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Will Web 3.0 Do Away With Science 2.0? Is Science Falling Behind?

Posted in Artificial Intelligence Applications in Health Care, BioIT: BioInformatics, Blockchain Transactions System, Curation, Curation methodology, De novo synthesis, Discovery process, Historical relevance, IP Development by LPBI Group Team, IP Development by LPBI Group Team & Other Organizations, Key Opinion Leaders in eScientific Publishing - Interviews with, LPBI Group, e-Scientific Media, DFP, R&D-M3DP, R&D-Drug Discovery, US Patents: SOPs and Team Management, Open Access Journals, REAL TIME Conference Coverage Twitter's Hashtags and Handles per Presentation/session, Scientific Publishing, tagged #science2_0, #science3_0, communication of scientific findings, Curation of Scientific Content, Curation of Scientific Findings, LinkedIn, NFT, science communication, Twitter, web 2.0, web 3.0 on April 1, 2022| Leave a Comment »

Will Web 3.0 Do Away With Science 2.0? Is Science Falling Behind?

Curator: Stephen J. Williams, Ph.D.

UPDATED 4/06/2022

A while back (actually many moons ago) I had put on two posts on this site:

Scientific Curation Fostering Expert Networks and Open Innovation: Lessons from Clive Thompson and others

Twitter is Becoming a Powerful Tool in Science and Medicine

Each of these posts were on the importance of scientific curation of findings within the realm of social media and the Web 2.0; a sub-environment known throughout the scientific communities as Science 2.0, in which expert networks collaborated together to produce massive new corpus of knowledge by sharing their views, insights on peer reviewed scientific findings. And through this new media, this process of curation would, in itself generate new ideas and new directions for research and discovery.

The platform sort of looked like the image below:

 

This system lied above a platform of the original Science 1.0, made up of all the scientific journals, books, and traditional literature:

Previous image source: PeerJ.com

To index the massive and voluminous research and papers beyond the old Dewey Decimal system, a process of curation was mandatory. The dissemination of this was a natural for the new social media however the cost had to be spread out among numerous players. Journals, faced with the high costs of subscriptions and their only way to access this new media as an outlet was to become Open Access, a movement first sparked by journals like PLOS and PeerJ but then begrudingly adopted throughout the landscape. But with any movement or new adoption one gets the Good the Bad and the Ugly (as described in my cited, above, Clive Thompson article). The bad side of Open Access Journals were

1. costs are still assumed by the individual researcher not by the journals
2. the arise of the numerous Predatory Journals

 

Even PeerJ, in their column celebrating an anniversary of a year’s worth of Open Access success stories, lamented the key issues still facing Open Access in practice

• which included the cost and the rise of predatory journals.

In essence, Open Access and Science 2.0 sprung full force BEFORE anyone thought of a way to defray the costs

 

Can Web 3.0 Finally Offer a Way to Right the Issues Facing High Costs of Scientific Publishing?

What is Web 3.0?

From Wikipedia: https://en.wikipedia.org/wiki/Web3

Web 1.0 and Web 2.0 refer to eras in the history of the Internet as it evolved through various technologies and formats. Web 1.0 refers roughly to the period from 1991 to 2004, where most websites were static webpages, and the vast majority of users were consumers, not producers, of content.^[6][7] Web 2.0 is based around the idea of “the web as platform”,^[8] and centers on user-created content uploaded to social-networking services, blogs, and wikis, among other services.^[9] Web 2.0 is generally considered to have begun around 2004, and continues to the current day.^[8][10][4]

Terminology[edit]

The term “Web3”, specifically “Web 3.0”, was coined by Ethereum co-founder Gavin Wood in 2014.^[1] In 2020 and 2021, the idea of Web3 gained popularity^{[citation needed]}. Particular interest spiked towards the end of 2021, largely due to interest from cryptocurrency enthusiasts and investments from high-profile technologists and companies.^[4][5] Executives from venture capital firm Andreessen Horowitz travelled to Washington, D.C. in October 2021 to lobby for the idea as a potential solution to questions about Internet regulation with which policymakers have been grappling.^[11]

Web3 is distinct from Tim Berners-Lee‘s 1999 concept for a semantic web, which has also been called “Web 3.0”.^[12] Some writers referring to the decentralized concept usually known as “Web3” have used the terminology “Web 3.0”, leading to some confusion between the two concepts.^[2][3] Furthermore, some visions of Web3 also incorporate ideas relating to the semantic web.^[13][14]

Concept[edit]

Web3 revolves around the idea of decentralization, which proponents often contrast with Web 2.0, wherein large amounts of the web’s data and content are centralized in the fairly small group of companies often referred to as Big Tech.^[4]

Specific visions for Web3 differ, but all are heavily based in blockchain technologies, such as various cryptocurrencies and non-fungible tokens (NFTs).^[4] Bloomberg described Web3 as an idea that “would build financial assets, in the form of tokens, into the inner workings of almost anything you do online”.^[15] Some visions are based around the concepts of decentralized autonomous organizations (DAOs).^[16] Decentralized finance (DeFi) is another key concept; in it, users exchange currency without bank or government involvement.^[4] Self-sovereign identity allows users to identify themselves without relying on an authentication system such as OAuth, in which a trusted party has to be reached in order to assess identity.^[17]

Reception[edit]

Technologists and journalists have described Web3 as a possible solution to concerns about the over-centralization of the web in a few “Big Tech” companies.^[4][11] Some have expressed the notion that Web3 could improve data security, scalability, and privacy beyond what is currently possible with Web 2.0 platforms.^[14] Bloomberg states that sceptics say the idea “is a long way from proving its use beyond niche applications, many of them tools aimed at crypto traders”.^[15] The New York Times reported that several investors are betting $27 billion that Web3 “is the future of the internet”.^[18][19]

Some companies, including Reddit and Discord, have explored incorporating Web3 technologies into their platforms in late 2021.^[4][20] After heavy user backlash, Discord later announced they had no plans to integrate such technologies.^[21] The company’s CEO, Jason Citron, tweeted a screenshot suggesting it might be exploring integrating Web3 into their platform. This led some to cancel their paid subscriptions over their distaste for NFTs, and others expressed concerns that such a change might increase the amount of scams and spam they had already experienced on crypto-related Discord servers.^[20] Two days later, Citron tweeted that the company had no plans to integrate Web3 technologies into their platform, and said that it was an internal-only concept that had been developed in a company-wide hackathon.^[21]

Some legal scholars quoted by The Conversation have expressed concerns over the difficulty of regulating a decentralized web, which they reported might make it more difficult to prevent cybercrime, online harassment, hate speech, and the dissemination of child abuse images.^[13] But, the news website also states that, “[decentralized web] represents the cyber-libertarian views and hopes of the past that the internet can empower ordinary people by breaking down existing power structures.” Some other critics of Web3 see the concept as a part of a cryptocurrency bubble, or as an extension of blockchain-based trends that they see as overhyped or harmful, particularly NFTs.^[20] Some critics have raised concerns about the environmental impact of cryptocurrencies and NFTs. Others have expressed beliefs that Web3 and the associated technologies are a pyramid scheme.^[5]

Kevin Werbach, author of The Blockchain and the New Architecture of Trust,^[22] said that “many so-called ‘web3’ solutions are not as decentralized as they seem, while others have yet to show they are scalable, secure and accessible enough for the mass market”, adding that this “may change, but it’s not a given that all these limitations will be overcome”.^[23]

David Gerard, author of Attack of the 50 Foot Blockchain,^[24] told The Register that “web3 is a marketing buzzword with no technical meaning. It’s a melange of cryptocurrencies, smart contracts with nigh-magical abilities, and NFTs just because they think they can sell some monkeys to morons”.^[25]

Below is an article from MarketWatch.com Distributed Ledger series about the different forms and cryptocurrencies involved

From Marketwatch: https://www.marketwatch.com/story/bitcoin-is-so-2021-heres-why-some-institutions-are-set-to-bypass-the-no-1-crypto-and-invest-in-ethereum-other-blockchains-next-year-11639690654?mod=home-page

by Frances Yue, Editor of Distributed Ledger, Marketwatch.com

Clayton Gardner, co-CEO of crypto investment management firm Titan, told Distributed Ledger that as crypto embraces broader adoption, he expects more institutions to bypass bitcoin and invest in other blockchains, such as Ethereum, Avalanche, and Terra in 2022. which all boast smart-contract features.

Bitcoin traditionally did not support complex smart contracts, which are computer programs stored on blockchains, though a major upgrade in November might have unlocked more potential.

“Bitcoin was originally seen as a macro speculative asset by many funds and for many it still is,” Gardner said. “If anything solidifies its use case, it’s a store of value. It’s not really used as originally intended, perhaps from a medium of exchange perspective.”

For institutions that are looking for blockchains that can “produce utility and some intrinsic value over time,” they might consider some other smart contract blockchains that have been driving the growth of decentralized finance and web 3.0, the third generation of the Internet, according to Gardner. 

“Bitcoin is still one of the most secure blockchains, but I think layer-one, layer-two blockchains beyond Bitcoin, will handle the majority of transactions and activities from NFT (nonfungible tokens) to DeFi,“ Gardner said. “So I think institutions see that and insofar as they want to put capital to work in the coming months, I think that could be where they just pump the capital.”

Decentralized social media？ 

The price of Decentralized Social, or DeSo, a cryptocurrency powering a blockchain that supports decentralized social media applications, surged roughly 74% to about $164 from $94, after Deso was listed at Coinbase Pro on Monday, before it fell to about $95, according to CoinGecko.

In the eyes of Nader Al-Naji, head of the DeSo foundation, decentralized social media has the potential to be “a lot bigger” than decentralized finance.

