Graphene nanoplatelets (GNPs) offer promising properties for neural interface applications, particularly in intracranial direct current (DC) stimulation, due to their high charge injection capacity, flexibility, and biocompatibility. In this study, we comprehensively evaluate the biological and functional performance of GNP-based electrodes for acute and chronic cortical stimulation and recording. GNP electrodes were fabricated via screen-printing on flexible polyimide substrates. They were assessed for cytotoxicity using RAW 264.7 macrophage cell lines and primary hippocampal cultures, showing no evidence of cell death or toxicity. Long-term implantation in non-human primates (50 days) revealed no signs of infection, inflammation, or cortical damage, supporting the biostability of the material. Magnetic resonance imaging confirmed that GNPs produce no imaging artifacts, affirming their compatibility with standard neuroimaging protocols. Functionally, epidural DC stimulation using GNP electrodes modulated neuronal firing rates in the auditory cortex in a polarity-dependent manner, consistent with classical effects of DC stimulation. Furthermore, the same electrodes reliably recorded auditory-evoked potentials, demonstrating dual functionality for stimulation and recording. These findings position screen-printed GNP electrodes as safe, versatile, and effective tools for neurophysiological applications, and highlight their potential for future use in translational neuroscience and neurotherapeutic settings.
IBM has unveiled chip technology that could help extend Moore’s Law another decade
IBM has built a new prototype chip with around 100 billion transistors on an area the size of a fingernail, which is twice the density of the company’s previous state-of-the-art technology announced in 2021. The design could pave the way for faster and more energy efficient computers for years to come.
For more than half a century, chipmakers have been able to make ever more powerful computers by following the key principle of Moore’s Law: Cram more transistors onto the chip. To do this, they shrank transistors—the tiny switches that perform computations—to incrementally smaller sizes. But in the last 15 years, transistors have gotten close to the point where quantum mechanics starts to interfere with their function: just a few dozen nanometers in size. They can’t get smaller.
So to fit more transistors on a chip, engineers across the industry are eyeing a pivot to an approach familiar to urban planners: build up. On Thursday, IBM announced it has created a chip that uses this strategy. The new architecture, known as a nanostack, vertically stacks transistors in two layers on a silicon chip.
“It’s not just an incremental step,” Jay Gambetta, the director of IBM Research, said during a press conference on Tuesday. “It’s a meaningful leap forward.” Within a decade, Gambetta expects, chips with nanostacking will be widely used in data centers, where their improved efficiency could help the facilities better manage their energy consumption.
“Absolutely, it’s transformational,” says Dan Hutcheson, vice chair of TechInsights, a technology analysis company. “This puts another 10, 15 years on the roadmap.”
Compared with IBM’s previous state-of-the-art architecture, the company reports, chips built with this new approach can do as much as 50% more work in the same amount of time and be up to 70% more energy efficient.
The architecture offers a general way of laying out transistors, and IBM will partner with semiconductor manufacturers to make the actual chips. It anticipates that chip designers will deploy the design in many different types of chips, including GPUs and CPUs. “I expect to have many conversations with designers about how they can use this technology,” Huiming Bu, IBM’s vice president of global semiconductor R&D, said in the press conference announcing the new design.
A layer cake
Engineers created IBM’s new chip layer by layer, like a cake. They start by fabricating transistors on one layer of silicon. Then they place a silicon layer on top of these devices, and they fabricate another layer of transistors directly on top of that. Finally, they create the electrical connections between the two layers of transistors. This kind of vertical stack, which combines two types of transistors, is known as a complementary field-effect transistor, or CFET, explains Qing Cao, a professor of materials science and engineering at the University of Illinois at Urbana-Champaign, who was not involved with the work.
The company isn’t the only one pursuing this general approach. The biggest chip manufacturers—Intel, Samsung, and TSMC—and the competing research lab Imec in Belgium have been investigating CFETs. IBM says its design is distinguished by the fact that the transistors in the second layer do not sit directly on top of the first layer’s transistors; rather, they are staggered, which the company says simplifies wiring, among other advantages.
CFETs like those in IBM’s nanostack architecture contrast with another common approach to making two-tiered chips, such as AMD’s 3D V-Cache and Huawei’s forthcoming LogicFolding technology, Cao says. In those approaches, engineers fabricate the transistors on each layer of the chip independently before bonding the two together. IBM’s new method allows for more precise alignment of the layers, which is important for performance because transistors are so tiny, says Cao.
