India Will Use Blockchain to Secure Genetic Data

Blockchain will secure the DNA database for 50 million citizens in the eighth-largest state in India. The government of Andhra Pradesh signed a Memorandum of Understanding with a German genomics and precision medicine start-up, Shivom, which announced to start the pilot project soon. The move falls in line with a trend for governments turning to population genomics, and at the same time securing the sensitive data through blockchain.

Andhra Pradesh, DNA, and blockchain

Storing sensitive genetic information safely and securely is a big challenge. Shivom builds a genomic data-hub powered by blockchain technology. It aims to connect researchers with DNA data donors thus facilitating medical research and the healthcare industry.

With regards to Andhra Pradesh, the start-up will first launch a trial to determine the viability of their technology for moving from a proactive to a preventive approach in medicine, and towards precision health. “Our partnership with Shivom explores the possibilities of providing an efficient way of diagnostic services to patients of Andhra Pradesh by maintaining the privacy of the individual data through blockchain technologies,” said J A Chowdary, IT Advisor to Chief Minister, Government of Andhra Pradesh.

Other Articles in this Open Access Journal on Digital Health include:

Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.

Medical Applications and FDA regulation of Sensor-enabled Mobile Devices: Apple and the Digital Health Devices Market

 

How Social Media, Mobile Are Playing a Bigger Part in Healthcare

 

E-Medical Records Get A Mobile, Open-Sourced Overhaul By White House Health Design Challenge Winners

 

Medcity Converge 2018 Philadelphia: Live Coverage @pharma_BI

 

Digital Health Breakthrough Business Models, June 5, 2018 @BIOConvention, Boston, BCEC

 

 

 

 

 

 

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Live Conference Coverage @Medcitynews Converge 2018 Philadelphia:Liquid Biopsy and Gene Testing vs Reimbursement Hurdles

9:25- 10:15 Liquid Biopsy and Gene Testing vs. Reimbursement Hurdles

Genetic testing, whether broad-scale or single gene-testing, is being ordered by an increasing number of oncologists, but in many cases, patients are left to pay for these expensive tests themselves. How can this dynamic be shifted? What can be learned from the success stories?

Moderator: Shoshannah Roth, Assistant Director of Health Technology Assessment and Information Services , ECRI Institute @Ecri_Institute
Speakers:
Rob Dumanois, Manager – reimbursement strategy, Thermo Fisher Scientific
Eugean Jiwanmall, Senior Research Analyst for Medical Policy & Technology Evaluation , Independence Blue Cross @IBX
Michael Nall, President and Chief Executive Officer, Biocept

 

Michael: Wide range of liquid biopsy services out there.  There are screening companies however they are young and need lots of data to develop pan diagnostic test.  Most of liquid biopsy is more for predictive analysis… especially therapeutic monitoring.  Sometimes solid biopsies are impossible , limited, or not always reliable due to metastasis or tough to biopsy tissues like lung.

Eugean:  Circulating tumor cells and ctDNA is the only FDA approved liquid biopsies.  However you choose then to evaluate the liquid biopsy, PCR NGS, FISH etc, helps determines what the reimbursement options are available.

Rob:  Adoption of reimbursement for liquid biopsy is moving faster in Europe than the US.  It is possible in US that there may be changes to the payment in one to two years though.

Michael:  China is adopting liquid biopsy rapidly.  Patients are demanding this in China.

Reimbursement

Eugean:  For IBX to make better decisions we need more clinical trials to correlate with treatment outcome.  Most of the major cancer networks, like NCCN, ASCO, CAP, just have recommendations and not approved guidelines at this point.  From his perspective with lung cancer NCCN just makes a suggestion with EGFR mutations however only the companion diagnostic is approved by FDA.

Michael:  Fine needle biopsies are usually needed by the pathologist anyway before they go to liquid biopsy as need to know the underlying mutations in the original tumor, it just is how it is done in most cancer centers.