“Today there are only a few companies that control most of what we see online,” Al-Naji told Distributed Ledger in an interview. But DeSo is “creating a lot of new ways for creators to make money,” Al-Naji said.

“If you find a creator when they’re small, or an influencer, you can invest in that, and then if they become bigger and more popular, you make money and they make and they get capital early on to produce their creative work,” according to AI-Naji.

BitClout, the first application that was created by AI-Naji and his team on the DeSo blockchain, had initially drawn controversy, as some found that they had profiles on the platform without their consent, while the application’s users were buying and selling tokens representing their identities. Such tokens are called “creator coins.”

AI-Naji responded to the controversy saying that DeSo now supports more than 200 social-media applications including Bitclout. “I think that if you don’t like those features, you now have the freedom to use any app you want. Some apps don’t have that functionality at all.”

 

But Before I get to the “selling monkeys to morons” quote,

I want to talk about

THE GOOD, THE BAD, AND THE UGLY

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

THE GOOD

My foray into Science 2.0 and then pondering what the movement into a Science 3.0 led me to an article by Dr. Vladimir Teif, who studies gene regulation and the nucleosome, as well as creating a worldwide group of scientists who discuss matters on chromatin and gene regulation in a journal club type format.

For more information on this Fragile Nucleosome journal club see https://generegulation.org/fragile-nucleosome/.

Fragile Nucleosome is an international community of scientists interested in chromatin and gene regulation. Fragile Nucleosome is active in several spaces: one is the Discord server where several hundred scientists chat informally on scientific matters. You can join the Fragile Nucleosome Discord server. Another activity of the group is the organization of weekly virtual seminars on Zoom. Our webinars are usually conducted on Wednesdays 9am Pacific time (5pm UK, 6pm Central Europe). Most previous seminars have been recorded and can be viewed at our YouTube channel. The schedule of upcoming webinars is shown below. Our third activity is the organization of weekly journal clubs detailed at a separate page (Fragile Nucleosome Journal Club).

 

His lab site is at https://generegulation.org/ but had published a paper describing what he felt what the #science2_0 to #science3_0 transition would look like (see his blog page on this at https://generegulation.org/open-science/).

This concept of science 3.0 he had coined back in 2009.  As Dr Teif had mentioned

So essentially I first introduced this word Science 3.0 in 2009, and since then we did a lot to implement this in practice. The Twitter account @generegulation is also one of examples

 

This is curious as we still have an ill defined concept of what #science3_0 would look like but it is a good read nonetheless.

His paper,  entitled “Science 3.0: Corrections to the Science 2.0 paradigm” is on the Cornell preprint server at https://arxiv.org/abs/1301.2522 

 

Abstract

Science 3.0: Corrections to the Science 2.0 paradigm

Vladimir B. Teif

The concept of Science 2.0 was introduced almost a decade ago to describe the new generation of online-based tools for researchers allowing easier data sharing, collaboration and publishing. Although technically sound, the concept still does not work as expected. Here we provide a systematic line of arguments to modify the concept of Science 2.0, making it more consistent with the spirit and traditions of science and Internet. Our first correction to the Science 2.0 paradigm concerns the open-access publication models charging fees to the authors. As discussed elsewhere, we show that the monopoly of such publishing models increases biases and inequalities in the representation of scientific ideas based on the author’s income. Our second correction concerns post-publication comments online, which are all essentially non-anonymous in the current Science 2.0 paradigm. We conclude that scientific post-publication discussions require special anonymization systems. We further analyze the reasons of the failure of the current post-publication peer-review models and suggest what needs to be changed in Science 3.0 to convert Internet into a large journal club. [bold face added]

In this paper it is important to note the transition of a science 1.0, which involved hard copy journal publications usually only accessible in libraries to a more digital 2.0 format where data, papers, and ideas could be easily shared among networks of scientists.

As Dr. Teif states, the term “Science 2.0” had been coined back in 2009, and several influential journals including Science, Nature and Scientific American endorsed this term and suggested scientists to move online and their discussions online.  However, even at present there are thousands on this science 2.0 platform, Dr Teif notes the number of scientists subscribed to many Science 2.0 networking groups such as on LinkedIn and ResearchGate have seemingly saturated over the years, with little new members in recent times. 

The consensus is that science 2.0 networking is:

1. good because it multiplies the efforts of many scientists, including experts and adds to the scientific discourse unavailable on a 1.0 format
2. that online data sharing is good because it assists in the process of discovery (can see this evident with preprint servers, bio-curated databases, Github projects)
3. open-access publishing is beneficial because free access to professional articles and open-access will be the only publishing format in the future (although this is highly debatable as many journals are holding on to a type of “hybrid open access format” which is not truly open access
4. only sharing of unfinished works and critiques or opinions is good because it creates visibility for scientists where they can receive credit for their expert commentary

There are a few concerns on Science 3.0 Dr. Teif articulates:

A.  Science 3.0 Still Needs Peer Review

Peer review of scientific findings will always be an imperative in the dissemination of well-done, properly controlled scientific discovery.  As Science 2.0 relies on an army of scientific volunteers, the peer review process also involves an army of scientific experts who give their time to safeguard the credibility of science, by ensuring that findings are reliable and data is presented fairly and properly.  It has been very evident, in this time of pandemic and the rapid increase of volumes of preprint server papers on Sars-COV2, that peer review is critical.  Many of these papers on such preprint servers were later either retracted or failed a stringent peer review process.

Now many journals of the 1.0 format do not generally reward their peer reviewers other than the self credit that researchers use on their curriculum vitaes.  Some journals, like the MDPI journal family, do issues peer reviewer credits which can be used to defray the high publication costs of open access (one area that many scientists lament about the open access movement; where the burden of publication cost lies on the individual researcher).

An issue which is highlighted is the potential for INFORMATION NOISE regarding the ability to self publish on Science 2.0 platforms.

 

The NEW BREED was born in 4/2012

An ongoing effort on this platform, https://pharmaceuticalintelligence.com/, is to establish a scientific methodology for curating scientific findings where one the goals is to assist to quell the information noise that can result from the massive amounts of new informatics and data occurring in the biomedical literature. 

B.  Open Access Publishing Model leads to biases and inequalities in the idea selection

The open access publishing model has been compared to the model applied by the advertising industry years ago and publishers then considered the journal articles as “advertisements”.  However NOTHING could be further from the truth.  In advertising the publishers claim the companies not the consumer pays for the ads.  However in scientific open access publishing, although the consumer (libraries) do not pay for access the burden of BOTH the cost of doing the research and publishing the findings is now put on the individual researcher.  Some of these publishing costs can be as high as $4000 USD per article, which is very high for most researchers.  However many universities try to refund the publishers if they do open access publishing so it still costs the consumer and the individual researcher, limiting the cost savings to either.  

However, this sets up a situation in which young researchers, who in general are not well funded, are struggling with the publication costs, and this sets up a bias or inequitable system which rewards the well funded older researchers and bigger academic labs.

C. Post publication comments and discussion require online hubs and anonymization systems

Many recent publications stress the importance of a post-publication review process or system yet, although many big journals like Nature and Science have their own blogs and commentary systems, these are rarely used.  In fact they show that there are just 1 comment per 100 views of a journal article on these systems.  In the traditional journals editors are the referees of comments and have the ability to censure comments or discourse.  The article laments that comments should be easy to do on journals, like how easy it is to make comments on other social sites, however scientists are not offering their comments or opinions on the matter. 

In a personal experience, 

a well written commentary goes through editors which usually reject a comment like they were rejecting an original research article.  Thus many scientists, I believe, after fashioning a well researched and referenced reply, do not get the light of day if not in the editor’s interests.  

Therefore the need for anonymity is greatly needed and the lack of this may be the hindrance why scientific discourse is so limited on these types of Science 2.0 platforms.  Platforms that have success in this arena include anonymous platforms like Wikipedia or certain closed LinkedIn professional platforms but more open platforms like Google Knowledge has been a failure.

A great example on this platform was a very spirited conversation on LinkedIn on genomics, tumor heterogeneity and personalized medicine which we curated from the LinkedIn discussion (unfortunately LinkedIn has closed many groups) seen here:

Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

 

 

Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

 

In this discussion, it was surprising that over a weekend so many scientists from all over the world contributed to a great discussion on the topic of tumor heterogeneity.

But many feel such discussions would be safer if they were anonymized.  However then researchers do not get any credit for their opinions or commentaries.

A Major problem is how to take the intangible and make them into tangible assets which would both promote the discourse as well as reward those who take their time to improve scientific discussion.

This is where something like NFTs or a decentralized network may become important!

See

https://pharmaceuticalintelligence.com/portfolio-of-ip-assets/

 

UPDATED 5/09/2022

Below is an online @TwitterSpace Discussion we had with some young scientists who are just starting out and gave their thoughts on what SCIENCE 3.0 and the future of dissemination of science might look like, in light of this new Meta Verse.  However we have to define each of these terms in light of Science and not just the Internet as merely a decentralized marketplace for commonly held goods.

This online discussion was tweeted out and got a fair amount of impressions (60) as well as interactors (50).

