Elevance Health has sued the U.S. government, alleging that federal efforts to recalculate its Medicare Advantage quality ratings didn’t align with a recent court ruling, costing the health insurer $115 million.
The lawsuit, filed Wednesday in U.S. District Court for the Southern District of Georgia, represents a new tier of drama in the Medicare Advantage program, which is the alternative to traditional Medicare that is run by private insurers.
The suit centers on star ratings, which are supposed to measure the quality of a health plan’s care and customer service. Plans that meet certain quality thresholds get extra taxpayer-funded bonuses and rebates.
During the 2026 BIO International convention in San Diego, GEN sat down with Aki Ko, CEO of Elixirgen Therapeutics, to discuss the company’s multi-platform technology development efforts. The company, which was founded in 2017, is developing what it believes are breakthrough technologies that target telomere biology disorders (TBD) and aging as well as mRNA-based therapies.
Its therapy for addressing telomere biology disorders, based on its proprietary ZSCAN4 approach, is the furthest to the clinic. The biotech company will also target other aging-related diseases with the technology. Meanwhile, its efforts in the mRNA space are currently focused on Duchenne Muscular Dystrophy (DMD), although there are plans to pursue other targets there as well.
Ko founded the company with CSO Minoru Ko, MD, PhD, in 2017. Elixirgen’s 15 employees are based in Baltimore in its office space in the Johns Hopkins Medical campus, although there is no affiliation with the university. This location offers some advantages to the company, according to Ko. Specifically, “we have a wet lab and an animal lab” that has “helped us go from in vivo to in vitro very quickly to test concepts or optimize formulations and things like that.”
Recently, Elixirgen announced an option agreement with Japan’s Nippon Shinyaku focused on DMD. Under the terms of the agreement, Elixirgen will be responsible for the development of an asset dubbed EXG-7001, a locally administered, full-length dystrophin mRNA therapeutic that is currently in preclinical development for the treatment of DMD.
As part of the deal, Nippon Shinyaku will provide funding for the developmental costs of the therapy. Meanwhile, Elixirgen will receive an upfront payment and is eligible to receive additional development and sales-based milestone payments if the option were to be exercised. Also, Nippon Shinyaku may obtain exclusive worldwide rights to commercialize EXG-7001.
“Current approaches for treating DMD focus on delivering or restoring an incomplete dystrophin protein, and there still remains a significant unmet need for a therapy that can successfully deliver a full-length dystrophin protein,” Ko said in comments about the announcement. “By design, EXG-7001 has the potential to deliver the full-length, complete dystrophin protein that is missing in DMD patients, regardless of their genetic mutation.”
EXG-7001 leverages one of Elixirgen’s core technologies. The company has developed a platform for delivering mRNA-based therapies that it claims addresses the major delivery limitations of current methods. “The key features are that it is a lipid nanoparticle-free, localized mRNA therapeutics platform,” Ko explained to GEN. With this approach, “we’re avoiding some of the complications of gene therapies and delivering genes systemically by going local” and avoiding liver accumulation, which remains “a big issue” for mRNA therapeutics.
The system has two components. The first component, called RNA tether, is designed to ensure that the RNA stays in the tissue that is injected without migrating to the liver. The second component is the mRNA cargo itself, which the company calls Bobcat®mRNA. Though the lead indication for this technology is DMD, there are other diseases involving large genes that the company could target.
“We’re able to express the full length protein as mRNA as a single strand” and “it stays where you administer it, which is kind of unusual,” Ko said. Combining RNA tether and Bobcat makes it possible to express large genes and localize them to target tissues even without accumulation in the liver. Preclinical data has demonstrated its effectiveness in mice with no safety concerns associated with administration or treatment. “A full length dystrophin being given to kind of key muscles could potentially change quality of life,” particularly for the non-ambulatory population, Ko said.
Beyond EXG-7001, Elixigen has other candidates in its pipeline that are much closer to the clinic. Its lead candidate is currently in Phase I/II testing at Cincinnati Children’s Hospital Medical Center. This is an ex vivo cell therapy based on the company’s ZSCAN4 technology, which is designed to extend the telomeres of stem cells in “a controlled way” using a telomerase-independent mechanism. EXG-34217 is comprised of autologous CD34+ hematopoietic stem cells that have been treated ex vivo with EXG-001, a non-integrating, non-transmissible, temperature-sensitive Sendai virus vector encoding human ZSCAN4.
The features of that technology were identified by the company’s CSO and his team while he worked at the National Institutes of Health’s National Institute on Aging. In 2024, the U.S. Food and Drug Administration granted Rare Pediatric Disease Designation to the treatment, dubbed EXG-34217, for the treatment of patients with dyskeratosis congenita and related telomere biology disorders.
“Telomeres obviously have a relationship with aging, and there are in fact genetic diseases associated with short telomeres and telomerase mutations,” CEO Ko told GEN at BIO. People with TBDs are “born with shorter telomeres typically, but also have a mutation in their telomerase so they are not necessarily maintaining them either.” The result is a type of premature aging, so conditions like bone marrow failure and cytopenia happen earlier in the life of the patient. In fact, “bone marrow failure is one of the largest issues” affecting both adults and young children, CEO Ko said.
One treatment option in these cases is allogeneic hematopoietic stem cell transplantation (HSCT), he continued. However, people with short telomeres have more fragile genomes that are less resistant to chemotherapy and radiotherapy and are at greater risk of cancer even after HSCT treatment. In an ideal scenario, it would be possible to postpone or avoid HSCT for these patients, and the company’s ZSCAN4-based therapy could make it possible to do that.
The treatment is currently being tested in adult and pediatric patients in Cincinnati. “We started in adults because this is first-in-human,” but the disease is also very severe in children, Ko said. “Our target ultimately is to make sure as many people with TBDs can get this if they need it.” Early clinical results published in 2025 in a paper in NEJM Evidenceshow durable telomere extension overall with no treatment-related safety concerns observed over a 24-month and 5-month period after infusion. The trial has been going on for some time, and “we have a lot of longer-term data now” and are “looking toward potential accelerated approval.”
But targeting TBDs is just one indication. “Short telomeres manifest in many different ways,” Ko said. Other potential targets for the company’s technology are aging-related diseases, including things like idiopathic pulmonary fibrosis.
