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Archive for July, 2019

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

Curator: Aviva Lev–Ari, PhD, RN

UPDATED on 4/4/2022

Analytics for e-Reputation based on LinkedIn 1st Degree Connections, +7,500 of LPBI Group’s Founder, 2012-2022: An Intangible Asset – Connections’ Position Seniority & Biotech / Pharma Focus

Author: Aviva Lev-Ari, PhD, RN, Founder of 1.0 LPBI, 2012-2020 & 2.0 LPBI, 2021-2025 and Data Scientist, Research Assistant III: Tianzuo George Li

https://pharmaceuticalintelligence.com/2022/04/04/analytics-for-e-reputation-based-on-linkedin-1st-degree-connections-7500-of-lpbi-groups-founder-2012-2022-an-intangible-asset-connections-position-seniority-biotech-pharma-focus/

 

UPDATED on 7/30/2021

Analysis of a corporate Stream of Innovation as reputation builder for venture valuation is presented, below

2.0 LPBI is a Very Unique Organization

Author: Aviva Lev-Ari, PhD, RN, Founder of 1.0 LPBI and 2.0 LPBI, April 2012 to Present

https://pharmaceuticalintelligence.com/2021/03/02/2-0-lpbi-is-a-very-unique-organization/

Direct reputation, feedback reputation and signaling effects are present; and shows that better sellers are always more likely to brand stretch. The comparative statics with respect to the initial reputation level, however, are not obvious. … a higher reputation firm can earn a higher direct reputation effect premium. But a higher reputation firm also has more to lose. The trade-off between using one’s reputation and protecting it can go both ways.

Luıs M B Cabral, New York University and CEPR, 2005

Part 1:   A Digital Business Defined and the Intangible Asset of Firm’s Reputation

  1.  Claiming Distinction
  2.  Recognition Bestowed
  3.  The Technology
  4.  The Sphere of Influence
  5.  The Industrial Benefactors in Potential
  6.  The Actors at Play – Experts, Authors, Writers – Life Sciences & Medicine as it applies to HEALTH CARE
  7.  1st Level Connection on LinkedIn = +7,100 and Endorsements = +1,500
  8.  The DIGITAL REPUTATION of our Venture – Twitter for the Professional and for Institutions
  9.  Growth in Twitter Followers and in Global Reach: Who are the NEW Followers? they are OUR COMPETITION   and   other Media Establishments – that is the definition of Trend Setter, Opinion Leader and Source for Emulation
  10.  Business Aspects of the Brick & Mortar World render OBSOLETE

Part 2:   Business Perspectives on Reputation

Part 3:   Economics Perspectives on Reputation

Part 1:   A Digital Business Defined and the Intangible Asset of Firm’s Reputation

This curation attempts to teach-by-example the new reality of the Intangible Asset of Firm’s Reputation when the business is 100% in the cloud, 100% electronic in nature (paperless), the customers are the Global Universe and the organization is 100% Global and 100% virtual.

A Case in Point: Intellectual Property Production Process of Health Care Digital Content using electronic Media Channels

Optimal Testimonial of e-Product Quality and Reputation for an Open Access Online Scientific Journal pharmaceuticalintelligence.com 

 1.   Claiming Distinction

Executive Summary

WHAT ARE LPBI Group’s NEEDS in June 2019: Aviva’s BOLD VISION on June 11, 2019

2.   Recognition Bestowed 

Our Books are here

  • On 8/17/2018, Dr. Lev-Ari, PhD, RN was contacted by the President elect of the Massachusetts Academy of Sciences (MAS), Prof. Katya Ravid of Boston University, School of Medicine, to join MAS in the role of Liaison to the Biotechnology and eScientific Publishing industries for the term of August 2018-July 2021. In the MAS, Dr. Lev-Ari serve as Board member, Fellow, and Advisor to the Governing Board.

http://www.maacadsci.org

MAS FELLOWS 

GOVERNING BOARD

ACTIVITIES

BUNDLED BY AMAZON.COM INTO A SIX-VOLUME SERIES FOR $515

https://lnkd.in/e6WkMgF

Sixteen Volumes ARE ON AMAZON.COM, average book length – 2,400 pages

https://lnkd.in/ekWGNqA

3.   The Technology

Curation Methodology – Digital Communication Technology to mitigate Published Information Explosion and Obsolescence in Medicine and Life Sciences

Detailed Technology Description

LPBI’s Pipeline Map: A Positioning Perspectives – An Outlook to the Future from the Present

4.   The Sphere of Influence 

LPBI Group’s Social Media Presence

JOURNAL Statistics on 2/24/2019

  • LPBI Platform is been used by GLOBAL Communities of Scientists for interactive dialogue of SCIENCE – Four case studies are presented in the link, below

Electronic Scientific AGORA: Comment Exchanges by Global Scientists on Articles published in the Open Access Journal @pharmaceuticalintelligence.com – Four Case Studies

Curator and Editor-in-Chief: Journal and BioMed e-Series, Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2018/04/10/electronic-scientific-agora-comment-exchanges-by-global-scientists-on-articles-published-in-the-open-access-journal-pharmaceuticalintelligence-com-four-case-studies/

5.   The Industrial Benefactors in Potential

Opportunities Map in the Acquisition Arena

Dynamic Contents for LPBI Group’s PowerPoint Presentation

Potential Use of LPBI IP as Value Price Driver by Potential Acquirer: Assumptions per Asset Class 

6.   The Actors at Play – Experts, Authors, Writers – Life Sciences & Medicine as it applies to HEALTH CARE

Founder’s Role in the Development of Venture’s Factors of Content Production – Biographical Notes by Aviva Lev-Ari, PhD, RN, LPBI Group

Top Authors by Number of eReaders Views

Top Articles by Number of e-Readers for All Days ending 2019-02-17

FIT Members Contribute to Opportunities Map

FINAL IMPROVEMENT TEAM (FIT): Definition of Active, Lapsing of Active Status, COMPs Formulas

FIT members – Who works on WHAT?

Summer 2019 Plan – Research Associates Tasks

7.   1st Level Connection on LinkedIn = +7,100 and Endorsements = +1,500

Connections First Level on LinkedIn: 500 CEOs, 200 Big Pharma Professionals, 7,000 in Total: LPBI Group Founder – Aviva Lev-Ari, PhD, RN

8.   The DIGITAL REPUTATION of our Venture – Twitter for the Professional and for Institutions

Mostly HONORED to be followed by [from an Excerpt of 117 Followers of the Twitter Account @AVIVA1950 from the List of 359 Followers] by the Number of their Followers on 2/24/2019

LPBI Group is mostly HONORED to be followed by [from an Excerpt of 136 Followers of the Twitter Account @pharma_BI from the List of 505 Followers] by the Number of their Followers on 3/20/2019

Excerpt of 136 Followers of @pharma_BI (from the List of 505 Followers) by the Number of their Followers on 3/20/2019

Excerpt of 117 Followers of @AVIVA1950 (from the List of 359 Followers) by the Number of their Followers

REACH – Two Handles on Twitter.com @AVIVA1950 @pharma_BI

9.   Growth in Twitter Followers and in Global Reach: Who are the NEW Followers: OUR COMPETITION and other Media Establishments – that is the definition of Trend Setter, Opinion Leader and Source for Emulation

@4openjournalFollows you

Follow

4open is a multi- & inter-disciplinary, online, peer-reviewed, open access journal publishing across a broad range of subjects in the STEM domain.

@roll_clausFollows you

Follow

Publishing Editor at 

@EDPSciences

@PubtextoPFollows you

Following

Pubtexto is an International online publishing organization that publishes Scientific literature through its different open access Journals.

@alexanderlabrieFollows you

Following

CEO 

@sphereinc

@BjoernBruecherFollows you

Following

THEODOR-BILLROTH-ACADEMY® 

(link: http://linkedin.com/in/bruecher)

linkedin.com/in/bruecher // 

(link: http://4open-sciences.org)

4open-sciences.org – Editor-in-Chief // Science Profile – 

(link: http://researchgate.net/profile/Bjoern)

researchgate.net/profile/Bjoern

@MPDexpertFollows you

Follow

translate research into life-changing Global manufactured Medical Products – drugs, devices, biotech, combination; anything requiring FDA approval#MedProdDev

@P_A_MORGONFollows you

Following

Life science expert & investor_travel, wine & golf amateur_Proud father of 2 girls_My Tweets are only mine 

@INmuneBioFollows you

Follow

INmune Bio, Inc. is developing therapies that harness patient’s #immunesystem to treat #cancer. Our focus is on #NKcells and #myeloid derived suppressor cells.

@sallyeavesFollows you

Following

Innovating #tech #education #business CEO CTO Advisor & Prof. #blockchain #AI 

@OxfordSBS

@Forbes

 #FinTech #speaker #SDGs #STEM #techforgood #sustainability

@sciencetracker2Follows you

You will hear more recent and cool scientific news here. Besides, some health and tech news. Follow us in

(link: http://facebook.com/sciencetracker2)

facebook.com/sciencetracker2

13.8K Following

24.6K Followers

Followed by Stanford Tweets, Biotech Week Boston, and 23 others you follow

@sgruenwaldFollows you

Following

MD, PhD, scientist, futurist, entrepreneur, managing director of 

(link: http://www.genautica.com)

genautica.com, co-founder 

(link: http://www.diagnomics.com)

diagnomics.com

(link: http://www.scoop.it/t/amazing-science)

scoop.it/t/amazing-scie…user

10.  Business Aspects of the Brick & Mortar World render OBSOLETE

Financial Valuation of Three Health Care Intellectual Property (IP) Content Asset Classes

Global Market Penetration Forecast for each Volume in the 16 Volume BioMed e-Series

2013-2019, On the Medical & Scientific Bookshelf in Kindle Store: eReader Behaviors: Browsing, Page Downloads and Buying e-Books – LPBI Group’s BioMed e-Series, Royalties Payment Analysis 

Part 2: BUSINESS PERSPECTIVES on Reputation

Warren Buffett on reputation: the economic value of values, integrity and corporate culture

Warren Buffett understands that reputation and integrity have economic value. Research that shows that a good reputation is worth real money — in fact, some research indicates that a good reputation might replace a line of credit at the bank. In his book Berkshire Beyond Buffett: The Enduring Value of Values, Lawrence Cunningham argues that one of Berkshire Hathaway’s greatest assets is reputation.

https://www.finn.agency/fr/warren-buffett-reputation-berkshire-hathaway

The Value of Reputation

Thomas Pfeiffer1,2,4,*, Lily Tran5, Coco Krumme5 and David G Rand1,3,* 1 Program for Evolutionary Dynamics, FAS, 2 School of Applied Sciences and Engineering, and 3 Department of Psychology, Harvard University, Cambridge MA 02138, USA 4 New Zealand Institute for Advanced Study, Massey University, Auckland 0745, New Zealand 5 MIT Media Laboratory, Cambridge MA 02139, USA

Reputation plays a central role in human societies.