Set a reminder for my upcoming Space! https://t.co/7mOpScZfGN @Pharma_BI @PSMTempleU #science3_0 @science2_0

— Stephen J Williams (@StephenJWillia2) April 28, 2022

 For the recording on both Twitter as well as on an audio format please see below

<blockquote class=”twitter-tweet”><p lang=”en” dir=”ltr”>Set a reminder for my upcoming Space! <a href=”https://t.co/7mOpScZfGN”>https://t.co/7mOpScZfGN</a&gt; <a href=”https://twitter.com/Pharma_BI?ref_src=twsrc%5Etfw”>@Pharma_BI</a&gt; <a href=”https://twitter.com/PSMTempleU?ref_src=twsrc%5Etfw”>@PSMTempleU</a&gt; <a href=”https://twitter.com/hashtag/science3_0?src=hash&amp;ref_src=twsrc%5Etfw”>#science3_0</a&gt; <a href=”https://twitter.com/science2_0?ref_src=twsrc%5Etfw”>@science2_0</a></p>&mdash; Stephen J Williams (@StephenJWillia2) <a href=”https://twitter.com/StephenJWillia2/status/1519776668176502792?ref_src=twsrc%5Etfw”>April 28, 2022</a></blockquote> <script async src=”https://platform.twitter.com/widgets.js&#8221; charset=”utf-8″></script>

 

 

To introduce this discussion first a few startoff material which will fram this discourse

 

The Intenet and the Web is rapidly adopting a new “Web 3.0” format, with decentralized networks, enhanced virtual experiences, and greater interconnection between people. Here we start the discussion what will the move from Science 2.0, where dissemination of scientific findings was revolutionized and piggybacking on Web 2.0 or social media, to a Science 3.0 format. And what will it involve or what paradigms will be turned upside down?

Old Science 1.0 is still the backbone of all scientific discourse, built on the massive amount of experimental and review literature. However this literature was in analog format, and we moved to a more accesible digital open access format for both publications as well as raw data. However as there was a structure for 1.0, like the Dewey decimal system and indexing, 2.0 made science more accesible and easier to search due to the newer digital formats. Yet both needed an organizing structure; for 1.0 that was the scientific method of data and literature organization with libraries as the indexers. In 2.0 this relied on an army mostly of volunteers who did not have much in the way of incentivization to co-curate and organize the findings and massive literature.

Each version of Science has their caveats: their benefits as well as deficiencies. This curation and the ongoing discussion is meant to solidy the basis for the new format, along with definitions and determination of structure.

We had high hopes for Science 2.0, in particular the smashing of data and knowledge silos. However the digital age along with 2.0 platforms seemed to excaccerbate this somehow. We still are critically short on analysis!

 

We really need people and organizations to get on top of this new Web 3.0 or metaverse so the similar issues do not get in the way: namely we need to create an organizing structure (maybe as knowledgebases), we need INCENTIVIZED co-curators, and we need ANALYSIS… lots of it!!

Are these new technologies the cure or is it just another headache?

 

There were a few overarching themes whether one was talking about AI, NLP, Virtual Reality, or other new technologies with respect to this new meta verse and a concensus of Decentralized, Incentivized, and Integrated was commonly expressed among the attendees

The Following are some slides from representative Presentations

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Other article of note on this topic on this Open Access Scientific Journal Include:

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

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

e-Scientific Publishing: The Competitive Advantage of a Powerhouse for Curation of Scientific Findings and Methodology Development for e-Scientific Publishing – LPBI Group, A Case in Point

@PharmaceuticalIntelligence.com –  A Case Study on the LEADER in Curation of Scientific Findings

Real Time Coverage @BIOConvention #BIO2019: Falling in Love with Science: Championing Science for Everyone, Everywhere

Old Industrial Revolution Paradigm of Education Needs to End: How Scientific Curation Can Transform Education

 

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Medical Startups – Artificial Intelligence (AI) Startups in Healthcare

Posted in Academic Publishing, Advanced Computing Platform, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Health Care - Tools & Innovations, Big Data, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Machine Learning, Natural Language Processing (NLP) on March 2, 2022| Leave a Comment »

 

Medical Startups – Artificial Intelligence (AI) Startups in Healthcare

Reporters: Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN and Shraga Rottem, MD, DSc,

The motivation for this post is two fold:

First, we are presenting an application of AI, NLP, DL to our own medical text in the Genomics space. Here we present the first section of Part 1 in the following book. Part 1 has six subsections that yielded 12 plots. The entire Book is represented by 38 x 2 = 76 plots.

Second, we bring to the attention of the e-Reader the list of 276 Medical Startups – Artificial Intelligence (AI) Startups in Healthcare as a hot universe of R&D activity in Human Health.

Third, to highlight one academic center with an AI focus

	Dear friends of the ETH AI Center, We would like to provide you with some exciting updates from the ETH AI Center and its growing community. We would like to provide you with some exciting updates from the ETH AI Center and its growing community. The ETH AI Center now comprises 110 research groups in the faculty, 20 corporate partners and has led to nine AI startups. As the Covid-19 restrictions in Switzerland have recently been lifted, we would like to hear from you what kind of events you would like to see in 2022! Participate in the survey to suggest event formats and topics that you would enjoy being a part of. We are already excited to learn what we can achieve together this year. We already have many interesting events coming up, we look forward to seeing you at our main and community events!

SOURCE

https://news.ethz.ch/html_mail.jsp?params=%2FUnFXUQJ%2FmiOP6akBq8eHxaXG%2BRdNmeoVa9gX5ArpTr6mX74xp5d78HhuIHTd9V6AHtAfRahyx%2BfRGrzVL1G8Jy5e3zykvr1WDtMoUC%2B7vILoHCGQ5p1rxaPzOsF94ID

 

 

LPBI Group is applying AI for Medical Text Analysis with Machine Learning and Natural Language Processing: Statistical and Deep Learning

Our Book 

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

Medical Text Analysis of this Books shows the following results obtained by Madison Davis by applying Wolfram NLP for Biological Languages on our own Text. See below an Example:

Part 1: Next Generation Sequencing (NGS)

 

1.1 The NGS Science

1.1.1 BioIT Aspect

 

Hypergraph Plot #1 and Tree Diagram Plot #1 for 1.1.1 based on 16 articles & on 12 keywords protein, cancer, dna, genes, rna, survival, immune, tumor, patients, human, genome, expression

(more…)

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Papers citing PharmaceuticalIntelligence.com

Posted in Academic Publishing on December 8, 2020| Leave a Comment »

Papers citing PharmaceuticalIntelligence.com

Reporter: Stephen J. Williams, PhD

(10 papers, 5 peer reviewed, 2 web page, 2 teaching lectures, 1 preprint) Search from years 2012 – 2018

UPDATED on 5/22/2022

Summary

Peer Reviewed

1. Yan-Fang Guan, Gai-Rui Li, Rong-Jiao Wang, Yu-Ting Yi, Ling Yang, Dan Jiang, Xiao-Ping Zhang, and Yin Peng. Application of next-generation sequencing in clinical oncology to advance personalized treatment of cancer. Chin J Cancer. 2012 Oct; 31(10): 463–470. Using 44. Lev-Ari A. Sunitinib brings Adult acute lymphoblastic leukemia (ALL) to Remission-RNA Sequencing-FLT3 Receptor Blockade. 2012. Available at: http://pharmaceuticalintelligence.com/2012/07/09/sunitinib-brings-adult-all-to-remission-rna-sequencing/
2. Pedro A. Moreno. Bioinformática multifractal: Una propuesta hacia la interpretación no-lineal del genoma: Multifractal bioinformatics: A proposal to the nonlinear interpretation of genome. Ing. compet. vol.16 no.1 Cali Jan./June 2014. Using (http://pharmaceuticalintelligence.com/tag/fractal-geometry/).

 

3. Gökmen Altay1* and David E. Neal2. Genome-wide differential gene network analysis R software and its application in LnCap prostate cancer in https://www.biorxiv.org/content/biorxiv/early/2017/04/24/129742.full.pdf citing an R package in the paper citing us in reference to CXC4 inhibitors as (http://pharmaceuticalintelligence.com/2015/12/15/are-cxc4-antagonists-making-a-comebackin-cancer-chemotherapy, 2015).

 

4. D.Sairam Course : Fundamentals of Biology- Life Sciences Course Code: BSBT-201 Course Instructor: Dr. Subhabrata Kar citing in presentation Comparison of Glycolysis between a Normal Tissue and Tumour/ Proliferated Tissue • http://pharmaceuticalintelligence.com/2012/10/17/is-the- warburg-effect-the-cause-or-the-effect-of-cancer-a-21st- century-view/

 

5. Sasaki, Takaaki, Scott J. Rodig, Lucian R. Chirieac, and Pasi A. Janne. “The Biology and Treatment of EML4-ALK Non-Small Cell Lung Cancer.” NCBI. US National Library of Medicine, 24 Apr. 2010. In website https://prezi.com/rbtv_450s3wd/eml4-alk-fusion-gene/ citing us as https://pharmaceuticalintelligence.files.wordpress.com/2014/08/membrane_receptor_tk.jpg?w=500&h=326
6. Github project on GENEREGULATION,Deciphering the code of gene regulation using massively parallel assays of designed sequence at https://github.com/arquivo/Research-Websites-Preservation/blob/master/classifier/projects_classification.csv.3 citing us as http://pharmaceuticalintelligence.com/tag/transcription/page/2/,0

 

7. Website https://tginnovations.wordpress.com/2012/05/08/cancer/ citing us as Cancer Metastasis(pharmaceuticalintelligence.com) {one of our chapters in Cancer Volume 1}

 

8. Website  https://www.minipiginfo.com/mini-pig-cancer.html citing us https://pharmaceuticalintelligence.com/the-scid-pig-how-pigs-are-becoming-a-great-alternate-model-for-cancer-research
9. Shashi Shekhar Anand, Navgeet, Balraj Singh Gill*. In https://www.pharmatutor.org/articles/breakthroughs-in-epigenetics

Citing as figure source for Fig 5. Phosphorylation of serine chain
Fig.source:pharmaceuticalintelligence.com

 

10. Larry H Bernstein. Ca2+-Stimulated Exocytosis: The Role of Calmodulin and Protein Kinase C in Ca2+ Regulation of Hormone and Neurotransmitter in https://www.archivesofmedicine.com/medicine/ca2stimulated-exocytosis-the-role-of-calmodulin-and-protein-kinase-c-in-ca2-regulation-of-hormone-and-neurotransmitter.php?aid=7058

 

11. Description: Multidimensional Representation of Concepts as Cognitive Engrams in the Human Brain. Clinical Trial NCT02834716 on Cognitive Impairment using https://pharmaceuticalintelligence.com/2013/02/27/ustekinumab-new-drug-therapy-for-cognitive-decline-resulting-from-neuroinflammatory-cytokine-signaling-and-alzheimers-disease/
    Description: Ustekinumab New Drug Therapy for Cognitive Decline resulting from Neuroinflammatory Cytokine Signaling and Alzheimer’s Disease

 

12. Beckman Coulter promotional material. In Post Translational Modification at https://www.beckman.com/resources/sample-type/bio-molecules/post-translational-modification

Using our material as reference “Overview of Posttranslational Modification (PTM) | Leaders in Pharmaceutical Business Intelligence (LPBI) Group.” Leaders in Pharmaceutical Business Intelligence (LPBI) Group, 29 July 2014, https://pharmaceuticalintelligence.com/2014/07/29/overview-of-posttranslational-modification-ptm/.