To date, Elixirgen has raised roughly $34 million from existing investors.
At the 74th American Society for Mass Spectrometry (ASMS) Conference in San Diego, the obvious story was hardware. Vendors showcased faster acquisition, higher sensitivity, alternative fragmentation, spatial workflows, and software ecosystems. New or highlighted platforms and workflows came from Waters, Thermo Fisher Scientific, Sciex, Bruker, Biognosys, and Evosep.
But after several days of talks, posters, hallway conversations, and interviews with senior figures in mass spectrometry (MS)-based proteomics, the deeper story was not simply that instruments are getting better. The field is beginning to look past the instrument. The mass spectrometer is still central, but the question is shifting: what has to happen around it for proteomics to become clinically useful, scalable, trusted, and routine?
Beyond the instrument
Jennifer Van Eyk, PhD, professor of cardiology, biomedical sciences, pathology, and laboratory medicine, and director of the Advanced Clinical Biosystems Research Institute at Cedars-Sinai Health Science University, put it most directly: “I think mass spec is no longer the limitation. We have the sensitivity, the throughput, and the accuracy at discovery and targeted levels.”
Jennifer Van Eyk, PhD [Gustav Ceder]
That is a remarkable statement in a field long defined by instrument performance. Van Eyk was not saying that MS innovation is finished. She pointed to continuing gains in quantitation, protein structure, conformational analysis, post-translational modifications (PTMs), top-down proteomics, and protein dynamics. But for clinical impact, she argued, the next bottlenecks are increasingly sample preparation, data analysis, standardization, harmonization, and quality control.
Joshua Coon, PhD, professor of biomolecular chemistry at the University of Wisconsin-Madison and the Pyle Chair at the Morgridge Institute for Research, saw instrument speed as the force opening new applications. Faster scanning mass analyzers are allowing deeper proteome coverage, more post-translational modification (PTM) measurements, and shorter runs. Ryan Kelly, PhD, professor of chemistry and biochemistry at Brigham Young University, framed the same shift as a throughput problem. “Now the mass spec is so fast that we need to figure out how to feed it faster,” he said. In plasma proteomics, Coon said, faster instruments, nanoparticle-based enrichment, and improved chromatography are moving the field from hundreds
Joshua Coon, PhD [Gustav Ceder]
toward thousands of detectable proteins in blood.
John R. Yates III, PhD, the John Lytton Young Endowed Chair in the department of integrative structural and computational biology at Scripps Research, highlighted electron activation dissociation methods and the possibility that high-throughput workflows could push MS deeper into plasma and population-level studies. He described targeted affinity platforms as powerful for “known knowns” because they measure targets defined in advance. “But with mass spectrometry,” he added, “you can look for unknown unknowns, which is where the gold lies.”
John R. Yates III, PhD [Gustav Ceder]
The point cuts to the heart of where the field now stands, and a recurring ASMS tension. The future of proteomics is not a choice between platforms. It is a division of labor. Targeted affinity technologies have become central to large-scale plasma proteomics and population studies. MS remains uniquely powerful for unbiased discovery, tissue proteomics, complex sample matrices, protein modifications, structural diversity, and biology that is not yet named.
From depth to trust
If the first era of modern proteomics was about seeing more, the next may be about measuring better. Devin Schweppe, PhD, assistant professor in the Department of Genome Sciences at the University of Washington, described the current moment as a “duality.” Instruments can now deliver deep coverage, and computational tools are making interpretation faster. Together, he said, they are creating “a comfort level with trusting the data.”
Devin Schweppe, PhD [Gustav Ceder]
Trust came up repeatedly. For discovery biology, a strong signal can be enough to generate a hypothesis. For clinical practice, it is not. Van Eyk said clinical-grade assays are “way harder than people think they are.” A research study can iterate. A clinical assay has to deliver the same measurement today, in five weeks, in six months, and years later. Once a test is locked, “you can’t go, ‘Oh no, we should have had this extra protein in there,’” she said. “It’s done.”
This distinction matters across assay types. Targeted MS methods such as multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) can provide absolute quantification, but only for preselected proteins. Data-independent acquisition (DIA), meanwhile, has moved discovery proteomics closer to translation by improving reproducibility and scalability. DIA is still often used for relative quantification, but its ability to capture patterns across tens or hundreds of proteins may become important as clinical decision-making moves beyond single biomarkers and reference intervals.
The field is responding to these demands. David Kotol, PhD, R&D manager at ProteomEdge, discussed an independently validated nine-protein plasma panel designed to improve emergency department triage and imaging decisions for patients with suspected venous thromboembolism, compared with D-dimer alone.
David Kotol, PhD [Gustac Ceder]
Kotol described a shift “from relative protein measurements toward robust, multiplexed absolute quantification.” He emphasized stable isotope-labeled protein standards added early in sample preparation to monitor digestion efficiency, downstream analytical variation, and multi-peptide quantification. These standards cannot remove variation introduced during sample collection, handling, or storage. But they can make the analytical workflow more transparent and transferable.
The clinical gap
Mathieu Lavallée-Adam, PhD, associate professor in the department of biochemistry, microbiology and immunology and director of the specialization in bioinformatics at the University of Ottawa, gave the least glamorous answer to what still blocks clinical translation. “My answer is going to be boring,” he said. “It’s going to be education.”
Mathieu Lavallée-Adam, PhD [Gustav Ceder]
Lavallée-Adam argued that many clinicians and biomedical researchers still do not fully understand what modern MS can do. Too often, the outside view is still: give me a list of differentially expressed proteins. But MS-based proteomics has moved beyond lists, into proteoforms, structural information, PTMs, protein dynamics, and flexible acquisition. “We’re past that now,” he said. “The main barrier is our inability to communicate the possibilities that we offer.”
Sasha Singh, PhD, assistant professor of medicine at Harvard Medical School, associate scientist at Brigham and Women’s Hospital, and director of proteomics research at the Center for Interdisciplinary Cardiovascular Sciences (CICS), described this translation role from inside a hospital environment. “That’s actually my role at the hospital,” Singh said. “I am a liaison between the technology and the application scientist.”