Empirical and theoretical work indicates that a good reputation is valuable in that it increases one’s expected payoff in the future. Here, we explore a game that couples a repeated Prisoner’s Dilemma (PD), in which participants can earn and can benefit from a good reputation, with a market in which reputation can be bought and sold. This game allows us to investigate how the trading of reputation affects cooperation in the PD, and how participants assess the value of having a good reputation. We find that depending on how the game is set up, trading can have a positive or a negative effect on the overall frequency of cooperation. Moreover, we show that the more valuable a good reputation is in the PD, the higher the price at which it is traded in the market. Our findings have important implications for the use of reputation systems in practice.

Keywords: evolution of cooperation; reciprocal altruism; indirect reciprocity; reputation

http://decisionlab.harvard.edu/_content/research/papers/Krumme_Pfieffer_Tran_and_Rand_Value_of_Reputation.pdf

The Impact of Reputation on Market Value by Simon Cole

One of the most familiar, but least understood, intangible assets is a firm’s reputation.

Simon Cole is the founding partner of the corporate reputation and branding consultancy Reputation Dividend (www. reputationdividend.com).

http://www.reputationdividend.com/files/4713/4822/1479/Reputation_Dividend_WEC_133_Cole.pdf

Part 3:   ECONOMICS PERSPECTIVES on Reputation

The Economics of Trust and Reputation: A Primer

Luıs M B Cabral New York University and CEPR, June 2005, lecture series at the University of Zurich

lcabral@stern.nyu.edu

https://pdfs.semanticscholar.org/24e5/2f3bd22d4bfa86902e5ae07d57039480004f.pdf

Notes on the literature

Important note: The notes in this section are essentially limited to the ideas discussed in the present version of these lectures notes. They cannot therefore be considered a survey of the literature. There are dozens of articles on the economics of reputation which I do not include here. In a future version of the text, I hope to provide a more complete set of notes on the literature. The notes below follow the order with which topics are presented.

Bootstrap models. The bootstrap mechanism for trust is based on a general result known as the folk theorem (known as such because of its uncertain origins). For a fairly general statement of the theorem (and its proof) see Fudenberg and Makin (1986). One of the main areas of application of the folk theorem has been the problem of (tacit or explicit) collusion in oligopoly. This is a typical problem of trust (or lack thereof): all firms would prefer prices to be high and output to be low; but each firm, individually, has an incentive to drop price and increase output. Friedman (1971) presents one of the earliest formal applications of the folk theorem to oligopoly collusion. He considers the case when firms set prices and history is perfectly observable. Both of the extensions presented in Section 2.2 were first developed with oligopoly collusion applications in mind. The case of trust with noisy signals (2.2.1) was first developed by Green and Porter (1984). A long series of papers have been written on this topic, including the influential work by Abreu, Pearce and Stacchetti (1990). Rotemberg and Saloner (1986) proposed a model of oligopoly collusion with fluctuating market demand. In this case, the intuition presented in Section 2.2.2 implies that firms collude on a lower price during periods of higher demand. This suggests that prices are counter-cyclical in markets where firms collude. Rotemberg and Saloner (1986) present supporting evidence from the cement industry. A number of papers have built on Rotemberg and Saloner’s analysis. Kandori (1992) shows that the i.i.d. assumption simplifies the analysis but is not crucial. Harrington (19??) considers a richer demand model and looks at how prices vary along the business cycle. The basic idea of repetition as a form of ensuring seller trustworthiness is developed in Klein and Leffler (1981). See also Telser (1980) and Shapiro (1983). When considering the problem of free entry, Klein and Leffler (1981) propose advertising as a solution, whereas Shapiro (1983) suggests low intro25 ductory prices. Section ?? is based on my own research notes. The general analysis of selfreinforcing agreements when there is an outside option of the kind considered here may be found in Ray (2002). Watson (1999, 2002) also considers models where the level of trust stars at a low level and gradually increases.

Bayesian models. The seminal contributions to the study of Bayesian models of reputation are Kreps and Wilson (1982) and Milgrom and Roberts (1982). The model in Section 3.2.1 includes elements from these papers as well as from Diamond (1989). H¨olmstrom (1982/1999) makes the point that separation leads to reduced incentives to invest in reputation. The issue of reputation with separation and changing types is treated in detail in the forthcoming book by Mailath and Samuelson (2006). In Section 3.3, I presented a series of models that deal with name as carriers of reputations. The part on changing names (Section 3.3.1) reflects elements from a variety of models, though, to the best of my knowledge, no study exists that models the process of secret, costless name changes in an infinite period adverse selection context. The study of markets for names follows the work by Tadelis (1999) and Mailath and Samuelson (2001). All of these papers are based on the Bayesian updating paradigm. Kreps (1990) presents an argument for trading reputations in a bootstrap type of model. The analysis of brand stretching (Section 3.3.3) is adapted from Cabral (2000). The paper considers a more general framework where the direct reputation, feedback reputation and signalling effects are present; and shows that better sellers are always more likely to brand stretch. The comparative statics with respect to the initial reputation level, however, are not obvious. As we saw above, a higher reputation firm can earn a higher direct reputation effect premium. But a higher reputation firm also has more to lose. The trade-off between using one’s reputation and protecting it can go both ways. For other papers on brand stretching and umbrella branding see Choi (1998), Anderson (2002).

Bibliography

Abreu, Dilip, David Pearce and Ennio Stacchetti (1990), “Toward a Theory of Discounted Repeated Games with Imperfect Monitoring,” Econometrica 58, 1041–1064. Andersson, Fredrik (2002), “Pooling reputations,” International Journal of Industrial Organization 20, 715–730. Bernhein, B. Douglas and Michael D. Whinston (1990), “Multimarket Contact and Collusive Behavior,” Rand Journal of Economics 21, 1–26. Cabral, Lu´ıs M B (2000), “Stretching Firm and Brand Reputation,” Rand Journal of Economics 31, 658-673. Choi, J.P. (1998), “Brand Extension and Informational Leverage,” Review of Economic Studies 65, 655–69. Diamond, Douglas W (1989), “Reputation Acquisition in Debt Markets,” Journal of Political Economy 97, 828–862. Ely, Jeffrey C., and Juuso Valim ¨ aki ¨ (2003), “Bad Reputation,” The Quarterly Journal of Economics 118, 785–814. Fishman, A., and R. Rob (2005), “Is Bigger Better? Customer Base Expansion through Word of Mouth Reputation,” forthcoming in Journal of Political Economy. Friedman, James (1971), “A Noncooperative Equilibrium for Supergames,” Review of Economic Studies 28, 1–12. Fudenberg, Drew and Eric Maskin (1986), “The Folk Theorem in Repeated Games with Discounting or with Imperfect Public Information,” Econometrica 54, 533–556. Green, Ed and Robert Porter (1984), “Noncooperative Collusion Under Imperfect Price Information,” Econometrica 52, 87–100. Holmstrom, Bengt ¨ (1999), “Managerial Incentive Problems: A Dynamic Perspective,” Review of Economic Studies 66, 169–182. (Originally (1982) in Essays in Honor of Professor Lars Wahlback.) Kandori, Michihiro (1992), “Repeated Games Played by Overlapping Generations of Players,” Review of Economic Studies 59, 81–92. Klein, B, and K Leffler (1981), “The Role of Market Forces in Assuring Contractual Performance,” Journal of Political Economy 89, 615–641. 27 Kreps, David (1990), “Corporate Culture and Economic Theory,” in J Alt and K Shepsle (Eds), Perspectives on Positive Political Economy, Cambridge: Cambridge University Press, 90–143. Kreps, David M., Paul Milgrom, John Roberts and Robert Wilson (1982), “Rational Cooperation in the Finitely Repeated Prisoners’ Dilemma,” Journal of Economic Theory 27, 245–252. Kreps, David M., and Robert Wilson (1982), “Reputation and Imperfect Information,” Journal of Economic Theory 27, 253–279. Mailath, George J, and Larry Samuelson (2001), “Who Wants a Good Reputation?,” Review of Economic Studies 68, 415–441. Mailath, George J, and Larry Samuelson (1998), “Your Reputation Is Who You’re Not, Not Who You’d Like To Be,” University of Pennsylvania and University of Wisconsin. Mailath, George J, and Larry Samuelson (2006), Repeated Games and Reputations: Long-Run Relationships, Oxford: Oxford University Press. Milgrom, Paul, and John Roberts (1982), “Predation, Reputation, and Entry Deterrence,” Journal of Economic Theory 27, 280–312. Phelan, Christopher (2001), “Public Trust and Government Betrayal,” forthcoming in Journal of Economic Theory. Ray, Debraj (2002), “The Time Structure of Self-Enforcing Agreements,” Econometrica 70, 547–582. Rotemberg, Julio, and Garth Saloner (1986), “A Supergame-Theoretic Model of Price Wars During Booms,” American Economic Review 76, 390–407. Shapiro, Carl (1983), “Premiums for High Quality Products as Rents to Reputation,” Quarterly Journal of Economics 98, 659–680. Tadelis, S. (1999), “What’s in a Name? Reputation as a Tradeable Asset,” American Economic Review 89, 548–563. Tadelis, Steven (2002), “The Market for Reputations as an Incentive Mechanism,” Journal of Political Economy 92, 854–882. Telser, L G (1980), “A Theory of Self-enforcing Agreements,” Journal of Business 53, 27–44. Tirole, Jean (1996), “A Theory of Collective Reputations (with applications to the persistence of corruption and to firm quality),” Review of Economic Studies 63, 1–22. 28 Watson, Joel (1999), “Starting Small and Renegotiation,” Journal of Economic Theory 85, 52–90. Watson, Joel (2002), “Starting Small and Commitment,” Games and Economic Behavior 38, 176–199. Wernerfelt, Birger (1988), “Umbrella Branding as a Signal of New Product Quality: An Example of Signalling by Posting a Bond,” Rand Journal of Economics 19, 458–466.

