NHS England is rolling out AI tools across the health service, including integrated ambient voice technology, as part of £10bn tech funding.
Strong Public Support for Embryonic Genome Editing to Eliminate Severe Conditions, European Survey Shows
A European survey launched at the 42nd Annual Meeting of the European Society of Human Reproduction and Embryology (ESHRE) suggests broad public support for fertility treatment and research, including genome editing in human embryos for specific reasons.
The report, “Fertility, Embryo Research and Genome Editing: Public Attitudes in Europe,” was commissioned by the charity Progress Educational Trust (PET), which aims to improve choices for people affected by infertility and/or genetic conditions, and was supported by ESHRE. It explored public attitudes towards fertility treatment, embryo research, genome editing, surrogacy, and related topics.
The authors say the findings will “inform ESHRE’s ongoing work in Europe, linked to implementation of the European Union’s SoHO (Substances of Human Origin) Regulation and to the ethical considerations that arise in our field.”
The survey included 8688 participants aged 16–75 years across the U.K., Netherlands, Spain and Italy, with more than 2000 respondents in each country.
Across all four countries, a large proportion of respondents supported state-funded fertility treatment for people experiencing infertility and wishing to conceive, ranging from 54% in the Netherlands to 57% in the U.K., 62% in Spain, and 64% in Italy. Support was highest for heterosexual couples (47–59%) and lowest for transgender people (12–18%).
Conversely, most respondents said they did not support people being able to choose the biological sex of their child, based on personal preference. Opposition was strongest in the Netherlands (72%) followed by the U.K. (59%), Italy (55%) and Spain (47%). Nonetheless, there was still a significant minority that expressed support for sex selection. This support was strongest in Spain (32%) followed by the U.K. (26%), Italy (22%) and the Netherlands (18%), and was more common among younger participants than their older counterparts.
The age differential across responses could mean that with time, there will be a shift in views and potential changes in policy, the authors note.
The survey also found public backing for the use of human embryos in research to better understand and develop treatments for congenital diseases. Support ranged from 41% in Italy to 48% in the Netherlands and Spain, substantially exceeding opposition in all four countries (15–24%), including Italy, where research uses of human embryos are currently prohibited.
Respondents were then asked whether they supported or opposed the use of genome editing in human embryos, in three different scenarios:
- For scientific and medical research to help understand or develop treatments for congenital disease, without human implantation.
- In human embryos that will be transferred to a human to establish pregnancy to help eliminate a severe or life-threatening condition, like cystic fibrosis, in the resulting child.
- In human embryos that will be transferred to a human to establish pregnancy to help eliminate a common or medically manageable condition, like asthma, in the resulting child.
In all four of the countries, more respondents supported than opposed all three of the uses of genome editing, with the highest level of support given if the technique helps eliminate a severe or life-threatening condition.
It is important to note that this use of genome editing—which was supported by 55% in the Netherlands, by 53% in Spain, by 52% in the U.K. and by 46% in Italy—is not permitted by law in any of these four countries at present.
PET commented: “It is heartening to see such substantial support for uses of genome editing in human embryos, across all four of the countries surveyed. That said, these findings present an interesting conundrum. Respondents seem to be more ready to countenance the use of genome-edited embryos in treatment—at least, if helps to eliminate a severe or life-threatening condition—than they are to countenance the use of genome-edited embryos in research. Realistically, research must occur first. There is therefore a need for wide-ranging public conversations, where the vital role played by research—in enabling treatment, and ensuring that treatment is safe and effective—can be conveyed.”
Professor Karen Sermon, immediate past chair of ESHRE, said, “Reproductive medicine and embryo research are advancing rapidly, and these findings show the importance of understanding how the public views those developments. It is particularly striking that support for some applications extends beyond what is currently permitted in certain countries. As science advances, it is essential that public awareness keeps pace, so that decisions about future treatments are informed by both evidence and societal values.”
The post Strong Public Support for Embryonic Genome Editing to Eliminate Severe Conditions, European Survey Shows appeared first on Inside Precision Medicine.