Eugean:  Whatever the established way of doing things, you have to outperform the clinical results of the old method for adoption of a newer method.

Reimbursement issues have driven a need for more research into clinical validity and utility of predictive and therapeutic markers with regard to liquid biopsies.  However although many academic centers try to partner with Biocept Biocept has a limit of funds and must concentrate only on a few trials.  The different payers use different evidence based methods to evaluate liquid biopsy markers.  ECRI also has a database for LB markers using an evidence based criteria.  IBX does sees consistency among payers as far as decision and policy.

NGS in liquid biopsy

Rob: There is a path to coverage, especially through the FDA.  If you have a FDA cleared NGS test, it will be covered.  These are long and difficult paths to reimbursement for NGS but it is feasible. Medicare line of IBX covers this testing, however on the commercial side they can’t cover this.  @IBX: for colon only kras or nras has clinical utility and only a handful of other cancer related genes for other cancers.  For a companion diagnostic built into that Dx do the other markers in the panel cost too much?

Please follow on Twitter using the following #hash tags and @pharma_BI

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#cancertreatment

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5:00 – 5:45 PM Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing

Reporter: Stephen J. Williams, Ph.D.

 

Diagnosing cancer early is often the difference between survival and death. Hear from experts regarding the new and emerging technologies that form the next generation of cancer diagnostics.

Moderator: Heather Rose, Director of Licensing, Thomas Jefferson University
Speakers:
Bonnie Anderson, Chairman and CEO, Veracyte @BonnieAndDx
Kevin Hrusovsky, Founder and Chairman, Powering Precision Health @KevinHrusovsky

Bonnie Anderson and Veracyte produces genomic tests for thyroid and other cancer diagnosis.  Kevin Hrusovksy and Precision Health uses peer reviewed evidence based medicine to affect precision medicine decision.

Bonnie: aim to get a truth of diagnosis.  Getting tumor tissue is paramount as well as properly preserved tissue.  They use deep RNA sequencing  and machine learning  in their clinically approved tests.

Kevin: Serial biospace entrepreneur.  Two diseases, cancer and neurologic, have been diseases which have been hardest to get reproducible and validated biomarkers of early disease.  He concentrates on protein biomarkers.

Heather:  FDA has recently approved drugs for early disease intervention.  However the use of biomarkers can go beyond patient stratification in clinical trials.

Kevin: 15 approved drugs for MS but the markers are scans looking for brain atrophy which is too late of an endpoint.  So we need biomarkers of early disease progression.  We can use those early biomarkers of disease progression so pharma can target those early biomarkers and or use those early biomarkers of disease progression  for endpoint

Bonnie: exciting time in the early diagnostics field. She prefers transcriptomics to DNA based methods such as WES or WGS (whole exome or whole genome sequencing).  It was critical to show data on the cost savings imparted by their transcriptomic based thryoid cancer diagnostic test for payers to consider this test eligible for reimbursement.

Kevin: There has been 20 million  CAT scans for  cancer but it is estimated 90% of these scans led to misdiagnosis. Biomarker  development  has revolutionized diagnostics in this disease area.  They have developed a breakthrough panel of ten protein biomarkers in serum which he estimates may replace 5 million mammograms.

All panelists agreed on the importance of regulatory compliance and the focus of new research should be on early detection.  In addition they believe that Dr. Gotlieb’s appointment to the FDA is a positive for the biomarker development field, as Dr. Gotlieb understands the potential and importance of early detection and prevention of disease.  Kevin also felt Dr. Gotlieb understands the importance of incorporating biomarkers as endpoints in clinical trials.  Over 750 phase 1,2, and 3 clinical trials use biomarker endpoints but the pharma companies still need to prove the biomarkers clinical relevance to the FDA.They also agreed it would be helpful to involve advocacy groups in putting more pressure on the healthcare providers and policy makers on this importance of diagnostics as a preventative measure.