https://pdfs.semanticscholar.org/24e5/2f3bd22d4bfa86902e5ae07d57039480004f.pdf

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

Reporter: Aviva Lev-Ari, PhD, RN

3.4.15

3.4.15   Forbes Opinion: 13 Industries Soon To Be Revolutionized By Artificial Intelligence, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 3: AI in Medicine

UPDATED on 11/18/2019

Amazon Saw 15-Fold Jump In Forecast Accuracy With Deep Learning And Other AI Stats

https://www.forbes.com/sites/gilpress/2019/11/14/amazon-saw-15-fold-jump-in-forecast-accuracy-with-deep-learning-and-other-ai-stats/#2a03ca8a748f

China Leads in Highly Cited AI Papers

by GilPress

China AI Development Report 2018 (p. 20f.): Using the Web of Science database, this report puts China in the lead ahead of the US and any European country, both for the decade between 2007 and 2017 and in terms of annual output in 2017. Aggregating all European countries out of the top 10 from 2007 to 2017 (the UK, Germany, France, Italy, Spain), they almost catch up to the US and China, reaching 2,096 highly cited papers, compared to 2,241 and 2,349, respectively. The same applies to “hot papers.” Unfortunately, I do not know how they operationalized either “highly cited” or “hot.” Eyeballing combined European output in 2017 alone; China is still in the lead with an increasingly large margin, ahead of the US and Europe, which seem to be roughly tied.

Source: Stefan Torges

2020 Predictions for the Internet of Things (IoT)

by GilPress

IDC 2020 predictions for the Internet of Things (IoT):

In 2020, 90% of organizations will have determined key performance indicators to measure the success of their IoT projects.

By 2021, 75% of organizations embarking on an IoT project will work with a systems integrator to strategize, plan, deploy, and/or manage the initiative.

By 2022, 70% of new enterprise IoT applications built on IoT platforms will leverage container deployment.

By 2023, 70% of enterprises will run varying levels of data processing at the IoT edge. In tandem, organizations will spend over $16 billion on IoT edge infrastructure in that time.

By 2023, 20% of cybersecurity incidents will stem from Smart City IoT device deployments, forcing double-digit increases in cybersecurity software and staff training budgets.

To lessen critical equipment failures, by 2024, 40% of manufacturers will use field asset IoT data to intelligently diagnose issues and resolve autonomously, improving unplanned downtime by 25%.

By 2023, 70% of IoT deployment will include AI solutions for autonomous or edge decision making, supporting organizations’ operational and strategic agendas.

By 2025, there will be 79ZB of data created by billions of IoT devices, causing organizations to reevaluate their data governance, retention, and usage policies.

By 2025, 60% of manufacturers will use IoT platforms with digital innovation platforms to operate networks of asset, product, and process digital twins for a 25% reduction in cost of quality.

By 2023, enterprises will struggle to manage all the different access types used to connect their IoT endpoints, with 75% adopting more than one connectivity type.

GilPress | November 12, 2019 at 9:15 am | Tags: 2020 predictions, IDC | Categories: Data growth, Internet of Things | URL: https://wp.me/p1AMkl-2J2

13 Industries Soon To Be Revolutionized By Artificial Intelligence

7:00 am

30,112 views|Jan 16, 2019,

Post written by

Expert Panel, Forbes Technology Council

Successful CIOs, CTOs & executives from Forbes Technology Council offer firsthand insights on tech & business.

Artificial intelligence (AI) and machine learning (ML) have a rapidly growing presence in today’s world, with applications ranging from heavy industry to education. From streamlining operations to informing better decision making, it has become clear that this technology has the potential to truly revolutionize how the everyday world works.

While AI and ML can be applied to nearly every sector, once the technology advances enough, there are many fields that are either reaping the benefits of AI right now or that soon will be. According to a panel of Forbes Technology Council members, here are 13 industries that will soon be revolutionized by AI.

1. Cybersecurity

The enterprise attack surface is massive. There are countless permutations and combinations in which the adversary can get in. It is exceptionally hard for organizations to analyze and improve their security posture. With its power to bring complex reasoning and self-learning in an automated fashion at massive scale, AI will be a game-changer in how we improve our cyber-resilience. – Gaurav Banga, Balbix

2. DevOps And Cloud Hosting

AI is starting to make its mark in DevOps. Currently, Amazon has rolled out machine learning for their Elastic Compute Cloud (EC2) instances, which applies to predictive instance autoscaling. Other cloud vendors are following suit with similar technology. Within the next 10 years, I see the same being applied to bigger things like code deployments and infrastructure provisioning. – Rick Conlee, Meticulosity

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

3. Manufacturing

Artificial intelligence in the world of manufacturing has limitless potential. From preventative maintenance to the automation of human tasks, AI will enable more efficient work that’s less prone to error and has higher quality. Initiatives from tech giants like Microsoft (AI for Accessibility) and smaller leading companies like AtBot will revolutionize AI for all information workers. – Dan Sonneborn, Aerie Consulting

4. Healthcare

Healthcare is only starting on its AI journey. Computer vision against X-rays shows promises to help pinpoint diseases; natural language processing (NLP) shows promises in drug safety; ML shows promises to find patterns within a population. Once we reach a point of true information interoperability, supporting the secure exchange of health data, all these promises will join forces to become breakthroughs for the patients. – Florian Quarré, Ciox Health

5. Construction

The construction industry has long been underserved by the technology and software sector. Many new startups like ours are using AI in a big way to slingshot the construction industry into tomorrow. Bringing AI and machine learning into this industry will make the construction process faster, safer and more cost effective by reducing human error and better utilizing big data.  – Karuna Ammireddy, Pype

6. Senior Care

With the aging Baby Boomer generation, we need solutions that provide continued efficiency for seniors to make them feel more confident about living alone or receiving support from their caregivers. While AI may not be able to understand the cultural, physical and emotional needs of people, it can provide updates to many outdated resources. – Abdullah Snobar, DMZ at Ryerson University

7. Retail

The retail industry will be one that is most impacted by AI. Its global spending is expected to grow to $7.3 billion per year by 2022. Retailers will use augmented and virtual reality functionality in advertising. Immersive product catalog visualization will grow dramatically, and shoppers will experience products before buying. It’s predicted that by 2020, chatbots will power 85% of all customer service interactions. – Nacho De Marco, BairesDev

8. Business Intelligence

Enterprises are overwhelmed by the volume of data generated by their customers, tools and processes. They are finding traditional business intelligence tools are failing. Spreadsheets and dashboards will be replaced by AI-powered tools that explore data, find insights and make recommendations automatically. These tools will change the way companies use data and make decisions. – Sean Byrnes, Outlier AI

9. City Planning

Infrastructure planning and development will get a big boost from AI. So much data can be processed and organized to help understand urban areas and how they are changing. AI data can also provide a different way of looking at growth and development, utility use, safety, and more. – Chalmers Brown, Due

10. Mental Health Diagnosis And Treatment

We are starting to see an increase in mental health issues among young people. Whether it is device addiction or withdrawal from the physical world, some are starting to isolate themselves online. This can ultimately lead to a breakdown of social cohesion. I see potential in using AI to identify people at risk and recommend therapy before they fall into a hole of depression and hopelessness. – Chris Kirby, Retired

11. Education

The basic concepts of education have not changed much across generations, and it is quite obvious that change is needed. The most pressing question is what that change should be and how to achieve it. Harnessing AI to create a personalized, dynamic and effective learning path for any subject can prove to be an amazing enabler for such a revolution. – Ofer Garnett, YouAPPi Inc.

12. Fashion

Using AI to learn about buying patterns of users across the world and predict fashion trends would be a great implementation. Having a great recommendation engine backed by AI would help users tremendously. – Amit Ojha, Diamond Foundry

13. Supply Chain Management

AI can account for more factors and complicated nonlinear and correlated dependencies of data much better than a human can do. AI can predict the future without human bias, but with a proper risk assessment, and find optimal decisions even under asymmetric cost profile. This leads to improvements in every decision. – Michael Feindt, Blue Yonder

Forbes Technology Council is an invitation-only, fee-based organization comprised of leading CIOs, CTOs and technology executives. Find out if you qualify at forbestechcoRead More

SOURCE

https://www.forbes.com/sites/forbestechcouncil/2019/01/16/13-industries-soon-to-be-revolutionized-by-artificial-intelligence/amp/?__twitter_impression=true

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Article ID #272: Broad@15 – In 2004, the Broad Institute of MIT and Harvard launched with a mission to improve human health. Published on 7/31/2019

WordCloud Image Produced by Adam Tubman

Broad@15 – In 2004, the Broad Institute of MIT and Harvard launched with a mission to improve human health, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

Broad@15 – In 2004, the Broad Institute of MIT and Harvard launched with a mission to improve human health

Reporter: Aviva Lev-Ari, PhD, RN

THANK YOU @broadinstitute for following me @AVIVA1950

 

Following
A unique, collaborative community pioneering a new model of biomedical science

When I launched pharmaceuticalintelligence.com in April 2012, the first 26 categories of research where inspired by browsing the Broad Institute website.

Happy to report on 7/31/2019:

1,648,985 views

5,667 Posts

687 Categories – Our first 26 were in pursuit at the Broad Institute

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#Broad@15

broadinstitute.org/15

In 2004, the Broad Institute of MIT and Harvard launched with a mission to improve human health.

This year marks our 15th anniversary. During that time, biology and medicine have evolved in astonishing ways, and so have we. Our community now includes more than four thousand scientists, software engineers, and more, with collaborations in more than three dozen countries.

We think the amazing pace of scientific progress is a story worth sharing. Beginning in the summer of 2019 and continuing through spring of 2020, we’ll host a series of public talks to trace the evolution of key fields of science and medicine over the last 15 years, and look ahead to how they might continue to evolve in the future.

These engaging discussions will be in place of our regular Midsummer Nights’ Science and Science for All Seasons series, which will return later in 2020. 