DETAILS

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3777453/

Chin J Cancer. 2012 Oct; 31(10): 463–470.

doi: 10.5732/cjc.012.10216

PMCID: PMC3777453

PMID: 22980418

Application of next-generation sequencing in clinical oncology to advance personalized treatment of cancer

Yan-Fang Guan, Gai-Rui Li, Rong-Jiao Wang, Yu-Ting Yi, Ling Yang, Dan Jiang, Xiao-Ping Zhang, and Yin Peng

Author information Article notes Copyright and License information Disclaimer

This article has been cited by other articles in PMC.

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Abstract

With the development and improvement of new sequencing technology, next-generation sequencing (NGS) has been applied increasingly in cancer genomics research over the past decade. More recently, NGS has been adopted in clinical oncology to advance personalized treatment of cancer. NGS is used to identify novel and rare cancer mutations, detect familial cancer mutation carriers, and provide molecular rationale for appropriate targeted therapy. Compared to traditional sequencing, NGS holds many advantages, such as the ability to fully sequence all types of mutations for a large number of genes (hundreds to thousands) in a single test at a relatively low cost. However, significant challenges, particularly with respect to the requirement for simpler assays, more flexible throughput, shorter turnaround time, and most importantly, easier data analysis and interpretation, will have to be overcome to translate NGS to the bedside of cancer patients. Overall, continuous dedication to apply NGS in clinical oncology practice will enable us to be one step closer to personalized medicineWith the development and improvement of new sequencing technology, next-generation sequencing (NGS) has been applied increasingly in cancer genomics research over the past decade. More recently, NGS has been adopted in clinical oncology to advance personalized treatment of cancer. NGS is used to identify novel and rare cancer mutations, detect familial cancer mutation carriers, and provide molecular rationale for appropriate targeted therapy. Compared to traditional sequencing, NGS holds many advantages, such as the ability to fully sequence all types of mutations for a large number of genes (hundreds to thousands) in a single test at a relatively low cost. However, significant challenges, particularly with respect to the requirement for simpler assays, more flexible throughput, shorter turnaround time, and most importantly, easier data analysis and interpretation, will have to be overcome to translate NGS to the bedside of cancer patients. Overall, continuous dedication to apply NGS in clinical oncology practice will enable us to be one step closer to personalized medicine.

44. Lev-Ari A. Sunitinib brings Adult acute lymphoblastic leukemia (ALL) to Remission-RNA Sequencing-FLT3 Receptor Blockade. 2012. Available at: http://pharmaceuticalintelligence.com/2012/07/09/sunitinib-brings-adult-all-to-remission-rna-sequencing/

http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0123-30332014000100011

Ingeniería y competitividad

Print version ISSN 0123-3033

Ing. compet. vol.16 no.1 Cali Jan./June 2014

 

Bioinformática multifractal: Una propuesta hacia la interpretación no-lineal del genoma

Multifractal bioinformatics: A proposal to the nonlinear interpretation of genome

Pedro A. Moreno
Escuela de Ingeniería de Sistemas y Computación, Facultad de Ingeniería, Universidad del Valle, Cali, Colombia
E-mail: pedro.moreno@correounivalle.edu.co

Eje temático: Ingeniería de sistemas / System engineering
Recibido: 19 de septiembre de 2012
Aceptado: 16 de diciembre de 2013

Resumen

El primer borrador de la secuencia completa del genoma humano (GH) se publicó en el año 2001 por parte de dos consorcios competidores. Desde entonces, varias características estructurales y funcionales de la organización del GH han sido reveladas. Hoy en día, más de 2000 genomas humanos han sido secuenciados y todos estos hallazgos están impactando fuertemente en la academia y la salud pública. A pesar de esto, un gran cuello de botella llamado la interpretación del genoma persiste; esto es, la falta de una teoría que integre y explique el complejo rompecabezas de características codificantes y no codificantes que componen el GH como un todo. Diez años después de secuenciado el GH, dos trabajos recientes, abordados dentro del formalismo multifractal permiten proponer una teoría no-lineal que ayuda a interpretar la variación estructural y funcional de la información genética de los genomas. El presente artículo de revisión trata acerca de este novedoso enfoque, denominado: “Bioinformática multifractal”.

Palabras clave: Ciencias ómicas, bioinformática, genoma humano, análisis multifractal.

Abstract

The first draft of the human genome (HG) sequence was published in 2001 by two competing consortia. Since then, several structural and functional characteristics for the HG organization have been revealed. Today, more than 2.000 HG have been sequenced and these findings are impacting strongly on the academy and public health. Despite all this, a major bottleneck, called the genome interpretation persists. That is, the lack of a theory that explains the complex puzzles of coding and non-coding features that compose the HG as a whole. Ten years after the HG sequenced, two recent studies, discussed in the multifractal formalism allow proposing a nonlinear theory that helps interpret the structural and functional variation of the genetic information of the genomes. The present review article discusses this new approach, called: “Multifractal bioinformatics”.

1. Introduction

Omic Sciences and Bioinformatics

In order to study the genomes, their life properties and the pathological consequences of impairment, the Human Genome Project (HGP) was created in 1990. Since then, about 500 Gpb (EMBL) represented in thousands of prokaryotic genomes and tens of different eukaryotic genomes have been sequenced (NCBI, 1000 Genomes, ENCODE). Today, Genomics is defined as the set of sciences and technologies dedicated to the comprehensive study of the structure, function and origin of genomes. Several types of genomic have arisen as a result of the expansion and implementation of genomics to the study of the Central Dogma of Molecular Biology (CDMB), Figure 1 (above). The catalog of different types of genomics uses the Latin suffix “-omic” meaning “set of” to mean the new massive approaches of the new omics sciences (Moreno et al, 2009). Given the large amount of genomic information available in the databases and the urgency of its actual interpretation, the balance has begun to lean heavily toward the requirements of bioinformatics infrastructure research laboratories Figure 1 (below).

The bioinformatics or Computational Biology is defined as the application of computer and information technology to the analysis of biological data (Mount, 2004). An interdisciplinary science that requires the use of computing, applied mathematics, statistics, computer science, artificial intelligence, biophysical information, biochemistry, genetics, and molecular biology. Bioinformatics was born from the need to understand the sequences of nucleotide or amino acid symbols that make up DNA and proteins, respectively. These analyzes are made possible by the development of powerful algorithms that predict and reveal an infinity of structural and functional features in genomic sequences, as gene location, discovery of homologies between macromolecules databases (Blast), algorithms for phylogenetic analysis, for the regulatory analysis or the prediction of protein folding, among others. This great development has created a multiplicity of approaches giving rise to new types of Bioinformatics, such as Multifractal Bioinformatics (MFB) that is proposed here.

1.1 Multifractal Bioinformatics and Theoretical Background

MFB is a proposal to analyze information content in genomes and their life properties in a non-linear way. This is part of a specialized sub-discipline called “nonlinear Bioinformatics”, which uses a number of related techniques for the study of nonlinearity (fractal geometry, Hurts exponents, power laws, wavelets, among others.) and applied to the study of biological problems (http://pharmaceuticalintelligence.com/tag/fractal-geometry/). For its application, we must take into account a detailed knowledge of the structure of the genome to be analyzed and an appropriate knowledge of the multifractal analysis.

https://www.biorxiv.org/content/biorxiv/early/2017/04/24/129742.full.pdf

Genome-wide differential gene network analysis R software and its application in LnCap prostate cancer

Gökmen Altay1* and David E. Neal2 1*La Jolla Institute for Allergy and Immunology, CA, USA 2Nuffield Department of Surgical Sciences, University of Oxford, Headington, OX3 7DQ , Oxford, United Kingdom *Corresponding author altay@lji.org Abstract We introduce an R software package for condition-specific gene regulatory network analysis based on DC3NET algorithm. We also present an application of it on a real prostate dataset and demonstrate the benefit of the software. We performed genome-wide differential gene network analysis with the software on the LnCap androgen stimulated and deprived prostate cancer gene expression datasets (GSE18684) and inferred the androgen stimulated prostate cancer specific differential network. As an outstanding result, CXCR7 along with CXCR4 appeared to have the most important role in the androgen stimulated prostate specific genome-wide differential network. This blind estimation is strongly supported from the literature. The critical roles for CXCR4, a receptor over-expressed in many cancers, and CXCR7 on mediating tumor metastasis, along with their contributions as biomarkers of tumor behavior as well as potential therapeutic target were studied in several other types of cancers. In fact, a pharmaceutical company had already developed a therapy by inhibiting CXCR4 to block non-cancerous immuno-suppressive and pro-angiogenic cells from populating the tumor for disrupting the cancer environment and restoring normal immune surveillance functions. Considering this strong confirmation, our inferred regulatory network might reveal the driving mechanism of LnCap androgen stimulated prostate cancer. Because, CXCR4 appeared to be in the center of the largest subnetwork of our inferred differential network. Moreover, enrichment analyses for the largest subnetwork of it appeared to be significantly enriched in terms of axon guidance, fc gamma R-mediated phagocytosis and endocytosis. This also conforms with the recent literature in the field of prostate cancer. We demonstrate how to derive condition-specific gene targets from expression datasets on genome-wide level using differential gene network analysis. Our results showed that differential gene network analysis worked well in a prostate cancer dataset, which suggest the use of this approach as essential part of current expression data processing. Availability: The introduced R software package available in CRAN at https://cran.r-project.org/web/packages/dc3net and also at https://github.com/altayg/dc3net