Nanostacking builds on an approach called nanosheet technology, which has been used to make current state-of-the-art transistors since around 2022. A transistor is essentially a hose through which electrons flow, with a valve that can turn the flow on or off. Inside the transistor, electrons move through a patch of the silicon called a channel. In IBM’s nanostack approach, the channel consists of three nanosheets that are each 15 atoms thick, spaced nine nanometers apart.
Every chip generation gets a name. IBM refers to its nanostack technology as “sub-nanometer” or “0.7 nanometer,” following a longtime industry convention where each generation is named for a smaller and smaller length. But “0.7 nanometer” is a marketing term and does not correspond to any physical characteristics of the chip. The distance between transistors “has been staying at about 40 nanometers for quite a long period of time,” says Cao.
Putting it into production
Looking ahead, chipmakers can try increasing transistor density by building on more tiers, as Bu suggested in the press conference. However, they will face practical challenges, according to Cao. Manufacturing introduces errors, which means a certain number of chips are faulty upon creation. “Here you’re building another layer on top, so if either top layer or bottom layer fail, your entire chip is going to fail,” says Cao. The resulting failure rate will be higher than for single-layer chips, and that will be costly.
Another central challenge is what Cao calls “the thermal budget.” Essentially, it means that engineers need to figure out how to build each layer without melting the connections to the one underneath. This means keeping manufacturing processes below 400 °C. IBM figured out how to make the second stack at low enough temperature, although the company is mum about its methods.
Academics are also on the case. Cao’s group, for example, has created a method for stacking transistors layer by layer where the second layer is created with processes below 200 °C. They manage this by using a type of transistor known as the junctionless transistor, which can be created without a typically required step called doping—a process that injects non-silicon atoms into silicon to tune the material’s properties. Doping is usually the hottest part of fabricating transistors. Cao thinks from a thermal management perspective, his approach could be easier to scale up to multiple tiers, although his demonstration is just a proof of principle.
But Cao thinks IBM’s work is “transformative” because it demonstrates how to stack transistors “on a full wafer using a state‑of‑the‑art manufacturing line.” The new approach pushes the industry forward, he says: “I’m interested in what’s their killer application.”
STAT+: Facing a brutal run, battered vaccine makers still see cause for hope
SAN DIEGO — It is an unusual time to be in the vaccine business. But in the view of those gathered here at the BIO international conference this week, it’s not altogether a bleak one.
In fact, some vaccine makers are feeling more optimistic than they were a year ago — considering the circumstances, at least.
In interviews with STAT, they acknowledged that Health Secretary Robert F. Kennedy Jr., a longtime vaccine critic, has brought once-unthinkable disruptions — the cancellation of major mRNA vaccine contracts, the dismantling of universal vaccine recommendations, cuts to government funding for research — and that Washington could deliver more ahead.
Oxford AI studies secure NIHR funding to tackle NHS waiting times
Two studies have received funding from the National Institute for Health and Care Research (NIHR) as part of an £8 million initiative supporting AI projects aimed at reducing NHS waiting times and improving patient care. Through its Invention for Innovation (i4i) programme, the NIHR awarded £8,136,409 to six projects testing a range of AI and […]
The emergence of the web data infrastructure layer for AI
AI is booming. New use cases are emerging each day. To capitalize on the technology’s potential, enterprises require data at scale. In many cases, though, the relevant information is blocked or unstructured, which limits its use by AI models.
To understand this challenge, consider the foundation of the web itself. The web was not designed for the automated discovery and retrieval that new AI applications demand. Overcoming this inherent design constraint requires infrastructure.
The next frontier in AI may depend on a new web data infrastructure layer that can enable models to discover and map this ever-expanding digital realm. This layer must be able to navigate hundreds of millions of existing web domains and billions of new URLs created each week, delivering real-time information and overcoming technical barriers.
“The data suggests there’s far more data out there,” says Or Lenchner, CEO of Bright Data, a web data collection platform. “Think of the universe: It’s out there, but you don’t know what you don’t know.”
