DeepSeek-V3 and Reasoning Model R1: Four Views (a) Explanations (b) The Chinese Perspective (c) DeepSeek Impact on Demand for Inference Chips & Training Chips, and (d) LPBI Group: Expert Content for ML Models in Healthcare, Pharmaceutical, Medical and Life Sciences

January 28, 2025 by 2012pharmaceutical

DeepSeek-V3 and Reasoning Model R1: Four Views (a) Explanations (b) The Chinese Perspective (c) DeepSeek Impact on Demand for Inference Chips & Training Chips, and (d) LPBI Group: Expert Content for ML Models in Healthcare, Pharmaceutical, Medical and Life Sciences

Curator: Aviva Lev-Ari, PhD, RN

With the announcement of DeepSeek on January 27, 2025, it became compelling to cover several aspects of this hot Artificial Intelligence Technology.

This curation has four Parts: 

Part A: Explanations 

Part B: The Chinese Perspective

Part C: DeepSeek potential Impact on Demand for Inference Chips & Training Chips, and

Part D: LPBI Group: Expert Content for ML Models in Healthcare, Pharmaceutical, Medical and Life Sciences

 

Part A: Explanations by Morgan Brown

@morganb

Jan 27   Read on X

🧵 Finally had a chance to dig into DeepSeek’s r1…

Let me break down why DeepSeek’s  AI innovations are blowing people’s minds (and possibly threatening Nvidia’s $2T market cap) in simple terms… 

0/ first off, shout out to @doodlestein who wrote the must-read on this here:

The Short Case for Nvidia StockAll the reasons why Nvidia will have a very hard time living up to the currently lofty expectations of the market.https://youtubetranscriptoptimizer.com/blog/05_the_short_case_for_nvda

1/ First, some context: Right now, training top AI models is INSANELY expensive. OpenAI, Anthropic, etc. spend $100M+ just on compute. They need massive data centers with thousands of $40K GPUs. It’s like needing a whole power plant to run a factory. 

2/ DeepSeek just showed up and said “LOL what if we did this for $5M instead?” And they didn’t just talk – they actually DID it. Their models match or beat GPT-4 and Claude on many tasks. The AI world is (as my teenagers say) shook. 

3/ How? They rethought everything from the ground up. Traditional AI is like writing every number with 32 decimal places. DeepSeek was like “what if we just used 8? It’s still accurate enough!” Boom – 75% less memory needed. 

4/ Then there’s their “multi-token” system. Normal AI reads like a first-grader: “The… cat… sat…” DeepSeek reads in whole phrases at once. 2x faster, 90% as accurate. When you’re processing billions of words, this MATTERS. 

5/ But here’s the really clever bit: They built an “expert system.” Instead of one massive AI trying to know everything (like having one person be a doctor, lawyer, AND engineer), they have specialized experts that only wake up when needed.

[color added by curator, See Part D, below]

6/ Traditional models? All 1.8 trillion parameters active ALL THE TIME. DeepSeek? 671B total but only 37B active at once. It’s like having a huge team but only calling in the experts you actually need for each task. 

7/ The results are mind-blowing:
– Training cost: $100M → $5M
– GPUs needed: 100,000 → 2,000
– API costs: 95% cheaper
– Can run on gaming GPUs instead of data center hardware 

8/ “But wait,” you might say, “there must be a catch!” That’s the wild part – it’s all open source. Anyone can check their work. The code is public. The technical papers explain everything. It’s not magic, just incredibly clever engineering. 

9/ Why does this matter? Because it breaks the model of “only huge tech companies can play in AI.” You don’t need a billion-dollar data center anymore. A few good GPUs might do it. 

10/ For Nvidia, this is scary. Their entire business model is built on selling super expensive GPUs with 90% margins. If everyone can suddenly do AI with regular gaming GPUs… well, you see the problem. 

11/ And here’s the kicker: DeepSeek did this with a team of <200 people. Meanwhile, Meta has teams where the compensation alone exceeds DeepSeek’s entire training budget… and their models aren’t as good. 

12/ This is a classic disruption story: Incumbents optimize existing processes, while disruptors rethink the fundamental approach. DeepSeek asked “what if we just did this smarter instead of throwing more hardware at it?” 

13/ The implications are huge:
– AI development becomes more accessible
– Competition increases dramatically
– The “moats” of big tech companies look more like puddles
– Hardware requirements (and costs) plummet 

14/ Of course, giants like OpenAI and Anthropic won’t stand still. They’re probably already implementing these innovations. But the efficiency genie is out of the bottle – there’s no going back to the “just throw more GPUs at it” approach. 

15/ Final thought: This feels like one of those moments we’ll look back on as an inflection point. Like when PCs made mainframes less relevant, or when cloud computing changed everything.

 AI is about to become a lot more accessible, and a lot less expensive. The question isn’t if this will disrupt the current players, but how fast.

/end 

P.S. And yes, all this is available open source. You can literally try their models right now. We’re living in wild times! 🚀 

Momma, I’m going viral! No substack or gofundme to share but a few things to add/clarify:

1/ The DeepSeek app is not the same thing as the model. Apps are owned and operated by a Chinese corporation, the model itself is open source.

2/ Jevon’s paradox is the counter argument. Thanks papa @satyanadella. Could be a mix shift in chip type, compute type, etc. but we’re constrained by power and compute right now, not demand constrained.

3/ The techniques used are not ground breaking. It’s the combination of them w/the relative model performance that is so exciting. These are common eng techniques that combined really fly in the face of more compute is the only answer for model performance. Compute is no longer a moat.

4/ Thanks to all for pointing out my NVIDIA market cap numbers miss and other nuances – will do better next time, coach. 🫡 

SOURCE

https://threadreaderapp.com/thread/1883686162709295541.html#google_vignette

 

Part B: The Chinese Perspective

© 2025 Jordan Schneider

DeepSeek: The View from China

China’s takes are better than yours

JORDAN SCHNEIDER, IRENE ZHANG, ANGELA SHEN, AND YIWEN JAN 28

 

SOURCE

From: ChinaTalk <chinatalk@substack.com> on behalf of ChinaTalk <chinatalk@substack.com>
Reply-To: ChinaTalk <reply+2ktto4&8t4ds&&6fa8442469b96573268378f7538ff49c28c45589f5811b2a55b30e89ee8ff94d@mg1.substack.com>
Date: Tuesday, January 28, 2025 at 9:54 AM
To: Aviva Lev-Ari <avivalev-ari@alum.berkeley.edu>
Subject: DeepSeek: The View from China

And

https://www.chinatalk.media/p/deepseek-the-view-from-china

https://open.substack.com/pub/chinatalk/p/deepseek-the-view-from-china?r=8t4ds&utm_campaign=post&utm_medium=email

The Mystical DeepSeek. ‘The most important thing about DeepSeek is pushing intelligence’

1. Founder and CEO Liang Wenfeng is the core person of DeepSeek. He is not the same type of person as Sam Altman. He is very knowledgeable about technology.
2. DeepSeek has a good reputation because it was the first to release the reproducible MoE, o1, etc. It succeeded in acting early, but whether or not it did the absolute best remains to be seen. Moving forward, the biggest challenges are that resources are limited and can only be invested in the most high-potential areas. DeepSeek’s research and culture are still strong, and if given 100,000 or 200,000 chips, they might be able to do better.
3. From its preview to its official release, DeepSeek’s model’s long-context capabilities have improved rapidly. DeepSeek’s long-context 20K can be achieved with very conventional methods.
4. The CEO of Scale.ai said that DeepSeek has 50,000 chips, but that is definitely not reality. According to public information, DeepSeek had 10,000 old A100 chips and possibly 3,000 H800 cards before the ban. DeepSeek pays great attention to compliance and has not purchased any non-compliant GPUs, so it should have few chips. The way the United States uses GPUs is too extravagant.
5. DeepSeek focused all its efforts on a single goal and subsequently gave up many things, such as multimodality. DeepSeek is not just serving people, but seeking intelligence itself, which may have been a key factor in its success.
6. In some ways, quant trading can be said to be the business model of DeepSeek. Huanfang (another quantitative investment company founded by Liang Wenfeng) is the product of the last round of machine learning. DeepSeek’s highest priority is to push intelligence. Money and commercialization are not high priorities. China needs several leading AI labs to explore things that can beat OpenAI. Intelligence takes a long time to develop, and has begun to differentiate again this year, so new innovations are bound to result.
7. From a technical perspective, DeepSeek has been instrumental as a training ground for talent.
8. The business model of AI labs in the United States is not good either. AI does not have a good business model today and will require viable solutions in the future. Liang Wenfeng is ambitious; DeepSeek does not care about the model and is just heading towards AGI.
9. Many of the insights from DeepSeek’s paper involve saving hardware costs. On a couple of big dimensions of scaling, DeepSeek’s techniques are able to reduce costs.
10. In the short-term, everyone will be driven to think about how to make AI more efficient. In the long-run, questions about computing power will remain. Demand for compute remains strong and no company has enough.
11. Discussing DeepSeek’s organization:
    1. When investing, we always choose the most advanced talent. But we see from DeepSeek’s model (the team is mostly smart young people who graduated from domestic universities) that a group that coheres well may also gradually advance their skills together. It has yet to be seen whether poaching one person might break DeepSeek’s advantage, but for now this seems unlikely.
    2. While there’s a lot of money in the market, DeepSeek’s core advantage is its culture. The research culture of DeepSeek and ByteDance are similar, and both are critical for determining the availability of funding and long-term viability. Only with an important business model can there be a sustainable culture. Both DeepSeek and ByteDance have very good business models.
12. Why did DeepSeek catch up so fast?
    1. Reasoning models require high-quality data and training. For LLMs or multimodal AI, it’s difficult to catch up with a closed source model from scratch. The architecture of pure reasoning models hasn’t changed much, so it’s easier to catch up in reasoning.
    2. One reason R1 caught up quickly was that the task was not particularly difficult. Reinforcement learning only made the model choices more accurate. R1 did not break through the efficiency of Consensus 32, spending 32 times the efficiency, which is equivalent to moving from deep processing to parallelization, which is not pushing the boundaries of intelligence, just making it easier.

Pioneers vs. Chasers: ‘AI Progress Resembles a Step Function – Chasers Require 1/10th the Compute’

Points 13 – 17

[Points 18-48 was a long technical discussion we’ve machine-translated below]

Why didn’t the other companies take the DeepSeek approach: ‘Models from the big labs need to maintain a low profile’

Points 49, 50

The Divergence and Bets of 2025 Technology: ‘Can We Find Architectures Beyond Transformer?’

