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sales pitches are often from biased sources, the choices can be overwhelming and impartial help is not equally available to all

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theconversation.com – Grace McCormack, Postdoctoral researcher of Health Policy and Economics, of Southern California – 2024-10-10 07:32:00

It can take a lot of effort to understand the many different Medicare choices.

Halfpoint Images/Moment via Getty Images

Grace McCormack, University of Southern California and Melissa Garrido, Boston University

The 67 million Americans eligible for Medicare make an important every October: Should they make changes in their Medicare health insurance plans for the next calendar year?

The decision is complicated. Medicare has an enormous variety of coverage options, with large and varying implications for people’s health and finances, both as beneficiaries and taxpayers. And the decision is consequential โ€“ some choices lock beneficiaries out of traditional Medicare.

Beneficiaries choose an insurance plan when they turn 65 or become eligible based on qualifying chronic conditions or disabilities. After the initial sign-up, most beneficiaries can make changes only during the open enrollment period each fall.

The 2024 open enrollment period, which runs from Oct. 15 to Dec. 7, marks an opportunity to reassess options. Given the complicated nature of Medicare and the scarcity of unbiased advisers, however, finding reliable information and understanding the options available can be challenging.

We are health care policy experts who study Medicare, and even we find it complicated. One of us recently helped a relative enroll in Medicare for the first time. She’s healthy, has access to health insurance through her employer and doesn’t regularly take prescription drugs. Even in this straightforward scenario, the number of choices were overwhelming.

The stakes of these choices are even higher for people managing multiple chronic conditions. There is help available for beneficiaries, but we have found that there is considerable room for improvement โ€“ especially in making help available for everyone who needs it.

The choice is complex, especially when you are signing up for the first time and if you are eligible for both Medicare and Medicaid. Insurers often engage in aggressive and sometimes deceptive advertising and outreach through brokers and agents. Choose unbiased resources to guide you through the process, like www.shiphelp.org. Make sure to start before your 65th birthday for initial sign-up, look out for yearly plan changes, and start well before the Dec. 7 deadline for any plan changes.

2 paths with many decisions

Within Medicare, beneficiaries have a choice between two very different programs. They can enroll in either traditional Medicare, which is administered by the government, or one of the Medicare Advantage plans offered by private insurance companies.

Within each program are dozens of further choices.

Traditional Medicare is a nationally uniform cost-sharing plan for medical services that allows people to choose their providers for most types of medical care, usually without prior authorization. Deductibles for 2024 are US$1,632 for hospital costs and $240 for outpatient and medical costs. Patients also have to chip in starting on Day 61 for a hospital stay and Day 21 for a skilled nursing facility stay. This percentage is known as coinsurance. After the yearly deductible, Medicare pays 80% of outpatient and medical costs, leaving the person with a 20% copayment. Traditional Medicare’s basic plan, known as Part A and Part B, also has no out-of-pocket maximum.

Pen, glasses and medicare health insurance card

Traditional Medicare starts with Medicare parts A and B.

Bill Oxford/iStock via Getty Images

People enrolled in traditional Medicare can also purchase supplemental coverage from a private insurance company, known as Part D, for drugs. And they can purchase supplemental coverage, known as Medigap, to lower or eliminate their deductibles, coinsurance and copayments, cap costs for Parts A and B, and add an emergency foreign travel benefit.

Part D plans prescription drug costs for about $0 to $100 a month. People with lower incomes may get extra financial help by signing up for the Medicare program Part D Extra Help or state-sponsored pharmaceutical assistance programs.

There are 10 standardized Medigap plans, also known as Medicare supplement plans. Depending on the plan, and the person’s gender, location and smoking status, Medigap typically costs from about $30 to $400 a month when a beneficiary first enrolls in Medicare.

The Medicare Advantage program allows private insurers to bundle everything together and offers many enrollment options. Compared with traditional Medicare, Medicare Advantage plans typically offer lower out-of-pocket costs. They often bundle supplemental coverage for hearing, vision and dental, which is not part of traditional Medicare.