4. Discussion By employing differential gene network analysis approach, the present study aims to investigate the molecular mechanisms that may drive disease progression in prostate cancer using our presented software dc3net. Top four hub nodes, identified in the present study, have been strongly associated with prostate cancer metastatic process, including CXCR7, STK39, ELOVL7 and ACSL3. Hub nodes are genes that are highly connected with other genes and they were proposed to have important roles in biological development. Since hub nodes have more complex interactions than other genes, they may have crucial roles in the underlying mechanisms of disease (Guo, 2015). Identification of hub genes involved in progression of prostate cancer may lead to the development of better diagnostic methods and providing therapeutic approaches. According to our analysis, CXCR7 (chemokine (C-X-C motif) receptor 7) is by far the top hub gene in the androgen stimulated differential network and it is also part of the largest independent subnetwork as seen in Figure 3. In (Wang, 2008), it is reported that staining of high-density tissue microarrays shows that the levels of CXCR7/RDC1 expression increase as the tumors become more aggressive. Also, In vitro and in vivo studies with prostate cancer cell lines propose that alterations in CXCR7/RDC1 expressions are associated with enhanced invasive and adhesive activities in addition to a survival advantage. Along other papers on CXCR7 (Zheng, 2010), it was shown that increased CXCR7 expression was found in hepatocellular carcinoma (HCC) tissues. Knockdown of CXCR7 expression by transfected with CXCR7shRNA significantly inhibited SMMC-7721 angiogenesis, adhesion and cells invasion. Moreover, down-regulation of CXCR7 expression leads to a reduction of tumor growth in a xenograft model of HCC (Zheng, 2010). Another study demonstrated that the IL8–regulated Chemokine Receptor CXCR7 stimulates EGFR Signaling to promote prostate cancer growth (Singh, 2011). In a study conducted by Yun et al., it is reported that CXCR7 expression is increased in most of the tumor cells compared with the normal cells and is involved in cell proliferation, migration, survival, invasion and angiogenesis during the initiation and progression of many cancer types including prostate cancer (Yun, 2015). A more recent study indicated that there appeared to be disconnect of the effect of DHT on CXCL12/CXCR4/CXCR7 chemokine axis between transcriptional and translation machinery in androgen-responsive LnCaP cell line. There are many other studies that showed the strong role of CXCR7 in metastatic type cancer that strongly validates our blind foremost prediction is very likely to be true and thus needs further experimental work on its targets that we inferred in this study. However, available under aCC-BY-NC 4.0 International license. not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made bioRxiv preprint doi: https://doi.org/10.1101/129742; this version posted April 24, 2017. The copyright holder for this preprint (which was 11 It was also observed that CXCR7/RDC1 levels are regulated by CXCR4 (Singh, 2011). This is a very interesting supporting information from literature for our blind estimation because in our predicted largest independent subnetwork, as shown in Figure 3, CXCR7 and CXCR4 appear to be very close and interacting over only one gene. Although CRCX4 is not a hub gene, it appears to be as a bridge that connects both halves of the largest subnetwork. According to KEGG analysis, CXCR4 was found in the gene list of two different significantly enriched KEGG pathways, axon guidance and endocytosis which are strongly associated with prostate cancer (Table 3). Considering the prediction was made on global level, this literature confirmation seems assuring but not a coincidence. Therefore, this relation is worth experimenting in LnCap cancer too. It is also reported (Shanmugam, 2011) that inhibition of CXCR4/CXCL12 signaling axis by ursolic acid leads to suppression of metastasis in transgenic adenocarcinoma of mouse prostate model and CXCR4 induced a more aggressive phenotype in prostate cancer (Miki, 2007). In another study, it is reported that CXCR4 and CXCR7 have critical roles on mediating tumor metastasis in various types of cancers as both being a receptor for an important α-chemokine, CXCL12 (Sun, 2010). Furthermore, a more recent study concluded that CXCR4 plays a crucial role in cancer proliferation, dissemination and invasion and the inhibition of CXCR4 strongly affects prostate cancer metastatic disease (Gravina, 2015). The chief officer of Massachusetts based X4 Pharmaceuticals company recently stated that CXCR4 protein “acts as a beacon to attract cells to surround a tumor, effectively hiding the tumor from the body’s T cells that would otherwise destroy them”. He indicated that X4 company is beginning human trials using CXCR4 inhibitors which aims to develop a therapy to block the protein, CXCR4 (http://pharmaceuticalintelligence.com/2015/12/15/are-cxc4-antagonists-making-a-comebackin-cancer-chemotherapy, 2015).

https://www.slideshare.net/dsairamsairam/warburg-effect

1. 1. Warburg Effect Presented By: D.Sairam Course : Fundamentals of Biology- Life Sciences Course Code: BSBT-201 Course Instructor: Dr. Subhabrata Kar Presentation Code: U3P1
2. 2. Overview Ø Introduction Ø Causes of Warburg Effect Ø Significance of Warburg Effect ØReferences
3. 3. Introduction • Warburg, considered by many the pre-eminent bio chemist of the first half of the twentieth century, made vital contributions to many other areas of biochemistry, including respiration, photosynthesis, and the enzymology of intermediary metabolism. • In the mid 1920s Warburg and co-workers showed that, under Aerobic conditions, tumour tissues metabolize approximately tenfold more glucose to lactate in a given time than normal tissues. • This phenomenon later came to be known as “Warburg Effect”. • Warburg purified and crystallized seven of the enzymes of glycolysis. He used a tool called as “ Warburg Manometer” which measured directly the consumption of oxygen by monitoring changes in gas volume, and therefore allowed quantitative measurement of any enzyme with oxidase activity.
4. 4. Note: Warburg Effect in Plant Physiology and Oncology are different • In Plant physiology, Warburg Effect refers to the phenomenon in which Oxygen acts as a competitive inhibitor to Carbon dioxide fixation under the influence of RuBisCO, which initiates photosynthesis. Source : Bild-1928
5. 5. Comparison of Glycolysis between a Normal Tissue and Tumour/ Proliferated Tissue • http://pharmaceuticalintelligence.com/2012/10/17/is-the- warburg-effect-the-cause-or-the-effect-of-cancer-a-21st- century-view/

https://prezi.com/rbtv_450s3wd/eml4-alk-fusion-gene/

EML4-ALK fusion gene

CK

Published with reusable license by Camille Kawawa-Beaudan

July 2, 2015

Sasaki, Takaaki, Scott J. Rodig, Lucian R. Chirieac, and Pasi A. Janne. “The Biology and Treatment of EML4-ALK Non-Small Cell Lung Cancer.” NCBI. US National Library of Medicine, 24 Apr. 2010. Web. 30 June 2015. <http%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC2888755%2F>.

Shaw, Alice T., and Benjamin Solomon. “Targeting Anaplastic Lymphoma Kinase in Lung Cancer.” Clinical Cancer Research. N.p., 2 Feb. 2011. Web. 30 June 2015. <http%3A%2F%2Fclincancerres.aacrjournals.org%2Fcontent%2F17%2F8%2F2081.full>.

Webb, Thomas R., Jake Slavish,et al. “Anaplastic Lymphoma Kinase: Role in Cancer Pathogenesis and Small-molecule Inhibitor Development for Therapy.” Expert Review of Anticancer Therapy. U.S. National Library of Medicine, 1 Jan. 2010. Web. 30 June 2015. <http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2780428/&gt;.

Webb, Thomas R., Jake Slavish, et al. “Anaplastic Lymphoma Kinase: Role in Cancer Pathogenesis and Small-molecule Inhibitor Development for Therapy.” Expert Review of Anticancer Therapy. U.S. National Library of Medicine, 1 Jan. 2010. Web. 30 June 2015. <http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2780428/&gt;.

https://pharmaceuticalintelligence.com/wp-content/uploads/2014/08/membrane_receptor_tk.jpg?w=500&h=326

https://github.com/arquivo/Research-Websites-Preservation/blob/master/classifier/projects_classification.csv.3

GENEREGULATION,Deciphering the code of gene regulation using massively parallel assays of designed sequence libraries,http://pharmaceuticalintelligence.com/tag/transcription/page/2/,0
	EXACTA,

https://tginnovations.wordpress.com/2012/05/08/cancer/

Cancer

MAY 8, 2012 · TGI – BIOMARKERS, TGI – CANCER, TGI – EPIGENETICS, TGI – HEALTH, TGI – INTEGRATIVE MEDICINE, TGI – MASS SPECTROMETRY

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• DR. Karra

2 Votes

Cancer is a broad group of various diseases involving unregulated cell growth. It is medically known as a malignant neoplasm. In cancer, cells divide and grow uncontrollably and invade nearby parts of the body. The cancer may also spread to more distant parts of the body through the lymphatic system or bloodstream, it is called metastasis. However, not all tumors are cancerous. Some tumors do not grow uncontrollably, do not invade neighboring tissues, and do not spread throughout the body which are called Benign tumors.