In addition, the discovery and use of biomarkers as disease endpoints has led to a resurgence of Alzheimer’s disease drug development by companies which have previously given up on these type of neurodegenerative diseases.

Kevin feels proteomics offers great advantages over DNA-based diagnostics, especially in cancer such as ovarian cancer, where a high degree of specificity for a diagnostic test is required to ascertain if a woman should undergo prophylactic oophorectomy.  He suggests that a new blood-based protein biomarker panel is being developed for early detection of some forms of ovarian cancer.

Please follow on Twitter using the following #hash tags and @pharma_BI

#MCConverge

#cancertreatment

#healthIT

#innovation

#precisionmedicine

#healthcaremodels

#personalizedmedicine

#healthcaredata

And at the following handles:

@pharma_BI

@medcitynews

 

Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal

LIVE – Real Time – 16th Annual Cancer Research Symposium, Koch Institute, Friday, June 16, 9AM – 5PM, Kresge Auditorium, MIT

Real Time Coverage and eProceedings of Presentations on 11/16 – 11/17, 2016, The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

Tweets Impression Analytics, Re-Tweets, Tweets and Likes by @AVIVA1950 and @pharma_BI for 2018 BioIT, Boston, 5/15 – 5/17, 2018

BIO 2018! June 4-7, 2018 at Boston Convention & Exhibition Center

https://pharmaceuticalintelligence.com/press-coverage/

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SNP-based Study on high BMI exposure confirms CVD and DM Risks – no associations with Stroke

Reporter: Aviva Lev-Ari, PhD, RN

Genes Affirm: High BMI Carries Weighty Heart, Diabetes Risk – Mendelian randomization study adds to ‘burgeoning evidence’

by Crystal Phend, Senior Associate Editor, MedPage Today, July 05, 2017

 

The “genetically instrumented” measure of high BMI exposure — calculated based on 93 single-nucleotide polymorphisms associated with BMI in prior genome-wide association studies — was associated with the following risks (odds ratios given per standard deviation higher BMI):

  • Hypertension (OR 1.64, 95% CI 1.48-1.83)
  • Coronary heart disease (CHD; OR 1.35, 95% CI 1.09-1.69)
  • Type 2 diabetes (OR 2.53, 95% CI 2.04-3.13)
  • Systolic blood pressure (β 1.65 mm Hg, 95% CI 0.78-2.52 mm Hg)
  • Diastolic blood pressure (β 1.37 mm Hg, 95% CI 0.88-1.85 mm Hg)

However, there were no associations with stroke, Donald Lyall, PhD, of the University of Glasgow, and colleagues reported online in JAMA Cardiology.

The associations independent of age, sex, Townsend deprivation scores, alcohol intake, and smoking history were found in baseline data from 119,859 participants in the population-based U.K. Biobank who had complete medical, sociodemographic, and genetic data.

“The main advantage of an MR approach is that certain types of study bias can be minimized,” the team noted. “Because DNA is stable and randomly inherited, which helps to mitigate errors from reverse causality and confounding, genetic variation can be used as a proxy for lifetime BMI to overcome limitations such as reverse causality and confounding, a process that hampers observational analyses of obesity and its consequences.”

 

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

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Reporter and Curator: Irina Robu, PhD

Monitoring cancer patients and evaluating their response to treatment can sometimes involve invasive procedures, including surgery.

The liquid biopsies have become something of a Holy Grail in cancer treatment among physicians, researchers and companies gambling big on the technology. Liquid biopsies, unlike traditional biopsies involving invasive surgery — rely on an ordinary blood draw. Developments in sequencing the human genome, permitting researchers to detect genetic mutations of cancers, have made the tests conceivable. Some 38 companies in the US alone are working on liquid biopsies by trying to analyze blood for fragments of DNA shed by dying tumor cells.