Sasha Singh, PhD [Gustav Ceder]
The translation is becoming harder because proteomics is diversifying. End users often need to distinguish among discovery MS, which can provide broad relative quantification; targeted MS, which can provide absolute concentrations for selected proteins; and targeted affinity proteomics, which can scale well for plasma cohorts but is limited by predefined assays and available binding reagents. Singh added that different technologies may produce profiles that do not fully overlap. Rather than treating that as a failure, she suggested it reveals something real: the circulation contains many subproteomes, and different technologies enrich different views.
AI with guardrails
No 2026 conference escapes artificial intelligence (AI), and ASMS was no exception. But the mood among the researchers was cautious rather than breathless.
Lavallée-Adam said agent-based AI was dominating conversations in his part of the field. The dream is seductive: put a sample on an instrument, ask an AI agent to maximize protein identifications or optimize a method, and let it select the best protocol. But he drew a clear line between potential and reality. “Are such agents really driving change? It’s unclear at this point,” he said. “I think it’s unproven.”
Still, AI-assisted acquisition strategies are entering workflows. Lavallée-Adam’s group works on real-time MS data acquisition, where software analyzes data as it is acquired and adapts the run to the biological question. Instead of measuring the same abundant proteins repeatedly, the system can decide it has seen enough and move on to new targets. In that sense, AI becomes less a magical oracle than an instrument assistant.
Faster instruments are generating more data, and faster analysis is needed to keep up. Schweppe also argued that open-source tools remain essential because they let laboratories build on one another’s work rather than rebuild it.
More than abundance
Much of the clinical proteomics effort is focused on plasma because it is minimally invasive and suitable for screening, longitudinal sampling, and routine monitoring. But even in blood, researchers are learning that plasma is only part of the story.
Roman Fischer, PhD, associate professor and head of the Discovery Proteomics Facility at the Target Discovery Institute, University of Oxford, pushed the conversation back toward biology. Plasma alone does not capture the full circulating system, he noted. Peripheral blood mononuclear cells, extracellular vesicles, microvesicles, and other compartments may contain disease-relevant information that conventional workflows miss. “We have to be more sophisticated in addressing the compartments of the blood,” Fischer said.
Roman Fischer, PhD [Gustav Ceder]
He also pointed to the proteoform problem. A single gene can give rise to many transcripts, isoforms, modified proteins, and glycosylated forms. These differences may affect activity, localization, disease pathways, and therapy response. Capturing that diversity is not possible with targeted affinity assays alone. It requires deeper characterization of the proteome, not only quantification.
Yates offered a clinical example. His group has been developing protein-footprinting approaches that can detect conformational changes in proteins in blood. In one transthyretin amyloid cardiomyopathy project, he said, abundance alone was not the answer. The important signal was how the protein folded or misfolded. That kind of assay moves proteomics beyond proteins going up or down, into structural disease biology.
Van Eyk’s work on remote sampling devices pointed to another future: patient-collected blood samples that make longitudinal cardiovascular studies easier, more inclusive, and better matched to real clinical questions.
In the background was a broader translational arc: discovery, verification, clinical validation, health economics, and access. Plasma proteomics highlights included Lekha Sleno, PhD, professor at Université du Québec à Montréal, who is combining nanoparticle enrichment with isotope-enabled targeted proteomics, and a CinderBio breakfast seminar featuring Fredrik Edfors, PhD, assistant professor at KTH Royal Institute of Technology and SciLifeLab, and Simion Kreimer, PhD, senior research project advisor in the Proteomics and Metabolomics Core at Cedars-Sinai Health Science University.
The seminar focused on accelerated plasma proteomics, rapid digestion workflows, stable isotope standards, Human Protein Atlas resources, and faster enzyme workflows that can reduce lead times. The common message was that sample preparation, quantification, and validation may become as decisive as instrument resolution.
The next bottleneck
ASMS 2026 was not short on technical spectacle. High-resolution instruments, electron-based fragmentation, narrow-window DIA, rapid acquisition, MS imaging, top-down workflows, and AI-enabled software all had their moment. But the most interesting conversations were less about spectacle than maturity.
Proteomics is no longer trying only to prove that it can see more. It is trying to prove that it can measure consistently, explain biology more deeply, support drug development, fit into clinical laboratories, and eventually improve patient decisions.
That means the next bottleneck is distributed across the ecosystem: sample preparation, standards, software, education, reimbursement, clinical menus, regulatory validation, open tools, and the ability to translate technical power into something a clinician can use.
Longer term, integrated proteomics, other omics, imaging, clinical data, and AI may support not only single biomarkers, but interpretable molecular patterns, longitudinal trajectories, and digital-twin-like models of patient biology.
The field spent decades making proteins visible. The next challenge is making proteomic measurements dependable enough to act on.
A set of blood-based circular RNAs (circRNAs) could change how we diagnose and monitor Alzheimer’s disease, providing a simple, noninvasive test that can detect the disease with remarkable accuracy and predict its progression years before symptoms appear.
Washington University School of Medicine researchers analyzed blood samples from 1,221 individuals, including people with Alzheimer’s disease and cognitively healthy participants, making it one of the largest investigations of blood circRNAs in Alzheimer’s to date. The findings, published in a Nature Medicine study, identified 34 circRNAs whose combined expression patterns accurately distinguished Alzheimer’s disease from healthy aging.
Unlike conventional RNA molecules, circRNAs form single-stranded closed loops that resist degradation and are abundant in the brain. Their stability and ability to cross the blood-brain barrier make them attractive candidates for blood-based biomarkers that reflect changes occurring in the brain.
The research indicated that a predictive model built from the 34 circRNAs achieved an area under the curve (AUC) of 0.945 for identifying biomarker-confirmed Alzheimer’s disease, outperforming the widely used plasma biomarker pTau217 (AUC 0.877). When circRNA measurements were combined with pTau217, diagnostic performance increased further to an AUC of 0.977.
Beyond diagnosis, the circRNA signature demonstrated exceptional ability to predict disease progression. Individuals with elevated circRNA scores were nearly three times more likely to progress to symptomatic Alzheimer’s disease than those with lower scores. The model also outperformed pTau217 in forecasting progression and remained highly specific for Alzheimer’s, showing limited predictive ability for other neurodegenerative disorders such as Parkinson’s disease, frontotemporal dementia, and dementia with Lewy bodies.