Use of Electronic Patient Record Systems for Rapid Response to an MHRA Public Assessment Report: Retrospective Observational Study
Background: Digital health data and infrastructure facilitate rapid analysis to provide actionable data, thereby fulfilling the principles of a learning health system. In response to a report from the UK Medicines and Healthcare Products Regulatory Agency (MHRA), a rapid service evaluation was carried out to identify patterns of modified-release (MR) opioid use after elective surgery. Objective: We aimed to describe the prescribing patterns of MR opioids, methods to repurpose existing infrastructure, and the experience of collaboration between clinical and research teams using shared data pipelines. Methods: A retrospective case-control study was conducted at a tertiary care organization across multiple hospital sites in London, United Kingdom. Prescription and administration data for adult patients undergoing elective surgery between March 31, 2019, and June 20, 2025, were extracted from a standardized research data pipeline within 4 weeks of the publication of the MHRA report. Patients were screened for MR opioid prescriptions in the postoperative period and at hospital discharge. Counts and proportions of encounters in which MR opioids were administered or prescribed were evaluated across the study period. Reflections on the application of the infrastructure for this purpose were also documented. Results: Of 126,882 elective surgeries screened, 102,879 (81.1%) met the eligibility criteria. Over the study period, patients received a new MR opioid prescription after 7525 (7.3%) of the 102,879 eligible encounters, with 2438 (2.4%) encounters receiving a new MR opioid prescription at hospital discharge. Postoperative administration of MR opioids and prescribing at discharge have declined since 2020. As a result of this study, a new context-aware alert system was developed to monitor and reduce MR opioid prescribing in this surgical cohort. Reflections on the implementation experience demonstrated how collaboration between clinical and research teams in conjunction with integrated and seamless research pipelines allowed rapid knowledge generation. Key issues raised were the difficulty of validation between parallel data extraction systems and how the two different teams compared nonequitable data points and results. Conclusions: Mature digital and analytical infrastructure within health care institutions can enable swift evaluation of local practices in the context of national medication safety alerts. This can shorten action response times and improve patient care but requires close collaboration between clinicians and research teams. Shared infrastructure between teams across the learning health system improves data quality and provides easy access to the key users. Further work is needed to understand the benefits and challenges of infrastructure built for other use cases and the effectiveness of the intervention.
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Pathology transformation could unlock £450m a year for the NHS
Completing the NHS’s pathology transformation programme could unlock around £450m a year for the health service, a review has found.
The Download: a smoking “endgame” and a new Elizabeth Bear story
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.
The UK’s generational tobacco ban might not work. I’m supporting it anyway.
—Jessica Hamzelou
As the parent of two little girls, I often think about how their childhood is different from mine. The seven-year-old is learning about AI at school. The five-year-old is given internet-based homework every week. And they are both absolutely repulsed by the idea of smoking.
That was not the prevailing sentiment when I was young. Smoking was a central part of our culture. Which is why the UK’s recent passing of a generational sales ban on tobacco products feels like such a big deal.
This is what’s described as an “endgame” approach. While many tobacco control strategies—such as taxation or gory imagery—aim to reduce consumption, policies like the UK’s are designed to eliminate it entirely. It’s a new approach, and no one knows whether it will work. But it’s an enticing prospect—and it’s starting to look a lot less radical.
Find out why generational tobacco bans are gaining support.
This story is from The Checkup, our weekly biotech newsletter. Sign up to receive it in your inbox every Thursday.
You do your own time
—You do your own time is a short story by Elizabeth Bear, an award-winning speculative fiction author.
There we were, a regular murderers’ row of librarians. Turning around in the nave of our library to greet the sound of footsteps, pistols leveled in case whoever was coming in didn’t respect sanctuary.
I pulled down a solid-state drive full of biographies and case studies of people who had spent time—and sometimes their whole lives—in labor camps or chattelhood. It was illegal to possess, and the feds used smart agents to track down and obliterate any copies. Which was why we were sending one to the stars.
What’s left behind when a name is erased from the system? No legacy, no memory—that is the point of media and narrative control. So that was our plan: to preserve it, for later generations, or just as a silent record of our existence.
Read the rest of this short story in full.
—Elizabeth Bear
This story is from the latest edition of our magazine, which is all about engineering. Subscribe now to get a copy, plus all our other issues and a range of subscriber-only content.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 An EU lawmaker investigating spyware was hacked by that spyware
Citizen Lab found Pegasus spyware on Stelios Kouloglou’s phone. (Wired $)
+ It said the EU “looks the other way” on spyware abuses. (Guardian)
+ Meet the director of Citizen Lab. (MIT Technology Review)
2 Anthropic is closing loopholes that allow Chinese access to Claude
It’s targeting VPNs, relay services, and overseas accounts. (FT $)
+ Users in China keep finding new workarounds. (Wired $)
3 A Tesla driver has been charged with manslaughter after a fatal crash
Court records show he was using automated driver-assistance. (WSJ $)
+ Tesla sales have surged 25% after a rebound in Europe. (NYT $)
4 Trump bought lots of tech stock the day he unveiled his AI Action Plan
He acquired up to $5 million in stock from Amazon and others. (Engadget)
+ His AI Action Plan was a distraction. (MIT Technology Review)
5 Companies are throttling employees’ AI use because it’s too expensive
They’re pleading with workers to use less powerful models. (404 Media)
+ Tesla has capped their AI spending at $200 per week. (The Information $)
6 The Energy Dept wants data centers on backup power in heat waves
It wants them to free up power for AC. (NYT $)
+ People near data centers are dreading heat wave pollution. (Politico $)
+ No one wants a data center in their backyard. (MIT Technology Review)
7 A Meta glasses feature just went from free to a subscription service
“Conversation Focus” will now cost $19.99 per month. (BBC)
+ The move heralds a new era of consumer tech subscriptions. (Wired $)
8 Random wobbles in time could solve gravity’s greatest mystery
A new idea could reconcile gravity and quantum mechanics. (New Scientist $)
9 Peter Thiel claims the pope is “working for the Chinese Communists”
By pushing for stricter AI rules that may benefit Chinese interests. (CNN)
+ Pope Leo XIV said AI must be “disarmed” in his first major teaching. (BBC)
+ His encyclical offered a template for steering AI. (MIT Technology Review)
10 Supersonic flight over land could finally be legal again
Regulators want to lift a ban—so long as the planes are quiet. (Ars Technica)
Quote of the day
“We don’t have robots that are nearly as good at understanding the physical world as a rat.”