Points 51 – 56

Have developers moved from closed-source models to DeepSeek? ‘Not yet’

Points 57 – 62

OpenAI Stargate’s $500B Narrative and Changes in Computing Power Demand

63. The emergence of DeepSeek has led people to question the latest $500B narrative from Nvidia and OpenAI. There’s no verdict yet on compute — and OpenAI’s $500B narrative is their attempt to throw themselves a lifeline.
64. Regarding the doubts about OpenAI’s $500B infrastructure investment: because OpenAI is a commercial company, it could be risky if debt is involved.
65. $500B is an extreme number — likely to be executed over 4 or 5 years. SoftBank and OpenAI are the leading players (the former providing capital, the latter technology) — but SoftBank’s current funds can’t support $500B; rather SoftBank is using its assets as collateral. OpenAI, meanwhile, isn’t very cash-rich either, and other AI companies are more technical participants than they are funding providers. So it will be a struggle to fully realize the $500B vision.
66. OpenAI’s $500B computing power makes sense: during the exploration phase, the cost of trial and error is high, with both human and investment costs being substantial. But although the path isn’t clear and getting from o1 to R1 won’t be easy, at least we can see what the finish line looks like: we can track the intermediate markers, and from day one, aim for others’ proven end states; this gives us a better bearing on our progress. Being at the frontier exploring the next generation is most resource-intensive. The followers don’t bear exploration costs — they’re always just following. If Google/Anthropic succeed in their exploration areas, they might become the frontier company.
67. In the future, Anthropic might replace all their inference with TPU or AWS chips.
68. Domestic Chinese companies were previously constrained by computing power, but now it’s proven that the potential technical space is vast. For more efficient models, we might not need especially large cards — we can provide relatively customized chips that can be adapted for compatibility with AMD and ASIC. From an investment perspective, Nvidia’s moat is very high, but ASIC will have yet greater opportunities.
69. The DeepSeek situation isn’t really about compute — it’s about America realizing China’s capabilities and efficiency.DeepSeek isn’t Nvidia’s vulnerability; Nvidia will grow as long as AI grows. Nvidia’s strength is its ecosystem, which has been built up over a long time. Indeed, when technology develops rapidly, the ecosystem is crucial. The real crisis comes, though, when technology matures like electricity: it becomes commoditized; then, everyone will focus on products, and many ASIC chips will emerge for specific scenario optimization.

 

Impact on the Secondary Market: ‘Short-term sentiment is under pressure, but the long-term narrative continues’

Points 70 – 74

Open-Source vs Closed Source: ‘If capabilities are similar, closed source will struggle.’

Points 75 – 78

The Impact of DeepSeek’s Breakthrough: ‘Vision Trumps Technology’

79. DeepSeek’s breakthrough made the outside world realize China’s AI strength. Previously, outsiders thought China’s AI progress lagged America by two years, but DeepSeek shows the gap is actually 3 to 9 months, and in some areas, even shorter.
80. When it comes to technologies and sectors that America has historically blocked China from accessing, if China can break through nonetheless, those sectors ultimately become highly competitive. AI might follow this pattern — and DeepSeek’s success may well prove this.
81. DeepSeek didn’t suddenly explode. R1’s impressive results reverberated throughout America’s entire AI establishment.
82. DeepSeek stands on the shoulders of giants — but exploring the frontier still requires much more time and human capital cost. R1 doesn’t mean that future training costs will decrease.
83. AI explorers definitely need more computing power; China, as a follower, can leverage its engineering advantages. How Chinese large-model teams use less computing power to produce results, thereby having some definite resilience — or even doing better — might end up being how the US-China AI landscape plays out in the future.
84. China is still replicating technical solutions; reasoning was proposed by OpenAI in o1, so the next gap between various AI labs will be about who can propose the next reasoning. Infinite-length reasoning might be one vision.
85. The core difference between different AI labs’ models lies not in technology, but in what each lab’s next vision is.
86. After all, vision matters more than technology.

Technical Discussion

There was a deep technical discussion in the article that we’ve machine-translated below.

Technical Detail 1: Supervised Fine-Tuning (SFT). ‘No need for SFT on the reasoning level’

Points 18 – 27

Technical Detail 2: Data. ‘DeepSeek values data annotation’

Points 28 – 30

Technical Detail 3: Distillation. ‘The limit of distillation is that model diversity drops

Points 31 – 43

Technical Detail 4: Process Reward. ‘The upper limit of process reward is human, but the upper limit of outcome supervision is the model itself.’

Points 44 – 48

 

SOURCE of the Chinese Perspective

https://www.chinatalk.media/p/deepseek-the-view-from-china?utm_source=substack&publication_id=4220&post_id=155916148&utm_medium=email&utm_content=share&utm_campaign=email-share&triggerShare=true&isFreemail=true&r=8t4ds&triedRedirect=true

 

Part C: DeepSeek Impact on Demand for “Inference Chips” and “Training Chips”

 

Watch Full Interviews with Ark’s Cathie Wood

 

• Ark’s Wood on DeepSeek, AI, Crypto, Trump | Cathie Wood Full Interview

https://youtu.be/EKELCEW8lNo?si=Zri9QqcMHsESgO8N

 

• Cathie Wood Talks DeepSeek Lessons, Musk, Driverless Cars & UK

https://youtu.be/aThejSuMX-I?si=e9uM7TpoQ1Neb-cT

 

“Inference Chips” and “Training Chips”: Technology explained

 

AI Chips Explained: Training vs. Inference Processors Unveiled

https://www.friendsofthemetaverse.com/blog/ai-chips-explained-training-vs-inference-processors-unveiled

 

Inference chips and training chips are both types of AI chips that serve different purposes. Training chips are used to develop AI models, while inference chips are used to deploy those models in real-world applications. 

An “inference chip” is designed to efficiently execute a trained AI model on new data to make predictions in real-time, prioritizing low latency and power consumption, while a “training chip” is optimized for the computationally intensive process of initially training a machine learning model, requiring high processing power and memory bandwidth, often at the cost of power efficiency; essentially, inference chips are for “applying” the learned model, while training chips are for “learning” the model itself. 

Key Differences: 

• Focus:

Inference chips prioritize speed and energy efficiency to run predictions on new data in real-time, whereas training chips focus on maximum computational power to process large datasets during model development. 

• Application:

Inference chips are typically used in edge devices like smartphones and IoT gadgets where power consumption is critical, while training chips are usually deployed in data centers for large-scale model training. 

• Architecture Design:

Inference chips often have optimized architectures for specific operations like matrix multiplication, while training chips may have more flexible architectures to handle diverse training tasks. 

Example: 

• Inference Chip:

A dedicated AI chip in a smartphone that quickly identifies objects in a camera image. 

• Training Chip:

A high-performance GPU in a cloud computing platform used to train a complex language model on massive amounts of text data. 

 

SOURCE

https://www.google.com/search?q=training+chips+vs+inference+chips&oq=Training+Chips+vs+Inference+chips&gs_lcrp=EgZjaHJvbWUqBggAEEUYOzIGCAAQRRg7Mg0IARAAGIYDGIAEGIoFMg0IAhAAGIYDGIAEGIoFMg0IAxAAGIYDGIAEGIoFMgoIBBAAGIAEGKIEMgoIBRAAGIAEGKIEMgoIBhAAGIAEGKIEMgoIBxAAGIAEGKIE0gEKMTc2OTVqMGoxNagCCLACAQ&sourceid=chrome&ie=UTF-8

 

Training vs. Inference (But, Really: Training Then Inference)

To recap: the AI training stage is when you feed data into your learning algorithm to produce a model, and the AI inference stage is when your  algorithm uses that training to make inferences from data. Here’s a chart for quick reference: 

Table	Inference
Feed training data into a learning algorithm	Apply the model to the inference data
Produces a model comprising code and data	Produces output data
One time-ish (Requirement to retain training data in case of re-training.)	Often continuous

Inference
Apply the model to the inference data
Produces output data
Often continuous

The difference may seem inconsequential at first glance, but defining these two stages helps to show implications for AI adoption particularly with businesses. That is, given that it’s much less resource intensive (and therefore, less expensive), it’s likely to be much easier for businesses to integrate already-trained AI algorithms with their existing systems. 

And, as always, we’re big believers in demystifying terminology for discussion purposes. Let us know what you think in the comments, and feel free to let us know what you’re interested in learning about next.

SOURCE

AI 101: Training vs. Inference

November 9, 2023 by Stephanie Doyle

https://www.backblaze.com/blog/ai-101-training-vs-inference/

 

r/AMD_Stock

CryptOHFrank

“AI is really two markets, training and inference. Inference is going to be 100 times bigger than training. Nvidia is really good at training but very miscast at inference.” – Chamath Palihapitiya

Let’s discuss.

Below I layout AMD investor relevant time stamps:

7:35 – Meta AI business strategy

10:00 – Open source impact on LLM marketplace

12:10 – Telecom analogy (capex discussion)

16:35 – Closed source model economic viability

19:50 – Meta overspend on training (Nvidia)

SOURCE

https://www.reddit.com/r/AMD_Stock/comments/1cf765y/ai_is_really_two_markets_training_and_inference/

 

Part D: LPBI Group: Expert Content for ML Models in Healthcare, Pharmaceutical, Medical and Life Sciences

 

LPBI Group’s Journal http://pharmaceuticalintelligence.com had a fully developed ontology for the Healthcare, Pharmaceutical, Medical and Life Sciences domains of knowledge.

The ontology comprises of +750 categories of research. Each category consists of multiple scientific articles that were curated by domain knowledge experts in the fields of Healthcare, Pharmaceutical, Medical and Life Sciences.

• Each article is a token, a Non Fungible Token (NFT) = a mutually exclusive scientifically written piece that makes a Prior Art artifact from the intellectual property law perspective and copyright law.
• Each category of research is “An expert system knowledge base”
• Examples: The last column in this table represents the number of articles in this category of research
• Each curation is written by an expert in this domain, and
• Each one of the 469 articles in Example #1, in this category of research had been assigned THIS category by an EXPERT in this domain. 
• The universe of 469 articles represents an “Expert System Knowledge Base” in the domain of biological networks, gene regulation and evolution
• Example #1 comprises of 469 NFTs
• Example #2 comprises of 1,022 NFTs
• Example #3 comprises of 681 NFTs
• An ML model can be trained on the content of a Master file that included the content of all the 469 article files mentioned in Example #1 – that process is performed on Training Chips
• The outcomes of the model involve the phase of Inference. That process is performed on Inference Chips.

 

Example #1: 469 articles in Biological Networks, Gene Regulation and Evolution

Expert, Author, Writer (EAW): Dr. Larry Bernstein
Degree: BS, MS, MD
Specialty: Clinical Pathology
e-Mail: larry.bernstein@gmail

N = 469

Biological Networks, Gene Regulation and Evolution

 

Points (a) to (f) are applicable as well to Example #2, and #3, below. Or for any other category of research from the universe of +750 categories that consists of +50 articles

 

Example #2: 1,022 articles in CANCER BIOLOGY & Innovations in Cancer Therapy

Contributor EAW: Prabodh kumar Kandala, PhD Specialty: Preclinical Oncology, Prabodh.kandala@gmail.com

Contributor EAW: Ritu Saxena, PhD
ritu.uab@gmail.com

Contributor EAW: Dr. Larry Bernstein
Degree: BS, MS, MD
Specialty: Clinical Pathology
e-Mail: larry.bernstein@gmail.com

Contributor EAW: Stephen J. Williams
Degree: Ph.D. Pharmacology
Specialty: cancer pharmacology, ovarian specialty
e-Mail: sjwilliamspa@comcast.net
Phone: 215-487-0259

Contributor EAW: Tilda Barliya
Degree: PhD
Specialty: Cancer biology, cell biology, nanotechnology and drug delivery
e-Mail: tildabarliya@gmail.com
Phone: +972-50-8622289

N = 1,022

CANCER BIOLOGY & Innovations in Cancer Therapy

 

Example #3: 681 articles in Frontiers in Cardiology and Cardiovascular Disorders

EAW: Aviva Lev-Ari, PhD, RN

EAW: Justin D. Pearlman
Degree: MD ME PhD MA FACC
Specialty: Internal Medicine, Cardiology, Cardiovascular Radiology, Image Processing, Computer Science, Electronic Records
jdpmdphd@gmail.com
Phone:617-894-6888

N = 681

Frontiers in Cardiology and Cardiovascular Disorders

 

Respectively, the categories of research are

• “Expert systems domain knowledge bases”
• They are ready for ML model development in each of the domains that a category comprises more than 50 articles.
• Total number of categories of research in the Journal’s Ontology N = 757 on 1/28/2025

Share this:

• X
• LinkedIn
• Facebook
• Print
• Email

Like this:

Like Loading...