We hope you’ll join us in person or online! Sign up here to stay up to date!

Broad@15 Talk Series

Topics

The Human Genomic Revolution: Past, Present, and Future

Eric Lander 

Thursday, August 1, 2019

Over 15 years ago, the scientific community celebrated the sequencing of the first human genome. It’s time to ask how this monumental effort has transformed biomedical science, from basic research to the understanding and treatment of disease. Eric Lander, Broad Institute president and founding director and one of the principal leaders of the Human Genome Project, will survey the impact — what we’ve learned, and what lies ahead.

This lecture is presented in memory of Eliana Hechter and is supported by the Eliana Hechter Memorial Fund.

Cancer

Todd Golub

September 19, 2019

Mental Health

Benjamin Neale and Beth Stevens

October 7, 2019

Therapeutics

Anna Greka and Florence Wagner

Thursday, November 14, 2019

Genome Editing

David Liu and Feng Zhang

January 21, 2020

Infectious Disease

Deborah Hung and Pardis Sabeti

Thursday, February 13, 2020

Sequencing and Data Sciences

Jonathan Bloom and Stacey Gabriel

Wednesday, March 4, 2020

Single-cell Biology

Aviv Regev

May 5, 2020

SOURCE

https://www.broadinstitute.org/15

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MinneBOS 2019, Field Guide to Data Science & Emerging Tech in the Boston Community

August 22, 2019, 8AM to 5PM at Boston University Questrom School of Business, 595 Commonwealth Avenue, Boston, MA

 

 

MinneBOS – Boston’s Field Guide to Data Science & Emerging Tech

Announcement

Leaders in Pharmaceutical Business Intelligence (LPBI) Group

 

REAL TIME Press Coverage for

 http://pharmaceuticalintelligence.com 

by

 Aviva Lev-Ari, PhD, RN

Director & Founder, Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston

Editor-in-Chief, Open Access Online Scientific Journal, http://pharmaceuticalintelligence.com

Editor-in-Chief, BioMed e-Series, 16 Volumes in Medicine, https://pharmaceuticalintelligence.com/biomed-e-books/

@pharma_BI

@AVIVA1950

#MinneBos

 

Logo, Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston

Our BioMed e-series

WE ARE ON AMAZON.COM

 

https://lnkd.in/ekWGNqA

 

UPDATED AGENDA

Thursday, August 22 • 9:30am – 10:15am
Histopathological images are the gold standard tool for cancer diagnosis, whose interpretation requires manual inspection by expert pathologists. This process is time-consuming for the patients and subject to human error. Recent advances in deep learning models, particularly convolutional neural networks, combined with big databases of patient histopathology images will pave the path for cancer researchers to create more accurate guiding tools for pathologists. In this talk, I will review the latest advances of big data in healthcare analytics and focus on deep learning applications in cancer research. Targeted at a general audience, I will provide a high-level overview of technical concepts in deep learning image analysis, and describe a typical cloud-based workflow for tackling such big data problems. I will conclude my talk by sharing some of our most recent results based on a wide range of cancer types.

Speakers

avatar for Mohammad Soltanieh-ha, PhD

Mohammad Soltanieh-ha, PhD

Clinical Assistant Professor, Boston University – Questrom
Mohammad is a faculty at Boston University, Questrom School of Business, where he teaches data analytics and big data to master’s students. Mohammad’s current research area involves deep learning and its applications in cancer research.

10:15am

10:30am

Thursday, August 22 • 10:30am – 11:00am

Deep learning image recognition and classification models for fashion items

Large scale image recognition and classification is an interesting and challenging problem. This case study uses fashion-MNIST dataset that involves 60000 training images and 10000 testing images. Several popular deep learning models are explored in this study to arrive at a suitable model with high accuracy. Although convolutional neural networks have emerged as a gold-standard for image recognition and classification problems due to speed and accuracy advantages, arriving at an optimal model and making several choices at the time of specifying model architecture, is still a challenging task. This case study provides the best practices and interesting insights.

Speakers

avatar for Bharatendra Rai

Bharatendra Rai

Professor, UMass Dartmouth
Bharatendra Rai, Ph.D. is Professor of Business Analytics in the Charlton College of Business at UMass Dartmouth. His research interests include machine learning & deep learning applications.
  • Train data: 60,000
  • Test data: 10,000
  • Dataset available from Google MNIST Fashion Data – items in DB: data already labelled
  • Label and Description
  • Architecture: Input >> Conv >> Conv >> Pooling >> Dropout << Dense <<Flatten << Dropout >> Output
  • CNN vs Fully connected: 320 parameters: 3x3x1x32 + [32 BIAS TERM] = 320 vs
  • fully connected network parameters is 16 million
  • Train the model: 15 iterations – Training and Validation
  • Actual vs Predicted: 94% was classified correctly = Accuracy: 94% 5974 vs 4700 (78%)
  • Confusion Matrix – Test 720 correctly classified for item 6  – Probability va Actual Vs Predicted
  • Image generation: Noise . gnerator Network > fake Image vs Real image – GAN Loss va Discriminator Loss
  • CNN network help reduce # of parameter
  • Droppot layers can help reduce overfitting
  • validation split of x%chooses last x% of train data
  • Generation of new data is challenging

11:00am

11:15am

Thursday, August 22 • 11:15am – 12:00pm

Rapid Data Science

Most companies today require fast, traceable, and actionable answers to their data questions. This talk will present the structure of the data science process along with cutting edge developments in computing and data science technology (DST) with direct applications to real world problems (with a lot of pictures!). Everything from modeling to team building will be discussed, with clear business applications.

Speakers

avatar for Erez Kaminski

Erez Kaminski

Leaders Global Operations Fellow, MIT
Erez has spent his career helping companies solve problems using data science. He is currently a graduate student in computer science and business at MIT. Previously, he worked in data science at Amgen Inc. and as a technologist at Wolfram Research.

12:00pm

1:00pm

Thursday, August 22 • 1:00pm – 1:45pm

Health and Healthcare Data Visualization – See how you’re doing

Health and healthcare organizations are swimming in data but few have the skills to show and see the story in their data using the best practices of data visualization. This presentation raises awareness about the research that inform these best practice and stories from the front of groups who are embracing them and re-imagining how they display their data and information. These groups include the NYC Dept of Health & Mental Hygiene, The Centers for Medicare and Medicaid (CMS), and leading medical centers and providers across the country.

Speakers

avatar for Katherine Rowell

Katherine Rowell

Co-Founder & Principal, Health Data Viz
Katherine Rowell is a health, healthcare, and data visualization expert. She is Co-founder and Principal of HealthDataViz, a Boston firm that specializes in helping healthcare organizations organize, design and present visual displays of data to inform their decisions and stimulate… Read More →
  • dashboard for Hospital CEOs

1:45pm

2:00pm

Thursday, August 22 • 2:00pm – 2:45pm

AI in Healthcare

Benefits, challenges and impact of AI and Cybersecurity on medicine.

Speakers

avatar for Vinit Nijhawan

Vinit Nijhawan

Lecturer, Boston University
Vinit Nijhawan is an Entrepreneur, Academic, and Board Member with a track record of success, including 4 startups in 20 years.
  • US: Spends the most on Health Care (HC) death per 100K people is the highest
  • Eric Topol – Diagnosis is not done correctly, AI will help with diagnosis
  • Diagnosis — AI will have the most impact; VIRAL infections are diagnosed as bacterial infections and get antibiotics for treatment
  • Image Classification my ML – decline below to human misclassification
  • Training Data sets – Big data
  • Algorithms getting better
  • Data Capture getting better – HC as well
  • Investment in HC is the greatest
  • SECURITY related to Implentable Medical Devices = security attacks – hacking and sending signal to implentable devices

2:45pm

3:00pm

Empower

Thursday, August 22 • 3:00pm – 3:30pm

Patient centric AI: Saving lives with ML driven hospital interventions

This presentation will cover the use of machine learning for maximizing the impact of a hospital readmissions intervention program. With machine learning, clinical care teams can identify and focus their intervention efforts on patients with the highest risk of readmission. The talk will go over the goals, logistics, and considerations for defining, implementing, and measuring our ML driven intervention program. While covering some technical details, this presentation will focus on the business implementation of advanced technology for helping people live healthier lives.

Speakers

avatar for Miguel Martinez

Miguel Martinez

Data Scientist, Optum
Miguel Martinez is a Data Scientist at Optum Enterprise Analytics. Relied on as a tech lead in advancing AI healthcare initiatives, he is passionate about identifying and developing data science solutions for the benefit of organizations and people.

 

3:30pm

3:45pm

Thursday, August 22 • 3:45pm – 4:15pm

Using Ontologies to Power AI Systems

There’s a great deal of confusion about the role of a knowledge architecture in artificial intelligence projects. Some people don’t believe that any reference data is necessary. But in reality reference data is required- even if there is no metadata or architecture definitions outside defined externally for an AI algorithm, someone has made the decisions about architecture and classification within the program. However, this will not work for every organization because there are terms, workflows, product attributes, and organizing principles that are unique to the organization and that need to be defined for AI tools to work most effectively.

Speakers

avatar for Seth Earley

Seth Earley

CEO, Earley Information Science
Seth Earley is a published author and public speaker about artificial intelligence and information architecture. He wrote “There’s no AI without IA” which has become an industry catchphrase used by a number of people including Ginny Rometty, the CEO of IBM.
  • Ontology, taxonomies, thesauri – conceptual relationships
  • Object-Oriented Programming and Information Architecture using AI is Old wine in new bottles

4:15pm

Thursday, August 22

TBA

 Senior Leadership Panel: Future Directions of Analytics

This panel includes senior leaders from across industry, academia & government to discuss challenges they are tackling, needs they anticipate and goals they will achieve

Moderators

avatar for Bonnie Holub, PhD

Bonnie Holub, PhD

Industry & Business Data Science, Teradata
Bonnie has a PhD in Artificial Intelligence and specializes in correlating disparate sets of Big Data for actionable results.

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The Sylvester Comprehensive Cancer Center of the University of Miami named 71st NCI designated Cancer Center

 

Reporter: Stephen J. Williams, PhD

As seen in the Cancer Letter at https://cancerletter.com/articles/20190729_1/

Conversation with The Cancer Letter

Sylvester becomes 71st NCI-designated cancer center

Stephen Nimer

Director,

Sylvester Comprehensive Cancer Center

 

After six years of  aggressively recruiting and spending more than $250 million to build up its programs, Sylvester Comprehensive Cancer Center has become the 71st NCI-designated cancer center in the US and the only such institution in South Florida.