—Yann LeCun, the founder of AMI Labs and Meta’s former chief AI scientist, tells the BBC that AI isn’t as smart as many think.
One More Thing
MARCO GIANNAVOLA
How two brothers became go-to experts on America’s “mystery drone” invasion
On a Friday evening in December, every tier of US law enforcement was dispatched to a military research installation outside Boston after a squadron of 15 to 20 drones was spotted violating restricted airspace. The culprits could not be found.
It was the latest in a series of purported drone sightings along the US East Coast. Lacking coordination or clarity from the White House, the Pentagon, and the intelligence community, law enforcement officers turned to an unlikely source: twin brothers from Long Island who hunt UFOs.
The Tedescos have built a mobile field lab to investigate unexplained aerial phenomena. Now members of the FBI want their support.
Discover how the brothers are helping law enforcement investigate UFOs.
—Matthew Phelan
We can still have nice things
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ This record-breaking drone show is a mind-bending display of aerial light.
+ A Paris bakery is taking a bite out of food waste by repurposing croissants.
+ Relive your childhood with a classic episode from the Mister Rogers archive.
+ See graffiti through new eyes with this project that prettifies tags and makes them legible.
Exercise interventions are most consistently supported for depressive disorders: an umbrella review of diagnosed depressive and anxiety disorders
BackgroundExercise is increasingly discussed as part of lifestyle-based and multimodal care for mood and anxiety disorders, but review-level evidence often mixes formally diagnosed clinical populations with symptom-defined or medically mixed samples.MethodsWe conducted an umbrella review of systematic reviews, meta-analyses, and network meta-analyses of structured exercise interventions for adults with depressive or anxiety disorders. Six databases were searched from inception to 1 March 2026. Primary outcomes were depressive and anxiety symptom severity, remission, and response; secondary outcomes were acceptability and tolerability. Review quality was appraised with AMSTAR 2, and primary-study overlap was quantified with corrected covered area (CCA), including overall and symptom-cluster analyses. The synthesis was designed to summarize review-level credibility and clinical interpretability rather than to generate a second-order pooled efficacy estimate.ResultsNine reviews met eligibility criteria; four supplied directly extractable primary overall review-level estimates for core psychiatric symptom outcomes. AMSTAR 2 appraisal rated one review as high, three as low, and five as critically low. Recalculated overall overlap was slight (112 primary-study occurrences, 89 unique primary studies; CCA = 3.23%), although cluster-level analyses identified localized redundancy, particularly within anxiety-disorder-specific reviews. In major depressive disorder, one clinically focused review reported a large reduction in depressive symptoms for aerobic exercise versus non-exercise comparators (Hedges’ g = -0.79, 95% CI -1.00 to -0.57; I² = 21%). Across diagnosed depressive and/or anxiety disorders, broader review-level estimates also favored exercise for depressive symptoms (SMD = -0.97, 95% CI -1.28 to -0.66) and anxiety symptoms (SMD = -0.66, 95% CI -1.09 to -0.23), but heterogeneity was high. Anxiety-disorder-specific evidence was less secure: the primary DSM-IV anxiety-disorder pooled estimate showed no clear benefit over selected controls (SMD = 0.02, 95% CI -0.20 to 0.24). Acceptability estimates were close to null, and adverse-event reporting was too sparse to support confident safety conclusions.ConclusionExercise is best supported as an adjunctive, patient-centered component of care for depressive disorders. Anxiety-disorder-specific efficacy remains uncertain when comparator rigor, diagnostic heterogeneity, and localized overlap are considered, and safety reporting needs substantial improvement.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD420261364264.
The UK’s generational tobacco ban might not work. I’m supporting it anyway.
As the parent of two little girls, I often think about how their childhood is different from mine. The seven-year-old is learning about AI at school. The five-year-old is given internet-based homework every week. And they are both absolutely repulsed by the idea of smoking.