But Medicare Advantage plans also limit provider networks, meaning that people who are enrolled in them can see only certain providers without paying extra. In comparison to traditional Medicare, Medicare Advantage enrollees on average go to lower-quality hospitals, nursing facilities, and home health agencies but see higher-quality primary care doctors.

Medicare Advantage plans also often require prior authorization โ€“ often for important services such as stays at skilled nursing facilities, home health services and dialysis.

Choice overload

Understanding the tradeoffs between premiums, access and out-of-pocket health care costs can be overwhelming.

Graphic of a person flow lines pointing to text boxes on either side that have smaller arrows to more text boxes holding plan choice descriptions.

Turning 65 begins the process of taking one of two major paths, which each have a thicket of health care choices.

Rika Kanaoka/USC Schaeffer Center for Health Policy & Economics

Though options vary by county, the typical Medicare beneficiary can choose between as many as 10 Medigap plans and 21 standalone Part D plans, or an average of 43 Medicare Advantage plans. People who are eligible for both Medicare and Medicaid, or have certain chronic conditions, or are in a long-term care facility have additional types of Medicare Advantage plans known as Special Needs Plans to choose among.

Medicare Advantage plans can vary in terms of networks, benefits and use of prior authorization.

Different Medicare Advantage plans have varying and large impacts on enrollee health, including dramatic differences in mortality rates. Researchers found a 16% difference per year between the best and worst Medicare Advantage plans, meaning that for every 100 people in the worst plans who die within a year, they would expect only 84 people to die within that year if all had been enrolled in the best plans instead. They also found plans that cost more had lower mortality rates, but plans that had higher federal quality ratings โ€“ known as โ€œstar ratingsโ€ โ€“ did not necessarily have lower mortality rates.

The quality of different Medicare Advantage plans, however, can be difficult for potential enrollees to assess. The federal plan finder website lists available plans and publishes a quality rating of one to five for each plan. But in practice, these star ratings don’t necessarily correspond to better enrollee experiences or meaningful differences in quality.

Online provider networks can also contain errors or include providers who are no longer seeing new patients, making it hard for people to choose plans that give them access to the providers they prefer.

While many Medicare Advantage plans boast about their supplemental benefits , such as vision and dental coverage, it’s often difficult to understand how generous this supplemental coverage is. For instance, while most Medicare Advantage plans offer supplemental dental , cost-sharing and coverage can vary. Some plans don’t cover services such as extractions and endodontics, which includes root canals. Most plans that cover these more extensive dental services require some combination of coinsurance, copayments and annual limits.

Even when information is fully available, mistakes are likely.

Part D beneficiaries often fail to accurately evaluate premiums and expected out-of-pocket costs when making their enrollment decisions. Past work suggests that many beneficiaries have difficulty processing the proliferation of options. A person’s relationship with health care providers, financial situation and preferences are key considerations. The consequences of enrolling in one plan or another can be difficult to determine.

The trap: Locked out

At 65, when most beneficiaries first enroll in Medicare, federal regulations guarantee that anyone can get Medigap coverage. During this initial sign-up, beneficiaries can’t be charged a higher premium based on their health.

Older Americans who enroll in a Medicare Advantage plan but then want to switch back to traditional Medicare after more than a year has passed lose that guarantee. This can effectively lock them out of enrolling in supplemental Medigap insurance, making the initial decision a one-way street.

For the initial sign-up, Medigap plans are โ€œguaranteed issue,โ€ meaning the plan must cover preexisting health conditions without a waiting period and must allow anyone to enroll, regardless of health. They also must be โ€œcommunity rated,โ€ meaning that the cost of a plan can’t rise because of age or illness, although it can go up due to other factors such as inflation.

People who enroll in traditional Medicare and a supplemental Medigap plan at 65 can expect to continue paying community-rated premiums as long as they remain enrolled, regardless of what happens to their health.