Importantly, the findings were independently replicated in two additional cohorts, including 551 participants from the Knight Alzheimer’s Disease Research Center and 1,767 participants enrolled in the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) study. This independent validation demonstrates that the circRNA signature is robust across multiple populations and study designs.
The researchers also found evidence that circRNA changes begin approximately two to four years before the onset of clinical symptoms, suggesting that these molecules may capture biological processes closely linked to the transition from silent pathology to cognitive decline. Such biomarkers could become increasingly valuable as disease-modifying therapies enter clinical practice, where monitoring ongoing neurodegeneration is just as important as detecting amyloid pathology.
The work builds on intellectual property protected by patent PCT/US2026/017857, “Blood Circular RNA as a Noninvasive Biomarker of Alzheimer’s Disease,” which Circular Genomics has licensed. The company, based in San Diego, California, is developing next-generation molecular blood biomarker diagnostics for precision neurology. The patent covers the use of blood circRNA signatures for the diagnosis and monitoring of Alzheimer’s disease and supports continued development of clinically deployable blood tests.
While the authors emphasize that larger prospective studies are still needed before widespread clinical implementation, the findings position blood circRNAs as a promising new class of biomarkers for Alzheimer’s disease. By combining high diagnostic accuracy with strong prediction of disease progression using a simple blood sample, circRNA-based testing could help identify patients earlier, improve clinical trial enrollment, and provide physicians with new tools to monitor disease over time.
Researchers at the University of Texas MD Anderson Cancer Center have identified a strategy to reverse resistance to standard treatment in BRAF-mutant advanced melanoma. Their findings, published today in Nature Communications, support using a biomarker-guided approach to improve outcomes for patients with treatment-resistant melanoma.
“Patients whose melanoma has stopped responding to standard therapies currently have very few effective treatment options,” said Vashisht Gopal Yennu Nanda, PhD, associate professor of melanoma medical oncology and translational molecular pathology at UT MD Anderson. “Our findings could help address this critical need for these patients by guiding clinicians toward combinations tailored to each individual’s tumors.”
About 50% of melanoma tumors carry BRAF mutations that drive uncontrolled tumor growth. Although the standard of care, consisting of a combination of BRAF and MEK inhibitors, is initially effective in most patients, about 80% will develop resistance within two years. In many cancers, but especially in melanoma, acquired resistance is often driven by the tumor increasing production of proteins from the BCL2 family, which block apoptosis and support the survival of cancer cells.
Yennu Nanda and colleagues tested the effects of adding a BCL2 inhibitor drug to the standard two-drug regimen in patient-derived xenograft models, which were established using melanoma tumors that had acquired resistance to standard therapy. Results showed that tumors that expressed high levels of BCL2 responded well to the triple combination, reversing resistance and inducing a complete tumor regression.
However, tumors that expressed high levels of MCL1—another protein from the BCL2 family—did not respond to this combination. In these tumors, the researchers tested an alternative treatment course adding an experimental MCL1 inhibitor to standard treatment, which successfully led to complete tumor regression.
“Targeted therapy works by shutting down the main signal driving melanoma growth, but tumors often have backup systems that keep them alive,” said Yennu Nanda. “By identifying which protein a tumor relies on for survival, we may be able to match patients to drug combinations tailored to their specific tumor biology.”
MCL1 inhibitors have previously shown promising antitumor activity, but early clinical trials flagged concerning heart-related side effects that have prevented them from moving through clinical development and receiving approval. In this study, however, the combination of an MCL1 inhibitor with standard BRAF-MEK inhibitors seemed to protect cardiac cells from the harmful effects associated with these experimental drugs.
“We did not anticipate that pairing these drugs would reduce MCL1 inhibitor toxicity,” said Michael A. Davies, MD, PhD, chair of melanoma medical oncology at UT MD Anderson. “If this finding is confirmed in clinical trials, it could give a second life to a class of drugs that has struggled to advance through development. It also reinforces that the most effective combinations are those that eliminate cancer while sparing healthy tissue.”
Building on these findings, the researchers are now working on analyzing tumor samples from a recent Phase II clinical trial in melanoma patients who received standard treatment and a BCL2 inhibitor, with the goal of studying whether MCL1 expression can predict clinical response. Down the line, their goal is to design clinical trials where melanoma patients are matched with drug combinations based on the expression of BCL2 or MCL1 biomarkers.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
LLMs are stuck in a groupthink groove. This startup is trying to get them out.
Open up your chatbot of choice—Claude, ChatGPT, Gemini—and type “Give me a random number between 1 and 10.” You’re going to get 7. Almost always.
That won’t work every time—but if it did for you, you may wonder if I have superpowers. I don’t.
The truth is that most large language models are stuck in a rut. They are far more predictable and far less creative in their responses than you might expect. That’s fine for tasks like coding or research, but groupthink is a problem when you’re brainstorming or planning your next vacation.
The Australian startup Springboards has a solution. It built an LLM called Flint, which has been trained to come up with a wider variety of responses than mainstream LLMs to open-ended questions such as “Where should I go in Europe?”