Enabling access to fresh, relevant, and trustworthy data
While early AI breakthroughs were driven by scaling training data and model size, organizations are now encountering a fundamental bottleneck: They need to keep pace with the dynamic, unstructured, and constantly evolving nature of web data in order to ground outputs in current and verifiable information. AI performance increasingly depends not just on model architecture but on a system’s compute, networking, retrieval, and data engineering capabilities—that is, the system’s ability to quickly and reliably retrieve data that is fresh, relevant, and trustworthy.
Traditional model training relies on snapshots of information collected at a particular point in time. Training AI on such static data is no longer sufficient. To track fluctuations such as competitor pricing, consumer sentiment, and market trends, companies need a constant feed of new information, pulling data in real time along with relevant context. Their infrastructure must therefore be able to handle millions of simultaneous interactions across websites that vary by geography, language, format, and access rules.
“If it can’t retrieve real-time information, it lacks context,” Lenchner says. “In a business setting, that’s not acceptable anymore. Stale answers lead to bad decisions and disappointed consumers.”
Speed is not merely a matter of convenience; it’s a matter of necessity. Today’s organizations operate in environments where prices, inventory, markets, security threats, and customer behavior change continuously. Delayed data retrieval can reduce the usefulness of an otherwise sophisticated model.
Using live, high-quality web data can also reduce AI hallucinations because the model has a more relevant knowledge base. This builds user trust. In fact, one survey found that 56% of AI practitioners said businesses need access to real-time web data to improve trust in AI outputs. To ensure the model runs efficiently and effectively, the information must also be pared down to the appropriate essentials.
Despite the introduction of retrieval-augmented generation (RAG), where models pull in external data at the moment of a query, many AI systems still struggle to deliver outputs that are current, contextually relevant, and trustworthy in operational settings. According to Gartner, 60% of AI projects that are not supported by AI-ready data—accurate, structured, organized, and contextualized—will be abandoned by the end of the year.
This is because large-scale retrieval alone does not solve the problem. As Lenchner puts it, “You need to retrieve data at scale, but also in real time. Latency becomes an issue because of the end user who is waiting for the output.”
Accessing fresh, AI-ready data at scale introduces technical and structural challenges. In practice, many enterprise systems combine public web retrieval with APIs, licensed datasets, and proprietary internal data in their AI applications. Integrating these fragmented sources into a timely and usable knowledge layer requires specialized capabilities. Some research has found that 97% of AI organizations depend on real-time web data infrastructure, but 90% feel boxed in by various restrictions. Companies are increasingly developing technical approaches to navigate these constraints.
Lenchner draws this metaphor: “Think of the trained model as intelligence and relevant data as knowledge. A powerful intelligence layer sitting on top of a hollow knowledge layer is like a genius who knows nothing—useless in practice. Intelligence and knowledge have to come together.”
The promise of new infrastructure
A new layer of web data infrastructure can address this developing need for stronger AI inputs by enabling discovery of data, real-time access, and tailoring to a specific context. As Lechner describes it, “It’s all about collecting data at scale, super-low latency, without being blocked.”
Rather than relying on increased computing power, this type of platform emulates human browsing behavior to access available content and transform raw code into structured data feeds. It can work with websites that might not interact with traditional scraping tools, such as those heavy in JavaScript, or with aggressive antibot software.
As Lenchner explains, “It’s basically having infrastructure that can mimic a web user with identifying information—IP address, location, and 1,000 more parameters. And at scale. Think of doing that 80 billion times a day for millions of websites. And every single time, you are looking exactly as the website expects you to look.”
Of course, continuous retrieval introduces new data governance challenges. To address them, platforms can enforce strict compliance protocols aligned with global privacy frameworks, such as the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). They can also be limited to openly accessible, public information, avoiding paywalls or private logins. Any networks used can be vetted and consent-based, and incentives can be provided to owners of IP addresses. In this way, systems can be designed to comply with tightening regulation.
Such complex capabilities do not come easy. “When this is critical infrastructure for a company,” Lenchner says, “doing it in-house becomes a full-time engineering problem that competes with the actual AI work.” Addressing this complexity requires organizations to commit significant resources, leading many to seek specialized platforms designed specifically for data retrieval, orchestration, and observability.
Infrastructure for the real world
Real-time data retrieval is changing what AI systems can do inside organizations. For example, a retail company can use public information to enable a dynamic pricing engine, and global brands can track trademark infringements.