In most states, however, people who switch from Medicare Advantage to traditional Medicare don’t have as many protections. Most regulations permit plans to deny coverage, impose waiting periods or charge higher Medigap premiums based on their expected health costs. Only Connecticut, Maine, Massachusetts and New York guarantee that people can get Medigap plans after the initial sign-up period.

Deceptive advertising

Information about Medicare coverage and assistance choosing a plan is available but varies in quality and completeness. Older Americans are bombarded with ads for Medicare Advantage plans that they may not be eligible for and that include misleading statements about benefits.

A November 2022 report from the U.S. Senate Committee on Finance found deceptive and aggressive sales and marketing tactics, including mailed brochures that implied government endorsement, telemarketers who called up to 20 times a day, and salespeople who approached older adults in the grocery store to ask about their insurance coverage.

The Department of Health and Human Services tightened rules for 2024, requiring third-party marketers to include federal resources about Medicare, including the website and toll- phone number, and limiting the number of contacts from marketers.

Although the government has the authority to marketing materials, enforcement is partially dependent on whether complaints are filed. Complaints can be filed with the federal government’s Senior Medicare Patrol, a federally funded program that prevents and addresses unethical Medicare activities.

Meanwhile, the number of people enrolled in Medicare Advantage plans has grown rapidly, doubling since 2010 and accounting for more than half of all Medicare beneficiaries by 2023.

Nearly one-third of Medicare beneficiaries seek information from an insurance broker. Brokers sell health insurance plans from multiple companies. However, because they receive payment from plans in exchange for sales, and because they are unlikely to sell every option, a plan recommended by a broker may not meet a person’s needs.

Help is out there โˆ’ but falls short

An alternative source of information is the federal government. It offers three sources of information to assist people with choosing one of these plans: 1-800-Medicare, medicare.gov and the State Health Insurance Assistance Program, also known as SHIP.

The SHIP program combats misleading Medicare advertising and deceptive brokers by connecting eligible Americans with counselors by phone or in person to help them choose plans. Many people say they prefer meeting in person with a counselor over phone or internet support. SHIP staff say they often help people understand what’s in Medicare Advantage ads and disenroll from plans they were directed to by brokers.

Telephone SHIP services are available nationally, but one of us and our colleagues have found that in-person SHIP services are not available in some . We tabulated areas by ZIP code in 27 states and found that although more than half of the locations had a SHIP site within the county, areas without a SHIP site included a larger proportion of people with low incomes.

Virtual services are an option that’s particularly useful in rural areas and for people with limited mobility or little access to transportation, but they require online access. Virtual and in-person services, where both a beneficiary and a counselor can look at the same computer screen, are especially useful for looking through complex coverage options.

We also interviewed SHIP counselors and coordinators from across the U.S.

As one SHIP coordinator noted, many people are not aware of all their coverage options. For instance, one beneficiary told a coordinator, โ€œI’ve been on Medicaid and I’m aging out of Medicaid. And I don’t have a lot of money. And now I have to pay for my insurance?โ€ As it turned out, the beneficiary was eligible for both Medicaid and Medicare because of their income, and so had to pay less than they thought.

The interviews made clear that many people are not aware that Medicare Advantage ads and insurance brokers may be biased. One counselor said, โ€œThere’s a lot of backing (beneficiaries) off the ledge, if you will, thanks to those TV commercials.โ€

Many SHIP staff counselors said they would benefit from additional training on coverage options, including for people who are eligible for both Medicare and Medicaid. The SHIP program relies heavily on volunteers, and there is often greater demand for services than the available volunteers can offer. Additional counselors would help meet needs for complex coverage decisions.

The key to making a good Medicare coverage decision is to use the help available and weigh your costs, access to health providers, current health and medication needs, and also consider how your health and medication needs might change as time goes on.

This article is part of an occasional series examining the U.S. Medicare system.