That was not the prevailing sentiment when I was young. My parents smoked. The customers at our family’s restaurant smoked. Cartoon characters smoked. My friends and I would buy little cigarette-box-shaped packets of sugary white sticks and pretend to smoke in the playground. Smoking was a central part of our culture.
Which is why the UK’s recent passing of a generational sales ban on tobacco products feels like such a big deal. As part of the Tobacco and Vapes Act 2026, retailers are prohibited from selling tobacco products to anyone born after January 1, 2009, in perpetuity. It doesn’t matter when those people turn 18—or 38 or 68, for that matter. It will always be illegal to sell to anyone born after that date.
This is what’s described as an “endgame” approach. While many tobacco control strategies—such as taxation or gory imagery—aim to reduce consumption, policies like the UK’s are designed to eliminate it entirely. It’s a new approach, and no one knows whether it will work.
The Maldives was the first country to implement a generational smoking ban, in November last year. It’s too soon to say how that has panned out.
Nor do we know if these laws will even last. In 2022, New Zealand passed a similar generational sales ban as part of a broader anti-smoking law. But it was never enacted—the law was repealed by a new government in February 2024.
In the UK, both major parties support the ban. But Nigel Farage, whose right-wing party has seen a recent surge in support, has promised that “the generational smoking ban will not last long if Reform gets the chance to start rebuilding our mismanaged country.”
Chris Bostic, an attorney and former policy director for the advocacy group Action on Smoking and Health, says he and his colleagues began promoting the idea of a generational ban in the United States 11 years ago. Back then, they struggled to win support, even from major health charities. “People said we were crazy … [and] that this was impossible,” he says. Opponents argued that bans would infringe on personal freedoms.
“The public health argument is: Well, what about freedom from addiction?” says Britta Matthes, a tobacco control researcher at the University of Bath in the UK. Most people who smoke began when they were teenagers, want to quit, and wish they’d never started. Tobacco is arguably the most harmful consumer product of all time. It will kill half its users who don’t quit, according to the World Health Organization.
It also kills people who don’t smoke. Of the 7 million who die from tobacco every year, 1.6 million are nonsmokers who were exposed to secondhand smoke, according to the WHO.
Generational sales bans are a long-term strategy that will only protect future smokers. Most experts agree that people who already smoke should be a main consideration for any policy, and that a multipronged approach is probably the best way to go. Janet Hoek at the University of Otago, who has explored tobacco control policies in New Zealand, believes that enforcing very low limits on nicotine levels and banning filters—an environmental scourge that does not make smoking safer, as many people believe—might be a “powerful combination,” for example.
But preventing teenagers from starting to smoke in the first place is an enticing prospect, even among the majority of people who smoke. And it’s starting to look a lot less radical.
The US has quietly been making progress on a smaller scale. Since 2021, Brookline, a town in the Boston area, has banned the sale of tobacco products to anyone born after January 1, 2000. The idea has spread. Today there are 23 towns in Massachusetts with similar bans, says Bostic. Nine towns across Minnesota, New York, and California have implemented other endgame policies.
The UK law has normalized the idea more than ever, he adds. His colleagues are already fielding calls from health agencies around the world. “People [are] saying, Wow I can’t believe the UK just did this—can we do this here?” he says.
Norms change. Like many other millennials, I vividly remember my first night out after a ban on indoor smoking took effect. My clothes didn’t stink! My hair still felt clean! And my throat wasn’t scratchy the next morning! Now that’s just normal. I hope a tobacco-free world can be the new normal for my kids.
ASMS 2026: Solving Proteomics’ Next Bottleneck
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.”
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
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.”
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.”
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.
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.”
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.”
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.
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.
The post ASMS 2026: Solving Proteomics’ Next Bottleneck appeared first on GEN – Genetic Engineering and Biotechnology News.
