The approval is based on results from the MCL cohort of the TRANSCEND NHL 001 trial.

Bristol Myers Squibb’s Breyanzi gains FDA approval for lymphoma

In the years since gaining an initial FDA approval for Breyanzi, Bristol Myers Squibb has worked hard to expand the reach of its cell therapy. | In the years since gaining an initial FDA approval for Breyanzi, Bristol Myers Squibb has worked hard to expand the reach of its cell therapy.

Bristol Myers secures another FDA go-ahead for Breyanzi, this time in mantle cell lymphoma | Fierce Pharma

Bristol Myers&#x27; (BMY) Opdivo combination regimen receives the European Commission&#x27;s approval for the first-line treatment of unresectable or metastatic urothelial carcinoma.

Bristol Myers (BMY) Gets EC Nod for Opdivo Label Expansion

Bristol Myers (BMY) has received quite a bit of attention from Zacks.com users lately. Therefore, it is wise to be aware of the facts that can impact the stock&#x27;s prospects.

Bristol Myers Squibb Company (BMY) Is a Trending Stock: Facts to Know Before Betting on It

Prothena (PRTA) announces that Bristol Myers is exercising its option for the second time, thereby in-licensing exclusive global rights for PRX019 to potentially treat neurodegenerative diseases.

Prothena (PRTA), Bristol Myers Tie Up for Second Neuro Candidate

Breaking news and real-time stock market updates from Seeking Alpha. Check out the latest investing news and financial headlines.

Bristol-Myers gets EU approval for Opdivo for bladder cancer (NYSE:BMY)

Prothena plans to initiate a Phase I trial in an undisclosed indication by the end of 2024.

BMS stakes $80m on Prothena’s neurodegenerative candidate - Pharmaceutical Technology

British drugmaker GSK said Wednesday that sales of its new respiratory syncytial virus vaccine were £709 million ($859 million) in the third quarter of the year, significantly topping analysts’ expectations,

GSK Reports Earnings Wednesday. Here’s What to Expect.

Bristol Myers Squibb (NYSE:BMY) has named Christopher Boerner as their new Chair of the Board of Directors and CEO. Boerner, who is set to take over as Chair in April 2024, will assume the role of CEO from November 1.

Christopher Boerner To Take The Helm At Bristol Myers Squibb As Chair Of The Board, CEO

(RTTNews) - Bristol Myers Squibb (BMY) announced that its Board elected Christopher Boerner to serve as Chair of the Board, effective April 1, 2024. He succeeds Giovanni Caforio, who will step down as Chair of the Board on April 1, 2024 and retire from the company after 23 years of service.