This story has been updated to remove a graphic that contained incorrect information about SHIP locations, and to correct the date of the open enrollment period.The Conversation

Grace McCormack, Postdoctoral researcher of Health Policy and Economics, University of Southern California and Melissa GarridoBoston University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Machine learning cracked the protein-folding problem and won the 2024 Nobel Prize in chemistry

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theconversation.com – Marc Zimmer, Professor of Chemistry, Connecticut College – 2024-10-09 12:57:00

Protein molecules can have complicated structures that dictate their functions.
Christoph Burgstedt/Science Photo Library via Getty Images

Marc Zimmer, Connecticut College

The 2024 Nobel Prize in chemistry recognized Demis Hassabis, John Jumper and David Baker for using machine learning to tackle one of biology’s biggest challenges: predicting the 3D shape of proteins and designing them from scratch.

This year’s award stood out because it honored research that originated at a tech company: DeepMind, an AI research startup that was acquired by Google in 2014. Most previous chemistry Nobel Prizes have gone to researchers in academia. Many laureates went on to form startup companies to further expand and commercialize their groundbreaking work โ€“ for instance, CRISPR gene-editing technology and quantum dots โ€“ but the research, from start to end, wasn’t done in the commercial sphere.

Although the Nobel Prizes in physics and chemistry are awarded separately, there is a fascinating connection between the winning research in those fields in 2024. The physics award went to two computer scientists who laid the foundations for machine learning, while the chemistry laureates were rewarded for their use of machine learning to tackle one of biology’s biggest mysteries: how proteins fold.

The 2024 Nobel Prizes underscore both the importance of this kind of artificial intelligence and how science today often crosses traditional boundaries, blending different fields to achieve groundbreaking results.

The challenge of protein folding

Proteins are the molecular machines of . They make up a significant portion of our bodies, muscles, enzymes, hormones, blood, hair and cartilage.

schematic of 20 amino acids in a chain and then how a protein structure folds into a unique shape
Proteins are chains of amino acid molecules that form a 3D shape based on their atoms’ interactions.
ยฉJohan Jarnestad/The Royal Swedish Academy of Sciences

Understanding proteins’ structures is essential because their shapes determine their functions. Back in 1972, Christian Anfinsen won the Nobel Prize in chemistry for showing that the sequence of a protein’s amino acid building blocks dictates the protein’s shape, which, in turn, influences its function. If a protein folds incorrectly, it may not work properly and could lead to diseases such as Alzheimer’s, cystic fibrosis or diabetes.

A protein’s overall shape depends on the tiny interactions, the attractions and repulsions, between all the atoms in the amino acids its made of. Some want to be together, some don’t. The protein twists and folds itself into a final shape based on many thousands of these chemical interactions.

For decades, one of biology’s greatest challenges was predicting a protein’s shape based solely on its amino acid sequence. Although researchers can now predict the shape, we still don’t understand how the proteins maneuver into their specific shapes and minimize the repulsions of all the interatomic interactions in a few microseconds.

To understand how proteins work and to prevent misfolding, scientists needed a way to predict the way proteins fold, but solving this puzzle was no easy task.

In 2003, University of Washington biochemist David Baker wrote Rosetta, a computer program for designing proteins. With it he showed it was possible to reverse the protein-folding problem by designing a protein shape and then predicting the amino acid sequence needed to create it.

It was a phenomenal jump forward, but the shape chosen for the calculation was simple, and the calculations were complex. A major paradigm shift was required to routinely design novel proteins with desired structures.

A new era of machine learning

Machine learning is a type of AI where computers learn to solve problems by analyzing vast amounts of data. It’s been used in various fields, from game-playing and speech recognition to autonomous vehicles and scientific research. The idea behind machine learning is to use hidden patterns in data to answer complex questions.

This approach made a huge leap in 2010 when Demis Hassabis co-founded DeepMind, a company aiming to combine neuroscience with AI to solve real-world problems.