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence Applications in Health Care, Deep Learning, Machine Learning, Natural Language Processing (NLP) | Leave a Comment »

Recipients of the 2024 National Medal of Technology and Innovation, administered by President Joe Biden and Laureates of the National Medal of Science, administered by NSF

January 13, 2025 by 2012pharmaceutical

Recipients of the 2024 National Medal of Technology and Innovation, administered by President Joe Biden and Laureates of the National Medal of Science, administered by NSF

Reporter: Aviva Lev-Ari, PhD, RN

NSF congratulates recipients of the prestigious National Medal of Science and National Medal of Technology and Innovation awards

January 7, 2025

President Joe Biden revealed the newest honorees of the recipients of the  National Medal of Science and the National Medal of Technology and Innovation. The laureates were honored during a prestigious ceremony at the White House last Friday. These esteemed awards celebrate groundbreaking contributions that have advanced knowledge, driven progress and tackled the world’s most critical needs while underscoring the vital role of research and creativity in fostering a brighter, more sustainable future.

Among this year’s honorees are several distinguished individuals with ties to NSF. John Dabiri, Feng Zhang and Jennifer Doudna are former recipients of NSF’s prestigious Alan T. Waterman Award, which recognizes exceptional early-career scientists and engineers for their transformative contributions. Keivan Stassun, a current member of the National Science Board and a former member of NSF’s Committee for Equal Opportunity in Science and Engineering, has been a leader in advancing diversity, equity and inclusion in STEM.

These honorees exemplify NSF’s enduring role in fostering groundbreaking research, nurturing talent and driving innovation across the scientific and engineering enterprise. Among the recipients, NSF has funded, at some point in their careers, all 14 recipients of the National Medal of Science and eight of the nine recipients of the National Medal of Technology and Innovation.

SOURCES

https://new.nsf.gov/honorary-awards/national-medal-science

White House Briefing

https://www.whitehouse.gov/briefing-room/statements-releases/2025/01/03/president-biden-honors-nations-leading-scientists-technologists-and-innovators/

 

The 2024 National Medal of Technology and Innovation (NMTI) Laureates were honored and celebrated at the White House on Friday, January 3 for their trailblazing achievements in science, technology, and innovation.

Nine individuals and two companies were recognized for their groundbreaking accomplishments, ranging from the “camera-on-a-chip” technology integrated into most smartphones today, to improvements in mammogram and other optoelectric technologies that can better detect breast cancer, to the mRNA vaccines that treated a global pandemic, and more.

Acting Under Secretary of Commerce for Intellectual Property and Acting Director of the U.S. Patent and Trademark Office (USPTO) Derrick Brent delivered remarks at the special medaling ceremony of the NMTI, which is administered by the USPTO. Director of the White House Office of Science and Technology Policy Arati Prabhakar presented the Laureates with their NMTI medals alongside 14 Laureates of the National Medal of Science, administered by the National Science Foundation (NSF).

“These medals celebrate some of your greatest achievements,” said Acting USPTO Director Brent in his remarks. “Yet, they also bestow upon you a unique responsibility: mentoring and inspiring the next generation of innovators. Paying it forward is our obligation to history, and to our future.”

Recipients of the 2024 National Medal of Technology and Innovation

Martin Cooper, Illinois Institute of Technology and Dyna LLC

For inventing the handheld cellular phone and revolutionizing worldwide communications. Martin Cooper delivered breakthroughs for cellular telephone and network technologies that have dramatically altered the world as we know it—changing our sense of proximity to others around the globe, the way we perceive ourselves, and our universe of possibilities.

Jennifer A. Doudna, Innovative Genomics Institute

For development of the revolutionary CRISPR-Cas9 gene editing technology, with widespread applications in agriculture and health research. Jennifer Doudna’s innovations are fundamentally transforming our collective health and well-being and have contributed to the development of treatments for sickle cell disease, cancer, type 1 diabetes, and more.

Eric R. Fossum, Dartmouth College

For inventing world-changing “camera-on-a-chip” technology that has turned billions of phones into cameras and transformed everyday life. When NASA needed smaller cameras to take into space, Eric Fossum developed a groundbreaking image sensor and then worked to use it in medicine, business, security, entertainment, and more, while also mentoring legions of young entrepreneurs pushing the bounds of innovation.

Paula T. Hammond, Massachusetts Institute of Technology

For groundbreaking research in nanoscale engineering. Paula Hammond pioneered novel materials that have revolutionized how we deliver cancer drugs to cancer patients and how we store solar energy. An inventor and mentor, Paula has paved the way for a more diverse, inclusive scientific workforce that taps into the full talents of our nation.

Kristina M. Johnson, Johnson Energy Holdings, LLC

For pioneering work that has transformed optoelectronic devices, 3D imaging, and color management systems. Kristina Johnson has channeled her ingenuity and optimism into developing technologies that have improved processes for mammograms and pap smears, promoted clean energy, elevated the entertainment industry, and more—while working to expand the field of STEM for all Americans.

Victor B. Lawrence, Bell Labs and Stevens Institute of Technology

For a lifetime of prolific innovation in telecommunications and high-speed internet technology. Victor Lawrence has dedicated his life to expanding the realm of possibilities worldwide. By bringing fiber-optic connectivity to the African continent and improving global internet accessibility, he has enhanced the security, opportunity, and well-being of people around the world.

David R. Walt, Harvard Medical School

For setting a new gold standard in genetic analysis that is transforming medical research, care, and well-being. David Walt pioneered the use of microwell arrays to analyze thousands of genes at once and detect single molecules, making DNA sequencing exponentially more accurate and affordable, and promising simple biomarker blood tests that may revolutionize our approach to cancer and other conditions—giving people renewed hope.

Paul G. Yock, Stanford University 

For innovations in interventional cardiology. Paul Yock’s visionary work understanding the human heart is applied around the world today to improve patient care and save countless lives. His creation of the Biodesign approach to training future leaders of biotechnology and health care ensures his insights and experience will benefit generations to come.

Feng Zhang, Massachusetts Institute of Technology 

For development of the revolutionary CRISPR-Cas9 gene editing technology, with widespread applications in agriculture and health research. Feng Zhang’s innovations are fundamentally transforming our collective health and well-being and have contributed to the development of treatments for sickle cell disease, cancer, type 1 diabetes, and more.

National Medal of Technology and Innovation Organization Recipients

Moderna, Inc.

For saving millions of lives around the world by harnessing mRNA vaccine technology to combat a global pandemic. In 2020, Moderna rapidly developed and deployed a COVID-19 mRNA vaccine that was essential to ending the COVID-19 pandemic, opening new frontiers in immunology and advancing America’s leadership in research innovation.

Pfizer Inc.

For saving millions of lives around the world by harnessing mRNA vaccine technology to combat a global pandemic. In 2020, Pfizer rapidly developed and deployed a COVID-19 mRNA vaccine that was essential to ending the COVID-19 pandemic, opening new frontiers in immunology and advancing America’s leadership in research innovation.

SOURCES

https://www.uspto.gov/about-us/news-updates/2024-national-medal-technology-and-innovation-laureates-honored-white-house

National Medal of Technology and Innovation (NMTI)

https://www.uspto.gov/learning-and-resources/ip-programs-and-awards/national-medal-technology-and-innovation-nmti

 

On January 3, 2025, President Biden honored the nation’s leading scientists, technologists, and innovators

Jennifer Doudna, professor of chemistry and molecular and cell biology, and a Nobel Laureate in chemistry, has been honored by President Biden with the National Medal of Technology and Innovation as a pioneer of CRISPR gene editing. This award is one of the nation’s highest honors for exemplary achievement and leadership in science and technology. Read the White House briefing(link is external) to read about Doudna and the other recipients of the National Medal of Technology and Innovation.

SOURCE

https://chemistry.berkeley.edu/news/doudna-receives-national-medal-technology-and-innovation

14 Laureates of the National Medal of Science, administered by the National Science Foundation (NSF).

• Huda Akil: University of Michigan
• Barry Barish: California Institute of Technology
• Gebisa Ejeta: Purdue University
• Eve Marder: Brandeis University
• Gregory Petsko: Harvard Medical School and Brigham and Women’s Hospital
• Myriam Sarachik: The City College of New York
• Subra Suresh: Massachusetts Institute of Technology and Brown University
• Shelley Taylor: UCLA
• Sheldon Weinbaum: The City College of New York
• Richard B. Alley: Pennsylvania State University
• Larry Martin Bartels: Vanderbilt University
• Bonnie L. Bassler: Princeton University
• Angela Marie Belcher: Massachusetts Institute of Technology
• Helen M. Blau: Stanford University

The 2024 National Medal of Science recipients made contributions in many fields, including astronomy, biology, and engineering. 

Astronomy 

• Wendy Freedman

  University of Chicago astronomer who studied the Hubble constant and the expansion of the universe
• Keivan Stassun

  Vanderbilt University astrophysicist who studied star formation and exoplanets

Biology

• Teresa Woodruff

  Michigan State University professor who studied ovarian biology, fertility preservation, and women’s health 
• Helen Blau

  Stanford University researcher who contributed to the development of gene editing techniques 

Engineering

• Ingrid Daubechies

  Duke University mathematician who developed wavelet theory, which improved signal processing and image compression 
• John Dabiri

  California Institute of Technology aeronautics engineer who studied fluid mechanics and biomechanics, particularly in designing wind turbines 
• Emery Brown

  Massachusetts General Hospital professor who studied the effects of anesthesia on the brain 

The National Medal of Science is the highest science award in the United States. The NSF administers the award, which is selected by a presidential committee.

SOURCE

The National Medal of Science

https://new.nsf.gov/honorary-awards/national-medal-science

 

ADDENDUM

Established in 1959, the National Medal of Science is administered for the White House by the National Science Foundation. The medal recognizes individuals who have made outstanding contributions to science and engineering.

The National Medal of Technology and Innovation was established in 1980 and is administered for the White House by the U.S. Department of Commerce’s Patent and Trademark Office. It recognizes individuals and organizations for their lasting contributions to America’s competitiveness and quality of life and helped strengthen the nation’s technological workforce.