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Scientists say they have built a cell from scratch for the first time Built with lab-made DNA, it can feed, grow, and multiply. (CNN) + It brings us closer to creating synthetic life. (Quanta) + And is arguably the greatest feat of bioengineering yet. (New Scientist $) + But also raises concerns over the dangers of synthetic biology. (NYT $) + Mirror organisms could threaten life on Earth. (MIT Technology Review)
2 OpenAI has proposed giving the Trump administration a 5% stake Talks over a public ownership deal come amid rising political pressure.(FT $) + OpenAI also proposed other US AI giants providing a 5% stake. (CNBC) + That could include Anthropic, Google, and Meta. (Bloomberg $) + President Trump says he wants the public to have a stake in AI. (BBC)
3 Singapore has seized a $42 million mansion tied to Nvidia chip smuggling It was seized as part of an investigation into alleged illegal trading. (BBC) + Days earlier, Supermicro’s Taiwan offices were raided in the probe. (FT $)
4 Anthropic’s Fable 5 is back online But queries posing security risks may be routed to less powerful models. (Axios) + Anthropic restored access yesterday after the US lifted an export ban. (BBC) + But the battle over how to tame AI has just begun. (WSJ $) + Anthropic has launched a new AI science product. (MIT Technology Review)
5 Meta is building its own cloud infrastructure business It’s exploring two ways of monetizing AI compute and models. (Bloomberg $) + One is selling access to models hosted on Meta’s infrastructure. (CNBC) + The other is selling “raw” computing power. (TechCrunch)
6 PlayStation will stop releasing games on discs in 2028 Future PS5 games will be digital-only releases. (Verge) + The news comes days after reports that GTA VI will have no disc. (BBC) + It’s put a nail in physical media’s coffin. (Wired $)
7 A low-cost Chinese AI model is catching up with US giants on their home turf Western customers are drawn to GLM-5.2’s cheap but powerful model. (Reuters $) + Chinese open-source models are spreading fast. (MIT Technology Review)
8 Google has lost its fight against a record €4.1 billion EU antitrust fine It was charged in 2018 for using Android to block rivals. (CNBC)
9 The UN has launched an “AI for Good” commission Salesforce CEO Benioff and Rwandan President Kagame will co-chair it. (Axios)
10 People prefer AI impersonators over politicians The study’s findings raise alarm bells around potential public deception. (404 Media)
Quote of the day
“If AI overdelivers, it will impact financial stability. If AI underdelivers, it will impact financial stability.”
—Torsten Slok from Apollo Global Management shares common concerns about AI at the European Central Bank’s annual conference, Reuters reports.
One More Thing
America was winning the race to find Martian life. Then China jumped in.
In July 2024, after more than three years on Mars, the Perseverance rover came across a peculiar rocky outcrop. Instead of the usual crystals or sedimentary layers, this one had spots. Those specks were the best hint yet of alien life.
NASA began a new mission to bring the rocks back to Earth to study. But now, just over a year and a half later, the project is on life support. As a result, those oh-so-promising rocks may be stuck out there forever.
This also means that, in the race to find evidence of alien life, America has effectively ceded its pole position to its greatest geopolitical rival: China. Beijing is now moving full steam ahead with its own version of NASA’s mission.
Something stinks in California’s climate policies.
Years ago, the state set up a system that pays cattle farmers across the country to turn the methane emitted from cattle manure into natural gas, encouraging the dairy sector to produce a gas we burn instead of one that just pollutes the air.
It’s become wildly popular because the subsidies are extremely lucrative. But a growing body of research suggests the program is a case study in the shortcomings of our preferred approaches to climate action. Instead of simply forcing industries to directly cut their pollution or pay for it as a cost of doing business, legislators have repeatedly opted to set up convoluted incentive systems that swap climate responsibilities between parties and regions. As studies have shown again and again, these carbon offsetting and trading schemes often dramatically overstate the emissions reductions actually achieved in the one place that matters: the atmosphere.
The dairy program illustrates a particular version of this problem, muddling the impacts of different types of greenhouse gases in a way that researchers argue will lock in more warming in the future.
Despite this and other concerns, California regulators decided in 2024 to extend parts of the program beyond 2050. And a recent proposal by the state’s air resources board could send millions of additional dollars to dairy farmers as part of a plan that would ease restrictions on major greenhouse-gas producers.
Here’s how the system works: The state’s climate regulations require the transportation fuels industry to lower the carbon dioxide levels in its products over time—or purchase credits from other parties that cut fuel emissions, including cattle farmers.
Dairies generally spray cattle manure into giant open lagoons, where microbes gobble up organic matter and produce methane as a by-product. But if farmers set up what are known as anaerobic digesters, the sludge is redirected into covered vessels that capture the biogas, which can be converted into natural gas and injected into a pipeline. It can then be used to fuel certain vehicles or generate electricity in a power plant. Either way, petroleum companies can pay those farmers for Low Carbon Fuel Standard (LCFS) credits, to meet regulatory requirements in lieu of reducing the emissions from their own fuels.
Burning biogas in a bus or turbine still releases carbon dioxide, but the idea is that this process reduces market demand to extract natural gas from the ground and avoids the release of methane, which is a far more powerful greenhouse gas (at least initially). In fact, methane is so much more powerful that under California’s program, “adding one average biogas-powered vehicle to the fleet would produce enough LCFS credits to cover the deficits incurred by 26 similar gasoline-powered vehicles,” according to Aaron Smith, a UC Berkeley economist.
But there’s a problem with this carbon math. California assumes that methane exerts about 25 times the warming effect of carbon dioxide over a 100-year period. That’s not how it really works in the atmosphere, though.
Methane is very powerful, but it also breaks down quickly, generally within a couple of decades. Meanwhile, carbon dioxide builds up cumulatively in the atmosphere—and much of whatever we emit will continue heating up the planet for hundreds to thousands of years.
So, in effect, the state has created a system that reduces short-term warming at the cost of increasing all-but-permanent warming. Any methane that digesters capture today would have caused extra-powerful warning if released, but by 2050 that effect would have mostly faded away. Meanwhile, that additional carbon dioxide we permitted in its place could continue warming the world for millennia.
It is a good idea to cut methane emissions, and dairy digesters achieve this (though not always as effectively as hoped). But we can’t swap a decrease in short-lived greenhouse gases for an increase in long-lived ones if we hope to keep global temperatures within relatively safe levels in the coming century, as researchers have long warned. We have to slash both.
The problem I keep returning to, after years of covering carbon markets and offsets, is this: We need to clean up every sector, completely, over the next few decades. It’s increasingly untenable for so many of our climate ambitions to turn on getting one industry to make progress on paper by paying another one to reduce emissions, at a point when every business in every industry needs to be racing toward net zero.
It’s time to move past the idea that we need to reward sectors for doing us the favor of not polluting the atmosphere, and simply require them to stop unloading the huge environmental burden of their business onto society.