As the ecosystem matures, organizations that invest in this emerging data infrastructure layer will be better positioned to build AI systems that are more responsive, reliable, and aligned with real-world conditions—AI systems that can continuously adapt using current web data. Over time, the distinction between AI models and the infrastructure that feeds them may even begin to disappear.
As Lenchner says, “The world is changing. And everything that is happening in the world is being uploaded to the public web. The amount of new data that is being generated is growing and accelerating.”
To learn more from Bright Data, read the Data for AI 2026 report.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
Stripe, Anthropic, and OpenAI are backing an effort to stop respiratory infections
The common cold comes for us all—often more than once a year. And there is no way to prevent it. The best you can do is take vitamin C and stay away from people with the sniffles.
Now the payment company Stripe, founded by brothers Patrick and John Collison, says it will fund a new $500 million nonprofit whose goal is preventing both the common cold and the flu. Its eventual aim is to get rid of respiratory viruses altogether.
The new organization, called Intercept, will use grants and investments to back prevention approaches, including vaccines, as well as large-scale air-cleaning systems for schools, offices, and other public spaces.
In addition to Stripe, other funders include Anthropic, Flu Lab, and the OpenAI Foundation, as well as Bill Gates and several traders at the quantitative investing fund Jane Street Capital, according to an Intercept spokesperson.
“I think we treat respiratory infections as a minor nuisance, but have really underweighted the burden that they impose on society,” says Nan Ransohoff, the Stripe executive leading the initiative along with Charlie Petty, a venture capitalist who joined Stripe this year. On average, people spend 5% of their lifetime fighting a cold or the flu, according to Ransohoff.
Despite that, drug companies put relatively little effort into preventing colds. Part of the problem is that the sniffles are caused by more than 200 different viruses, according to the American Lung Association, with rhinoviruses being the most common culprits. There are so many that it typically doesn’t pay to try to stop any one of them with a vaccine. “When pharma companies look at it, it’s not as attractive as other things they could work on,” says Ransohoff. “So it hasn’t attracted the resources.”
Stripe previously organized a $1.8 billion program called Frontier to encourage the development of carbon removal technology, as a way of countering climate change. Ransohoff says removing carbon from the atmosphere and getting rid of respiratory viruses are similar in that each is “technically possible” but they “lack commercial incentives.”
The concept for Intercept took shape after Ransohoff started talking to David Veesler, a structural biologist and vaccine designer at the University of Washington, who argued that it’s possible to come up with broad countermeasures that work against many viruses at once.
“He effectively sort of nerd-sniped me,” Ransohoff says of Veesler. “He convinced me that this is technically possible. He also helped me understand that some of the reasons that this hasn’t been done before was sort of an incentive problem.”
Veesler says the growing tool kit available to scientists includes RNA drugs, antibodies, and computational protein design. For instance, one idea is to engineer virus-grabbing proteins that people could spray in their nasal passages, to catch viruses before they cause infection.
“Most people just accept these viruses as a fact of life, and that got us thinking: Do we have to accept it?” says Veesler. “The more we thought about it, the more we realized that many of these problems have not been worked on with modern technologies.”
The project takes inspiration from efforts to fight the covid-19 virus, where Veesler’s group was among those involved in the speedy development of vaccines, antiviral drugs, and antibodies.
According to Ransohoff, Intercept’s advisors will include Peter Marks, a former top FDA official, as well as Moncef Slaoui, the pharmaceutical executive who led the US coronavirus vaccine effort, Operation Warp Speed.
A key challenge for Intercept will be coming up with ways to counter many viruses at one time. That accounts for the interest in air-cleaning technology, such as using strong ultraviolet light to inactivate viruses. The idea, the group says, is to remove them from the air in the same way municipalities remove impurities from the water supply before it’s piped to people’s homes.
The US funds about $6.5 billion a year in virus research through the National Institute of Allergy and Infectious Disease, or NIAID. But that agency’s budget hasn’t grown in recent years, leaving more room for private philanthropy.
And Stripe’s Collison brothers have become some of the most reliable philanthropists in viral research. After giving away “fast grants” to help labs during the covid-19 pandemic, they later joined other donors who committed $650 million to establish the Arc Institute in Palo Alto, California, which has developed AI models for biological research.