There are more than 100 types of Cancers. Follow the link to know more:

http://www.cancer.gov/cancertopics/types/alphalist

Main sites of metastases for some common cancer types. Primary cancers are denoted by “…cancer” and their main metastasis sites are denoted by “…metastases”. List of included entries and references is found on main image page in Commons: (Photo credit: Wikipedia)

RELATED ARTICLES

• Commander Selvam(drcommanderselvamsiddhar28.wordpress.com)
• Cancer Cells Identified and Highly Preventable(guardianlv.com)
• Cancer Metastasis(com)

https://www.minipiginfo.com/mini-pig-cancer.html

Cancer and pigs

Pig cancer has been widely studied because pigs cancer advantages include the resemblance in anatomy, physiology, and genetic makeup with the human, as well as new methods to manipulate the pig genome. So there is alot of research that continues to be done with regards to pigs and cancer. Some of the articles are older and additonal research has been done, but the studies have not been published publicly, so I am including links to studies that can be viewed by anyone. I do have access to some studies that are published on sites only healthcare personnel can access. These are just a few things you should be aware of when adding a pig to your family. This does not mean your pig may get cancer, but it is certainly a possibility. In the past 15 years (2001–2016), the incidence of reported neoplasia in pet pot-bellied pigs has increased, mostly as a consequence of increased life spans resulting from improved veterinary medical care.

Pigs are predisposed to some genetic cancers as well as other types of cancer. Reproductive cancer is extremely common in pigs that were never spayed/neutered, but especially in the females. I have seen research studies indicating a percentage as high as 75% of ALL females will develop some kind of neoplasm or tumor in the reproductive organs as they age, which is why we let others know the importance of spaying and neutering your pig. These pigs age data were available for 27 of the 32 pigs and ranged from 4 months to 19 years. The study is linked below from Sage Pub.

Sources:
​https://www.ccsi.ca/reports/Melanoma_article.pdf

https://pharmaceuticalintelligence.com/2013/10/10/the-scid-pig-how-pigs-are-becoming-a-great-alternate-model-for-cancer-research/
​http://journals.sagepub.com/uterine-cancer-in-pigs/full
​https://www.ncbi.nlm.nih.gov/pigs-in-research-article
http://pubmedcentralcanada.ca/lukemia-in-mini-pigs/articles
https://www.ncbi.nlm.nih.gov/pmc/hepatocellular-carcinoma-potbellied-pig/articles
http://journals.sagepub.com/full/hepatocelluar-carcinoma-potbellied-pigs
http://journals.sagepub.com/doi/full/oral-squamous-cell-carcinoma-in-potbellied-pig
http://journals.sagepub.com/doi/pdf/gastric-carcinoma-in-mini-pigs

http://illinoisjltp.com/journal/wp-content/uploads/2014/12/Mehta.pdf

But, one key distinction between the Biologics Price Act and the HatchWaxman Act that deserves some attention is that even though the current FDA Draft guidelines are silent on the issue of labeling requirements for follow-on biologic drugs, it is most likely that the FDA will not only permit, but will require follow-on biologic drug manufacturers to maintain warning labels that are unique. (117) What this means is that brand-name biologic drug

117. It is worth emphasizing that there is a normative debate amongst industry as to whether follow-on biologics should have unique names. See Biosimilars: Intellectual Property Creation and Protection by Pioneer and by Biosimilar Manufacturers, LEADERS IN PHARMACEUTICAL BUS. INTELLIGENCE, http://pharmaceuticalintelligence.com/tag/BiologicsPriceAct/ (last visited Oct. 27, 2014) (“Having unique names will avoid unintended substitution, minimize risk of medication errors, allow for essential elements of pharmacovigilance such as traceability and follow-up of adverse drug reactions, as well as facilitate prescriberpatient decision making . . . .”). However, the debate seems to be tipping in favor of requiring unique labeling as opposed to identical labeling. See id (“[W]hile all biologics should be uniquely tracked, biosimilars should not require unique International Nonproprietary Names (INNs) from their reference products.

https://www.pharmatutor.org/articles/breakthroughs-in-epigenetics

ABOUT AUTHORS:
Shashi Shekhar Anand, Navgeet, Balraj Singh Gill*
Centre for Biosciences,
School of Basic and Applied Sciences,
Central University of Punjab, Bathinda, India
gillsinghbalraj@gmail.com

ABSTRACT
The word ‘epigenetic’ was first coined by Conrad Waddington in 1946. It deals with functionally relevant modifications to the genome that do not include a change in the nucleotide sequence. Till date observation has focused on the functions of genome sequences and how their regulation occurs. The emerging epigenetic changes and the interactions between cis-acting elements with protein factors  plays a central role in gene regulation as well as give insight to various diseases. To evaluate the crosstalk of DNA and protein by taking account of the whole genome, one new evolving technique which is called as ChIP-chip, works on the principle of combining chromatin immunoprecipitation with microarray. ChIP-chip has been recently used in basic biological studies and may be improved further and can be useful for other to aspects, like human diseases. Now a days large amount of discoveries by ChIP-chip and other high-throughput techniques like this   may be connected with evolving bioinformatics to add to our knowledge of life and diseases.

Histone Phosphorylation
Histone Phosphorylation is the modification in which there is an addition of a phosphate  group. Phosphorylation is catalyzed by the various specific protein kinases, whereas phosphatases mediate removal of the phosphate group (Figure 5). The most studied sites of histone phosphorylation are the serine. Phosphorylation of histones can also be a vital regulatory signal. H2A variant, H2AX that helps in the DNA repair by the process of phosphorylation. It has an important role in DNA damage response and DNA repair.

Fig 5. Phosphorylation of serine chain
Fig.source:pharmaceuticalintelligence.com

Histone H3 phosphorylation has been reported to play important roles in both transcription and chromatin condensation during mitosis.

https://www.archivesofmedicine.com/medicine/ca2stimulated-exocytosis-the-role-of-calmodulin-and-protein-kinase-c-in-ca2-regulation-of-hormone-and-neurotransmitter.php?aid=7058

Archives of Medicine

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Ca2+-Stimulated Exocytosis: The Role of Calmodulin and Protein Kinase C in Ca2+ Regulation of Hormone and Neurotransmitter

Larry H Bernstein^*

New York Methodist Hospital, Brooklyn, New York, USA

Corresponding Author:

Larry H Bernstein
New York Methodist Hospital
Brooklyn, New York, USA
Tel: 2032618671
E-mail: larry.bernstein@gmail.com

Visit for more related articles at Archives of Medicine

Abstract

This is a review of the role of calmodulin and protein kinase C inregulation of Ca++ – stimulated secretion. The molecular mechanisms underlying the Ca2+ regulation of hormone and neurotransmitter release are largely unknown. Using a reconstituted [3H] norepinephrine release assay in permeabilized PC12 cells, we found essential proteins that support the triggering stage of Ca2+-stimulated exocytosis are enriched in an EGTA extract of brain membranes. Fractionation of this extract allowed purification of two factors that stimulate secretion in the absence of any other cytosolic proteins. These are calmodulin and protein kinase Ca (PKCa). Their effects on secretion were confirmed using commercial and recombinant proteins. Calmodulin enhances secretion in the absence of ATP, whereas PKC requires ATP to increase secretion, suggesting that phosphorylation is involved in PKC-mediated stimulation but not calmodulin mediated stimulation. Both proteins modulate the half-maximal increase was elicited by 3 nM PKC and 75 nM calmodulin. These results suggest that calmodulin and PKC increase Ca2+-activated exocytosis by directly modulating the membrane- or cytoskeleton-attached exocytic machinery downstream of Ca2+ elevation.

Ca2+-Stimulated Exocytosis:  The Role of Calmodulin and Protein Kinase C in Ca2+ Regulation of Hormone and Neurotransmitter

Writer and Curator: Larry H Bernstein, MD, FCAP
and
Curator and Content Editor: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/12/23/calmodulin-and-protein-kinase-c-drive-the-ca2-regulation-of-hormone-and-neurotransmitter-release-that-triggers-ca2-stimulated-exocytosis/

 

Government Repositories Using our Curations

ClinicalTrials.gov

 

https://clinicaltrials.gov/ct2/history/NCT02835716?V_3=View

 

one of our articles used as a reference for this clinical trial entry

 

see below

 

Links:URL: http://print.ispub.com/api/0/ispub-article/7521
Description: Multidimensional Representation of Concepts as Cognitive Engrams in the Human Brain
URL: http://ispub.com/IJH/8/1/9567
Description: Evaluation of Cognitive Impairment
URL: https://pharmaceuticalintelligence.com/2013/02/27/ustekinumab-new-drug-therapy-for-cognitive-decline-resulting-from-neuroinflammatory-cytokine-signaling-and-alzheimers-disease/
Description: Ustekinumab New Drug Therapy for Cognitive Decline resulting from Neuroinflammatory Cytokine Signaling and Alzheimer’s Disease
Available IPD/Information:

Study NCT02835716
Submitted Date:  September 10, 2016 (v3)