Prediction of Clinically Significant Depressive Symptoms at 2-Year Follow-Up in Older Adults: Machine Learning Study Using the English Longitudinal Study of Ageing
Background: Depression in older adults is often underdiagnosed due to atypical symptom presentation and generational stigma, leading to delayed intervention. Early identification of individuals at risk of developing elevated depressive symptoms is therefore critical, but traditional approaches show limited predictive accuracy. To date, no study has applied machine learning (ML) models to predict clinically significant depressive symptoms at 2-year follow-up in older adults in the United Kingdom using data from the English Longitudinal Study of Ageing (ELSA). Moreover, the impact of encoding strategies for categorical health care variables has not been examined. Objective: This study aimed to develop and evaluate ML models to predict the clinically significant depressive symptoms at 2-year follow-up in older adults using ELSA data. We further compared ordinal and one-hot encoding strategies across different ML architectures and identified key predictors of depressive symptoms at follow-up. Methods: Data were drawn from 4 consecutive waves of ELSA, including participants aged ≥50 years without significant depressive symptoms at the baseline wave (waves 6‐9). Clinically significant depressive symptoms were defined as 8-item Center for Epidemiologic Studies Depression Scale (CES-D 8) scores of ≥4 at the subsequent wave (waves 7‐10). Over 120 features spanning sociodemographic, psychological, and health-related domains were analyzed. Eight ML models were applied, including tree-based ensembles, deep learning architectures for tabular data, distance-based methods, probabilistic methods, and linear methods. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and -score. Model interpretability was examined using Shapley additive explanations (SHAP). Sensitivity analyses assessed the robustness of results across alternative CES-D 8 thresholds (≥3, ≥4, and ≥5) and encoding strategies. Results: Across waves, the best-performing models achieved mean AUROC scores of 0.72‐0.73, with a peak of 0.75 in the highest-performing wave. Ordinal encoding consistently outperformed one-hot encoding across all ML models, yielding improvements in AUROCs and -scores, with the greatest increase in tree-based methods. SHAP consistently identified loneliness, sleep disturbances, and low social engagement as strong predictors of elevated depressive symptoms at follow-up. Sensitivity analyses across CES-D 8 thresholds demonstrated robust feature importance, with AUROCs ranging from 0.67 to 0.82. Traditional ML models (random forest, extreme gradient boosting, and support vector machines) generally achieved higher performance than the deep learning models for this task. Conclusions: Our findings demonstrate the feasibility of predicting clinically significant depressive symptoms at 2-year follow-up in UK older adults, with moderate accuracy. Ordinal encoding demonstrates superior performance for health care datasets with inherently ordered categorical features. The identification of consistent risk factors highlights opportunities for developing targeted clinical screening tools and preventive interventions. This study provides new evidence on depressive symptom prediction in the UK context, leveraging longitudinal data from ELSA, and contributes to advancing digital mental health research for aging populations.
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Teaching AI to run with the turbines
Artificial intelligence may have captured the public imagination through chatbots and image generators, but some of its most consequential use cases are unfolding far from consumer-facing tools. In industries where physical infrastructure, operational continuity, and safety are paramount, AI is becoming a core operating layer. With its sprawling industrial systems and constant stream of operational data, the energy sector offers a glimpse into what that future could look like.
At Woodside Energy, AI adoption did not begin with generative models or enterprise copilots. The company has spent years building predictive analytics, optimization systems, and machine learning tools across exploration, drilling, maintenance, and plant operations. “We’ve always had very large volumes of operational data coming from the equipment and the plants and the assets that we operate,” says the company’s vice president for digital Andrew Melouney. “Those have created really clear, quite high-value use cases for us.”
That long-term investment in infrastructure and governance is now enabling a broader shift toward agentic AI systems that can support complex industrial workflows. Rather than replace human operators, Woodside designs AI systems to augment expertise in high-stakes environments. A prime example is its “Startup Advisor,” an AI copilot that helps operators manage the complex process of starting liquefied natural gas (LNG) plants. “We’re really thinking about, how does it support the people in the organization in terms of empowering them to make better decisions, to make faster decisions,” Melouney explains.
The company’s approach reflects a wider evolution taking place across industrial AI: graduating from isolated experiments to enterprise-wide systems built on standardized platforms, governed data, and repeatable deployment patterns. That transition, Melouney argues, requires organizations to rethink both their technology stacks and how work itself gets done. “We’re not just bolting AI onto an existing process,” he says. “We’re deeply thinking about how that work needs to be reimagined.”
Melouney’s motto has become: “Think big, prototype small, and scale fast.”
As AI systems become more autonomous and interconnected, the companies poised to succeed may be those that spent years building the operational foundations beneath the hype.
“Our ambition is really for an autonomous enterprise, where we have agents with agency that are able to really deeply interact with our core workflows,” says Melouney.
This episode of Business Lab is produced in partnership with Infosys.
Full Transcript:
Megan Tatum: From MIT Technology Review, I’m Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.
This episode is produced in partnership with Infosys.
Now, when people think about artificial intelligence, they often picture chatbots or productivity tools, but some of the most sophisticated and high impact uses of AI are actually happening far from consumer apps, inside complex industrial environments where safety, reliability, and physical systems matter. The global energy sector is a prime example.
Companies like Woodside Energy, a global energy producer headquartered in Western Australia, have been applying AI for more than a decade now, from advanced analytics and operations, to remote decision support, to smarter maintenance, and energy efficiency across large scale assets. Today, Woodside is scaling that experience, embedding AI more deeply across its operations and the enterprise with a strong focus on governance, data quality, and human accountability.
Two words for you: technological fuel.
My guest today is Andrew Melouney, vice president for digital at Woodside Energy. Welcome, Andrew.
Andrew Melouney: Thanks, Megan. It’s great to be here.
Megan: Lovely to have you. Now, Andrew, as I said there, the energy sector has approached AI quite differently from technology or consumer businesses. Early value has emerged in operational and industrial environments, rather than consumer-facing generative AI tools. Why is that? And what differentiates the energy sector’s AI journey?