Bristol Myers Squibb Elects Christopher Boerner To Serve As Chair Of Board

Bristol-Myers Squibb

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Authors: (1) JunJie Wee, Department of Mathematics, Michigan State University; (2) Jiahui Chen, Department of Mathematical Sciences, University of Arkansas; (3) Kelin Xia, Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University & xiakelin@ntu.edu.sg; (4)Guo-Wei Wei, Department of Mathematics, Michigan State University, Department of Biochemistry and Molecular Biology, Michigan State University, Department of Electrical and Computer Engineering, Michigan State University & xiakelin@ntu.edu.sg. Table of Links Abstract & Introduction Results Discussion Conclusion Materials and Methods Software and resources, Code and Data Availability Supporting Information, Acknowledgments & References Supporting Information Supporting Information is available for supplementary tables, figures, and methods. Acknowledgments This work was supported in part by NIH grants R01GM126189, R01AI164266, and R01AI146210, NSF grants DMS-2052983, DMS-1761320, and IIS-1900473, NASA grant 80NSSC21M0023, MSU Foundation, Bristol-Myers Squibb 65109, and Pfizer. It was supported in part by Nanyang Technological University Startup Grant M4081842.110, Singapore Ministry of Education Academic Research fund Tier 1 RG109/19 and Tier 2 MOE-T2EP20120-0013, MOE-T2EP20220- 0010, and MOE-T2EP20221-0003. References [1] Y. Qiu and G.-W. Wei, “Persistent spectral theory-guided protein engineering,” Nature Computational Science, vol. 3, no. 2, pp. 149–163, 2023. [2] R. Guerois, J. E. Nielsen, and L. Serrano, “Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations,” Journal of molecular biology, vol. 320, no. 2, pp. 369–387, 2002. [3] P. Sormanni, F. A. Aprile, and M. 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The last month will go down in history as "black February" and not so much because of volatility in the markets as growing geopolitical tensions. Despite all the promises, Russian President Vladimir Putin gave the green light to a so-called "military operation" in Ukraine. It didn’t take long for Europe and the US to respond. In a matter of days, the Office of Foreign Assets Control of the US Department of the prohibited United States persons from engaging in transactions with the Central Bank of the Russian Federation, the National Wealth Fund of the Russian Federation, and the Ministry of Finance of the Russian Federation. Treasury This action effectively immobilizes any assets of the Central Bank of the Russian Federation held in the United States or by U.S. persons, wherever located. On top of that, the United States and its allies have announced that they are prepared to restrict imports of Russian technology products by 50%. Lose-lose situation The only problem is that anti-Russian sanctions have also punished the global economy: gas and oil prices are skyrocketing, whereas prices for metals, including palladium, which is widely used in the manufacture of automotive catalysts, have hit multi-year highs. In this context, it shouldn’t be a surprise that inflation expectations have spiked once again. According to the latest , Eurozone consumer prices rose by a record 5.8 percent in February. data The jump in the cost of living in the Eurozone to well above the ECB’s 2 percent inflation target has divided the central bank into two groups: those who are pushing for tighter monetary policy to tackle inflation and those who want a pause to assess the economic impact of Russia’s invasion of Ukraine. Prepare for High Gas Prices Regarding energy prices, the biggest issue is that the world's leading oil powers simply cannot increase production due to lack of capacity because of extremely low investment in the industry in recent years. Even the current quotas are not fully taken by OPEC+ countries. The good news is that the prospects for the Iran nuclear deal remain important for the oil market. Negotiators are gradually moving closer to a consensus, which could mean the removal of sanctions on Iranian oil exports and would add up to 1 million barrels to the market by 2022. Iran currently produces about 2.5 million b/d. It is forecasted that the rapid growth of commodity prices could end up pushing up inflation in the U.S. and EU. The monetary authorities will inevitably react to this by raising refinancing rates, which will raise the cost of loans for businesses. Given the rise in the cost of industrial raw materials, along with energy, this will inevitably lead to a downturn in the global economy. In particular, JPMorgan Chase & Co. a run-up to $150 a barrel would almost stall the global expansion and send inflation spiraling to over 7%, more than three times the rate targeted by most monetary policymakers. warns How to Save Money Despite rising inflation expectations, the major cryptocurrency failed to justify its safe-haven status. Possible reasons for the continuation of the sideways trend include geopolitical tensions, as well as increased hawkish sentiment at the Federal Reserve. If risk-off in the markets continues, Bitcoin's return to historical records may be delayed. In the meantime, Exchange-traded funds that in gold and other precious metals have seen massive inflows. Data from Refinitiv Lipper showed and other precious metal ETFs have seen an inflow of $4.7 billion this year, after witnessing outflows worth $7.8 billion last year. invest gold Regarding the stocks, the best idea would be to follow Buffett’s principle: not to rush and invest in enterprises with a long-term economic outlook. It is worth mentioning that in the last quarter, Berkshire Hathaway shares of Teva Pharmaceuticals (TEVA) and Sirius XM Holdings (SIRI), and bought securities of Formula One Group (FWONK), Activision Blizzard (ATVI), and Nu Holdings (NU). sold In addition, Berkshire increased its stake in Chevron (CVX) by more than 33%, decreasing its stake in Kroger (KR) by 0.61%, and Charter Communications, Inc. (CHTR) by 8.8%. Also, the company continued to get rid of pharma securities, with shares in AbbVie ( ) down -79%, Bristol Myers Squibb (BMY) down -76%, and Royalty Pharma (RPRX) down 34%. ABBV Another option would be to invest in real physical assets: real estate, cars, or bars of gold or other metals. In addition, it could be a good idea to focus on businesses that don't depend too much on market conditions and external circumstances. Everything that generates cash is a possible defense against inflation.