Share this:

• X
• LinkedIn
• Facebook
• Print
• Email

Like this:

Like Loading...

Posted in Intellectual Property, Intellectual Property, Innovations, Commercialization, Investment in technological breakthrough, Interviews with Scientific Leaders | Leave a Comment »

SNU-BioTalk 2025: Symphony of Cellular Signals in Metabolism and Immune Response – International Conference at Sister Nivedita University, Kolkata, India on 16 & 17 January 2025

November 26, 2024 by Dr. Sudipta Saha

SNU-BioTalk 2025: Symphony of Cellular Signals in Metabolism and Immune Response – International Conference at Sister Nivedita University, Kolkata, India on 16 & 17 January 2025

Joint Convenor: Dr. Sudipta Saha (Member of LPBI since 2012)

About the Conference:

The International Conference on ‘Symphony of Cellular Signals in Metabolism and Immune Response’ focuses on the complex signalling pathways governing cellular functions in health and disease. It will explore the cellular mechanisms that regulate metabolism, immune responses, and survival, highlighting advances in medical science and biotechnology. Bringing together leading experts and emerging researchers, the conference will feature keynote lectures, panel discussions, research presentations, and interactive sessions, all designed to foster collaboration and innovation. By promoting an exchange of ideas, the event aims to drive transformative insights and solutions that impact human health and sustainable healthcare practices.

The conference will also be livestreamed on YouTube and Facebook

This programme will also host I-STEM: Indian Science, Technology and Engineering facilities Map (I-STEM) is a dynamic and interactive national portal for research cooperation.

Thrust areas:

• Intracellular signalling processes of cellular metabolism
• Signalling pathways in physiological and pathological processes
• Signalling in innate and adaptive immunity

Conference Webpage: https://www.snuniv.ac.in/snu-biotalk-2025/

NU-BioTalk 2025 Abstract Submission Form: https://forms.gle/ygdGqtuBGa7DEhDFA

SNU-BioTalk 2025 Registration Form: https://forms.gle/unasPpByLmYwrRBM6

Programme Schedule:

YouTube Links of Live Telecast:

Day 1:

Day 2:

Media:

Newspaper:

The Telegraph – Click to View

 

Abstract Book

Scan to Download:

Click: 

Abstract Book

Share this:

• X
• LinkedIn
• Facebook
• Print
• Email

Like this:

Like Loading...

Posted in Adaptive Immune Response to Biomaterials and Tissue Repair, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Autoimmune Inflammatory DIseases, Calcium Signaling, CANCER BIOLOGY & Innovations in Cancer Therapy, cancer metabolism, Cancer Vaccines: Targeting Cancer Genes for Immunotherapy, Cancer-Immune Interactions, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, CNS-Immune Interface, combination immunotherapies., Competition in regenerative stem cell science, Conference Coverage with Social Media, Diagnostic Immunology, Human Immune System in Health and in Disease, Human neural progenitor cells, Immune Engineering, Immune-Mediation (independent immunopathology: lung and reticuloendothelial system), Immuno-Oncology & Genomics, Immunodiagnostics, Immunology, Immunotherapy, Infectious Disease Immunodiagnostics, Innovation in Immunology Diagnostics, Metabolic Immuno-Oncology, Monoclonal Immunotherapy, NK Cell-Based Cancer Immunotherapy, Population Health Management, Population Health Management, Genetics & Pharmaceutical, Population Health Management, Nutrition and Phytochemistry, Protection Against Autoimmune Disease, REAL TIME Conference Coverage Twitter's Hashtags and Handles per Presentation/session, Scientific & Biotech Conferences: Press Coverage, Signaling, Signaling & Cell Circuits, single cell sequencing, Stem Cells for Regenerative Medicine, Synthetic Immunology: Hacking Immune Cells, Tolerance-inducing Autoimmune Disease Therapeutics, Uncategorized | Tagged Cell, conference, I-STEM, Immunology, India, International, Kolkata, Metabolism, signalling, Sister Nivedita University | Leave a Comment »

Real Time Coverage Morning Session on Precision Oncology: Advancing Precision Medicine Annual Conference, Philadelphia PA November 2 2024

November 4, 2024 by sjwilliamspa

Real Time Coverage Morning Session on Precision Oncology: Advancing Precision Medicine Annual Conference, Philadelphia PA November 2 2024

Reporter: Stephen J. Williams, Ph.D.

9:20-9:50

How Can We Close the Clinical Practice Gaps in Precision Medicine?

Susanne Munksted, Diaceutics

Studies are showing that genetic tests are being ordered at a sufficient rate however it appears there are problems in interpretation and developing treatment plans based on omics testing results

 

• 30 % of patients in past and now currently half of all patients are not being given the proper treatment based on genomic testing results (ASCO)
• E.g. only 1.5% with NTRK fusions received a NTRK based therapy (this was > 4000 patients receiving wrong therapy)
• A lung oncologist may only see one patient with NTRK fusion in three years

 

Precision Medicine Practice Gaps

48% of oncologist surveyed  agreed pathologist needs to be more informed and relevant in the decision making process with regard to tests needing to be ordered

95% said need to flip cost issues ; what does it cost not to get a test … i.e. what is the cost of the wrong therapy

We need a new commercialization model for therapeutic development for this new era of “n of one” patient

9:50-10:15

Implementation of a CLIA-based Reverse Phase Protein Array Assay for Precision Oncology Applications: Proteomics and Phosphoproteomics at the Bedside (CME Eligible)

Emanuel Petricoin, George Mason University

There are some tumor markers approved by FDA that cant just be measured by NGS and are correlated with a pathologic complete response

 

• Many point mutations will have no actionable drug
• Many alterations are post-genomic meaning there is a post translational component to many prognostic biomarkers
• Prevalence of point mutation with no actionable mutation is a limit of NGS
• It is important to look at phospho protein spectrum as a potential biomarker

 

Reverse phase protein proteomic analysis

• Made into CLIA based array
• They trained centers around the US on the technology and analysis
• Basing proteomics or protein markers by traditional IHC requires much antibody validation so if the mass spectrometry field can catch up it would be very powerful
• With multiple MRM.MS there is too low abundance of phosphoproteins to allow for good detection

 

They  conducted the I-SPY2 trial for breast cancer and determining if phosphoproteins could be a good biomarker panel

• They found they could predict a HER2 response better than NGS
• There were patients who were predicted HER2 negative that actually had an activated HER2 signaling pathway by proteomics so NGS must have had a series of false negatives
• HER2 co phosphorylation predicts pathologic complete response and predicts therapy by herceptin
• They found patients classified as HER2 negative by FISH were HER2 positive by proteomics and had HER2 activation

10:15-11:10

Liquid Biopsy MRD to Escalate or De-escalate Therapy (CME Eligible)

Adrian Lee, UPMC

Marija Balic, UPMC

Howard McLeod, Utah Tech University

Muhammed, Murtaza, University of Wisconsin-Madison

 

11:15-11:25  PRODUCT PRESENTATION  204A

SpaceIQ™ – Powering Next Generation Precision Therapeutics with AI-Driven Spatial Biomarkers

Dusty Majumdar, PredxBio 

Single Cell and Spatial Omics

 

• Single cell transcriptomics technology have been scaled up very nicely over the past ten years
• Spatial informatics field is lacking in innovations
• Can get a terabyte worth of data from analysis of one slide

11:25-11:35  PRODUCT PRESENTATION  204C

10x Genomics

11:40-12:35

Transcriptomics and AI in Transforming Precision Diagnosis

Maher Albitar, Genomic Testing Cooperative

Transciptomica and AI:Transforming Precision diagnosis

-The Genomics Testing Coopererative at www.genomictestingcooperative.com

 

Advantages of transcriptomics

– mutation frequency and allele variant detection now at 80% (higher sensitivity in mutation detection)

 

– transcriptomics has good detection of chromosomal translocations

– great surrogate for IHC and detect splicing alterations

– can use AI to predict % of PDL1 in tumor cells versus immune cells

– they have developed a software UMAP (uniform manifold approximation and projection) to supervise cluster analysis

– the group has used AI to predict prognosis and survival using transcriptomics data

Marija Balic, UPMC

Andrew Pecora, Hackensack University Medical Center 

12:35-1:00

The Impact of Multi-Omics in the Context of the APOLLO-2 Moonshot Program (CME Eligible)

Thomas Conrads, Inova Fairfax Hospital

 

 

Share this:

• X
• LinkedIn
• Facebook
• Print
• Email

Like this:

Like Loading...

Posted in Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Medicine - Applications in Therapeutics, Big Data, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Genomics, Clinical Genomics, Genomic Expression, Genomic Testing: Methodology for Diagnosis, Personalized and Precision Medicine & Genomic Research, Proteomics, REAL TIME Conference Coverage Twitter's Hashtags and Handles per Presentation/session | Tagged 10X genomics, 10X Genomics Pacific Biosciences, functional proteomics, liquid biopsy, Proteomics, spatial genomics, therapeutic proteomics, Transcriptomics | Leave a Comment »

Real Time Coverage Advancing Precision Medicine Annual Conference, Philadelphia PA November 1,2 2024

November 1, 2024 by sjwilliamspa

Real Time Coverage Advancing Precision Medicine Annual Conference, Philadelphia PA November 1,2 2024

Reporter: Stephen J. Williams, Ph.D.

Source: https://www.advancingprecisionmedicine.com/apm-annual-conference-and-exhibition-in-philadelphia/ 

This event will be covered by the LPBI Group on Twitter.  Follow on

@Pharma_BI

@StephenJWillia2

@Aviva1950

@AdvancingPM

using the following meeting hashtags

#AdvancingPM #precisionmedicine

The Advancing Precision Medicine (APM) Annual Conference 2024 will take place at the Pennsylvania Convention Center in Philadelphia,  November 1-2, 2024. Located in the heart of the biopharma ecosystem and with easy access to some of the most renowned academic and research institutions in the world, the APM Annual Conference 2024 will attract all segments of the precision medicine landscape.

The event will consist of two parallel tracks composed of keynote addresses, panel discussions and fireside chats which will encourage audience participation. Over the course of the two-day event leaders from industry, healthcare, regulatory bodies, academia and other pertinent stakeholders will share an intriguing and broad scope of content.

his event will consist of three immersive tracks, each crafted to explore the multifaceted dimensions of precision medicine. Delve into Precision Oncology, where groundbreaking advancements are reshaping the landscape of cancer diagnosis and treatment. Traverse the boundaries of Precision Medicine Outside of Oncology, as we probe into the intricate interplay of genetics, lifestyle, and environment across a spectrum of diseases and conditions including rare disease, cardiology, ophthalmology, and neurodegenerative disease. Immerse yourself in AI for Precision Medicine, where cutting-edge technologies are revolutionizing diagnostics, therapeutics, and patient care. Additionally, explore the emerging frontiers of Spatial Biology and Mult-Omics, where integrated approaches are unraveling the complexities of biological systems with unprecedented depth and precision.

Whether you are a seasoned researcher, a dedicated clinician, or a visionary industry professional, this conference serves as a vibrant hub of knowledge exchange, collaboration, and innovation. Elevate your expertise, expand your network, and chart the course of your career trajectory amidst a community of like-minded individuals.  Join us as we embark on this transformative journey, where the possibilities are as limitless as the potential of precision medicine itself.