The designation was announced July 29.

Sylvester, which is a part of the University of Miami Leonard M. Miller School of Medicine, is one of 64 cancer centers with the NCI Cancer Center designation in the nation. Fifty of these centers hold the Comprehensive Cancer Center designation. Seven more are designated as Basic Laboratory Cancer Centers.

“There are over 21 million people who live in the state of Florida. In 2014, Florida became the third largest state in the United States, surpassing New York—yet New York has seven NCI designated cancer centers and Florida had had only one,” Stephen D. Nimer, director of Sylvester, said to The Cancer Letter.

“There are over six million people in our catchment area, South Florida, and if they wanted to go to an NCI-designated cancer center they’d have to either get on a plane or drive nearly 300 miles—to Tampa.”

Public health programs that helped Sylvester secure the NCI designation include the Game Changer vehicle, which brings evidence-based interventions to underserved communities in the cancer center’s catchment area (The Cancer Letter, April 27, 2018). The center’s cancer control program also includes the Firefighter Cancer Initiative, a long-term study of exposures to carcinogens and ways to reduce and prevent cancer risks for Florida firefighters.

 

 

The cancer center is working on deploying another Game Changer vehicle. Recently, Peter Tunney, a New York and Miami-based artist and gallerist who donated a painting for the first Game Changer van, donated another painting that Sylvester can sell to raise money for its programs (The Cancer Letter, April 27, 2018).

 

“When they got that designation, they were walking on sunshine,” Tunney said to The Cancer Letter. “I think it’s a universal idea. I think that’s the goal for all of us—for all of mankind, for sick and healthy—to have that feeling that is so rare today: I am walking on sunshine. It’s almost like a thing of the past. Who can walk on sunshine today, in this crazy world filled with suffering and illness? And I just feel like we can, we can, it’s possible to be grateful for the things we have.

The intense yellow wallpaper motif reminds Tunney of the wallpaper in his grandmother’s house in the 1960s and 1970s, the time when American astronauts walked on the moon. “It’s somebody’s grandmother’s wallpaper from the sixties. We look back at that time, we look back at landing on the moon, and everyone is aflutter, ‘Oh, those were the good old days.’ No, these are the good old days.”

The word “comprehensive” in Sylvester’s name doesn’t refer to its level of NCI designation. When it was founded in 1973, the institution was known as the Comprehensive Cancer Center for the State of Florida. In 1992, after receiving a $27.5 million gift from the philanthropist Harcourt Sylvester Jr., it was renamed Sylvester Comprehensive Cancer Center.

 

Sylvester director Nimer spoke with Paul Goldberg, editor and publisher of The Cancer Letter.

 

Paul Goldberg:

First of all, congratulations.

Stephen Nimer: 

Thank you; it’s a big deal.

 

PG:

How long did it take to get this done?

SN:

I’d say, six years. I arrived in 2012, seven years ago, and the first year started by assessing what’s going on at Sylvester. We then developed our first five-year strategic plan, which ran from 2014 to 2018, and we submitted our [Cancer Center Support Grant] application in September 2018. We’re now in the midst of our second five-year plan.

 

PG:

And how much money did it require?

SN:

I’d have to add it all up. One of the most important things for us was that the state, in 2014, started giving us a bit over $16 million a year so that we could become NCI-designated. The health system, over a five-to-six-year period, probably gave us somewhere between $90 and $100 million. And then we’ve raised philanthropy. The philanthropy over five to six years, is maybe close to $100 million. So, it’s probably $250 -$270 million.

 

PG:

How many people did you have to recruit?

SN:

We went in [to NCI] with 124 members on our CCSG application, but over the last seven years we’ve recruited nearly 150 people. In addition to recruiting researchers I’ve been given the opportunity to build the clinical programs also.

Many of the clinical people are not included on the grant, because the grant has very specific requirements to be a member. For example, we’ve hired a couple of breast cancer surgeons, and they are not listed on the grant, because they are not yet doing significant research.

The NCI doesn’t want to know about people who don’t have grants or aren’t running clinical trials. So, out of the 124, which is what we went in with, I believe nearly 50 of our members were new.

 

PG:

How is your cancer center different from all others?

SN:

One of the things that we got the highest marks on is our community outreach and engagement efforts and how relevant the research we’re doing is to our catchment area.

A couple of examples:

We have a West Indies population, so we have an endemic HTLV-1-infected population, and thus a significant number of HTLV-1-related adult T-cell leukemia patients. So, one of our physician scientists has an R01 studying ATL. And we have a number of clinical trials for people with adult T-cell leukemia.

We also have a large burden of advanced cervical cancer patients in our region, especially in Little Haiti. And so, we have a lot of efforts on early detection of high-risk HPV, prevention and clinical treatment trials for women with cervical cancer.

Another thing that distinguishes us from many centers is the diversity of our faculty, our students, and the patients we put on clinical trials. In our CCSG application, roughly 30% of the patients on interventional trials were black and 40% were Hispanic—so both racial and ethnic diversity. We also have incredible socio-economic diversity.

What’s unique among the black population in our catchment area is that it is Afro-Caribbean more than African American—different genetics, different cultures.

The Hispanic population is unique as well. MD Anderson is probably largely Mexican Americans. New York is probably mostly Dominican and Puerto Rican. We have significant populations of Cuban Americans, Venezuelans, Brazilians, Argentinians, Colombians—an incredibly diverse group.

One example of how this plays out is in our prostate cancer research. The watch-and-wait approach is an appropriate strategy for many people. We found that our black population has more anterior prostate cancer lesions, so when you do blind biopsies, you’re more likely to miss lesions.

And then we’ve looked among the Hispanic populations as to who has a better or worse prognosis and we’ve identified subgroups within the Hispanic population that have different genetics and a different biology. So, we are tailoring our approach. Based on genetic ancestry as well as other factors.

The other thing is, we have a very strong cancer epigenetics programs, a very strong program on infections and cancer, including H. Pylori, HPV, and hepatitis viruses B and C.

We are very focused on developing programs that meet the needs of the people in this six-million-plus community.

Our catchment area is four counties, somewhat famous, because of the election news nearly every cycle: Broward, Palm Beach County, Miami Dade and Monroe County.

 

PG:

New York, where you come from, has an NCI-designated cancer center on every street corner. And Miami—make that South Florida—has just one now. How is Florida different? You would have thought that there would be multiple NCI-designated cancer centers in South Florida.

SN:

Your point is very well taken. There are over 21 million people who live in the state of Florida. In 2014, Florida became the third largest state in the United States, surpassing New York—yet New York has seven NCI designated cancer centers and Florida had had only one.

Moffitt had gotten a huge investment from the state in the past, and that enabled them to become NCI-designated. And upon designation, they could recruit more researchers, attract more patients, and get more philanthropy, and get all the positives from that. And for the longest time, Florida has only had one.

There are over six million people in our catchment area, South Florida, and if they wanted to go to an NCI-designated cancer center they’d have to either get on a plane or drive nearly 300 miles—to Tampa.

Now, one problem that we face in our region, which is very splintered in terms of market share, etc. is that there’s a lot of community hospitals here that have cancer centers, but they are not necessarily conducting cancer research in any way.

I’ve been reading Joe Simone’s Journal of Clinical Oncology paper from 2002, where he talks about the fact that there are no criteria to call yourself a cancer center. And because people may feel like you can get great care anywhere, they may not seek out the experts.

Probably, in many markets throughout the US, there’s still an ongoing process of trying to educate people as to what’s the difference between an NCI-designated cancer center and one that’s not. And, obviously, the designation is given, because of the research that’s going on. And so, people wonder: “What is the connection between the research and me being a patient there?”

A big part of educating our community is to tell people that oftentimes the doctors who are doing research on a specific cancer have a deeper knowledge about its management. Also, experts more often make the correct diagnosis and come up with more exact multidisciplinary treatment approaches for many cancers.

NCI-designated cancer centers have more clinical trials and more investigator-initiated clinical trials. Now, with NCI designation, we’ll have access to the [NCI Cancer Therapy Evaluation Program] drugs and treatments. Already, we have a very robust phase I clinical trials program, having put 161 patients on phase I trials last year.

This means that we are doing more innovative things, not accepting the status quo, which is what you often get in community hospitals.

I get asked all the time: “Don’t only complicated cancers need to get seen in Sylvester?” and I usually say, “Any cancer that you have is complicated.”

There are other things we need to stress:  Sometimes patients spend more time figuring out which flat screen TV they’re going to buy than they do figuring out who should be taking care of them. And so, we tell patients to ask: “How sure are you that you have made the correct diagnosis?”

So many people are misdiagnosed in the US each year, and sometimes people are treated who don’t need to be treated and vice-versa.

For instance, we are working with Moffitt and the University of Florida on pancreas cancer. We’re hoping to look at how many patients in our state are told that with radiation, chemotherapy, and surgery there’s a potential for cure, as opposed to being told that pancreatic cancer is terrible, and you better get your affairs in order.

While the NCI designation, of course, relates to multidisciplinary and collaborative research efforts, we have—given the diversity of our catchment area and community—an important task to educate people in culturally appropriate ways.

 

PG:

Well, there’s a lot happening that actually very good. Having the University of Florida on the path to designation is also wonderful for the state. There’s so much room in there for growth.

SN:

Absolutely. Absolutely.

 

PG:

Since we are talking about Joe Simone’s paper, the word “comprehensive” is in the name of your cancer center. Yet, you don’t—yet—have the NCI-koshered comprehensive designation. Can you change the name? Do you need to?

SN:

The University of Miami’s cancer center started in 1973 shortly after Nixon signed the National Cancer Act. Later, with a naming gift from the Sylvester family, we opened our doors as the Sylvester Comprehensive Cancer Center in 1992. The comprehensive in our name does not refer to an NCI designation. It’s been our name because we have always delivered comprehensive cancer care.

 

PG:

Let’s talk about the Game Changer. That’s such a cool thing. That was one of your center’s great ideas.