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
Much of cancer research and therapy focuses on the direct impacts on cancer cells. However, understanding the broader context of cancer as a component of a patient, rather than an isolated invader, has opened a variety of insights and treatments for patients with cancer. Investigations of how the microbiome impacts cancer and immunotherapy was the prime focus of the second session on June 23, 2026 at the Frontiers in Cancer Immunotherapy Symposium hosted by The New York Academy of Sciences.
GVHD and the microbiome
Opening the discussion, Marcel van den Brink, MD, PhD, president of City of Hope Los Angeles and City of Hope Medical Center spoke about the role of the intestinal microbiome in cancer immunotherapy.
Marcel van den Brink, MD, PhD [Nick Fetty/The New York Academy of Sciences]
He began by describing the history of graft versus host disease (GVHD), pointing out that while early work from the 1970s suggested that germ free mice had reduced instance of GVHD following transplants, more current work has pointed to the intestinal microbiome as an immune system modulator.
“Protection of the commensal anaerobes is beneficial,” he said. He explained that Enterococcus has a habit of dominating a population with reduced diversity, pointing out that it “happens very frequently within the context of allogenic transplant, and again is linked with graft versus host [disease].”
“So we try to understand how that happens, why do you get that domination?” van den Brink described how damage to the internal lining of the gut by chemotherapy or XRT conditioning can lead to alloreactivity of immune cells. Damaged enterocytes are less able to produce lactase, leading to increase in lactose availability, which can help drive the growth of Enterococcus species, including E. faecillis—a primary species found in patients who develop GVHD. Concurrently, bile acids can have an immune suppressive effect.
He went on to share results of two published studies exploring the role of immune cells in this cycle and potential interventions. He summarized this work saying, “The protection of the commensal anaerobes is critical.
“That’s probably the easiest point that I can make, if you think about using the gut microbiome as a target to improve outcomes for cancer patients.”
As the gut is a complicated ecosystem, and there are many angles of research, the van den Brink lab is also now exploring other avenues of research that do not involve antibiotics in addition to their work with antibiotics and other therapies.
Probiotic engineering
The second talk in this session, presented by Nicholas Arpaia, PhD, associate professor of microbiology and immunology at Columbia University, explored the possibility of personalized cancer immunotherapy with the use of engineered probiotics.
Nicholas Arpaia, PhD [Nick Fetty/The New York Academy of Sciences]
His work has focused on exploring the interactions between bacteria and the tumor microenvironment (TME) and how bacteria can act as a sort of Trojan horse to access the inner tumor environment.
In terms of cancer immunotherapy, Arpaia began by saying, “hopefully I’ll be able to convince you that utilizing bacteria is a potential path forward.” Bacteria, he argues, have a bright future in the field with a strong and growing research background based on the publications, companies formed, and clinical trials over the last 20 to 30 years.
He continued describing the wide scope of the field, both in how bacteria are engineered, and in how those bacteria are delivered. While the immune-oncology space has tended towards engineering payloads that modify the TME or deliver neoantigens, there are other approaches aimed at delivering toxins or modifying the metabolism within the TME. Further, Arpaia shared details on the differences between intravenous (IV), intratumor, or oral delivery. He pointed out that bacteria injected intravenously have been found in the cores of tumors. “It’s been speculated that this occurs because of the amenable conditions within the tumor.”
The question then arises, how can this behavior be beneficial to cancer therapy? “Features of bacteria themselves can activate the innate immune system,” Arpaia said. “If you then couple that with something that’s going to help activate the adaptive immune system, it gives us all the signals we need to really get long-term durable and effective responses.”
While many bacterial strategies involve the bacteria bringing specific payloads to the TME, much of his work explores a strategy of quorum-based lysis or a synchronized lysing circuit.
“Essentially what we should observe is that there’s growth, they hit a quorum threshold, so this synchronized lysis event occurs, a few of the bacteria remain, and the entire population undergoes these cyclic events again.”
Following the lysis event, what remains is “just a massive bag of innate immune stimulatory ligands.” The payload is released over and over through this synchronized lysis of the bacteria. Arpaia summed the process: “They grow, they undergo a lytic event, they grow back, and the entire process again occurs again and again.”
Tumor-associated bacteria in space
The final talk seamlessly transitioned from the discussion of bacterial lysis deep within the tumor to a discussion on how the location of tumor-associated bacteria within the TME can impact therapy approaches.
Susan Bullman, PhD, associate professor of immunology at the University of Texas MD Anderson Cancer Center began her discussion by taking a step back from cancer. “What I’m going to talk about is the native colonization of tumors by bacteria, by members of our microbiome,” she said.
Susan Bullman, PhD [Nick Fetty/The New York Academy of Sciences]
She explained that her group is “particularly interested in oral gastrointestinal cancers and understanding how microbes disseminate from our microbiome and can infiltrate human tumors to modulate the TME.”
Bullman described how certain bacterial that or typically restricted to the oral cavity can migrate and infiltrate cancers throughout the gastrointestinal (GI) tract. She focused specifically on Fusobacterium nucleatum, which not only has been consistently identified in GI tract tumors, but has also been found to negatively impact patient outcomes.
“When this microbe is enriched in the tumor, patients tend to have an increased risk for relapse, metastases and overall poor prognosis,” she said. Bullman explained that there is variability between tumor types and likelihood of tumor infiltration by microbes—with GI tract tumors having a higher instance of bacterial infection. Further, there is a heterogenous distribution of the bacteria within the tumor itself and while this bacterium is not the only microbe within the tumor tissue, her work aims to understand how this species modulates the TME.
She asked, “When these microbes get into a tumor tissue or infiltrates the tumor tissue, what exactly are they doing?” She pointed out that in healthy tissue, bacteria will interact with epithelia cells and interact with the immune system, but it’s unclear what they do within the TME.
Through the use of sequencing of both tumor and bacterial cells, her lab was able to identify details on the genetic expression of tumor cells and have a better understanding of the TME. They found that just the mere presence of bacteria at all in the tumor also has a physical impact on the tumor. The bacteria have been shown to impact tumor cell density, increasing space between the human tumor cells. As a result, these cells become stressed and stay in temporary quiescence until the bacteria are removed.
“This is interesting for a range of perspectives, from an immunotherapy perspective and an immunology perspective,” she shared.