Dear Global Economy, Winter is Coming

Where would you invest in an endless world of the pandemic? Pharmaceuticals, 5G network, Apple stock, cloud storage? Well, if you believed in no end to lockdowns, you’d probably invest in all the above and diversify. That’s precisely what Buffett and his team at Berkshire Hathaway have done. So, let me walk you through Buffett’s eight most significant moves during the last quarter: 1.Dumping bank stocks Buffett continues along with the trend from the second quarter. He’s only added to 9.2% to Bank of America but drastically reduced all other JPMorgan, Wells Fargo, and BNY Mellon positions. He’s bearish on the US financials, and as I wrote in , he probably understands that “the great reset” and the emergence of digital assets and CBDCs will disrupt the traditional banks. my last article 2.Buying pharmaceuticals Four out six new buys for the quarter were pharmaceutical companies. Berkshire has made new positions in Merck, Pfizer, Bristol Myers Squibb, and AbbVie.  They invested approximately 1.8 billion in each one of them, which leads me to conclude that he’s merely diversifying into the sector. This allocation is essentially buying him insurance against the pandemic. And it’s an excellent vaccine play as well. 3.Trimming his position in Apple Buffett reduced the Apple position by 3.7%. This was the most impactful move in Berkshire’s portfolio in the third quarter. But overall, he’s still extremely bullish on Apple. It takes almost half of his portfolio. Holding Apple stock during global lockdowns is one of the most brilliant plays. 4.Buying T-Mobile T-Mobile is one of the largest mobile network providers focusing on rolling out the 5G network. After it acquired Sprint, it became the second-largest mobile network in the US. So, if you believed in a prolonged pandemic, you’d want to own a piece of a company providing a fast connection to the confined population. 5.Selling his gold This move is surprising. First, Buffett shocked the world by ; now, he’s quickly selling half of the position. Both things are the most unlike him. If I had to speculate, I would say he’s finally realized that Bitcoin is better than gold. Popular television host and Bitcoin proponent Max Keiser recently that “Buffett will start panic-buying bitcoin at $50,000, just like Peter Schiff.” buying into gold tweeted 6.Snowflake Snowflake is a data platform built for the cloud dealing with giants like Microsoft, Amazon, or Salesforce. Snowflake makes it easier to work from home. One can surmise that he’s making a bet on the unending pandemic and the consequent growth of cloud-based data warehousing. But it could have been just an opportunistic trade since Buffett bought the shares in the private placement for only at $120. That’s about a half-price from the current market price. 7.Bullish on Japan Berkshire acquired a little more than a 5% stake in five Japanese companies: Itochu, Marubeni, Mitsubishi, Mitsui, and Sumitomo. He made these investments over the previous year, so he likely purchased just a small fraction during the third quarter. Japan has been one of the most rational countries in their approach to Covid-19. Japan has never imposed lockdowns and will likely continue with its model in the future.  That will provide the Japanese economy with an edge over the economies suffering from the draconian measures. So what does it mean for investors? Many investors look to Buffett's wisdom when it comes to investing in turbulent times. Obviously, the Oracle of Omaha does not think the worst of the pandemic is behind us. As he once said, “predicting rain doesn't count, building the ark does.” So go ahead and build your ark too.