Premature research on the liquid biopsy has concentrated profoundly on patients with later-stage cancers who have suffered treatments, including chemotherapy, radiation, surgery, immunotherapy or drugs that target molecules involved in the growth, progression and spread of cancer. For cancer patients undergoing treatment, liquid biopsies could spare them some of the painful, expensive and risky tissue tumor biopsies and reduce reliance on CT scans. The tests can rapidly evaluate the efficacy of surgery or other treatment, while old-style biopsies and CT scans can still remain inconclusive as a result of scar tissue near the tumor site.

As recently as a few years ago, the liquid biopsies were hardly used except in research. At the moment, thousands of the tests are being used in clinical practices in the United States and abroad, including at the M.D. Anderson Cancer Center in Houston; the University of California, San Diego; the University of California, San Francisco; the Duke Cancer Institute and several other cancer centers.

With patients for whom physicians cannot get a tissue biopsy, the liquid biopsy could prove a safe and effective alternative that could help determine whether treatment is helping eradicate the cancer. A startup, Miroculus developed a cheap, open source device that can test blood for several types of cancer at once. The platform, called Miriam finds cancer by extracting RNA from blood and spreading it across plates that look at specific type of mRNA. The technology is then hooked up at a smartphone which sends the information to an online database and compares the microRNA found in the patient’s blood to known patterns indicating different type of cancers in the early stage and can reduce unnecessary cancer screenings.

Nevertheless, experts warn that more studies are essential to regulate the accuracy of the test, exactly which cancers it can detect, at what stages and whether it improves care or survival rates.

SOURCE

https://www.fastcompany.com/3037117/a-new-device-can-detect-multiple-types-of-cancer-with-a-single-blood-test

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4356857/

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

Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood – R&D @Worcester Polytechnic Institute, Micro and Nanotechnology Lab

Reporters: Tilda Barliya, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/12/28/liquid-biopsy-chip-detects-an-array-of-metastatic-cancer-cell-markers-in-blood-rd-worcester-polytechnic-institute-micro-and-nanotechnology-lab/

Liquid Biopsy Assay May Predict Drug Resistance

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2015/11/06/liquid-biopsy-assay-may-predict-drug-resistance/

One blood sample can be tested for a comprehensive array of cancer cell biomarkers: R&D at WPI

Curator: Marzan Khan, B.Sc

https://pharmaceuticalintelligence.com/2017/01/05/one-blood-sample-can-be-tested-for-a-comprehensive-array-of-cancer-cell-biomarkers-rd-wpi

 

 

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SomaticSeq: An Ensemble Approach with Machine Learning to Detect Cancer Variants

June 16 at 1pm EDT Register for this Webinar |  View All Webinars

Accurate detection of somatic mutations has proven to be challenging in cancer NGS analysis, due to tumor heterogeneity and cross-contamination between tumor and matched normal samples. Oftentimes, a somatic caller that performs well for one tumor may not for another.

In this webinar we will introduce SomaticSeq, a tool within the Bina Genomic Management Solution (Bina GMS) designed to boost the accuracy of somatic mutation detection with a machine learning approach. You will learn:

  • Benchmarking of leading somatic callers, namely MuTect, SomaticSniper, VarScan2, JointSNVMix2, and VarDict
  • Integration of such tools and how accuracy is achieved using a machine learning classifier that incorporates over 70 features with SomaticSeq
  • Accuracy validation including results from the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, in which Bina placed 1st in indel calling and 2nd in SNV calling in stage 5
  • Creation of a new SomaticSeq classifier utilizing your own dataset
  • Review of the somatic workflow within the Bina Genomic Management Solution

Speakers:

Li Tai Fang

Li Tai Fang
Sr. Bioinformatics Scientist
Bina Technologies, Part of
Roche Sequencing

Anoop Grewal

Anoop Grewal
Product Marketing Manager
Bina Technologies, Part of
Roche Sequencing

<Read full speaker bios here>

Cost: No cost!

Schedule conflict? Register now and you’ll receive a copy of the recording.

This webinar is compliments of: 

Bio-ITWorld.com/Bio-IT-Webinars

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