Andrew: Megan, I think it really comes down to the nature of the work we do. Energy operations and what Woodside does is very asset intensive, it’s very safety critical, and it’s highly physical. And when you think about how Woodside operates, we operate across the full value chain. We do exploration through to drilling and subsurface work, to project development, all the way through to operating assets, which are often operated in harsh and remote locations, and then global energy portfolio marketing and trading as well.
We’ve always had very large volumes of operational data coming from the equipment and the plants and the assets that we operate, and those have created really clear, quite high-value use cases for us. When you think about reliability, when you think about safety and efficiency, those are really critical things for a company like Woodside. We’ve been doing traditional AI for many years now. If you think about analytics, if you think about optimization, if you think about things like predictive models, those techniques we’ve been applying to our data sets and to our business since around 2015.
And more recently with the advent of generative AI, we’ve really found that we’ve got a pretty strong and awesome foundation to build on top of and to really solve problems in the service of improving the business. And again, whether that is keeping people safe, keeping the environments we operate in safe, or improving returns for the organization.
Megan: Fantastic. I mean you touched on it there, but how has this reality shaped your own AI strategy at Woodside? Where did you start, and where did the technology prove most impactful in those early days?
Andrew: Well, like I said, we’ve had a very long journey, in terms of understanding our operational data, recognizing the value of it, and collecting it at scale so that we can use it. And we’ve been very deliberate in that approach, Megan. We’ve really thought about where the value is and where the risks were manageable. And we’ve started looking at, in today’s world from an agentic AI perspective, we’ve started looking at the problems that were solved with traditional AI and machine learning and data science in the past. And we’ve started to think about, where can we then layer agentic AI over the top to provide an even better outcome?
For our asset intensive industry and organization, we’re looking at areas such as maintenance optimization. We’re looking at areas such as, how do we ensure our LNG plants start up reliably, consistently, and safely? And we’re considering really our frontline workforce and making sure that we’re giving people on the frontline the tools required to do their jobs. When we think about AI, we’re really thinking about, how does it support the people in the organization in terms of empowering them to make better decisions, to make faster decisions? I think over time, this has just evolved from what has been traditional analytics to now artificial intelligence and generative AI. And we’ve learned along the way that the technology is important, but it’s about aligning people, processes, and the technology together.
We’ve spent a long time not only in collecting the data and having a well-curated data set that we can build on top of, but we’ve also spent a lot of time teaching people how to work in agile ways, how to do design thinking, how to problem solve, and how to really make sure that the technology that, say, my team can bring to bear to the organization is adopted effectively and purposefully. And I think once we had that solid foundation in place from a technology perspective, from a data perspective, once we got strong trust built between our digital teams and the organization, we really saw quite a material uptick and the scaling of technology occur more broadly across the enterprise.
Megan: Fantastic. That people piece so important, isn’t it? It’s just a tool, technology, that needs to be in the right hands. And you touched on data there; industrial AI obviously depends on vast amounts of data. Can you walk us through how you’ve approached data at Woodside in a little more detail? How it’s structured and governed, and how tools like maintenance intelligence as well fit into that.
Andrew: Well, data is really foundational and fundamental to everything we do, particularly from a technology perspective. It gives us the ability to innovate at pace when we are building over the top of a strong foundation. As I said before, we’ve had the benefit of a long-term investment in our underlying operational data. I think the way we think about data is that it’s an asset for us.
And when you think about operating a facility where you’ve got sensors everywhere, you’ve got data streaming in real time, you’ve got operators needing to make decisions in real time, we have consciously made a decision over many, many years to invest in that enterprise scale data platform to make sure that it’s secure. We’ve got well-structured data assets, and we’ve got strong governance over the top of that data so that when it is used, when it’s built in a data science application or an AI agent, that we’ve got a level of trust in it that it’s going to be used responsibly. And that when it’s used, it can be trusted to give the outcome that we expect.
We have developed platforms that continuously ingest really high frequency data from the assets and from our enterprise systems. Once we’ve been able to develop solutions on top of that, parts of the business that might own the systems that collect that data, they see the value in it.
When you look at something like maintenance intelligence is a really good example of how we’ve been able to take something that we’ve been working on for a long time. Woodside does a lot of maintenance, it’s a very important part of our business, and it occurs across all of our operating assets. But we have been looking at how we do predictive analytics and predictive maintenance for a long time across that data set that we own. And something like maintenance intelligence is a solution that gives us the ability to optimize how we do that maintenance. And what it does is it analyzes historical maintenance records, alongside the performance of the equipment. And again, by having that data set well-governed and in one place, we get the ability to correlate different data sets, such as maintenance records out of SAP, alongside say equipment and performance coming from our time series data lake.