“We see that these quiescent cells, they reduce metabolism, they reduce gene expression, and they have reduced antigen presentation. So when the cancer epithelial cells are pushed into this dormant state, they become somewhat invisible to the immune system.”
From a chemotherapy perspective, this was an interesting discovery. “We know that many anti-metabolite chemotherapies that are used in the clinic, they are targeting hyperproliferative cells,” she explained.
Currently, the team is working to map the host-bacterial interactions within the TME, looking for co-localization of cells and function to better understand how the tumor responds to bacterial infection. While they are still trying to understand the mechanisms, Bullman is encouraged by the current data.
“There [are] hints towards impacts of microbes, the amount, the load of these microbes, the immune cells, the monoid cells that they’re recruiting, and their impact on immune checkpoints within the tumor microenvironment.”
A new blood test may help address one of the key challenges in prostate cancer detection—identifying aggressive forms of the disease early on. According to research led by a team at Karolinska Institutet, the Stockholm3 blood test detected more clinically significant cancer cases than the well-established, but problematic, Prostate Specific Antigen (PSA) screening test.
The study appears in the Annals of Internal Medicine. Swedish researchers collaborated with teams from Europe and the U.S. on this work.
Prostate cancer is one of the top cancers among men globally, with an estimated 1.5 million new cases and 397,000 deaths annually. PSA testing has long been used for early detection. But, although PSA is prostate-specific, it is not cancer-specific. Elevated PSA levels can be caused by benign conditions as well as cancer and up to 50% of diagnosed aggressive prostate cancers are in men with low PSA values—below today’s cutoffs of PSA 3 ng/ml or PSA 4 ng/ml.
“There are a number of tests in development to improve on PSA alone. A lot of them, like Stockholm3, are fairly ingenuous and do a good job,” Mark Pomerantz told Inside Precision Medicine. He is a medical oncologist at the Dana-Farber Cancer Institute in Boston. Until one of these newer tests emerges as a winner, though, MRI is the gold standard for evaluating men with high PSA. “With MRI we can see the prostate in some detail,” Pomerantz said, “But it is expensive.”
The Karolinska-led researchers analyzed data from 12,670 men aged 50–74 from the population-based STHLM3-MRI study, which compared MRI-targeted and standard biopsy in men with elevated PSAs. In this more recent Annals study, the men were tested first with both PSA and Stockholm3 and followed for two years via national cancer registries, which allowed researchers to also identify cancer cases missed during the initial screening.
Stockholm3 detected 90 percent of aggressive cancer cases, compared to 74 percent for PSA.
“The test incorporates plasma protein biomarkers, genetic risk information from a polygenic risk score, and clinical factors such as age, family history, and prior biopsy history,” the study’s lead author Thorgerdur Palsdottir, told Inside Precision Medicine. Palsdottir is a researcher at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet.
He added, “Together, these components provide a more comprehensive estimate of a risk of harboring clinically significant prostate cancer than PSA alone.”
The components, he explained, map onto distinct biological axes rather than a single one. The kallikreins (PSA, free PSA, hK2) reflect prostate epithelial and tumor secretory activity, and the free-to-total PSA relationship helps separate benign enlargement from cancer. The polygenic score and family history capture inherited susceptibility—a man’s baseline predisposition—rather than anything about a tumor. GDF-15 is a stress-response marker that has been associated with more aggressive disease across several cancers.
During the study follow-up, 443 men were diagnosed with clinically significant, i.e. aggressive, prostate cancer. Stockholm3 missed significantly fewer serious cancer cases than PSA, while the proportion of men incorrectly classified as high-risk was similar between the tests.
“These results point toward a potential change in how prostate cancer screening can be conducted. A more precise blood test could enable earlier detection of aggressive disease while reducing the number of unnecessary follow-up examinations and procedures,” said Palsdottir.
He adds that longer-term follow-up is needed to fully assess the effects on mortality and long-term outcomes. “The next important step is to evaluate longer-term outcomes, including disease progression, metastatic disease, and prostate cancer mortality.”
AI is often portrayed as either a technology that will revolutionize healthcare and cure disease or an overhyped force that could stifle science—but the reality is far more nuanced. While AI is already transforming biomedical research, meaningful advances in medicine require much more than powerful algorithms. That complexity is the focus of this conversation with Michael Antonov, co-founder of Oculus, who turned to biology and drug discovery after pioneering virtual reality.
To do so, he co-founded the computational drug discovery company Deep Origin. Rather than relying on AI alone, Antonov believes progress depends on integrating machine learning with physics-based molecular simulations, mechanistic models, and rigorous experimental validation. This philosophy has been fundamental for shaping Deep Origin’s AI-native platform to improve virtual drug screening, predict toxicity, and help researchers develop safer, more effective therapies.
In this episode of Behind the Breakthroughs, Anotonov examines how AI is changing drug discovery and the pharmaceutical industry’s opportunities and limitations, taking a pragmatic approach to claims that AI alone can improve human health from larger models and more computing power. This conversation offers a glimpse into biomedical innovation’s future for those interested in where AI is truly changing medicine and where human expertise and experimental science remain vital.
This interview has been edited for length and clarity.
IPM: Does virtual reality (VR) have a role in medicine and healthcare?
Antonov: VR is predominantly a visualization device, so it’s good for training and various other areas in terms of actual treatments. When VR has been used and is actually FDA approved, as far as I know, it presents modified images to each eye and kind of trains your brain to treat them both simultaneously. Similarly, it’s been used for PTSD treatments and some of the areas where you can maybe handle fears. I haven’t personally experimented with that.
Michael Antonov, co-founder of Oculus and Deep Origin [Deep Origin]
On the visualization side, for displayed molecules, there’s a company that has done a great job of allowing you to look at the molecules, and this could be useful for research. That said, it just gives you more spatial perception. It doesn’t actually solve the problem for you.
On the training side, there are potentially huge benefits, even though you would then have to require investing a lot in software to make it actually perform well. Now, one good example is, I have invested in this company called Osso VR, which does training for knee replacement surgery, and they actually practiced it, and they did a study where their surgeons trained with their knee replacement and got 230% more proficiency.