SN:

The Game Changer vehicle has been really incredible, already in its impact on our cancer education and early detection programs (The Cancer Letter, April 27, 2018). We’re accruing people for research, and we’re already following some of their health habits.

We’re in the process of delivering HPV vaccines. We have been working with our AIDS group, so you can get PrEP. And we go into communities, like Little Havana, Liberty City, Little Haiti. We are also going into areas to provide education on HIV. As you know, the incidence of HIV in the Miami Dade area is the highest in the nation. So, the vehicle is already having an impact in so many ways.

We’ve just gotten the second Game Changer!

Peter Tunney, the artist, is going to wrap this one also. And this one’s going to focus primarily on Monroe County, which has been hit hard by hurricanes, and also has very poor medical infrastructure.

If you travel to Miami, for business or pleasure, you don’t realize that it’s not that far to get to an extraordinarily rural area. The density of population in Monroe county is very low and access to health care is limited.

The areas that we’re trying to reach have so much socioeconomic gap and disparities. And the Game Changer vehicles are going to help us reach people who otherwise do not access traditional medical systems.

You asked me about the Game Changer vehicle as an idea, and I wanted to shout out the leadership team that we’ve been able to put together at Sylvester. They have been incredible. Our people have worked together in amazing ways. And so, when you say, “That’s a great idea of yours,” yours is the whole team, of course.

 

PG:

Of course.

SN:

It’s remarkable how much work it takes to build the research programs that allow us to even have a competitive application. There were so, so many people who spent so much time for the benefit of the cancer center, and not for their own research.

 

PG:

Can we talk about hurricanes? They have an impact on your mission.

SN:

It’s interesting, because the Sylvester Comprehensive Cancer Center opened its doors in 1992, which is just when Hurricane Andrew hit. I’ve looked through our archives: There are some great articles in the Miami newspaper, because we remained open and provided care right after Hurricane Andrew, which has been the most devastating hurricane here in, I don’t know exactly how many years, maybe 30 or 50 or whatever.

But even following the more recent hurricanes, we’ve been able to provide care for our patients. After Hurricane Irma, in one of our satellites we were open the next day, and we treated 30 patients with chemotherapy who needed it, even though many folks were without electricity.

It’s a unique challenge. We have hurricane preparedness for our laboratories. We have drills for the hospital. And we have a command center.

During Irma, because I live on Miami Beach, in a mandatory evacuation zone, I had to leave my home for a few days. And so, my wife and I slept in the hospital for three nights. There’s food, water, and air conditioning in the hospital. It’s not a bad place to be!

 

PG:

You’re driving now to one of the clinics, even as we speak; right? One of the satellite clinics?

SN:

Yes.

 

PG:

Can you tell me about that?

SN:

We have seven sites where we deliver clinical care. The main site in downtown Miami, and then we have three quite large facilities, one in Coral Gables, one in Plantation, one in Deerfield Beach. And we have three other satellites that are smaller, in Coral Springs, Hollywood, and Kendall.

And this allows us to deliver regional care. We’re all on the same EPIC electronic medical record. And we have patients enrolled on clinical trials in the satellites. Not all the satellites at the moment can have a research pharmacy. But the plan is we’re going to continue our expansion of facilities and services and increase the number of accruals and the sophistication of the trials that are available here. Everybody working in these satellites is a University of Miami employee.

The doctors are all part of our site disease groups, and they teleconference in to meetings and lectures. And many of them spend a day in Miami at the main satellite for education and clinical and other purposes.

Many of the doctors in the satellites are principal investigators on the clinical trials. And it’s important because people don’t want to travel necessarily on the freeways here to get to downtown Miami. And so, we can deliver academic care out in the community, which is always important and a challenging thing to do.

 

PG:

Is there anything we’ve forgotten, anything we need to address?

SN:

Maybe I can talk briefly about the state money for a minute. When Sen. [Rick] Scott [(R-FL)] was the governor, he got us together in his office, the University of Florida, Moffitt, and the University of Miami, and asked us what we needed to become major cancer centers and attain NCI designation so we could have three such facilities in the state.

The next year, the state gave us $10. 5 million to split three ways. So, we each got $3.5 million to bring in somebody from outside the state of Florida, a world-class scientist, and provide them with $500,000 a year for seven years.

We brought Ramin Shiekhattar from the Wistar Institute. He’s one of the leaders of our Cancer Epigenetics Program and a year and a half ago, Ramin won one of the highly prestigious NIH Director’s Pioneer Awards. I believe they give 10 out a year.

Next, the state set up a pool of $60 million to be shared between the three institutions each year for five and now six years. These funds are being used so that all three institutions can attain NCI designation. The directors of these cancer centers get along extremely well, and, in a pretty unique model, we created something called the Florida Academic Cancer Center Alliance.

It exists to promote collaborations across our institutions to conduct important cancer research and bring more federal research dollars to the state.

There are one or two other points I’d like to make: Another person we brought in, Gilberto Lopes, is the head of our Global Oncology Program and the editor of the Journal of Global Oncology for ASCO.

He just gave a plenary talk at 2018 ASCO, showing that immunotherapy is better than chemotherapy for the upfront treatment of certain subsets of lung cancer. His talk was one of four plenary talks we’ve recently given at important national cancer meetings.

I think the other message is just the level at which we’re operating on now. We are demonstrating to our community that we have people who are national leaders, and programs that are among the very best in the country. For this, I must thank the incredible team of researchers who work at Sylvester.

I think that, as we recruit more and more people, this designation is going to help us. I’m very pleased that when we submit NIH grants, the reviewers comment upon the environment in Miami, we now get the high scores for the research environment.

 

PG:

This brings up a problem that held back Sylvester for years, which was the lack of independence of the cancer center, or at least it was perceived to be that. Do you have the independence you need now?

SN:

First of all, I would never have left Sloan Kettering without the authority I needed from the leadership of the University of Miami, the health system and the Miller School of Medicine…

 

PG:

Yeah, that’s a good point.

SN:

I should point out, that I am the head of the cancer center, but I’m also the head of the oncology service line for UHealth health system. This arrangement allows me to align the clinical and the research missions in a way that many cancer center directors cannot.

It’s a real privilege, and I have great leadership and great people working on the service line to make our patient care and patient-related activities superb.

 

PG:

Well, that’s hugely important.

Copyright (c) 2018 The Cancer Letter Inc.

More on NCI Designated Cancer Centers can be found here: https://www.cancer.gov/research/nci-role/cancer-centers

Other articles on NCI Cancer Centers on the Open Access Online Journal include:

Salivary Gland Cancer – Adenoid Cystic Carcinoma: Mutation Patterns: Exome- and Genome-Sequencing @ Memorial Sloan-Kettering Cancer Center

Engineered Bacteria used as Trojan Horse for Cancer Immunotherapy

First Cost-Effectiveness Study of Multi-Gene Panel Sequencing in Advanced Non-Small Cell Lung Cancer Shows Moderate Cost-Effectiveness, Exposes Crucial Practice Gap

 

Read Full Post »

Artificial Intelligence and Cardiovascular Disease

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

3.3.18

3.3.18   Artificial Intelligence and Cardiovascular Disease, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair

Cardiology is a vast field that focuses on a large number of diseases specifically dealing with the heart, the circulatory system, and its functions. As such, similar symptomatologies and diagnostic features may be present in an individual, making it difficult for a doctor to easily isolate the actual heart-related problem. Consequently, the use of artificial intelligence aims to relieve doctors from this hurdle and extend better quality to patients. Results of screening tests such as echocardiograms, MRIs, or CT scans have long been proposed to be analyzed using more advanced techniques in the field of technology. As such, while artificial intelligence is not yet widely-used in clinical practice, it is seen as the future of healthcare.

The continuous development of the technological sector has enabled the industry to merge with medicine in order to create new integrated, reliable, and efficient methods of providing quality health care. One of the ongoing trends in cardiology at present is the proposed utilization of artificial intelligence (AI) in augmenting and extending the effectiveness of the cardiologist. This is because AI or machine-learning would allow for an accurate measure of patient functioning and diagnosis from the beginning up to the end of the therapeutic process. In particular, the use of artificial intelligence in cardiology aims to focus on research and development, clinical practice, and population health. Created to be an all-in-one mechanism in cardiac healthcare, AI technologies incorporate complex algorithms in determining relevant steps needed for a successful diagnosis and treatment. The role of artificial intelligence specifically extends to the identification of novel drug therapies, disease stratification or statistics, continuous remote monitoring and diagnostics, integration of multi-omic data, and extension of physician effectivity and efficiency.

Artificial intelligence – specifically a branch of it called machine learning – is being used in medicine to help with diagnosis. Computers might, for example, be better at interpreting heart scans. Computers can be ‘trained’ to make these predictions. This is done by feeding the computer information from hundreds or thousands of patients, plus instructions (an algorithm) on how to use that information. This information is heart scans, genetic and other test results, and how long each patient survived. These scans are in exquisite detail and the computer may be able to spot differences that are beyond human perception. It can also combine information from many different tests to give as accurate a picture as possible. The computer starts to work out which factors affected the patients’ outlook, so it can make predictions about other patients.

In current medical practice, doctors will use risk scores to make treatment decisions for their cardiac patients. These are based on a series of variables like weight, age and lifestyle. However, they do not always have the desired levels of accuracy. A particular example of the use of artificial examination in cardiology is the experimental study on heart disease patients, published in 2017. The researchers utilized cardiac MRI-based algorithms coupled with a 3D systolic cardiac motion pattern to accurately predict the health outcomes of patients with pulmonary hypertension. The experiment proved to be successful, with the technology being able to pick-up 30,000 points within the heart activity of 250 patients. With the success of the aforementioned study, as well as the promise of other researches on artificial intelligence, cardiology is seemingly moving towards a more technological practice.

One study was conducted in Finland where researchers enrolled 950 patients complaining of chest pain, who underwent the centre’s usual scanning protocol to check for coronary artery disease. Their outcomes were tracked for six years following their initial scans, over the course of which 24 of the patients had heart attacks and 49 died from all causes. The patients first underwent a coronary computed tomography angiography (CCTA) scan, which yielded 58 pieces of data on the presence of coronary plaque, vessel narrowing and calcification. Patients whose scans were suggestive of disease underwent a positron emission tomography (PET) scan which produced 17 variables on blood flow. Ten clinical variables were also obtained from medical records including sex, age, smoking status and diabetes. These 85 variables were then entered into an artificial intelligence (AI) programme called LogitBoost. The AI repeatedly analysed the imaging variables, and was able to learn how the imaging data interacted and identify the patterns which preceded death and heart attack with over 90% accuracy. The predictive performance using the ten clinical variables alone was modest, with an accuracy of 90%. When PET scan data was added, accuracy increased to 92.5%. The predictive performance increased significantly when CCTA scan data was added to clinical and PET data, with accuracy of 95.4%.