---

Study Identification Unique Protocol ID: PCD=OO ALZ Brief Title: Pre-Clinical (Alzheimers) Diagnosis PCD = Optimum Outcomes OO (PCD=OOALZ) Official Title: Pre-Clinical Alzheimer’s (ALZ) Diagnosis (PCD) = Optimum Outcomes (OO) Secondary IDs:
Study Status Record Verification: September 2016 Overall Status: Unknown status [Previously: Recruiting] Study Start: September 2016 Primary Completion: September 2019 [Anticipated] Study Completion: September 2020 [Anticipated] First Submitted: July 10, 2016 First Submitted that Met QC Criteria: July 13, 2016 First Posted: July 18, 2016 [Estimate] Last Update Submitted that Met QC Criteria: September 10, 2016 Last Update Posted: September 13, 2016 [Estimate]
Sponsor/Collaborators Sponsor: Millennium Magnetic Technologies, LLC Responsible Party: Sponsor Collaborators:
Oversight U.S. FDA-regulated Drug: U.S. FDA-regulated Device: Data Monitoring: No
Study Description Brief Summary: This Observational protocol will attempt to verify two recent and very critical concepts in ALZ Clinical Research by studying high-risk individuals who already are taking medications which may prevent the onset of ALZ. It may be possible to determine the future development of ALZ in a preclinical state in a cognitively normal but high risk individual at least 18-24 months before any symptoms develop of cognitive impairment. Early treatment of these cognitively normal high-risk persons with subclinical pre-ALZ can prevent of delay the occurrence and severity of ALZ. Caveats Neither this protocol nor the fMRI imaging are designed or intended to diagnose or treat ALZ, nor develop or use medications or diagnostic neuroimaging outside of already approved and accepted parameters. Persons who volunteer to be study subjects in this observational protocol will be under the care of their primary care / specialty physician, who will order tests and treatments as they see appropriate. Although there is a very large body of peer-reviewed scientific literature demonstrating that certain functional MRI patterns are associated with certain neurologic conditions, the utilization of fMRI for the evaluation of neurologic disorders is still considered an emerging science and therefore in the investigational stage. Although this protocol will report on brain patterns of certain neurologic conditions such as cognitive impairment and Alzheimer’s disease, based on patterns published in peer-reviewed journals, such findings are not considered stand alone or diagnostic per se and should always be considered by the PMD in conjunction with the patient’s clinical condition. These data should only be used as additional information to add to the PMD’s diagnostic impression. Detailed Description: Prospective observational study participants age 50-75 years are identified who are currently (<1 month) taking or will soon (within 3 months) start taking study medications of interest thru their own physician. Prospective observational study participants are explained how persons can be cognitively normal but at high risk for developing clinical ALZ, and that this risk can be prospectively identified. They are advised that there is some rationale that medications they are or will soon be taking may have a protective effect in delaying the onset of ALZ, and that protective effect can be monitored. They are asked if they would like to participate in a protocol that monitors their prospective risk for developing ALZ short term, and whether certain of their prescribed medications may have a protective effect. Those who are accepting to be participants are then enrolled in the study. Enrollees are tested for risk factors for having pre-clinical ALZ. Individuals identified as being at risk at baseline are followed at 6 month intervals for a 24 month period using psychometric testing and functional neuroimaging. Their maintenance of cognitive stability or cognitive decline is monitored while under the care of their PMD and while taking medications of interest.
Conditions Conditions: Alzheimer Disease Keywords: Phosphodiesterase type 4 inhibitor ustekinumab secukinumab P40 subunit Interleukin IL-12 Interleukin IL-23 Apoprotein e4 Interleukin IL-17A
Study Design Study Type: Observational [Patient Registry] Observational Study Model: Case-Only Time Perspective: Prospective Biospecimen Retention: Biospecimen Description: Enrollment: 150 [Anticipated] Number of Groups/Cohorts 0 Target Follow-Up Duration: 5 Years
Groups and Interventions Intervention Details: Drug: roflumilast DALIRESP is used in adults with severe chronic obstructive pulmonary disease (COPD) to decrease the number of flare-ups or the worsening of COPD symptoms (exacerbations Other Names: Daliresp Biological: ustekinumab STELARA is approved to treat adults 18 years and older with moderate or severe plaque psoriasis that involves large areas or many areas of their body Other Names: Stelara
Outcome Measures Primary Outcome Measures: 1. Number of Participants who Develop Cognitive Decline [ Time Frame: 18-24 month ]Cognitive decline is defined as a reduction from baseline performance within each individual of at least one standard deviation (SD) on at least one of the three principal outcome indices (DRS-2, RAVLT Sum of Trials 1-5 [T1- 5], RAVLT Delayed Recall [DR]).
Eligibility Study Population: Adults at high risk of developing ALZ within 18-48 months but are currently cognitively normal for their age sex matching group. Risk factors include parents with a history of ALZ, and/or positive APOE4 alleles. Sampling Method: Non-Probability Sample Minimum Age: 50 Years Maximum Age: 75 Years Sex: All Gender Based: Accepts Healthy Volunteers: Yes Criteria: Inclusion Criteria: 50 to 75 years old, Normal baseline cognitive function by standard psychometric testing. Able to privately fund psychometric and neuroimaging studies if not covered by their insurance, Able to give written Informed Consent, Ascent for collaboration by their primary care or specialty physician, Currently taking (<1 month) or planning to take (within the next 3 months) medications which are identified in the study group of interest, prescribed by and under the care of a PMD or specialty physician. Exclusion Criteria: • Inability to undergo MR Imaging : Claustrophobia, certain metal implants,
Contacts/Locations Study Officials: Donald H Marks, MD PhD Principal Investigator MMT Locations: United States, Connecticut Millennium Magnetic Technologies, LLC Westport, Connecticut, United States, 06880 Contact:Contact: Steve Levy, MD 203-423-9494 SL@MilMag.net Contact:Contact: Donald H Marks, MD PhD 9733070364 DHM@MilMag.net
IPDSharing Plan to Share IPD: Yes Supporting Information: Time Frame: Access Criteria: URL:
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The Road Ahead to Cure Alzheimer’s Disease: Development of Biological Markers and Neuroimaging Methods for Prevention Trials Across all Stages and Target Populations. J Prev Alzheimers Dis. 2014 Dec;1(3):181-202. PubMed 26478889 Chincarini A, Bosco P, Calvini P, Gemme G, Esposito M, Olivieri C, Rei L, Squarcia S, Rodriguez G, Bellotti R, Cerello P, De Mitri I, Retico A, Nobili F; Alzheimer’s Disease Neuroimaging Initiative. Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer’s disease. Neuroimage. 2011 Sep 15;58(2):469-80. doi: 10.1016/j.neuroimage.2011.05.083. Epub 2011 Jun 16. PubMed 21718788 Chiang K, Koo EH. Emerging therapeutics for Alzheimer’s disease. Annu Rev Pharmacol Toxicol. 2014;54:381-405. doi: 10.1146/annurev-pharmtox-011613-135932. Review. PubMed 24392696 Chao S, Roberts JS, Marteau TM, Silliman R, Cupples LA, Green RC. Health behavior changes after genetic risk assessment for Alzheimer disease: The REVEAL Study. Alzheimer Dis Assoc Disord. 2008 Jan-Mar;22(1):94-7. doi: 10.1097/WAD.0b013e31815a9dcc. PubMed 18317253 Deardorff WJ, Grossberg GT. Targeting neuroinflammation in Alzheimer’s disease: evidence for NSAIDs and novel therapeutics. Expert Rev Neurother. 2017 Jan;17(1):17-32. Epub 2016 Jun 24. PubMed 27293026 García-Osta A, Cuadrado-Tejedor M, García-Barroso C, Oyarzábal J, Franco R. Phosphodiesterases as therapeutic targets for Alzheimer’s disease. ACS Chem Neurosci. 2012 Nov 21;3(11):832-44. doi: 10.1021/cn3000907. Epub 2012 Oct 1. Review. PubMed 23173065 Galluzzi S, Marizzoni M, Babiloni C, Albani D, Antelmi L, Bagnoli C, Bartres-Faz D, Cordone S, Didic M, Farotti L, Fiedler U, Forloni G, Girtler N, Hensch T, Jovicich J, Leeuwis A, Marra C, Molinuevo JL, Nobili F, Pariente J, Parnetti L, Payoux P, Del Percio C, Ranjeva JP, Rolandi E, Rossini PM, Schönknecht P, Soricelli A, Tsolaki M, Visser PJ, Wiltfang J, Richardson JC, Bordet R, Blin O, Frisoni GB; PharmaCog Consortium. Clinical and biomarker profiling of prodromal Alzheimer’s disease in workpackage 5 of the Innovative Medicines Initiative PharmaCog project: a ‘European ADNI study’. J Intern Med. 2016 Jun;279(6):576-91. doi: 10.1111/joim.12482. Epub 2016 Mar 4. PubMed 26940242 Griffin WS. Neuroinflammatory cytokine signaling and Alzheimer’s disease. N Engl J Med. 2013 Feb 21;368(8):770-1. doi: 10.1056/NEJMcibr1214546. PubMed 23425171 Gurney ME, D’Amato EC, Burgin AB. Phosphodiesterase-4 (PDE4) molecular pharmacology and Alzheimer’s disease. Neurotherapeutics. 