Hassabis, a chess prodigy at age 4, quickly made headlines with AlphaZero, an AI that taught itself to play chess at a superhuman level. In 2017, AlphaZero thoroughly beat the world’s top computer chess program, Stockfish-8. The AI’s ability to learn from its own gameplay, rather than relying on preprogrammed strategies, marked a turning point in the AI world.

Soon after, DeepMind applied similar techniques to Go, an ancient board known for its immense complexity. In 2016, its AI program AlphaGo defeated one of the world’s top players, Lee Sedol, in a widely watched match that stunned millions.

two men on spiral staircase look up at camera
Demis Hassabis and John Jumper at Google DeepMind on Oct. 9, 2024, after being awarded the Nobel Prize in chemistry.
AP Photo/Alastair Grant

In 2016, Hassabis shifted DeepMind’s focus to a new : the protein-folding problem. Under the leadership of John Jumper, a chemist with a background in protein science, the AlphaFold began. The team used a large database of experimentally determined protein structures to train the AI, which it to learn the principles of protein folding. The result was AlphaFold2, an AI that could predict the 3D structure of proteins from their amino acid sequences with remarkable accuracy.

This was a significant scientific breakthrough. AlphaFold has since predicted the structures of over 200 million proteins โ€“ essentially all the proteins that scientists have sequenced to date. This massive database of protein structures is now freely available, accelerating research in biology, medicine and drug development.

Designer proteins to fight disease

Understanding how proteins fold and function is crucial for designing new . Enzymes, a type of protein, act as catalysts in biochemical reactions and can speed up or regulate these processes. To treat diseases such as cancer or diabetes, researchers often target specific enzymes involved in disease pathways. By predicting the shape of a protein, scientists can figure out where small molecules โ€“ potential drug candidates โ€“ might bind to it, which is the first step in designing new medicines.

In 2024, DeepMind launched AlphaFold3, an upgraded version of the AlphaFold program that not only predicts protein shapes but also identifies potential binding sites for small molecules. This advance makes it easier for researchers to design drugs that precisely target the right proteins.

Google bought Deepmind for reportedly around half a billion dollars in 2014. Google DeepMind has now started a new venture, Isomorphic Labs, to collaborate with pharmaceutical companies on real-world drug development using these AlphaFold3 predictions.

smiling seated man holds cell phone in his hand for a speaker call
David Baker speaks on the phone with Demis Hassabis and John Jumper just after they got the Nobel Prize on Oct. 9, 2024.
Ian C. Haydon/UW Medicine Institute for Protein Design

For his part, David Baker has continued to make significant contributions to protein science. His team at the University of Washington developed an AI-based method called โ€œfamily-wide hallucination,โ€ which they used to design entirely new proteins from scratch. Hallucinations are new patterns โ€“ in this case, proteins โ€“ that are plausible, meaning they are a good fit with patterns in the AI’s data. These new proteins included a light-emitting enzyme, demonstrating that machine learning can help create novel synthetic proteins. These AI tools offer new ways to design functional enzymes and other proteins that never could have evolved naturally.

AI will enable research’s next chapter

The Nobel-worthy achievements of Hassabis, Jumper and Baker show that machine learning isn’t just a tool for computer scientists โ€“ it’s now an essential part of the future of biology and medicine.

By tackling one of the toughest problems in biology, the winners of the 2024 prize have opened up new possibilities in drug discovery, personalized medicine and even our understanding of the chemistry of life itself.The Conversation

Marc Zimmer, Professor of Chemistry, Connecticut College

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Off-brand Ozempic, Zepbound and other weight loss products carry undisclosed risks for consumers

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theconversation.com – C. Michael White, Distinguished Professor of Pharmacy Practice, of Connecticut – 2024-10-09 07:34:00

C. Michael White, University of Connecticut

In just a few years, brand-name injectable such as Ozempic, Wegovy, Mounjaro and Zepbound have rocketed to fame as billion-dollar annual sellers for weight loss as well as to control blood sugar levels and reduce the risk of heart disease.