Another study findings showed that applying artificial intelligence (AI) to the electrocardiogram (ECG) enables early detection of left ventricular dysfunction and can identify individuals at increased risk for its development in the future. Asymptomatic left ventricular dysfunction (ALVD) is characterised by the presence of a weak heart pump with a risk of overt heart failure. It is present in three to six percent of the general population and is associated with reduced quality of life and longevity. However, it is treatable when found. Currently, there is no inexpensive, noninvasive, painless screening tool for ALVD available for diagnostic use. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3 percent, 85.7 percent, and 85.7 percent, respectively. Furthermore, in patients without ventricular dysfunction, those with a positive AI screen were at four times the risk of developing future ventricular dysfunction compared with those with a negative screen.

In recent years, the analysis of big data database combined with computer deep learning has gradually played an important role in biomedical technology. For a large number of medical record data analysis, image analysis, single nucleotide polymorphism difference analysis, etc., all relevant research on the development and application of artificial intelligence can be observed extensively. For clinical indication, patients may receive a variety of cardiovascular routine examination and treatments, such as: cardiac ultrasound, multi-path ECG, cardiovascular and peripheral angiography, intravascular ultrasound and optical coherence tomography, electrical physiology, etc. By using artificial intelligence deep learning system, the investigators hope to not only improve the diagnostic rate and also gain more accurately predict the patient’s recovery, improve medical quality in the near future.

The primary issue about using artificial intelligence in cardiology, or in any field of medicine for that matter, is the ethical issues that it brings about. Physicians and healthcare professionals prior to their practice swear to the Hippocratic Oath—a promise to do their best for the welfare and betterment of their patients. Many physicians have argued that the use of artificial intelligence in medicine breaks the Hippocratic Oath since patients are technically left under the care of machines than of doctors. Furthermore, as machines may also malfunction, the safety of patients is also on the line at all times. As such, while medical practitioners see the promise of artificial technology, they are also heavily constricted about its use, safety, and appropriateness in medical practice.

Issues and challenges faced by technological innovations in cardiology are overpowered by current researches aiming to make artificial intelligence easily accessible and available for all. With that in mind, various projects are currently under study. For example, the use of wearable AI technology aims to develop a mechanism by which patients and doctors could easily access and monitor cardiac activity remotely. An ideal instrument for monitoring, wearable AI technology ensures real-time updates, monitoring, and evaluation. Another direction of cardiology in AI technology is the use of technology to record and validate empirical data to further analyze symptomatology, biomarkers, and treatment effectiveness. With AI technology, researchers in cardiology are aiming to simplify and expand the scope of knowledge on the field for better patient care and treatment outcomes.

References:

https://www.news-medical.net/health/Artificial-Intelligence-in-Cardiology.aspx

https://www.bhf.org.uk/informationsupport/heart-matters-magazine/research/artificial-intelligence

https://www.medicaldevice-network.com/news/heart-attack-artificial-intelligence/

https://www.nature.com/articles/s41569-019-0158-5

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5711980/

www.j-pcs.org/article.asp

http://www.onlinejacc.org/content/71/23/2668

http://www.scielo.br/pdf/ijcs/v30n3/2359-4802-ijcs-30-03-0187.pdf

https://www.escardio.org/The-ESC/Press-Office/Press-releases/How-artificial-intelligence-is-tackling-heart-disease-Find-out-at-ICNC-2019

https://clinicaltrials.gov/ct2/show/NCT03877614

https://www.europeanpharmaceuticalreview.com/news/82870/artificial-intelligence-ai-heart-disease/

https://www.frontiersin.org/research-topics/10067/current-and-future-role-of-artificial-intelligence-in-cardiac-imaging

https://www.news-medical.net/health/Artificial-Intelligence-in-Cardiology.aspx

https://www.sciencedaily.com/releases/2019/05/190513104505.htm

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Artificial throat may give voice to the voiceless

Reporter
Irina Robu, PhD

Flexible sensors have fascinated more and more attention as a fundamental part of anthropomorphic robot research, medical diagnosis and physical health monitoring. The fundamental mechanism of the sensor is based on triboelectric effect inducing electrostatic charges on the surfaces between two different materials. Just like a plate capacitor, current is produced while the size of the parallel capacitor fluctuations caused by the small mechanical disturbances and therefore the output current/voltage is produced.

Chinese scientists combine ultra sensitive motion detectors with thermal sound-emitting technology invented an “artificial throat” that could enable speech in people with damaged or non-functioning vocal cords. Team members from University in Beijing, fabricated a homemade circuit board on which to build out their dual-mode system combining detection and emitting technologies.

Graphene is a wonder material because it is thinnest material in the universe and the strongest ever measured. And graphene is only a one-atom thick layer of graphite and possess a high Young’s modulus as well as superior thermal and electrical conductivities. Graphene-based sensors have attracted much attention in recent years due to their variety of structures, unique sensing performances, room-temperature working conditions, and tremendous application prospects.

The skin like device, wearable artificial graphene throat (WAGT) is as similar as a temporary tattoo, at least as perceived by the wearer. In order to make the device functional and flexible, scientists designed a laser-scribed graphene on a thin sheet of polyvinyl alcohol film. The device is the size of two thumbnails side by side and can use water to attach the film to the skin over the volunteer’s throat and connected to electrodes to a small armband that contained a circuit board, microcomputer, power amplifier and decoder. At the development phase, the system transformed subtle throat movements into simple sounds like “OK” and “No.” During the trial of the device, volunteers imitated throat motions of speech and the device converted these movements into single-syllable words.

It is believed that this device, would be able to train mute people to generate signals with their throats and the device would translate signals into speech.

SOURCE
https://www.aiin.healthcare/topics/robotics/artificial-throat-may-give-voice-voiceless?utm_source=newsletter

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Efficiency of PARP inhibitors beyond BRCA mutations

Reporter

Irina Robu, PhD

PARP inhibitors are a group of pharmacological inhibitors of the enzyme poly ADP ribose polymerase, which are developed for multiple indications but most visible is the treatment of cancer. Several forms of cancer are extra dependent on PARP than regular cells, making PARP an striking target for cancer therapy. PARP inhibitors seem to improve progression-free survival in women with recurrent platinum-sensitive cancer. In addition to their use in cancer therapy, PARP inhibitors can be a potential treatment for acute life-threatening diseases, such as stroke and myocardial infarction and neurodegenerative diseases.

With this knowledge in hand, Lee Kraus, di­rec­tor of the Green Cen­ter for Re­pro­duc­tive Bi­ol­o­gy Sci­ences at UT South­west­ern his team iden­ti­fied a po­ten­tial bio­mark­er, DDX21 protein, which is re­quired for the pro­duc­tion of ri­bo­somes in nu­cle­oli. Nonetheless, DDX21 in the nu­cle­o­lus re­quires PARP-1, which is tar­get­ed by ex­ist­ing PARP in­hibitors. The use of these drugs, blocks DDX21, hence in­hibit­ing ri­bo­some pro­duc­tion which as result means that en­hanced DDX21 lev­els in the nu­cle­o­lus could regulate can­cers that might be the most re­spon­sive to PARP in­hibitors.

Their data published in the journal Molecular Cell explains why breast cancer patients can be responsive to PARP inhibitors, even though they do not carry BRCA mutation. It is well known that the PARP inhibitors currently on the market such as As­traZeneca’s Lyn­parza, Clo­vis’ Rubra­ca and GSK’s Ze­ju­la work by disturbing PARP pro­teins that help re­pair dam­aged DNA in cell, hence steer­ing can­cer cells on­to a path of an­ni­hi­la­tion. Since cancer cells are addicted to ribosomes to grow and make proteins to support cell division, inhibiting PARP proteins can slow down the growth of the cell.

Kraus’s group is currently working to design clinical trials with UT South­west­ern on­col­o­gists to see if their hypothesis works. At the same time, they founded Ribon Therapeutics which is the first industrial biotech program going af­ter PARP7, a pro­tein al­so sim­i­lar­ly ac­ti­vat­ed by stress and cel­lu­lar re­sponse mech­a­nisms.

SOURCE

PARP inhibitors sometimes work beyond BRCA-mutations, researchers may finally know why

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

Targeting PARP

Curator: Larry H. Bernstein, MD, FCAP

Targeting PARP

 

 

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An Intelligent DNA Nanorobot to Fight Cancer by Targeting HER2 Expression

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

3.2.9

3.2.9   An Intelligent DNA Nanorobot to Fight Cancer by Targeting HER2 Expression, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair

HER2 is an important prognostic biomarker for 20–30% of breast cancers, which is the most common cancer in women. Overexpression of the HER2 receptor stimulates breast cells to proliferate and differentiate uncontrollably, thereby enhancing the malignancy of breast cancer and resulting in a poor prognosis for affected individuals. Current therapies to suppress the overexpression of HER2 in breast cancer mainly involve treatment with HER2-specific monoclonal antibodies. However, these monoclonal anti-HER2 antibodies have severe side effects in clinical trials, such as diarrhea, abnormal liver function, and drug resistance. Removing HER2 from the plasma membrane or inhibiting the gene expression of HER2 is a promising alternative that could limit the malignancy of HER2-positive cancer cells.

DNA origami is an emerging field of DNA-based nanotechnology and intelligent DNA nanorobots show great promise in working as a drug delivery system in healthcare. Different DNA-based nanorobots have been developed as affordable and facile therapeutic drugs. In particular, many studies reported that a tetrahedral framework nucleic acid (tFNA) could serve as a promising DNA nanocarrier for many antitumor drugs, owing to its high biocompatibility and biosecurity. For example, tFNA was reported to effectively deliver paclitaxel or doxorubicin to cancer cells for reversing drug resistance, small interfering RNAs (siRNAs) have been modified into tFNA for targeted drug delivery. Moreover, the production and storage of tFNA are not complicated, and they can be quickly degraded in lysosomes by cells. Since both free HApt and tFNA can be diverted into lysosomes, so,  combining the HApt and tFNA as a novel DNA nanorobot (namely, HApt-tFNA) can be an effective strategy to improve its delivery and therapeutic efficacy in treating HER2-positive breast cancer.