Agenda – What’s on when

Day 1 – Friday, Nov 1

Day 2 – Saturday, Nov 2

November 01, 2024

7:30 – 8:25

Registration and Check-in          Meeting Room 203          Philadelphia Convention Center

8:25 – 8:30

Welcome and Introduction

8:30 – 9:00

Opening Keynote

Advancing Precision Medicine in the Prevention and Treatment of Cardiometabolic Disease (CME Eligible)

Daniel Rader, Penn Medicine and Children’s Hospital of Philadelphia

9:00 – 10:20

9:00-10:20

Diagnosis to Treatment – A Case Study in Non Small Cell Lung Cancer

Moderator: Jason Crites, Assurance Health Data

Miriam Bredella, NYU Lagone Health

Rob Dumanois, Thermo Fisher Scientific

Joe Lennerz, BostonGene

9:00-9:20

Scaling Precision Therapeutic Development for Rare Disease (CME Eligible)Landscape Overview (CME Eligible)

Marni Falk, CHOP

9:20-9:40

Prognostic Biomarkers and Preclinical Modeling – the Cost of Inaction (CME Eligible)

Stephen Ekker, University of Texas at Austin

9:40-10:00

Regulatory Perspective on Rare Disease Drug Approvals and Efficacy Endpoints (CME Eligible)

Christine Nguyen, FDA

10:00-10:20

FDA’s Regulatory Flexibility Beyond One Positive Adequate & Well-Controlled Trial (CME Eligible)

Frank Sasinowski, Hyman, Phelps & McNamara

10:20 – 10:50

Networking, Exhibits and Product Presentations

10:25-10:35  PRODUCT PRESENTATION  204C

The Genexus Integrated Sequencer System:
NGS Results in 24 hours for Oncology Genomic Profiling

Jeff Smith,  Thermo Fisher Scientific

10:35-10:45  PRODUCT PRESENTATION  204A

Shifting the Paradigm in Patient Management with MRD Testing: Why Evidence-Generated Performance and Experience is Key

Karen Lin, Natera

10:50 – 12:50

10:50-11:50

Who Needs Liquid Biopsy? Opportunities to Increase Access and Improve Outcomes

Nicole St. Jean, GSK

Phil Febbo,  Veracyte, Inc.

Andrea Ferreira-Gonzalez, Virginia Commonwealth University

Lauren Leiman, BloodPAC

Nicole Sheahan, Global Colon Cancer Association

11:50-12:50

Advancing Digital Pathology and Precision Medicine – Where Are We Now?

Shruti Mathur, Genentech

Luke Benko, Roche Diagnostics

Kimberly Gasuad, JK Life Sciences

Eric Walk, PathAI

10:50-11:10

Real World Data vs Multi Modal Omics Data for Therapeutic Discovery (CME Eligible)

Adam Resnick, CHOP

11:10-11:30

An Academic Perspective on Rare Disease Target Discovery to Commercial Treatment Development (CME Eligible)

Hakon Hakonarson, CHOP

11:30-11:50

NCATS Perspective on Success and Failures of Drug Repurposing for Rare Disease (CME Eligible)

PJ Brooks, NIH

11:50-12:10

Pharma Perspective and Realities (CME Eligible)

Sundeep Dugar, Rarefy Therapeutics

12:10-12:50

A Panel Discussion: Scaling Precision Therapeutic Development for Rare Disease (CME Eligible)

Marni Falk, CHOP

Stephen Ekker, University of Texas at Austin

Christine Nguyen, FDA

Frank Sasinowski, Hyman, Phelps & McNamara

Adam Resnick, CHOP

Hakon Hakonarson, CHOP

Sundeep Dugar, Rarefy Therapeutics

PJ Brooks, NIH

12:50 – 1:50

Lunch & Product Presentations

1:10-1:25  PRODUCT PRESENTATION  204C

The Power of ctDNA Testing in Therapy Selection and Recurrence Monitoring

Taylor Jensen,  LabCorp

1:50 – 3:50

1:50-3:50

Unlocking the Next Quantum Leap in Precision Medicine – A Town Hall Discussion (CME Eligible)

Co-Chairs

Amanda Paulovich, Fred Hutchinson Cancer Center

Henry Rodriguez, NCI/NIH

Eric Schadt, Pathos

Participants

Ezra Cohen, Tempus

Jennifer Leib, Innovation Policy Solutions

Susan Monarez, ARPA-H

Nick Seddon, Optum Genomics 

Giselle Sholler, Penn State Hershey Children’s Hospital

Janet Woodcock, Former FDA

1:50-2:50

Advancing Precision Medicine in Non-Oncology Therapeutic Areas

Moderator: Mike Montalto, Amgen

Scott Friedman, Mt. Sinai

Sana Syed, University of Virginia

Lei Zhao, Amgen

2:50-3:20

Towards a Precision Neuroimmunology Platform (CME Eligible)

Amit Bar-Or, Penn Medicine

3:20-3:50

3:50 – 4:20

Networking and Exhibits

4:20 – 6:15

4:20-4:45

Precision Medicine, What’s So Difficult? The Interplay & Complexities of Delivering on the Promise of Precision Medicine (CME Eligible)

Sarah Hersey, BMS

4:45-5:30

Realizing Novel Treatment Paradigms with ctDNA-MRD (CME Eligible)

Mark Roschewski, NCI

5:30-6:30

The Potential Benefits of Next-day NGS for the Future of Precision Oncology

Allison Eck, Doylestown Health

Harry Erba, Duke Cancer Institute

Jeff Smith, Thermo Fisher Scientific

4:20-4:45

Advancing Precision Medicine: Polygenic Risk Scores and Beyond (CME Eligible)

Dokyoon Kim, Penn Medicine

4:45-5:30

The Rocky Road to Clinical Trial Diversity (CME Eligible)

Ysabel Duron, The Latino Cancer Institute

Porscha Johnson, PJW Clinical Pharmacy Consulting

Victor LaGroon, Department of Veterans Affairs

5:30-6:15

In the Rising Age of Women’s Health, How Do We Build Diagnostics to Last?

Oriana Papin Zoghbi, AOADx

Sarah Huah, Johnson & Johnson

6:30 – 7:00

Evening Keynote

Reimagining Health Equity in the Era of Precision Medicine (CME Eligible)

Rick Kittles, Morehouse School of Medicine

7:00 – 7:45

Cocktail Networking Reception 

November 02, 2024

8:00-8:55

Registration and Check-in          Meeting Room 203          Philadelphia Convention Center

Share this:

• X
• LinkedIn
• Facebook
• Print
• Email

Like this:

Like Loading...

Posted in BioBanking, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biomarkers & Medical Diagnostics, BioTechnology - Venture Creation, Venture Capital, Cancer Genomics, Cancer Informatics, Cardiovascular Pharmacogenomics, Circulating Tumor Cells (CTC), Computational Biology/Systems and Bioinformatics, Conference Coverage with Social Media, Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood, Personalized and Precision Medicine & Genomic Research, Precision Cancer Medicine, REAL TIME Conference Coverage Twitter's Hashtags and Handles per Presentation/session, Simulation Modeling in NGS | Tagged Advancing Precision Medicine, Cancer Genomics, genomics, liquid biopsy, personalized cancer therapy, Personalized medicine, precision medicine, precision oncoogy, real time conference coverage | Leave a Comment »

« Newer Posts - Older Posts »

• Follow Blog via Email

  Enter your email address to follow this blog and receive notifications of new posts by email.

  Email Address

  Follow

  Join 2,056 other subscribers

• Recent Posts

  • Grok thinks like PhDs – Hybrid Model for Autonomous Update of Doctoral Dissertations with Original PhD Student Author editing and acceptance of Grok’s updated output. 3rd Joint article, a pilot study for Validation of an Autonomous Journal Articles Updating System (AJAUS) tested on Autonomous Update of Aviva Lev-Ari, PhD, RN Doctoral Thesis, UC, Berkeley, 1983. January 31, 2026
  • 2026 World Economic Forum, 1/19 – 1/23/2026, Davos, Switzerland, LPBI Group’s KOLs interpretation of AI in Health Videos January 24, 2026
  • Evolving LPBI Group’s Portfolio of Intellectual Properties (IP): From 2021 Vision to 2026 Reality January 12, 2026
  • Aviva Lev-Ari, PhD, RN – Biography ->> Grokepedia Entry January 11, 2026
  • List of Articles included in the Article SELECTION from Collection of Aviva Lev-Ari, PhD, RN Scientific Articles on PULSE on LinkedIn.com for Training Small Language Models (SLMs) in Domain-aware Content of Medical, Pharmaceutical, Life Sciences and Healthcare by 15 Subjects Matter January 10, 2026
  • Article SELECTION from Collection of Aviva Lev-Ari, PhD, RN Scientific Articles on PULSE on LinkedIn.com for Training Small Language Models (SLMs) in Domain-aware Content of Medical, Pharmaceutical, Life Sciences and Healthcare by 15 Subjects Matter January 10, 2026
  • Collection of Aviva Lev-Ari, PhD, RN Scientific Articles on PULSE on LinkedIn.com January 10, 2026
  • The youngest leader in AI in Health: Tanishq Mathew Abraham, Ph.D. (@iScienceLuvr) January 8, 2026
  • 2026 Grok Multimodal Causal Reasoning on Proprietary Cariovascular Corpus: From 2021 Wolfram NLP Baseline to Thousands of Novel Relationships – A Second Head-to-Head Validation of LPBI’s Domain-Aware Training Advantage January 6, 2026
  • Workflow for Dynamic Linkage and Transition between two Authoring Systems: LPBI Group’s WordPress.com Multi-Authors Authoring System and Microsoft PowerPoint product for Slide show presentation – Part 10.1 in Composition of Methods January 6, 2026

