The Breakthrough Was There All Along

If you’ve ever played The New York Times game Connections, you know the feeling. You’re staring at a grid of words, knowing the solution is there, but unable to see how the pieces fit together. All you can do is work with the words in front of you. There are no extra clues, no new information coming. The only option is to shuffle, to look at the same information in a different arrangement until patterns begin to appear.

Nothing about the problem changes. Then something about how you see it does.

In 2014, a third-year medical student named David Fajgenbaum checked himself into the emergency room mid-exam. He felt off. By the time anyone understood why, he was in the ICU with multiple organ failure from a disease so rare it wasn’t taught in medical school: Castleman disease. The only approved drug didn’t work. A priest came to his bedside and read him his last rites. He was 25.

Fajgenbaum survived that relapse, and four more after it. As he recounted in a recent episode of NPR’s Radiolab, he understood that chemotherapy was keeping him alive without curing him, and that waiting for a new drug to be developed (a process that typically takes 10 to 15 years and billions of dollars) wasn’t an option he had. So he did something unusual. He started asking his doctors to save his blood samples, and he ran experiments on himself.

What he found was that a specific signaling pathway in his immune system, mTOR, was in overdrive. When he searched the existing pharmacological literature for something that could block it, he found an answer that had been sitting in pharmacies for 25 years. Sirolimus, a drug approved in 1999 to prevent organ transplant rejection, had never been used for Castleman disease. The biology of his disease hadn’t changed. The drug had always existed. The connection simply hadn’t been made.

He took it. It worked. He has been in remission for over a decade.

The detail worth holding onto isn’t the drug or the disease. It’s the instinct. Fajgenbaum didn’t wait for new knowledge to arrive. He looked differently at what already existed.

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Augmenting Human Capabilities with AI Tools

We hear a lot of stories about AI tools helping people complete tasks more quickly, or automating menial or redundant tasks. However, Promega isn’t just interested in speeding things up. We’re focused on leveraging AI tools to help us do things better. All over the organization, employees are leveraging large language models (LLMs) and machine learning systems to accomplish things that weren’t possible before, or to make their work more effective against their goals.

Three employees shared their recent successes, including strengthening supervisory skills, scaling up production processes and training new team members. Each of these examples uses AI in a unique way, while still elevating human expertise, creativity and decision-making.

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How Artificial Intelligence Revolutionized the 2024 Paris Olympics

At the end of July, many people across the globe were preparing to tune into the two-week, 2024 Olympic Games in Paris, France. The Olympics were slated to feature several high-profile athletes—including Simone Biles (USA, artistic gymnastics), Eluid Kipchoge (Kenya, marathon) and Marta Vieira da Silva (Brazil, football). However, in the lead-up to the Games, the International Olympic Committee (IOC) focused on a secondary player: Artificial Intelligence (AI). The IOC laid out an ambitious AI agenda aimed to enhance athlete performance, ensure fairness and optimize operations. The 2024 Paris Olympics represent a significant leap forward in integrating AI into the world of sports.

Together, we can unlock AI’s full potential to promote solidarity, further digitalization, improve sustainability and resilience, and reinforce the role of sport in society.” – Olympic AI Agenda

Here, we explore several applications of AI in the 2024 Paris Olympics.

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Can AI Replace High-Throughput Screens for Drug Discovery?

This image was created with the assistance of AI

For decades, pharmaceutical companies have relied on high-throughput screening (HTS) as the first step in the drug discovery process. After an initial screening of thousands of compounds, scientists select a smaller list of candidate drugs that is then used for further downstream testing. A major limitation to HTS, however, is the need to synthesize all compounds used in the screen—the compounds need to physically exist to be tested. This significantly limits the number of compounds that can be tested, hindering the discovery of new drugs.

What if we could test compounds even before they are synthesized?

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