But the price of these injections is steep: They cost about US$800-$1,000 per month, and if used for weight loss alone, they are not covered by most insurance policies. Both drugs mimic the naturally occurring hormone GLP-1 to regulate blood sugar and reduce cravings. They can be taken only with a prescription.

The Food and Drug Administration announced an official shortage of the active ingredients in these drugs in 2022, but on Oct. 2, 2024, the agency announced that the shortage has been resolved for the medicine tirzepatide, the active ingredient in Mounjaro and Zepbound.

Despite the soaring demand and limited supply of these drugs, there are no generic versions available. This is because the patents for semaglutide โ€“ the active ingredient in Ozempic and Wegovy, which is still in shortage โ€“ and tirzepatide don’t expire until 2033 and 2036, respectively.

As a result, nonbrand alternatives that can be purchased with or without a prescription are the market. Yet these products with real risks to consumers.

I am a pharmacist who studies weaknesses in federal oversight of prescription and over-the-counter drugs and dietary supplements in the U.S. My research group recently has investigated loopholes that are allowing alternative weight loss products to enter the market.

High demand is driving GLP-1 wannabes

The dietary supplement market has sought to cash in on the GLP-1 demand with pills, teas, extracts and all manner of other products that claim to produce similar effects as the brand names at a much lower price.

Products containing the herb berberine offer only a few pounds of weight loss, while many dietary supplement weight loss products contain stimulants such as sibutramine and laxatives such as phenolphthalein, which increase the risk of heart attacks, strokes and cancer.

Poison control centers have seen a steep rise in calls related to off-brand weight loss medications.

The role of compounding pharmacies

Unlike the dietary supplements that are masquerading as GLP-1 weight loss products, compounding pharmacies can create custom versions of products that contain the same active ingredients as the real thing for who cannot use either brand or generic products for some reason.

These pharmacies can also produce alternative versions of brand-name drugs when official drug shortages exist.

Since the demand for GLP-1 medications has far outpaced the supply, compounding pharmacies are legally producing a variety of different semaglutide and tirzepatide products.

These products may come in versions that differ from the brand-name companies, such as vials of powder that must be dissolved in liquid, or as tablets or nasal sprays.

Just like the brand-name drugs, you must have a valid prescription to them. The prices range from $250-$400 a month โ€“ still a steep price for many consumers.

Compounding pharmacies must adhere to the FDA’s sterility and quality production methods, but these rules are not as rigorous for compounding pharmacies as those for commercial manufacturers of generic drugs.

In addition, the products compounding pharmacies create do not have to be tested in humans for safety or effectiveness like brand-name products do.

Proper dosing can also be challenging with compounded forms of the drugs.

Companies that work the system

For people who cannot afford a compounding pharmacy product, or cannot get a valid prescription for semaglutide or tirzepatide, opportunistic companies are stepping in to fill the void. These include โ€œpeptide companies,โ€ manufacturers that create non-FDA approved knockoff versions of the drugs.

From November 2023 to March 2024, my team carried out a study to assess which of these peptide companies are selling semaglutide or tirzepatide products. We scoured the internet looking for these peptide companies and collected information about what they were selling and their sales practices.

We found that peptide sellers use a loophole to sell these drugs. On their websites, the companies that their drugs are for โ€œresearch purposes onlyโ€ or โ€œnot for human consumption,โ€ but they do nothing to verify that the buyers are researchers or that the product is going to a research facility.

By reading the comments sections of the company websites and the targeted ads on social media, it becomes clear that both buyers and sellers understand the charade. Unlike compounding pharmacies, these peptide sellers do not provide the supplies you need to dissolve and inject the drug, provide no instructions, and will usually not answer questions.

Peptide sellers, since they allegedly are not selling to consumers, do not require a valid prescription and will sell consumers whatever quantity of drug they wish to purchase. Even if a person has an eating disorder such as anorexia nervosa, the companies will happily sell them a semaglutide or tirzepatide product without a prescription. The average prices of these peptide products range from $181-$203 per month.