Given the time and the accuracy of a procedure and the speed of how they learn. But to me, that felt actually very incredible that it’s actually being used. I think they also do nursing trade trainings and such. Those are probably the top areas that will probably be more brain-oriented cognitive things you could do. It would just take time to explore it.
IPM: What will AI’s impact be on medicine and healthcare?
Antonov: I think that there is still a lot of uncertainty. The system is overloaded. There’s a whole spectrum, and the challenge is that there are hundreds of different companies and projects with a whole different range of funding.
For pharma, it would be a big job to sift through what is actually good and what will help me take my target forward. That’s a challenge because there’s a lot more noise and there are some really good companies, but there are also many me-too, not-so-great ones. There are also certain fundamental areas that haven’t been solved yet, like toxicity and other issues, although there has been progress in some areas. There are like dozens of predictors, but they’re not necessarily super great, though they’re better than nothing. It’s hard to tell where it’s going. The biggest thing is to see what you actually prove in the lab.
The other thing is that there is a range of medicinal chemists and other knowledgeable people who haven’t been exposed to the breakthroughs or effects we might see on our side. AI may surprise us in certain biological parts of the name for certain problems. Now, more holistically at Deep Origin, our plan is to support the discovery process for small molecule drugs and have predictable outcomes.
IPM: How will AI drive the future of precision medicine?
Antonov: The super exciting way it could look in 20 years in that type of timeframe is that we are starting to get personalized medicine. You’re really combining the patient and the system model so that whenever you have a disease, if you have maybe a novel genomic mutation or if you have a new virus, you can literally put the data into the system.
Here is basically experimental data about whatever you collect from the virus. I don’t know if you get the structure of its protease from crystallography. I will even tell you here are the steps you need to take and which lab to run them in. But once you provide it, the system will be able to decompose the pathways and targets it’s affecting and then identify the specific concentrations you might need for these patients.
Essentially, you can provide a target in just a few months. You have good candidates, and these candidates have a much higher probability of not being toxic and having good admin properties. Let’s say we are moving from 90% failure rate to maybe 60%. That would be a huge job. That’s what the toxicity models enable, though they are hard because they need both experimental and data collection. But actually, even things like physics can help with counter screening, asking, what are all these things we should not bind to? Go and check them computationally. This whole stack basically gives you data on how to run your trial. That’s ten years. But then you level it up with populations and the individual.
This is a 2030 year outlook because then you’re pulling in the genomics data, maybe various things, and this is where the industry really becomes much more powerful and individualized. To do that, you really need these more detailed models.
IPM: Do you have a prediction about a current AI trend that will be around for a while?
Antonov: One of the hot topics right now is the idea of AI scientists. In our case, we have an AI discovery engine. We actually did this earlier, which is this area grant from the U.K. for picking up the disease, which can be fully drugged by AI.
We ran our AI scientist system to pick a target for endometriosis. It uses our tools to come up with a molecule. It’s currently in progress, and it did a very detailed breakdown and analysis of hundreds of targets based on very specific criteria, and I picked a particular one with all the reasons.
It’s interesting to make those kinds of tools and this whole pipeline available to almost everyday people because then, much like some genomics tools, an available AI system, which can support the full path of drug development, can in fact let a patient or an interest group just come in and take lots of steps in the direction of saying, “Here’s either maybe an RNA or a gene therapy or a drug that can serve.”
That would be a huge step toward democratizing it. It doesn’t mean that AI will do all the steps for us, but it doesn’t mean that it can do a lot of the known steps, which have been done many times and can help us along the way. Of course, the real scientist will still be very critical to all the parts.
For general accessibility, this automation that is happening and these kinds of simulation tools and large language models in general are incredible. They’ve got a little bit of a long-winded thing, but I wanted to reflect on what you said.
IPM: What are the pros and cons of building Deep Origin in the U.S. or China?
Antonov: Some of the more recent wisdom that I’ve heard is that if you want to survive in the U.S. or more expensive countries, you need to be taking bigger risks, and you need to be more innovative in how you approach the type of modalities and things. So that’s one line of thinking.
Another way is to be distributed. In our case, a big part of our AI/ML team is in Armenia. My co-founder is Armenian. We have 40 people there. I have just come from spending a week and a half with the team there for model building and science. There is an AI, and there are definitely people in all of the areas. Automated labs could also probably be in any country.
In terms of the actual trials, it depends on the situation. There are certain things that it’s probably wiser to do in China for this time being, but also maybe India will be up and coming, and if there are certain scenarios where there are more rare diseases, it’s probably okay to also not stay in the States.
There’s no perfect answer. We have a challenging environment. At the end of the day, you have to have something really valuable and novel to keep going forward. They have really great scientific research there too. We have to be careful and just really go at it hard.
IPM: Where does China stand out from the United States in terms of pharmaceutical research and development?
Antonov: If I were to pick one area, it’s the cost of clinical trials and the way we select just all the aspects of this. And to be honest, I’m not an expert in this. And clearly there’s a lot of progress in China right now. Everybody talks about how it’s much more cost-effective and quicker to do things there. There are a lot of “right to try” opportunities that are helping.
That said, I believe that we can have a lot better kinds of social programs around this to make it like easier for people to participate and maybe take more highly educated guesses and risks. There’s software infrastructure to simplify and reduce the cost. That would be amazing. In some of those areas, AI also can help, and the models actually can help.
IPM: If you could work on anything, what would it be?
Antonov: I would say focusing on aging as a disease. If you look at the funding, things could shake up the type of research that the NIH and the National Institute on Aging (NIA) do, which is really fundamental to our biology because it drives the majority of diseases and has 3% of the budget, whereas oncology and Alzheimer’s have huge budgets. There’s probably more impact in aging than probably some other well-funded areas if we look at the fundamental parts. That would be a big area where you can have a multiplier effect just from the research side.
To really build an ecosystem of better computational and AI models, maybe creating some way to actually incentivize people to contribute to them, because that’s the challenge right now. You can publish a research paper, or you can build your model to make your proprietary hidden drug. But we need scientists to share those in an integrated way. How do we do that?
Maybe it’ll take some big AI companies to jump into it and do something there. But it’s not going to be solved with just a model. It really needs to be a true experiment-grounded framework where researchers can contribute their part and have it be a part of a whole.