2015 Jan;12(1):49-56. doi: 10.1007/s13311-014-0309-7. Review. PubMed 25371167 Christensen KD, Roberts JS, Whitehouse PJ, Royal CD, Obisesan TO, Cupples LA, Vernarelli JA, Bhatt DL, Linnenbringer E, Butson MB, Fasaye GA, Uhlmann WR, Hiraki S, Wang N, Cook-Deegan R, Green RC; REVEAL Study Group*. Disclosing Pleiotropic Effects During Genetic Risk Assessment for Alzheimer Disease: A Randomized Trial. Ann Intern Med. 2016 Feb 2;164(3):155-63. doi: 10.7326/M15-0187. Epub 2016 Jan 26. PubMed 26810768 Green RC, Christensen KD, Cupples LA, Relkin NR, Whitehouse PJ, Royal CD, Obisesan TO, Cook-Deegan R, Linnenbringer E, Butson MB, Fasaye GA, Levinson E, Roberts JS; REVEAL Study Group. A randomized noninferiority trial of condensed protocols for genetic risk disclosure of Alzheimer’s disease. Alzheimers Dement. 2015 Oct;11(10):1222-30. doi: 10.1016/j.jalz.2014.10.014. Epub 2014 Dec 9. PubMed 25499536 Haller S, Nguyen D, Rodriguez C, Emch J, Gold G, Bartsch A, Lovblad KO, Giannakopoulos P. Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data. J Alzheimers Dis. 2010;22(1):315-27. doi: 10.3233/JAD-2010-100840. PubMed 20847435 Heckman PR, Wouters C, Prickaerts J. Phosphodiesterase inhibitors as a target for cognition enhancement in aging and Alzheimer’s disease: a translational overview. Curr Pharm Des. 2015;21(3):317-31. Review. PubMed 25159073 Li NC, Lee A, Whitmer RA, Kivipelto M, Lawler E, Kazis LE, Wolozin B. Use of angiotensin receptor blockers and risk of dementia in a predominantly male population: prospective cohort analysis. BMJ. 2010 Jan 12;340:b5465. doi: 10.1136/bmj.b5465. PubMed 20068258 Langbaum JB, Fleisher AS, Chen K, Ayutyanont N, Lopera F, Quiroz YT, Caselli RJ, Tariot PN, Reiman EM. Ushering in the study and treatment of preclinical Alzheimer disease. Nat Rev Neurol. 2013 Jul;9(7):371-81. doi: 10.1038/nrneurol.2013.107. Epub 2013 Jun 11. Review. Erratum in: Nat Rev Neurol. 2013 Aug;9(8):418. PubMed 23752908 Lazarczyk MJ, Hof PR, Bouras C, Giannakopoulos P. Preclinical Alzheimer disease: identification of cases at risk among cognitively intact older individuals. BMC Med. 2012 Oct 25;10:127. doi: 10.1186/1741-7015-10-127. Review. PubMed 23098093 Peters KR, Lynn Beattie B, Feldman HH, Illes J. A conceptual framework and ethics analysis for prevention trials of Alzheimer Disease. Prog Neurobiol. 2013 Nov;110:114-23. doi: 10.1016/j.pneurobio.2012.12.001. Epub 2013 Jan 21. Review. PubMed 23348495 Riedel WJ. Preventing cognitive decline in preclinical Alzheimer’s disease. Curr Opin Pharmacol. 2014 Feb;14:18-22. doi: 10.1016/j.coph.2013.10.002. Epub 2013 Nov 13. Review. PubMed 24565007 Rizk-Jackson A, Insel P, Petersen R, Aisen P, Jack C, Weiner M. Early indications of future cognitive decline: stable versus declining controls. PLoS One. 2013 Sep 9;8(9):e74062. doi: 10.1371/journal.pone.0074062. eCollection 2013. PubMed 24040166 Zhou Y, Tan C, Wen D, Sun H, Han W, Xu Y. The Biomarkers for Identifying Preclinical Alzheimer’s Disease via Structural and Functional Magnetic Resonance Imaging. Front Aging Neurosci. 2016 Apr 27;8:92. doi: 10.3389/fnagi.2016.00092. eCollection 2016. PubMed 27199739 Vernarelli JA, Roberts JS, Hiraki S, Chen CA, Cupples LA, Green RC. Effect of Alzheimer disease genetic risk disclosure on dietary supplement use. Am J Clin Nutr. 2010 May;91(5):1402-7. doi: 10.3945/ajcn.2009.28981. Epub 2010 Mar 10. PubMed 20219963 Vom Berg J, Prokop S, Miller KR, Obst J, Kälin RE, Lopategui-Cabezas I, Wegner A, Mair F, Schipke CG, Peters O, Winter Y, Becher B, Heppner FL. Inhibition of IL-12/IL-23 signaling reduces Alzheimer’s disease-like pathology and cognitive decline. Nat Med. 2012 Dec;18(12):1812-9. doi: 10.1038/nm.2965. Epub 2012 Nov 25. PubMed 23178247 Woodard JL, Seidenberg M, Nielson KA, Smith JC, Antuono P, Durgerian S, Guidotti L, Zhang Q, Butts A, Hantke N, Lancaster M, Rao SM. Prediction of cognitive decline in healthy older adults using fMRI. J Alzheimers Dis. 2010;21(3):871-85. doi: 10.3233/JAD-2010-091693. PubMed 20634590 Xekardaki A, Rodriguez C, Montandon ML, Toma S, Tombeur E, Herrmann FR, Zekry D, Lovblad KO, Barkhof F, Giannakopoulos P, Haller S. Arterial spin labeling may contribute to the prediction of cognitive deterioration in healthy elderly individuals. Radiology. 2015 Feb;274(2):490-9. doi: 10.1148/radiol.14140680. Epub 2014 Oct 7. PubMed 25291458 Links: URL: http://print.ispub.com/api/0/ispub-article/7521  Description: Multidimensional Representation of Concepts as Cognitive Engrams in the Human Brain URL: http://ispub.com/IJH/8/1/9567  Description: Evaluation of Cognitive Impairment URL: https://pharmaceuticalintelligence.com/2013/02/27/ustekinumab-new-drug-therapy-for-cognitive-decline-resulting-from-neuroinflammatory-cytokine-signaling-and-alzheimers-disease/  Description: Ustekinumab New Drug Therapy for Cognitive Decline resulting from Neuroinflammatory Cytokine Signaling and Alzheimer’s Disease   Companies using our curations in promotional material Beckman Coulter Life Science, a $3 Billion a year revenue division of Beckman Coulter, had used our reference in the following promotional material. https://www.beckman.com/resources/sample-type/bio-molecules/post-translational-modification Post Translational Modification Beyond what genes can express While most recent estimates of the human genome’s size fall between 20,000 and 25,000 genes, that number is dwarfed by the human proteome our genome encodes, which comprises more than 1 million proteins. Single genes are responsible for the synthesis of multiple proteins, and one way this is accomplished is through the process of post-translational modification (PTM). Protein PTMs have been shown to play a key role in cellular processes such as differentiation, and regulation of gene expression. PTMs expand the range of gene products possible via the chemical addition or subtraction of functional groups to proteins already synthesized from an mRNA template without instructions for such alterations. These modifications play critical roles in the regulation of a protein’s activity and type of interaction with other molecules such as lipids, nucleic acids, enzymatic cofactors and other proteins. Types of post-translational modification Some of the most common and important PTMs so far identified include: Phosphorylation — The addition of a phosphate group to tyrosine, serine or threonine amino acid residues; accomplished by kinase enzymes; one of the most important and well-studied PTMs that occur within all organisms Methylation — The addition of a methyl group, via methyltransferase enzyme, to an arginine or lysine residue; methylation of histone (a DNA packaging protein) can either activate or repress genes depending on the amino acid modified Acetylation — The addition of an acetyl group to a protein; gene regulation is largely mediated by histone acetylation and deacetylation; acetylation of the p53 gene is essential to its tumor-suppressing activity Glycosylation — The addition of a glycan oligosaccharide to asparagine, serine or threonine residues; involved in antigen recognition and inflammatory response Ubiquitination — The addition of ubiquitin protein to a lysine residue, either singly (monoubiquitination) or in a chain (polyubiquitination); proteins modified in the latter fashion are tagged for degradation Lipidation — The addition of a lipid group to a cysteine or glycine residue; involved in biological regulation, membrane association and apoptosis (programmed cell death) Proteolysis — The splitting of proteins into constituent polypeptides or amino acids; removing an N-terminal methionine residue, for example, can activate an inactive protein Phosphorylation: adding a phosphate to 1 of a protein’s amino acid side chains. Phosphate groups carry 2 negative charges, and their addition to a protein results in a conformational change of the protein’s structure. References Deepthi, Surat P., Ph. D. Reviewed. “Types of Protein Post-Translational Modification.” News-Medical.Net, News-Medical, 10 May 2018, https://www.news-medical.net/life-sciences/Types-of-Protein-Post-Translational-Modification.aspx. “New Human Gene Tally Reignites Debate.” Nature, Macmillan Publishers Limited, part of Springer Nature, 19AD, https://www.nature.com/articles/d41586-018-05462-w. “Overview of Post-Translational Modification | Thermo Fisher Scientific – US.” Thermo Fisher Scientific – US, https://www.thermofisher.com/us/en/home/life-science/protein-biology/protein-biology-learning-center/protein-biology-resource-library/pierce-protein-methods/overview-post-translational-modification.html. “Overview of Posttranslational Modification (PTM) | Leaders in Pharmaceutical Business Intelligence (LPBI) Group.” Leaders in Pharmaceutical Business Intelligence (LPBI) Group, 29 July 2014, https://pharmaceuticalintelligence.com/2014/07/29/overview-of-posttranslational-modification-ptm/. “Posttranslational Modification – an Overview | ScienceDirect Topics.” ScienceDirect.Com | Science, Health and Medical Journals, Full Text Articles and Books., https://www.sciencedirect.com/topics/neuroscience/posttranslational-modification. “Post-Translational Modifications and Quality Control in the Rough ER – Molecular Cell Biology – NCBI Bookshelf.” National Center for Biotechnology Information, https://www.ncbi.nlm.nih.gov/books/NBK21741/. “Protein Phosphorylation vs. Ubiquitination in Drug Development.” GenScript – Make Research Easy – The Leader in Molecular Cloning and Gene Synthesis, Peptide Synthesis, Protein and Antibody Engineering., https://www.genscript.com/protein-phosphorylation-vs-ubiquitination.html.

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