How Buffett Is Hedging Against The Endless Pandemic

On the road to better healthcare So, how do I see the future of healthcare using AI? Well, let’s just face it. AI is heading to transform medicine and somewhere even replace real-medicine workers. Every year we observe the appearance of new and more advanced solutions. This, by the way, provides a whole slew of advantages, one of the most important of them is reducing the time needed to reach a diagnosis that allows medical workers to better prioritize patient case. Right now there is already a sufficient number of successful developments. AI and deep learning can analyze a lot more factors and cases than live-health workers do. To be more precise, we are able to use AI for genome research, drug development, medical imaging. AI-based devices can learn, analyze large amounts of information, and make decisions much faster than humans. Wow.. isn’t it just amazing? But what does it mean and is it really so? I suggest reviewing some of the recent advancements and startups to figure it out! and also for a healthy life check out these 12 amazing Healthy lifestyle facts . AI & Deep Learning: Current Status and Perspectives in Medicine In medical practice, there is such an important thing as medical intuition. When a patient visits a doctor, the doctor just looks and literally asks a couple of questions and he already knows what is going on and what disease a patient may have. Let’s look at this from a data analysis perspective. So, what is medical intuition? It is like a doctor using his built-in neural network. Having analyzed a sufficient number of cases, a doctor is able, consciously or not, to identify some additional factors that help him narrow down the solution space. And further, he comes to hypotheses, which he considers a possible diagnosis. Is AI better than medical intuition? Whether we like it or not, medical intuition is an automated thing that can be performed not less well by a machine. What is more, some diagnoses can be formulated better by machines. Let’s take a look at the most prominent cases. Top Recent AI Startups in The Medical Field This is a service that allows you to directly communicate with a person and give him your recommendations. The medical application communicates with the patient, asks about symptoms and complaints, and in response gives recommendations, including which doctor to visit, offers to contact a specialist for a remote consultation. Ada. About 20% of lung cancer and breast cancer go unnoticed in the diagnosis. Lunit is developing deep learning and 3D visualization software to help detect difficult-to-diagnose diseases such as airway cancer. Its use increases the probability of detecting cancer to 83–86%. Over time, the technology will improve and accuracy will become even higher. Lunit. This program monitors the condition of people who have recently undergone long-term treatment or suffer from chronic diseases. It was developed by a startup from San Francisco and raised 8 million venture capital investments initially. The application was created in order to structure data on the state of a person, it sends them to a specialist and gives recommendations. Also, the system is able to remind about the time of taking medications and when there is a need to visit a doctor. Sense.ly. The creation of new drugs is one of the most important tasks of modern medicine. The development of a single medicine in a research laboratory costs $ 2.6 billion, in total about $ 150 billion is spent on this process. Therefore, in 2014, only 46 new drugs appeared. Insilico Medicine creates artificial intelligence that will help in the development of drugs, biomarkers and the study of aging mechanisms. The company wants to improve the quality of life of every person. Insilico Medicine. (Boston) was founded by Andy Back, a graduate of Harvard Medical School. Back spent more than five years studying at Harvard and decided to help other doctors with the timely diagnosis of complex diseases, such as cancer, using machine learning to quickly and accurately analyze cell images. PathAI Startup opportunities are used not so much by doctors as by researchers at pharmaceutical companies. Among the startup client’s are such giants of the pharmaceutical industry like Novartis, Gilead Sciences and Bristol-Myers Squibb. (San Diego) helps blind and visually impaired people “see” the world by combining the capabilities of the human body and the capabilities of artificial intelligence in an application or smart glasses. The company claims that their AI development, Chloe, is still at the initial level of its capabilities: it can perform simple tasks like reading instructions on a medicine package, but the developers have big ambitions for its further development. Aira One of the company’s goals is to make navigation possible with computer vision. The product can be used free of charge for sessions shorter than five minutes and for all sessions in more than 25 thousand places where Aira has partnership agreements. For example, JFK Airport or the Wegmans chain of stores. Diagnostics Systems Using Julia Language Such programming languages like Python and R are popular for applications of machine learning. But in recent years, Julia is acquiring its place and has become the new de-facto for machine learning. Julia offers best-in-class support for modern ML-frameworks like TensorFlow and MXNet, making it easy to adapt to existing workflows. Here are some of the powerful projects built on this language. IBM and Julia Computing for Diagnosing Diabetic Retinopathy Diabetic retinopathy is a complication of diabetes, caused by high blood sugar levels damaging the back of the eye (retina). This disease affects more than 126 million diabetics. Timely screening and diagnosis can help prevent vision loss for millions of diabetics worldwide, but many of them lack access to health care. There are over 126.2 million people in the world (as of 2010) with diabetic retinopathy, and this is expected to rise to over 191.2 million by 2030. According to the WHO in 2006, it accounted for 5% of world blindness. Deep Learning in Julia comes to the rescue and helps to significantly reduce this number. By