Skirting regulations

Peptide sellers do not have to adhere to the rules or regulations that drug manufacturers or compounding pharmacies do. Many companies state that their products are 99% pure, but an independent investigation of three companies’ products from August 2023 to March 2024 found that the purity of the products were far less than promised.

One product contained endotoxin โ€“ a toxic substance produced by bacteria โ€“ suggesting that it was contaminated with microbes. In addition, the products’ promised dosages were off by up 29% to 39%. Poor purity can cause patients to experience fever, chills, nausea, skin irritation, infections and low blood pressure.

In this study, some companies never even shipped the drug, telling the buyers they needed to pay an additional fee to have the product clear customs.

If a consumer is harmed by a poor-quality product, it would be difficult to sue the seller, since the products specifically say they are โ€œnot for human consumption.โ€ Ultimately, consumers are being led to spend money on products that may never arrive, could cause an infection, might not have the correct dose, and contain no instructions on how to safely use or store the product.

Will prices for brand-name products come down?

To combat these alternative sellers, pharmaceutical company Eli Lilly began offering an alternative version of its brand-name Zepbound product for weight loss in September 2024.

Instead of its traditional injection pen products that cost more than $1,000 for a month’s supply, this product in vials that patients draw up and inject themselves. For patients who take 5 milligrams of Zepbound each week, the vial products would cost them $549 a month if patients buy it through the company’s online pharmacy and can show that they do not have insurance coverage for the drug.

After a grilling on Capitol Hill in September 2024, pharmaceutical company Novo Nordisk came under intense pressure to offer patients without prescription coverage a lower-priced product for its brand-name Wegovy as well.

In the next few years, additional brand-name GLP-1 agonist drugs will likely make it to market. As of October 2024, a handful of these products are in late-phase clinical trials, with active ingredients such as retatrutide, survodutide and ecnoglutide, and more than 18 other drug candidates are in earlier stages of development.

When new pharmaceutical companies enter this market, they will have to offer patients lower prices than Eli Lilly and Novo Nordisk in order to gain market share. This is the most likely medium-term solution to drive down the costs of GLP-1 drugs and eliminate the drug shortages in the marketplace.The Conversation

C. Michael White, Distinguished Professor of Pharmacy Practice, University of Connecticut

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How a subfield of physics led to breakthroughs in AI โ€“ and from there to this yearโ€™s Nobel Prize

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theconversation.com – Veera Sundararaghavan, Professor of Aerospace Engineering, University of Michigan – 2024-10-09 07:22:00

Neural networks have their roots in statistical mechanics.

BlackJack3D/iStock via Getty Images Plus

Veera Sundararaghavan, University of Michigan

John J. Hopfield and Geoffrey E. Hinton received the Nobel Prize in physics on Oct. 8, 2024, for their research on machine learning algorithms and neural networks that help computers learn. Their work has been fundamental in developing neural network theories that underpin generative artificial intelligence.

A neural network is a computational model consisting of layers of interconnected neurons. Like the neurons in your brain, these neurons and send along a piece of information. Each neural layer receives a piece of data, processes it and passes the result to the next layer. By the end of the sequence, the network has processed and refined the data into something more useful.

While it might seem surprising that Hopfield and Hinton received the physics prize for their contributions to neural networks, used in computer science, their work is deeply rooted in the principles of physics, particularly a subfield called statistical mechanics.

As a computational materials scientist, I was to see this area of research recognized with the prize. Hopfield and Hinton’s work has my colleagues and me to study a process called generative learning for materials sciences, a method that is behind many popular technologies like ChatGPT.

What is statistical mechanics?

Statistical mechanics is a branch of physics that uses statistical methods to explain the behavior of made up of a large number of particles.

Instead of focusing on individual particles, researchers using statistical mechanics look at the collective behavior of many particles. Seeing how they all act together helps researchers understand the system’s large-scale macroscopic properties like temperature, pressure and magnetization.