Researchers reported that a DNA framework-based intelligent DNA nanorobot for selective lysosomal degradation of tumor-specific proteins on cancer cells. An anti-HER2 aptamer (HApt) was site-specifically anchored on a tetrahedral framework nucleic acid (tFNA). This DNA nanorobot (HApt-tFNA) could target HER2-positive breast cancer cells and specifically induce the lysosomal degradation of the membrane protein HER2. An injection of the DNA nanorobot into a mouse model revealed that the presence of tFNA enhanced the stability and prolonged the blood circulation time of HApt, and HApt-tFNA could therefore drive HER2 into lysosomal degradation with a higher efficiency. The formation of the HER2-HApt-tFNA complexes resulted in the HER2-mediated endocytosis and digestion in lysosomes, which effectively reduced the amount of HER2 on the cell surfaces. An increased HER2 digestion through HApt-tFNA further induced cell apoptosis and arrested cell growth. Hence, this novel DNA nanorobot sheds new light on targeted protein degradation for precision breast cancer therapy.

It was previously reported that tFNA was degraded by lysosomes and could enhance cell autophagy. Results indicated that free Cy5-HApt and Cy5-HApt-tFNA could enter the lysosomes; thus, tFNA can be regarded as an efficient nanocarrier to transmit HApt into the target organelle. The DNA nanorobot composed of HApt and tFNA showed a higher stability and a more effective performance than free HApt against HER2-positive breast cancer cells. The PI3K/AKT pathway was inhibited when membrane-bound HER2 decreased in SK-BR-3 cells under the action of HApt-tFNA. The research findings suggest that tFNA can enhance the anticancer effects of HApt on SK-BR-3 cells; while HApt-tFNA can bind to HER2 specifically, the compounded HER2-HApt-tFNA complexes can then be transferred and degraded in lysosomes. After these processes, the accumulation of HER2 in the plasma membrane would decrease, which could also influence the downstream PI3K/AKT signaling pathway that is associated with cell growth and death.

However, some limitations need to be noted when interpreting the findings: (i) the cytotoxicity of the nanorobot on HER2-positive cancer cells was weak, and the anticancer effects between conventional monoclonal antibodies and HApt-tFNA was not compared; (ii) the differences in delivery efficiency between tFNA and other nanocarriers need to be confirmed; and (iii) the confirmation of anticancer effects of HApt-tFNA on tumors within animals remains challenging. Despite these limitations, the present study provided novel evidence of the biological effects of tFNA when combined with HApt. Although the stability and the anticancer effects of HApt-tFNA may require further improvement before clinical application, this study initiates a promising step toward the development of nanomedicines with novel and intelligent DNA nanorobots for tumor treatment.

References:

https://pubs.acs.org/doi/10.1021/acs.nanolett.9b01320

https://www.ncbi.nlm.nih.gov/pubmed/27939064

https://www.ncbi.nlm.nih.gov/pubmed/11694782

https://www.ncbi.nlm.nih.gov/pubmed/27082923

https://www.ncbi.nlm.nih.gov/pubmed/25365825

https://www.ncbi.nlm.nih.gov/pubmed/26840503

https://www.ncbi.nlm.nih.gov/pubmed/29802035

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Merck KGaA-owned Sigma-Aldrich has petitioned the US Patent and Trademark Office (USPTO) to open an interference proceeding between its own pending CRISPR-Cas9 patents and patents awarded to the University of California, Berkeley (UC Berkeley), Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair

Merck KGaA-owned Sigma-Aldrich has petitioned the US Patent and Trademark Office (USPTO) to open an interference proceeding between its own pending CRISPR-Cas9 patents and patents awarded to the University of California, Berkeley (UC Berkeley).

On Friday, July 19, Sigma-Aldrich submitted the request, available on blog PatentDocs, asking for a parallel interference to the one declared by the USPTO in June between UC Berkeley and the Broad Institute of MIT and Harvard.

Sigma-Aldrich recognizes, of course, that its pending applications’ claims have not yet been allowed, and thus declaring a patent interference now would – in ordinary U.S. Serial Nos. 15/188,911; 15/188,924; & 15/456,204 -2- circumstances – be premature. However, the facts here are truly extraordinary, and Sigma-Aldrich feels compelled to apprise the Director and the CAPJ of the current situation and to briefly explain why the PTAB’s declaration of a parallel interference in this instance would be in the long-term best interests of everyone, including the USPTO, the parties, and the public. Indeed, the sole issue raised by this Petition has already been effectively decided by both the PTAB and the Federal Circuit, and those decisions completely support Sigma-Aldrich’s request here; namely, does UC’s disclosure of CRISPR-Cas9 in in vitro cell-free and nucleus-free test tube environments (hereinafter, “prokaryotic environment”) render obvious claims directed to CRISPR-Cas9 in eukaryotic cells? The controlling answer to this question is decidedly “no.” SigmaAldrich respectfully submits that the PTAB’s and the Federal Circuit’s “no” answer compels the grant of this Petition. The following timeline – which shows the 2012 and early-2013 provisional applications of Sigma-Aldrich, UC, and Broad Inst. – is relevant to the issues presented.

https://patentdocs.typepad.com/files/181_petition.pdf

 

In June, the office revived the dispute between the Broad Institute and UC Berkeley over which first invented the CRISPR gene-editing technology by announcing that it would conduct an interference proceeding between 13 patents and one application to the Broad Institute and ten patent applications filed by UC Berkeley.

All of the Broad Institute and UC Berkeley patents and applications cover the use of CRISPR/Cas9 in eukaryotic cells.

Sigma-Aldrich’s pending patent applications (serial numbers 15/188,911, 15/456,204, and 15/188,924) are also directed to CRISPR-Cas9-based methods in eukaryotic cells.

“Of critical importance here, Sigma-Aldrich’s benefit applications pre-date the earliest possible benefit applications involved in the UC Berkeley v Broad Institute interference with respect to their respective disclosures of CRISPR-Cas9 in eukaryotic cells,” said Sigma-Aldrich in its petition.

The Merck-owned unit said that it “feels compelled to apprise the director and the chief administrative patent judge (CAPJ) of the current situation and to briefly explain why the Patent Trial and Appeal Board’s (PTAB) declaration of a parallel interference in this instance would be in the long-term best interests of everyone, including the USPTO, the parties, and the public”.

Sigma-Aldrich went on to claim that the sole issue raised by the petition has already been effectively decided by both the PTAB and the US Court of Appeals for the Federal Circuit.

‘Treated unfairly’

In February 2017, the PTAB held that the Broad Institute’s patents—which are all limited to CRISPR/Cas9 systems in a eukaryotic environment—do not interfere with patent claims (which are not restricted to any environment) filed by UC Berkeley and the University of Vienna.

UC Berkeley and the University of Vienna appealed against the decision, asking the Federal Circuit to determine whether the PTAB committed error in “ignoring overwhelming evidence” that the Broad Institute’s claims are obvious in light of UC Berkeley’s.

The PTAB’s finding was affirmed by the Federal Circuit in September 2018.

“Sigma-Aldrich respectfully submits that the PTAB’s and the Federal Circuit’s ‘no’ answer compels the grant of this petition,” said the Merck subsidiary.

Sigma-Aldrich has claimed that the USPTO is treating it “very differently and unfairly” when compared to the agency’s treatment of the Broad Institute and UC Berkeley.

It said: “Indeed, the USPTO has now granted Broad Inst over a dozen issued patents. In direct contrast, the USPTO continues to reject Sigma-Aldrich’s CRISPR-Cas9 eukaryotic claims as not patentable over those same UC CRISPR-Cas9 prokaryotic provisional applications that the USPTO has repeatedly found have been successfully overcome by Broad Inst’s eukaryotic claims.”

The petition claimed that this “blatant inconsistency” and the unfairness to Sigma-Aldrich could not be “more palpable”.

“In today’s highly charged political environment, certainly the director and CAPJ are sensitive to criticism levelled at the agency regarding issues of fairness and equity, eg whether the USPTO provides ‘a level playing field’,” added the petition.

A spokesperson for the Broad Institute said: “It is time for certainty around CRISPR and for the parties to come together to resolve these disputes and to simplify access to this important technology.”

Sigma-Aldrich is part of Merck’s life science business and, in combination with Merck’s other acquisition Millipore, operates as MilliporeSigma in North America.

A spokesperson for UC Berkeley said: “We remain confident that the USPTO will ultimately recognise that the Doudna and Charpentier team hold the priority of invention specific to the CRISPR-Cas9 gene-editing technology in eukaryotic cells, as well as other settings covered by the Doudna-Charpentier team’s previous patents.”

 

Keywords

Sigma-Aldrich, Merck KGaA, USPTO, CRISPR, Broad Institute, UC Berkeley, MIT, Harvard, Cas9, patent, genome editing

 

SOURCE

https://www.lifesciencesipreview.com/news/sigma-aldrich-submits-crispr-petition-at-uspto-3615

 

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

Multiple UPDATES for original article of April 13, 2017

UPDATED – Gene Editing Consortium of Biotech Companies: CRISPR Therapeutics $CRSP, Intellia Therapeutics $NTLA, Caribou Biosciences, ERS Genomics, UC, Berkeley (Doudna’s IP) and University of Vienna (Charpentier’s IP), is appealing the decision ruled that there was no interference between the two sides, to the U.S. Court of Appeals for the Federal Circuit, targeting patents from The Broad Institute.

Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/04/13/gene-editing-consortium-of-biotech-companies-crispr-therapeutics-crsp-intellia-therapeutics-ntla-caribou-biosciences-and-ers-genomics-uc-berkeley-doudnas-ip-and-university-of-vienna-charpe/

Part 2: CRISPR in 

Genomics Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology

https://pharmaceuticalintelligence.com/biomed-e-books/genomics-orientations-for-personalized-medicine/volume-two-genomics-methodologies-ngs-bioinformatics-simulations-and-the-genome-ontology/

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