combining Julia’s superior speed and performance with IBM’s Power8 server and NVIDIA Tesla K80 GPU accelerators, researchers increased image processing speed 57x — a dramatic improvement. They used MXNet.jl, a Julia package for deep learning. As a first step, they load a pre-trained model — ImageNet model called Inception with weights in their 39th epoch. On top of that, they specify a simple classifier. contextflow and Julia Help Radiologists Diagnose Difficult Cases Faster The first and foremost problem with the current state of radiologists’ working process is increasing workloads and lack of time. For diagnosing and treating disease, radiologists need to spend more time overall searching for information and a smaller proportion of their time actually interpreting examinations. contextflow’s image search engine in collaboration with Julia made efforts to resolve this dilemma. contextflow is a 3D image-based search engine that uses deep learning to put the knowledge encoded in millions of medical images and reports at fingertips. Simply mark a region of interest in an image, contextflow returns reference cases, and associated knowledge. While radiologists spend up to 20 minutes searching for information, contextflow using Julia promises to cut down search time to ~2 seconds. Taking into account a global shortage of radiologists, this could be a real solution. For doing so, contextflow uses Knet.jl — a great low-level Julia deep learning package, to build the models to identify reference cases for physicians. More Ways AI Is Making an Impact on Medical Care Google Deepmind Predicts an Exacerbation of Kidney Disease Two Days Before It Occurs This deep learning method can continuously predict the risk of future patient deterioration, drawing on recent work that models adverse events based on electronic medical records and using acute kidney damage — a common and potentially life-threatening condition — as an example. The model was developed on a large data set of electronic medical records covering various clinical environments, which included 703,782 adult patients in 172 inpatients and 1,062 outpatient settings. The model predicts 55.8% of all stationary episodes of acute kidney damage and 90.2% of all acute kidney damage that required subsequent dialysis, with a lead time of up to 48 hours and a ratio of 2 false alerts for each true alert. In addition to predicting future acute kidney damage, the model provides confidence estimates and a list of clinical features that are most common for each prognosis, as well as predicted future trajectories for clinically relevant blood tests. Despite the fact that recognition and quick treatment of acute kidney damage is known to be difficult, the developed approach may offer opportunities for identifying patients at risk within a time interval that allows for early treatment. Augmented Reality Microscope with Real-Time Integration of Artificial Intelligence for Cancer Diagnosis Microscopic evaluation of tissue samples plays an important role in the diagnosis and formulation of cancer and, therefore, directs therapy. However, these estimates demonstrate significant variability, and many regions of the world do not have access to trained pathologists. Although artificial intelligence promises to improve access and quality of care, the cost of digitizing images for pathology and the difficulty in deploying AI solutions remain barriers to real use. Augmented Reality Microscope (ARM) superimposes AI-based information on the current sample presentation in real-time, providing seamless AI integration into routine workflows. Researchers demonstrate the usefulness of ARM for detecting metastatic breast cancer and identifying prostate cancer, with latency compatible with real-time use. It is expected that ARM will be able to remove barriers to the use of AI designed to improve the accuracy and efficiency of a cancer diagnosis. Neural Network to Instantly Predict the Tertiary Structure of the Protein Another powerful breakthrough in healthcare made by AI. ProteinNet neural network is able to predict the structure of a protein in milliseconds. Well, predicting the structure of a protein from its sequence is a central biochemistry problem. Convolutional neural networks show promising results. Advances in deep learning that replace complex, human-developed conveyors with differentiable models optimized from start to finish suggest potential benefits. In their study, scientists presented an end-to-end differentiable model to study protein structure. The model links the local and global structure of the protein through geometric units that optimize global geometry without breaking local covalent chemistry. The tested model uses two complex tasks: forecasting new folds without co-evolutionary data and forecasting known folds without structural patterns. In the first problem, the model achieves modern accuracy, and in the second, it is within 1–2 Å; competing methods that use co-evolution and experimental patterns have been refined over the years, and it is likely that the differentiated approach has significant potential for further improvement, ranging from drug discovery to protein construction. Certainly, there are lots of other AI applications in medical care. This means only one thing for me, we have great potential for further development. Final word: What if AI do a mistake? For me now everything looks fine with AI plus medicine except for one thing. While cutting-edge technologies save time, money and serve patients more efficiently, the majority are curious — is it safe enough? What if AI software will give the wrong diagnosis and so on. Who will be responsible for the false results? It’s hard to answer. From the legal point of view, we do not understand who to shift the responsibility to. What’s the solution to this? Well, I think, medical workers should supervise the work of AI. In my opinion, it is highly important for the medicine to change not towards the complete replacement of doctors, but towards symbiosis and joint collaboration with AI. All existing AI-powered systems should act like supporting systems for making medical decisions. Also read - The Healthy Humans blogs Thanks for reading!