• Archives

  Archives Select Month January 2026  (10) December 2025  (8) November 2025  (8) October 2025  (12) September 2025  (2) July 2025  (4) June 2025  (8) May 2025  (4) April 2025  (2) March 2025  (1) February 2025  (2) January 2025  (3) December 2024  (1) November 2024  (5) October 2024  (7) September 2024  (7) August 2024  (1) June 2024  (1) May 2024  (2) April 2024  (2) January 2024  (1) November 2023  (1) October 2023  (3) September 2023  (3) August 2023  (2) July 2023  (2) June 2023  (7) May 2023  (4) April 2023  (2) March 2023  (5) February 2023  (2) January 2023  (3) December 2022  (2) October 2022  (6) September 2022  (2) August 2022  (1) July 2022  (3) June 2022  (3) May 2022  (8) April 2022  (10) March 2022  (7) February 2022  (7) January 2022  (3) December 2021  (3) November 2021  (4) October 2021  (11) September 2021  (5) August 2021  (8) July 2021  (21) June 2021  (8) May 2021  (10) April 2021  (8) March 2021  (15) February 2021  (17) January 2021  (20) December 2020  (10) November 2020  (12) October 2020  (26) September 2020  (17) August 2020  (22) July 2020  (27) June 2020  (33) May 2020  (26) April 2020  (42) March 2020  (22) February 2020  (11) January 2020  (7) December 2019  (20) November 2019  (13) October 2019  (11) September 2019  (3) August 2019  (8) July 2019  (25) June 2019  (47) May 2019  (40) April 2019  (27) March 2019  (29) February 2019  (17) January 2019  (50) December 2018  (14) November 2018  (17) October 2018  (18) September 2018  (17) August 2018  (8) July 2018  (20) June 2018  (22) May 2018  (17) April 2018  (12) March 2018  (20) February 2018  (26) January 2018  (16) December 2017  (6) November 2017  (20) October 2017  (13) September 2017  (16) August 2017  (16) July 2017  (19) June 2017  (20) May 2017  (32) April 2017  (21) March 2017  (10) February 2017  (24) January 2017  (21) December 2016  (43) November 2016  (8) October 2016  (40) September 2016  (67) August 2016  (59) July 2016  (75) June 2016  (37) May 2016  (95) April 2016  (152) March 2016  (175) February 2016  (196) January 2016  (139) December 2015  (90) November 2015  (287) October 2015  (237) September 2015  (115) August 2015  (96) July 2015  (63) June 2015  (44) May 2015  (53) April 2015  (76) March 2015  (95) February 2015  (83) January 2015  (75) December 2014  (75) November 2014  (88) October 2014  (135) September 2014  (117) August 2014  (88) July 2014  (88) June 2014  (109) May 2014  (60) April 2014  (85) March 2014  (58) February 2014  (72) January 2014  (107) December 2013  (104) November 2013  (136) October 2013  (62) September 2013  (32) August 2013  (46) July 2013  (74) June 2013  (110) May 2013  (112) April 2013  (86) March 2013  (92) February 2013  (63) January 2013  (63) December 2012  (46) November 2012  (71) October 2012  (84) September 2012  (82) August 2012  (122) July 2012  (59) June 2012  (34) May 2012  (46) April 2012  (2)