For example, physicist Ernst Ising developed a statistical mechanics model for magnetism in the 1920s. Ising imagined magnetism as the collective behavior of atomic spins interacting with their neighbors.

In Ising’s model, there are higher and lower energy states for the system, and the material is more likely to exist in the lowest energy state.

One key idea in statistical mechanics is the Boltzmann distribution, which quantifies how likely a given state is. This distribution the probability of a system being in a particular state โ€“ like solid, liquid or gas โ€“ based on its energy and temperature.

Ising exactly predicted the phase transition of a magnet using the Boltzmann distribution. He figured out the temperature at which the material changed from being magnetic to nonmagnetic.

Phase changes happen at predictable temperatures. Ice melts to at a specific temperature because the Boltzmann distribution predicts that when it gets warm, the water molecules are more likely to take on a disordered โ€“ or liquid โ€“ state.

Statistical mechanics tells researchers about the properties of a larger system, and how individual objects in that system act collectively.

In materials, atoms arrange themselves into specific crystal structures that use the lowest amount of energy. When it’s cold, water molecules freeze into ice crystals with low energy states.

Similarly, in biology, proteins fold into low energy shapes, which allow them to function as specific antibodies โ€“ like a lock and key โ€“ targeting a virus.

Neural networks and statistical mechanics

Fundamentally, all neural networks work on a similar principle โ€“ to minimize energy. Neural networks use this principle to solve computing problems.

For example, imagine an image made up of pixels where you only can see a part of the picture. Some pixels are visible, while the rest are hidden. To determine what the image is, you consider all possible ways the hidden pixels could fit together with the visible pieces. From there, you would choose from among what statistical mechanics would say are the most likely states out of all the possible options.

A diagram showing statistical mechanics on the left, with a graph showing three atomic structures, with the one at the lowest energy labeled the most stable. On the right is labeled neural networks, showing two photos of trees, one only half visible.

In statistical mechanics, researchers try to find the most stable physical structure of a material. Neural networks use the same principle to solve complex computing problems.

Veera Sundararaghavan

Hopfield and Hinton developed a theory for neural networks based on the idea of statistical mechanics. Just like Ising before them, who modeled the collective interaction of atomic spins to solve the problem with a neural network, Hopfield and Hinton imagined collective interactions of pixels. They represented these pixels as neurons.

Just as in statistical physics, the energy of an image refers to how likely a particular configuration of pixels is. A Hopfield network would solve this problem by finding the lowest energy arrangements of hidden pixels.

However, unlike in statistical mechanics โ€“ where the energy is determined by known atomic interactions โ€“ neural networks learn these energies from data.

Hinton popularized the development of a technique called backpropagation. This technique helps the model figure out the interaction energies between these neurons, and this algorithm underpins much of modern AI learning.

The Boltzmann machine

Building upon Hopfield’s work, Hinton imagined another neural network, called the Boltzmann machine. It consists of visible neurons, which we can observe, and hidden neurons, which the network learn complex patterns.

In a Boltzmann machine, you can determine the probability that the picture looks a certain way. To figure out this probability, you can sum up all the possible states the hidden pixels could be in. This gives you the total probability of the visible pixels being in a specific arrangement.

My group has worked on implementing Boltzmann machines in quantum computers for generative learning.

In generative learning, the network learns to generate new data samples that resemble the data the researchers fed the network to train it. For example, it might generate new images of handwritten numbers after being trained on similar images. The network can generate these by sampling from the learned probability distribution.

Generative learning underpins modern AI โ€“ it’s what allows the generation of AI art, videos and text.

Hopfield and Hinton have significantly influenced AI research by leveraging tools from statistical physics. Their work draws parallels between how nature determines the physical states of a material and how neural networks predict the likelihood of to complex computer science problems.The Conversation

Veera Sundararaghavan, Professor of Aerospace Engineering, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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