Artificial Intelligence & Deep Learning for Medical Diagnosis

On January 3rd 2019, Bristol-Myers Squibb ( ) Celgene ( ) for $74 billion. CELG rose instantly overnight from 66.64 to 87.86 per share, giving it a 31.8% gain. Although most professionals missed out on these gains, there were other hidden opportunities to be had, if you knew where and how to find them. BMY acquired CELG In this article, we’ll be discussing a group of equities that also rose in value but not immediately. This group has relatively unknown and indirect relationships to CELG. The delay in their rise was sufficient enough that it would have allowed for funds, traders or investors to take positions, and then profit. This is an example of information arbitrage in the financial markets. It happens from time to time and when it does, most people miss the opportunity. This also relates to a paper titled “ ,” (with Tomer Regev, Journal of Finance, February 2001, Vol. 56, №1, pp. 387–396). The research described an event with a company called EntreMed (ENMD was the symbol at the time): Contagious Speculation and a Cure for Cancer: A Non-Event that Made Stock Prices Soar “A Sunday New York Times article on a potential development of new cancer-curing drugs caused EntreMed’s stock price to rise from 12.063 at the Friday close, to open at 85 and close near 52 on Monday. It closed above 30 in the three following weeks. The enthusiasm spilled over to other biotechnology stocks. The potential breakthrough in cancer research already had been reported, however, in the journal , and in various popular newspapers ~including the Times! more than five months earlier. Thus, enthusiastic public attention induced a permanent rise in share prices, even though no genuinely new information had been presented.” Nature Among the many insightful observations made by the researchers, one stood out in the conclusion: “[Price] movements may be concentrated in stocks that have some things in common, but these need not be economic fundamentals.” Capturing information arbitrage opportunities can be done using advanced techniques in the area of (NLP/NLU). This also includes processing of correlation matrix datasets based on data such as public company profiles, encyclopedias, peer-reviewed scientific literature, news and patents located . Natural Language Processing and Understanding here Using a NLP/NLU correlation matrix dataset of US publicly traded equities, we generated a cluster (or basket) of companies that have relationships to CELG based on symbiotic, parasitic and sympathetic . latent entanglement What we found, with the top five scoring equities, were unique opportunities to profit ahead of the market. We’ve produced the following slides with charts to show where gains should have been locked in. Although not all equities performed, it’s clear to see where information arbitrage opportunities existed after the CELG acquisition. Especially pronounced movers were , and in relation to CELG. The benchmark used to compare against for the first set of charts is the S&P 500 and for the second set of charts, the IHE (iShares Pharma ETF). AGIO ADRO EPZM Benchmark: S&P 500 1. AGIO: 2. ADRO: 3. SHPG: 4. XLRN: 5. EPZM: Source: [ ] PDF Benchmark: IHE (iShares Pharma ETF) 1. AGIO: 2. ADRO: 3. SHPG: 4. XLRN: 5. EPZM: Source: ] [ PDF You can query the dataset used to generate the CELG basket . There’s also an API available on the same page. here The resulting output: Conclusion The basket ( , , , & ) generated by the dataset included many more equities, but as stated earlier, we chose to compare using the top five scoring symbols. AGIO ADRO SHPG XLRN EPZM The delayed reaction of , and is the result of a delay in the way the market absorbs information. AGIO ADRO EPZM We don’t know how many people missed out on these trades but one thing we do know is that the movement in CELG was nowhere near being priced into , and efficiently. This left an opportunity for a lot of money to be made for those that uncovered these relationships early. AGIO ADRO EPZM This means that when the news came out that CELG was going to be acquired for $74 billion, one could have generated a basket of equities with hidden relationships to CELG, positioned in them ahead of the market and profited from a rising tide created by CELG. As we continue to produce our correlation matrix datasets for traditional public companies and cryptocurrencies based on NLP/NLU, we’ll make them available, along with customized versions, to any holders of our token, . For more information feel free to reach out to us on or at anytime! VXV telegram vectorspace.ai

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