• Categories

  Categories Select Category 3D Printing for Medical Application  (239)    3D Plotting Scaffolds  (44)    BioInks  (34)       Biopolymer Blend Open Porous  (18)       Dental Applications  (10)    BioPrinting in Regenerative Medicine  (77)       Cell Level  (16)       MicroEngineering Cell-Tissue & Systems  (31)       Tissue Engineering  (37)    Cardiovascular and Vascular Systems  (30)       Artificial Vascular Structures  (9)       Cardiovascular Tissue  (8)    Drug Development using MultiOrgan Chip  (11)    Drug Development/Formulation using 3D Printing  (10)    MEMS  (16)       Bio-MEMS  (12)    Organ-on-a-Chip  (11)    Programmable Sensors (Carbon Nano Tubes)  (5) 3rd Party IP: Drug DIscovery  (1) 4D Printing and Meta Materials  (10) Academic Publishing  (223)    Key Opinion Leaders in eScientific Publishing – Interviews with  (8) Advanced Drug Manufacturing Technology  (96)    Antimalarial Preparation  (8)    Autologous Cell Therapy  (14)    Automated Cell Processing  (13) Allergy and Infectious Diseases  (12) Alzheimer’s Disease  (163)    Etiology  (77)    Medical Device Therapies for Altzheimer’s Disease  (15)    Pharmacotherapy and Cell Activity  (51) Anticancer Resistance  (31) Art Exhibits on the Human Condition  (1) Art Inspires Science  (4) Article Type  (2)    Authored  (1)    Curated  (2)    Scientific report  (1) Artificial Heart  (2) Artificial Intelligence – General  (232)    An executive’s guide to AI  (10)    Artificial Intelligence – Breakthroughs in Theories and Technologies  (115)    Artificial Intelligence Applications in Health Care  (125)       Artificial Intelligence in CANCER  (39)       Artificial Intelligence in Health Care – Tools & Innovations  (74)    Artificial Intelligence in Medicine – Application for Diagnosis  (69)       Artificial intelligence applications for cardiology  (31)          AI-assisted Cardiac MRI  (10)       Artificial Intelligence in Psychiatry  (6)       Deep Learning in Pathology  (18)    Artificial Intelligence in Medicine – Applications in Therapeutics  (64)    ChatGPT, GPT-4  (7)       ChatGPT at LPBI Group  (3)       ChatGPT in Academic Education  (3)    Machine Learning  (71)       Deep Learning  (25)       Natural Language Processing (NLP)  (45)    Machine learning in predicting type 2 diabetes  (5) Auditory and vision  (13) Autism Spectrum Disorders  (10) Autoimmune Inflammatory DIseases  (24)    Crohn’s disease  (5)    Tolerance-inducing Autoimmune Disease Therapeutics  (6)    Ulcerative Colitis  (3) Autophagosome  (4) Bacterial Resistance  (25) Behavior  (26) Behavioral Genetics  (21) Big Data  (88)    Intelligent Information Systems  (51) Bio Instrumentation in Experimental Life Sciences Research  (368)    Analytical Instruments Industry  (5)    Microfuidics  (8) Bio-Ethics  (15) BioBanking  (36) Biodegradable Drug-eluting Material  (8) Bioengineering & reverse engineering design  (43) BioIT: BioInformatics  (147) BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics  (135)    Advanced Computing Platform  (32)       Blockchain Transactions System  (6)          Platform for Content Monetization embedded Content Promotion  (1)    Pancreatic adenocarcinoma classifier  (4)    Simulation Modeling in NGS  (5) Biological Engineering  (43) Biological Networks  (193) Biological Networks, Gene Regulation and Evolution  (479)    Virology  (10) Biomarkers & Medical Diagnostics  (571)    VOC – Volatile Organic Compounds as BioMarkers  (2) Biomedical Measurement Science  (173) BioSimilars  (112) BioTechnology – Venture Creation  (131)    Foundations for supporting Science and Education  (14)    Philanthropy to Academic Institution  (3) BioTechnology – Venture Creation, Venture Capital  (108)    Seeking Talent  (3) BiVAD  (1) Blindness  (6) Bone Disease and Musculoskeletal Disease  (82) Brain Basis of Emotion  (5) Business Career Consideration  (1) Ca2+ triggered activation  (61) Calcium  (21) Calcium Signaling  (71) Calmodulin Kinase and Contraction  (36) Cancer – General  (186) Cancer and Current Therapeutics  (411)    interventional oncology  (49)       Breast Cancer – impalpable breast lesions  (20)       Prostate Cancer: Monitoring vs Treatment  (9) CANCER BIOLOGY & Innovations in Cancer Therapy  (1,122)    Anaerobic Glycolysis  (51)    Cachexia  (18)    Cancer Genomics  (84)       Circulating Tumor Cells (CTC)  (27)          Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood  (17)             mRNA  (6)          MagSifter chip  (1)       KRAS Mutation  (9)       Li-fraumeni syndrome.  (3)       TP53 – Germline mutations  (8)    cancer metabolism  (28)    Funding Opportunities for Cancer Research  (12)    Genomic Expression  (150)    Glioblastoma  (7)    Hexokinase  (14)    Loss of function gene  (18)    Metabolic Immuno-Oncology  (21)    Metastasis Process  (15)    Methylation  (33)    Microbiome and Responses to Cancer Therapy  (10)    Monoclonal Immunotherapy  (29)    mtDNA  (13)    Oxidative phosphorylation  (56)    Pancreatic cancer  (10)    Pyruvate Kinase  (27)    The NCI Formulary  (1)    tumor microenvironment  (16)    Warburg effect  (50) Cancer Informatics  (104) Cancer Prevention: Research & Programs  (131) Cancer Screening  (108) Cancer Vaccines: Targeting Cancer Genes for Immunotherapy  (30)    Engineering Enhanced Cancer Vaccines  (3) cancer-general  (6) Cardiac and Cardiovascular Surgical Procedures  (328)    Aortic Valve: TAVR, TAVI vs Open Heart Surgery  (91)       Prosthesis-Patient Mismatch (PPM) in TAVI vs SAVR  (2)       TAVI vs Open Heart Surgery  (6)       Transcatheter Aortic Valve Replacement via the Transcarotid Access  (3)       Transcaval TAVR Procedure  (3)       Valve-in-Valve Procedure  (5)    Atrial Fibrilation (a-Fib), Cardiac Pacing and Arrhythmias  (18)    BackBeat’s Cardiac Neuromodulation Therapy (CNT) bioelectronic therapy  (1)    CABG  (47)    Cancer Surgery of the Heart  (10)    Cardiovascular Fluid Management: algorithms  (1)    Heart Transplant  (16)    Heart-Lung Transplant  (11)    Implantation  (3)    Left Main Coronary Artery Disease (LMCAD)  (3)    Mechanical Assist Devices: LVAD, RVAD, BiVAD, Artificial Heart  (22)    Mitral Valve: Repair and Replacement  (64)       TMVR – Transcatheter Mitral Valve Repair  (5)    PCI  (108)       Bioresorbable Vascular Scaffold (BVS)  (4)       Invasive FFR for PCI  (2)       Multivessel PCI FFR Guidance  (1)       Robotic-assisted percutaneous coronary intervention  (2)       Stent Retriever in endovascular thrombectomy  (3)    Peripheral Arterial Disease & Peripheral Vascular Surgery  (65)       Abdominal Aorta  (20)       Carotid Artery  (15)       Limb ischemia  (4)       Thoracic Aorta  (16)    Pulmonary Valve Replacement and Repair  (3)    Renal Denervation  (20)    Replacement  (1)    Robotically assisted Cardiothoracic Surgery  (2)    Tricuspid Valve Repair  (7)    Vena Caval Filters: Device for Prevention of Pulmonary Embolism and Thrombosis  (2) Cardiovascular Pharmacogenomics  (171) Cargo  (3) Cell Biology  (323) Cell Biology, Signaling & Cell Circuits  (672)    Apoptosis  (17)    Autophagy-Modulating Proteins  (6)    “Antibody–enzyme conjugates”  (6)    Cell Processing System in Cell Therapy Process Development  (27)    Endoplasmic reticulum  (3)    Enzymatic mechanism underlying the synthesis of adenosine triphosphate (ATP)  (3)    Ubiquitin  (11) Cerebrovascular and Neurodegenerative Diseases  (123)    Stroke  (9) Chemical Biology and its relations to Metabolic Disease  (464)    #BIGAxisSummit  (1)    Gut Microbiome and Obesity  (8) Chemical Genetics  (350) Child and Adolescent Psychiatry  (13) Childhood cancer  (17) Childhood malnutrition  (3) children  (3) Circulating Progenitor Cells  (27)    Bone marrow derived cells  (17)    Endothelial cells  (7)    Umbilical cord cells  (6) Clinical & Translational  (238) Clinical Diagnostics  (220)    Mass automation of plasma proteins  (13) Clinical Genomics  (144) Coagulation Therapy and Internal Bleeding  (83) Cognition  (47) Commercialization  (20) Components and IRB related issues  (4) Computational Biology/Systems and Bioinformatics  (470)    Computational Histopathology  (3)    Meta-analysis of transcriptome data  (10) Conference Coverage with Social Media  (494)    MassBio  (3) coronavirus  (18) COVID-19  (20) CRISPR/Cas9 & Gene Editing  (192)    CRISPR alternative for editing genes without cutting  (14)       CAST – Alternative to CRISPR/Cas9  (3)       Transposon-encoded CRISPR–Cas systems direct RNA-guided DNA integration  (4)    CRISPR applied to Human Germ Line  (13)       Gene Editing Impact on Longevity  (6) CT  (20) Cytokines  (20) Cytoskeleton  (85) Developmental biology  (107) Diabetes Mellitus  (146)    Artificial Pancreas for Type 2 Diabetes  (4)    Artificial Pancreas for Type1 Diabetes  (8)    Gestational diabetes  (5) Diagnostic Immunology  (61) Diagnostics and Lab Tests  (128)    Liquid Biopsy: Circulating Tumor Cells in Urine and Blood  (9) Digital HealthCare – biotech & internet joint ventures  (47) Disease Biology  (334) Disease Biology, Small Molecules in Development of Therapeutic Drugs  (487) DNA repair  (73)    Apoptosis  (13)    Autophagy  (13)    Cell death pathways  (19)    Proteolysis  (8)    Proteosome  (8) Drug Delivery Platform Technology  (157)    Drug Carrier Design  (21)       Medication Remote Compliance Monitoring  (3)    Exosomes: Natural Carriers for siRNA Delivery  (9) Drug Toxicity  (74) Ecosystems & Industrial Concentration in the Medical Device Sector  (172)    Cardiac & Vascular Repair Tools Subsegment  (128)    Exec Compensation in the Cardiac & Vascular Repair Tools Subsegment  (14)    Massachusetts Niche Suppliers and National Leaders  (20) Electronic Health Record  (10) Embryology  (24) Empathy  (6) Endocrine Diseases  (60) Enzyme Induction  (46)    ion-transporting enzyme  (2)    K + -ATPase.  (1)    Na +  (2) Epigenetics and Environmental Factors  (35) Ethics and Leadership  (10) Executive Compensation – Big Pharma  (3) Fat soluble vitamins  (4) FDA  (71) FDA Regulatory Affairs  (358)    FDA, CE Mark & Global Regulatory Affairs: process management and strategic planning – GCP, GLP, ISO 14155  (52)    ISO 10993 for Product Registration: FDA & CE Mark for Development of Medical Devices and Diagnostics  (30) Fetal Ultrasound  (2) Fiction and medicine  (5) Frontiers in Cardiology and Cardiovascular Disorders  (929)    Acute Myocardial Infarction  (63)       Cardiogenic Shock  (5)    Anemia in CVD Patients  (6)    Cardio-Oncology  (4)    Cardiomyopathy  (57)    Cardiovascular Research  (187)       Myocardial metabolism, Myocardial ischemia, myocardial perfusion, Myocardial adenine nucleotide metabolism  (64)       Pre-Clinical Animal Model Development  (23)    Chronic Thromboembolic Pulmonary Hypertension (CTEPH) and Pulmonary Arterial Hypertension (PAH)  (20)    Cost of Inpatient Stay for Cardiovascular Diagnosis for Admission  (1)    Electrophysiology  (111)       Ablation Procedure vs Drug Therapy  (2)       Arrhythmia Detection with Machine Learning Algorithms  (7)       Cardiotoxic Risks of Immune Checkpoint Inhibitors  (1)       Genomic Analysis of EKG Electrophysiology Genomics  (3)       implantable cardioverter defibrillator (ICD) and External Defibrillation  (1)       implantable pacemaker  (2)       Rare heart rhythm disorders and Long QT syndrome (LQTS)  (7)    Epigenetics and Cardiovascular Risks  (57)    Heart Failure (HF)  (51)       Acute coronary syndrome  (7)       congestive heart failure  (19)       Hypertensive Disorders of Pregnancy  (1)    Medical Devices R&D and Inventions  (212)       Assist Devices: LV  (3)       RV  (1)       Stents & Tools  (90)       Valves & Tools  (67)    Origins of Cardiovascular Disease  (422)       Atherogenic Processes & Pathology  (180)       Congenital Heart Disease  (34)          Genetic Mutations in Congenital Heart Disease  (4)       inflammation independent of lipid levels  (13)       Systemic Inflammatory Diseases as CVD Risk  (12)    Pharmacotherapy of Cardiovascular Disease  (356)       HTN  (176)       HTN in Youth  (8)       Resident-cell-based  (43)       Subarachnoid Hemorrhage (SAH)  (1)       Transthyretin amyloid cardiomyopathy (ATTR-CM)  (1)    Spontaneous Coronary Artery Dissection (SCAD)  (6)    Stroke  (15)       endovascular thrombectomy  (6)    Vascular Diseases  (21)       mesenteric ischemia  (3) Future Pandemics Inform-graphics  (1) Gastroenterology  (38) Gene Regulation  (232) Gene Regulation and Evolution  (137) Genetics & Innovations in Treatment  (176) Genetics & Pharmaceutical  (203) Genome Biology  (967)    Exosomes  (25)    Gene Therapy & Gene Editing Development  (42)    mRNA Therapeutics  (20)    Mutagenesis  (27)    Single Cell Genomics  (25)    Single-cell sequencing  (20)    Variation in human protein-coding regions  (35) Genomic Endocrinology  (42) Genomic Testing: Methodology for Diagnosis  (416) Genomics Pharmacy  (40) Global Market of Medical Devices Technology  (2) Global Medical Publishers by Country  (3) Global Partnering & Biotech Investment  (47) GLP  (4) Glycobiology: Biopharmaceutical Production  (16) Glycobiology: Biopharmaceutical Production, Pharmacodynamics and Pharmacokinetics  (28) Health Care System by Country  (58)    AI Models in Healthcare  (12)    Centers for Medicare & Medicaid Services  (8)    Health in Israel  (2)    India  (1)    United States  (27)       Hospital-based Medical Innovations  (10)       Medical Recertification and Maintenance of Certification (MOC)  (1) Health Economics and Outcomes Research  (384)    Women Health  (18)       Heart and Brain Disease in Women  (3) Health Law & Patient Safety  (165)    and Bioethics  (14)    Biotechnology  (12)    Health Law Policy  (13) HealthCare IT  (138) Healthcare Reform  (117)    Accountable Care Organizations  (26)    Affordable Care Act  (29)       Repair  (1)       Repeal ACA  (1)       Replace  (1)    Federal Budget Appropriations  (26)    Healthcare costs and reimbursement  (59)    Indigent Nutrition  (14)    Medicare and Medicaid  (20)    Prescription Drugs Costs  (21)    Skilled Nursing Facilities  (6)    Technology Capital Expenses  (15)    Uninsured and Underinsured  (20) Hematology  (100)    Acute lymphocytic leukemia  (18)    Acute myelocytic leukemia  (21)    Hematopoiesis  (48)    Lymphoma  (21) History and physical exam  (6) Human aging  (27) Human Immune System in Health and in Disease  (246) Human Sensation and Cellular Transduction: Physiology and Therapeutics  (21) Image Processing/Computing  (41) Imaging-based Cancer Patient Management  (116) Immuno-Oncology & Genomics  (133)    mRNA platform in Drug DIscovery  (6) Immunodiagnostics  (139)    Cancer-Immune Interactions  (29)    CNS-Immune Interface  (5)    Neuronal Circuits  (4) Immunology  (123)    Adaptive Immune Response to Biomaterials and Tissue Repair  (13)    Antibody Responses Predict Antigen Exposure  (17)    Cytokine Receptor Structure  (8)    Human Antibody Response  (18)    Human Circulating Antibody Repertoire  (11)    Human Naive B Cell Repertoire  (8)    MHC Repertoires for Antigen Prediction  (8)    Protection Against Autoimmune Disease  (11)    Synthetic Immunology: Hacking Immune Cells  (15) Immunotherapy  (133)    CAR-T  (14)    combination immunotherapies.  (14)    Efficacy of Anti-Tumor T Cells  (13)    Engineering Better T Cells  (8)    Immune Engineering  (17)    Immune Modulatory  (15)    Integrin-targeted Combination Immunotherapy  (6)    Modulating Macrophages in Cancer Immunotherapy  (16)    NK Cell-Based Cancer Immunotherapy  (19)    Synergistic Innate and Adaptive Immunotherapy  (14) Income Disparity  (2) Infectious Disease & New Antibiotic Targets  (162)    Antibiotic resistance  (7)    Viral diseases  (37)       Myocarditis  (1) Infectious Disease Immunodiagnostics  (58)    Virology – Vector-borne DIsease  (17) Inflammasome  (29)    Anti-tumor necrosis factor drugs (TNF inhibitors)  (3) Innovation in Immunology Diagnostics  (111)    Universal Immune Cell Therapies (uICT)  (11) Innovations  (186)    R&D Expenditure  (8) Innovations in Neurophysiology & Neuropsychology  (145)    Age and Life Expectancy  (3)    autism spectrum disorder  (1)    Circadian Rhythm and Sleep  (6)    hNPCs  (1)    Nervous System Function  (2)    Relapsing Multiple Sclerosis  (1) Intellectual Property  (83)    Disputes and Settlements  (9)    IP Valuation Models – Pricing Intangible Assets  (7)    Patent Law in Biotech  (12) Intellectual Property, Innovations, Commercialization, Investment in technological breakthrough  (119) International Global Work in Pharmaceutical  (190) Interventional Oncology: Radiofrequency Ablation, Transarterial Chemoembolization, Microwave Ablation and Irreversible Electroporation (IRE)  (12) Interviews with Scientific Leaders  (315)    Albany Medical Center Prize in Medicine and Biomedical Research  (2)    Annual Breakthrough Prize  (4)    Annual Lewis S. Rosenstiel Award  (2)    Awards in Cardiology and Cardiovascular Medicine  (6)    CEOs of Biotech Companies  (3)    Gerald D Aurbach Award for Outstanding Translational Research  (2)    Interviews with Key Opinion Leaders (KOLs)  (10)    James Prize in Science and Technology Integration  (2)    Jessie Stevenson Kovalenko Medal – Medical Sciences  (2)    Lemelson-MIT Prize  (2)    Life Sciences Breakthrough Prize  (5)    Mosteller Statistician of the Year Award  (2)    Mourning the Loss of a Scientific Leader  (3)    National Academy of Sciences AWARDS  (4)    Nobel Prize Winners  (29)    Noble Prize (Not Nobel Prize)  (2)    Paul Marks Prize for Cancer Research  (2)    Russ Prize recognizes bioengineering achievements worldwide  (2)    The Dan David Prize  (6)    Warren Alpert Foundation Prize Recipients  (4)    Wolf Prize  (2)    Wolf Prize in Medicine  (2)    women in science  (5) Investment in Technological Breakthrough  (60) Ionic Transporters Na+  (25) IP Development by LPBI Group Team  (14) IP Development by LPBI Group Team & Other Organizations  (8) ISO 14155  (3) Joint Venture – SBH & M3DP: SOP and Arrangements  (1) Justice & Law  (1) K  (15) Lab-on-a-chip  (2) Landscape  (1) Lasers and photonics  (18)    Midrange IR spectroscopy  (1) Law and Medicine Conflicts  (14) Leadership, Power, Social Interlocking Connections  (13) Lipid metabolism  (109)    Fatty acids  (38)    Lipids  (38)    PCSK9 Inhibitor Therapy  (19) Lipidomics  (11) Liposome  (4) Liver & Digestive Diseases Research  (122) LPBI Group, e-Scientific Media, DFP, R&D-M3DP, R&D-Drug Discovery, US Patents: SOPs and Team Management  (143)    Award Nominations  (5)    BioMed e-Books e-Series by LPBI Group  (9)    Feedback from Investors: LPBI Portfolio of Five Businesses  (2)    LPBI Group Founder – Aviva Lev-Ari  (27)    PhD  (1)    RN  (1) LPBI Management  (23) Massachusetts Academy of Sciences (MAS)    (2) Materials Science & Engineering  (37)    Organic BioElectronics  (2) Math  (13)    Great Discoveries  (7) Mechanical Assist Devices: LVAD  (4) Medical and Population Genetics  (193) Medical Devices R&D Investment  (242)    21st Century Cures Act  (2)    CE Mark & Global Regulatory Affairs: process management and strategic planning – GCP  (14)    Rhythm management device hardware: dual-chamber pacemakers  (1)       Systolic HTN  (1) Medical Imaging Technology  (59) Medical Imaging Technology, Image Processing/Computing, MRI, CT, Nuclear Medicine, Ultra Sound  (164)    Circumferential-Intravascular-Radioluminescence-Photoacoustic-Imaging (CIRPI)  (3)    Echocardiography  (3)    MRI  (18)    Noninvasive Diagnostic Fractional Flow Reserve (FFR) CT  (6)    Ultra Sound  (10) Melatonin & Circadian Regulation  (9) memory loss  (1) Metabolism  (239)    Biochemical pathways  (144)       Enzymes and isoenzymes  (73)          Dehydrogenase  (16)          Kinase  (28)          Phosphatase  (9)          Phosphorylase  (22)          tyrosine decarboxylases  (3)    Inosine nucleotides  (9)    Leloir pathway  (6)    microbiome  (7)    Pentose monophosphate shunt  (14)    Pyridine nucleotides  (28)       Pyridine nucleotide transhydrogenase  (7) Metabolomics  (321) Methods  (37) Mg++  (10) Microbiologial genetics  (36) Microbiology  (68)    Virology  (14) Micronutrients  (16) Microvesicle  (7) Microwave Ablation and Irreversible Electroporation (IRE)  (1) Mobile Healthcare  (5) Molecular Genetics & Pharmaceutical  (377)    Organoids  (3) Monoclonal antibody therapy  (14) Mutant Gene Expression  (42) Myocardial adenine nucleotide metabolism  (6) Myocardial Ischemia  (26) Myocardial Metabolism  (36) Na-K transport  (40) Na-K-ATPase  (27) Nanotechnology for Drug Delivery  (99) Nephrology  (46) Nephrology & Regenerative medicine  (9) Neurodegenerative Diseases  (89)    hNPCs  (3)    MS  (5) Neurohumoral Transmission  (68)    Ca2+ triggered activation  (21)    Calmodulin  (13)    Parkinson’s disease dyskinesia levodopa  (3)    PKC  (13)    Synaptic vesicle  (20) Neurological Diseases  (121) Neuroscience  (126) Next Generation Sequencing (NGS)  (27)    Nanopore sequencing  (1) NIH Common Fund  (1) Nitric Oxide in Health and Disease  (136) Nuclear Medicine  (11) Nutrigenomics  (68) Nutrition  (195)    Nutritional Supplements: Atherogenesis, lipid metabolism  (50) Nutrition and Phytochemistry  (62)    Minerals in Medicine  (4)    Plant extract  (22) Nutrition Disorders  (31) Nutritional Supplements: Atherogenesis  (31) Obesity  (19) Oligonucleotide Therapeutics & Delivery  (7) Oncolytic virus & OncoViro-Therapy  (22)    Synthetic Virology  (1) Organ-on-a-Chip & 3D Printing in Life Sciences  (7) Pain: Etiology, Genetics & Innovations in Treatment  (24) Parasitology  (5)    Malaria  (4)    Tropical diseases  (1) Patents  (34) Patient Experience  (66)    Art therapy  (1)    Hospital reputation  (7)    Humor  (1)    Music therapy  (2)    Pain Alleviation  (7)    Patient Outlook  (28)    Reputation of Interventionist  (4)    Support Staff  (13)    Supportive therapies  (6)       laughfter  (1)    Surgical Procedure  (9) Patient Experience: Personal Memories of Invasive Medical Intervantion  (30) Patient’s Voice: Personal Experience with Invasive Medical Procedures  (18) Perioperative Statins at Noncardiac Surgery  (2) Personal Health Applications: Tech Innovations serves HealhCare  (43) Personalized and Precision Medicine & Genomic Research  (736)    Longevity  (3)    New Drug Approval  (5)    Patient-centered Medicine  (39)       cell-based therapy  (9)       stem cell biology and patient-specific  (7)    Personalized Medicine Coalition  (10)    Precision Cancer Medicine  (31) Phagophore  (2) Pharmaceutical Analytics  (261)    Pharmacologic toxicities  (62) Pharmaceutical Discovery  (109)    Rapid automation of plasma protein pools  (17) Pharmaceutical Drug Discovery  (200)    Drug Development Process  (55)       Drug Discovery Chemistry  (29)    drug repurposing  (10) Pharmaceutical Industry Competitive Intelligence  (343)    Pharmacovigilance  (7)    Value-based Drug Pricing  (8) Pharmaceutical R&D Informatics  (44) Pharmaceutical R&D Investment  (299) Pharmacodynamics and Pharmacokinetics  (24) Pharmacogenomics  (159) Photography Collection  (1) Physics  (12) Placenta  (7) Population genetics  (27) Population Health Management  (204)    Evolution of Biology Through Culture  (14)    U.S. Employment-to-Population Ratio  (8) Population Health Management, Genetics & Pharmaceutical  (643)    COVID-19  (157)       Biomarkers: Inflammation  (37)       Cancer Researchers Fighting COVID-19  (14)       COVID-19 effects on Human Heart  (15)       COVID-19: Pandemic Surgery Guidance  (19)       Economic Impact of Coronavirus Pandemic  (41)       number of asymptomatic infections  (27)       Treatment Protocols for COVID-19  (62)    SARS-CoV-2  (141)       Coronavirus Gene Expression  (66)          SARS-CoV-2 circulating variants   (14)          SARS-CoV-2 Viral Variants  (19)       Glycosylation and its Role in SARS-CoV-2 Viral Pathogenesis  (2)       Immune-Mediation (independent immunopathology: lung and reticuloendothelial system)  (30)       Mechanisms of infection by SARS-CoV-2  (21)       SAR-Cov-2 a vasculotropic (blood vessels) RNA Virus  (35)          SARS-COV2 Hijacking the Complement and Coagulation Systems  (19)       Seasonality & Environmental Factors in Resurgence  (26)       Serology tests for coronavirus antibodies  (44)       T-cell response to SARS-CoV-2 infection  (14)       Vaccinology  (51)          Mechanism of Thrombosis with AZ & J&J COVID-19 Vaccines  (5)       Virtual Drug Molecule Screening targeting SAR-CoV-2 proteins  (8)    Virus Infective Acute Respiratory Syndrome: SARS-CoV  (92) Population Health Management, Nutrition and Phytochemistry  (162) Preimplantation Genetic Diagnosis and Reproductive Genomics  (13) Protein-energy malnutrition  (12) Proteomics  (367)    Amino acids  (54)    Proteins  (138) Pulmonary diseases  (16)    Lung and pulmonology  (15) Pulmonary pathology  (8) Radio Frequency (RF)-based Surgical Solutions  (2) REAL TIME Conference Coverage Twitter’s Hashtags and Handles per Presentation/session  (50) Regenerative Biology and Medicine  (92) Regulated Clinical Trials: Design, Methods, Components and IRB related issues  (265) Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics  (113) Reproductive Biology & Bio Instrumentation  (78) RNA Biology  (81)    RNA interference (RNAi) therapeutic  (4) RNA Biology, Cancer and Therapeutics  (64) RVAD  (1) SBIR, SBA, NIH, NSF  (3) Schizophrenia  (16) Scientific & Biotech Conferences: Press Coverage  (182) Scientific Publishing  (609)    Curation  (585)       Curation methodology  (40)       De novo synthesis  (16)       Dialectic  (17)       Discovery process  (72)       Evolutionary cognition  (52)       Experimental validation  (91)       Explanatory  (91)       Historical relevance  (71)       Technology Advance Assessment of  (69)       Theoretic convergence  (27)    Open Access Journals  (37) Scientist: Career considerations  (197)    Big Data & Analytics  (17)    Data Science  (13)    Women in Life Sciences  (12) Scoop.it  (19) Sensors & Analytics  (18) Sepsis  (2) Severe Autism  (3) Signaling  (102)    Phosphorylation  (43)    S-nitrosylation  (13)    Ubiquitinylation  (34) Signaling & Cell Circuits  (130) single cell sequencing  (3) Small Molecules in Development of Therapeutic Drugs  (71) Social Development  (15) Specialized 3D BioPrinters (Cornea  (1) Statistical Methods for Research Evaluation  (137) Stem Cells for Regenerative Medicine  (177)    Cardiac muscle regeneration  (19)    Competition in regenerative stem cell science  (18)    Human neural progenitor cells  (8)    Skeletal muscle regeneration  (9) STORM  (2) Stress Disorders  (19) Substance Abuse  (4) Systemic Inflammatory Response Related Disorders  (161)    Inflammatory Pathophysiology  (9) Technology Transfer: Biotech and Pharmaceutical  (354) therapeutics  (3) Tissue Engineering and Regenerative Medicine  (22) Tissue Microenvironment  (5) Transarterial Chemoembolization  (2) Transcriptomics  (47) Transformative Technologies in Healthcare  (23) Translational Science  (230)    Best evidence  (52)    Bias measurement tools  (2)    Evidence-based decision-making  (29)    Inferential analysis  (25)    Intra-rater error  (1)    Quality Assurance  (8)    Random Error  (2)    Systematic Error (Bias)  (6)    Total error  (2)    Translational Effectiveness  (49)    Translational Research  (92) Trends in Global Economy  (6) Uncategorized  (1,019) Unfolded Protein Response (UPR)  (6) US and Global Economy: Trends and Findings  (9) Venture Capital  (47)    Income Geographic Distribution  (3)    Institutional Capital Raised by Female Founders  (6) virology  (10) Vision  (7) Voices of Patients and Healthcare Providers  (22) Water Transporters  (8) Wearable Tech + Digital Health  (5) Anemia  (22) Neutropenia  (8) Neutrophilia  (9) Platelet count disorder  (9) Folate and B12  (7) Iron deficiency  (5) Myelodysplasia  (13) Myelofibrosis  (10)

• Meta

  • Log in
  • Entries feed
  • Comments feed
  • WordPress.org

• • 2012pharmaceutical
  • sjwilliamspa