And when we build over the top of that, something like maintenance intelligence gives us the opportunity to recommend to the assets what the optimal timing for maintenance activities might be, and really give what is quite a simple aim, which is do the right work at the right time. And with something like maintenance intelligence, we have seen the opportunity, and we have the opportunity to reduce maintenance hours by up to 15% over five years on one of the assets that we’ve piloted this on. And as we’ve built out that underlying analytical model, we’re now able to put agentic AI over the top of that and provide better insights and optimize that solution more.
It really comes down to providing our asset teams and our operational teams with the right decision support capability that ensures they’re still accountable to make the decision and to ensure the right work is being done, but we are giving them the best possible opportunity to use their judgment and experience with the data that we provide to make the right decision.
Megan: Sounds like a really impactful change. Last year also marked a milestone in moving from early AI learnings to scale, using AI more deliberately as a force multiplier. What transition were you trying to make and how did you approach it?
Andrew: Well, Megan, we’ve had a philosophy for a long time in Woodside from an innovation perspective, where we really want to think big, we want to prototype small, and we want to scale fast. We want to find big opportunities that we can go after, but we want to ensure that we look at how we deploy those on a small scale first, and then provide the right learning and insight that then can scale it everywhere. Something like maintenance intelligence is a good example of that, or our Startup Advisor, where we know that we’ve got multiple plants that we need to start up. We know that we’ve got multiple assets that need to do maintenance, so we have a big, bold ambition about how we can improve and optimize that. We start with a small prototype; it might be one subsystem, it might be just a part of an asset, and then we scale it out, we learn, and we scale faster.
I think from an AI learning perspective, one of the key things we’ve learned is really the transition from moving from isolated AI solutions to a more coordinated enterprise-wide capability. If you look back maybe 18 months, two years, in our generative AI journey, we rarely started by deploying AI as broadly as we could in the organization from a personal productivity perspective. And probably being quite open in terms of the problems that we will solve, the business problems that we’ll solve with AI. That had a lot of benefits for us in terms of allowing our organization to get to know AI, get to know the capabilities, to build the trust in it.
What we’ve learned though is that we’ve needed to pivot from that to being a little bit tighter in terms of where we are going to invest our time and resources and more higher value solutions. How do we then enable and empower the rest of the organization so that they can actually effectively problem solve with technology in their domain or in their personal productivity without having to come to a central team?
When we think about that, think big, prototype small, scale fast, has been something really important for us. The transition from a more broader approach to use case development and solution development to now a narrower focus on the high value priorities. We’ve seen that paying dividends to us and allowing us to go after solutions and opportunities, things like Startup Advisor.
And so our Startup Advisor is a agentic AI solution that really aims to optimize and empower and better support our operators that sit in front of a panel and have to start up LNG plants, which are incredibly technical facilities and require really specialist skills to start up. And so our Startup Advisor is almost like a copilot that sits alongside those operators, and it gives them the ability to be able to play back previous startups. It gives them the ability to look at how the current startup is progressing, and it provides them better insights to optimize how they start up that facility. And again, starting up an LNG facility is incredibly complex.
Megan: I can imagine.
Andrew: When we think about opportunities like Startup Advisor, again, it goes back to that think big, prototype small, and scale fast. We started with a very bold vision of, how do we start up all of our LNG plants in a much more structured and optimized fashion? How do we better support our panel operators? How do we make, say, a more junior panel operator have a copilot that can help them almost like an experienced panel operator sitting next to them? And when we think about that vision and the ability then to prototype on a small scale and then scale fast, I think it’s been really successful for us.
As we scale, we’ve just naturally expanded into more agent-based solutions. Today, we’ve got around 50 AI agents in production, supporting both our operating assets and our enterprise workflows. These tools have been proven in live environments, and we have really seen the benefit of being able to shift from point solutions that maybe solve small scale problems in specific areas, to AI and agentic solutions with agency that can really work across our workflows.
We’re able to do this because we’ve standardized on the platform that we build on and we’ve got repeatable patterns. That’s been another really important learning for us, is that we don’t want to build 50 solutions in 50 different ways. We really want to be empowering our organization and our technical teams and the users of our solutions to roll them out quickly, to roll them out safely, and to do it in a patternized and platform manner.
But the last point I’ll make, Megan, from a learning perspective is that we’ve really understood that a strong governance around how AI is deployed and developed is critical for us, and it’s critical for us to go fast as well. The traditional ways of governing how we roll out different solutions or digital systems isn’t going to scale to the breadth that we need when we are thinking about AI. Being able to have a clear philosophy around how we innovate, transitioning from isolated solutions to that enterprise-wide capability, and making sure that we’ve got strong platforms with strong patterns and clear governance are the three really critical things that we’ve learned.
Megan: Such important pillars, all of them. And you’ve been working with Infosys on this journey. How has that partnership helped accelerate scaling and embedding AI across the business?
Andrew: Well, Infosys is our managed service provider, and so they play a really critical role in the operations of our core business. One of the things that I like to say is that our license to innovate is based on our license to operate. And so, for my team to be able to turn up to an operating asset or a corporate function and have the trust that’s needed to be able to innovate and reimagine and redesign how work gets done, to be able to do that, we need to make sure that our core platforms, our core systems, our applications are running really reliably, safely, and consistently every day. Having an experienced partner like Infosys looking after those core operations in partnership with our internal teams is really, really important to us.
As we move from pilots to enterprise-wide deployment, the ability to partner with someone like Infosys also gives us the ability to scale. And so being from Perth and Western Australia, while we’ve got a really strong local team in Western Australia, and we’ve also got a very strong team in some of our other operating locations, like everyone, we’re struggling to find people that can fill AI roles. Being able to partner with Infosys and have a number of different operating models at our disposal becomes really important for us. Having co-mingled teams where they are staff, they are Infosys staff, Woodside staff, and some of our other partners, really just brings diversity of thought and experience to how we solve problems.
Fundamentally, the partnership has allowed us to operate and innovate with more confidence. While Woodside always retains ownership of the strategy and where we’re going and the governance and my teams remain accountable for the outcomes, we can’t do what we do without strong partnerships like the one we have with Infosys.
Megan: Fantastic. And as AI adoption scales, you mentioned yourself, governance becomes increasingly important. How challenging has that been, and what guardrails have you put in place at Woodside?
Andrew: So, Megan, governance is really important to us, and we operate in a well-regulated environment. That means we’ve got to make really deliberate and well-reasoned decisions when we’re thinking about how we deploy technology into our organization, whether it’s artificial intelligence or anything else, for that matter. And so, governance is really central to how we approach the execution of our AI strategy at Woodside.
We’ve got maybe two or three really key things that we’ve put in place. The first one is just making sure that every AI use case goes through a structured assessment, and that’s making sure it meets our privacy controls, our cyber controls. We’re also asking the question, not just, could we do this, but should we do this? We’ve really got to bring together safety, ethics, transparency, accountability, and make sure that we make an informed decision. When an AI solution is going through that structured assessment, if there are concerns about how we might use that solution, it then goes to an AI council that’s made up of senior leaders across the organization. That council and that group really oversee some of the prioritization and risk management. That’s where we can have really strong, robust debates around, again, could we do something, should we do it, and how do we mitigate any of the risks that we might introduce here?
I think the last one, Megan, is really around lifecycle management. When you start thinking about, we’ve got 50 at the moment, but if we had 500 agents working in our organization, really amplifying the experience and the decision-making and the value creation of our staff, we really want to have an ability to manage the lifecycle of how those agents operate. We want to know, how many people are using them? What’s the efficacy and the outcome? Is there model drift? Do we need to retune or retrain? I think that’s an area where many organizations, including Woodside, are still leaning into and still figuring out the best way to do this. We can do it quite easily with 50 agents, but 500, 5,000, 50,000 becomes an opportunity for us. Again, thinking about how we partner with others, solving problems like that really present an opportunity to co-create and to co-solve with some of our partners, like with Infosys.
Megan: Fantastic. Just to close, what’s your long-term vision for AI at Woodside? How do you see this evolving over the years ahead, and what could it unlock for the sector in your view?
Andrew: So Megan, I think our ambition is really for an autonomous enterprise, where we have agents with agency that are able to really deeply interact with our core workflows. The outcome that we want to get from that is to protect our people, to protect the environments we operate in, and to be able to provide energy at a lower cost to the world. When we think about that ambition, we can really see that being applied to almost all of the areas that Woodside work in. Whether that’s from exploration through to project developments, through to operations or marketing, the scale of the opportunity in front of us and the ability for us to really change the way that work flows through the organization is really exciting.
For us, there’s three things that we have to get right in terms of being able to execute on that ambition. The first one is really thinking about how the work gets done in the organization so that we’re not just bolting AI onto an existing process, but we’re deeply thinking about how that work needs to be reimagined. We’ve also got to think about how we enable our workforce to work differently. Providing them with the skills and the tools and the ability to really harness the power of the technology that we provide.
Secondly, we’ve got to continue to move from and restrain ourselves from deploying point solutions that solve very narrow problems, to having more connected, agentic systems of systems that can interact with each other. To do that, and if we do that successfully, that’s where we really get the high value unlock from agents being able to interact with workflows and really change how the work gets done.
And lastly, Megan, it’s about how we must continue our philosophy of thinking big, prototyping small, and scaling fast.
Megan: Which is a fantastic lens to which to make all these decisions. Thank you so much, Andrew. That was Andrew Melouney, vice president for digital at Woodside Energy, whom I spoke with from Brighton in England.
That’s it for this episode of Business Lab. I’m your host, Megan Tatum. I’m a contributing editor and host for Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.
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