“The diversity of viruses is just too large and seems daunting, so people don’t even try,” says Veesler. “I’m happy that someone is ready to help scientists, not accepting the status quo, and doing something different.”
Associations between childhood trauma, intolerance of uncertainty, and symptom severity in obsessive-compulsive disorder
BackgroundChildhood trauma (CT) has been associated with obsessive-compulsive disorder (OCD), but its relationship with obsessive-compulsive symptom (OCS) severity remains inconsistent. Intolerance of uncertainty (IU) may represent one of the cognitive processes underlying this association. The present study aimed to examine differences in CT and IU between patients with OCD and healthy controls (HCs), and to test whether IU mediates the relationship between CT and OCS severity.MethodsThis study included 82 patients with OCD and 82 healthy controls (HCs) matched on age and sex. CT was assessed using the Childhood Trauma Questionnaire-33 (CTQ-33), IU using the Intolerance of Uncertainty Scale–Short Form (IUS-12), and OCS severity using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS).ResultsPatients with OCD had significantly higher scores than HCs on all CTQ-33 subscales and on IU measures. In particular, the patient group showed higher IUS-12 total scores than the HC group (39.30 ± 10.42 vs. 32.11 ± 8.62, p < 0.001), with higher prospective anxiety (22.11 ± 5.13 vs. 20.11 ± 4.59, p = 0.009) and inhibitory anxiety scores (17.19 ± 5.99 vs. 12.00 ± 4.82, p < 0.001). Within the patient group, physical abuse was the only CT dimension significantly associated with total Y-BOCS scores (r = 0.248, p = 0.025), whereas IU was positively associated with symptom severity (IUS-12 total: r = 0.346, p = 0.001). Path analysis showed that CT was associated with IU (β = 0.238, p = 0.023), IU was associated with OCS severity (β = 0.329, p = 0.007), and the direct effect of CT on OCS severity was no longer significant after IU was included in the model (c′ = 0.209, p = 0.093), supporting partial mediation.ConclusionCT appears to be elevated in patients with OCD, although its association with symptom severity is not uniform across trauma dimensions. IU may represent an important cognitive mechanism linking CT to OCS severity. These findings suggest that assessing and addressing IU may contribute to more individualized clinical approaches in OCD.
Drug Targets LDL Receptor Pathway to Control Cholesterol
Cholesterol-related heart disease remains the leading cause of death worldwide, and while doctors have more tools than ever to treat it, many patients still can’t achieve safe cholesterol levels or can’t tolerate the side effects of available medications. Researchers at the University of California (UC), San Diego, School of Medicine have now uncovered a hidden biological pathway, dependent on a protein known as Ral, which explains why high-cholesterol diets steadily chip away at our body’s ability to clear harmful low-density lipoprotein (LDL) cholesterol from the blood. The team‘s preclinical study, including tests in mice, also identified a drug candidate already proven safe in humans that could potentially target the pathway.
“We’ve known for a long time that a high-cholesterol diet reduces the liver’s ability to clear cholesterol from the blood, but we didn’t fully understand why,” said Alan Saltiel, PhD, professor of medicine at UC San Diego School of Medicine and director of the UC San Diego/UCLA Diabetes Research Center. “This new discovery explains a critical piece of that puzzle.” Saltiel is senior author of the researchers’ published paper in Nature, titled “Dietary cholesterol activates a Ral-dependent pathway driving LDLR turnover,” in which they concluded, “Together, our findings reveal a Ral-dependent signalling pathway as a key regulator of LDLR turnover and cholesterol homeostasis.”
Disruptions in cholesterol homeostasis are closely linked to an increased risk of atherosclerosis and cardiovascular disease (CVD), the authors wrote. “Elevated low-density lipoprotein cholesterol (LDL-C) significantly contributes to CVD by promoting the formation of atherosclerotic plaques in arteries.”
The liver is the main organ involved in removing cholesterol from the blood so it can be broken down and used elsewhere. This is done through LDL receptors (LDLRs), which sit on the surface of liver cells and act like docking stations, grabbing LDL cholesterol from the bloodstream and pulling it inside the cell for processing. “LDLRs have a crucial role in the uptake of LDL-C from the circulation by hepatocytes,” the investigators continued. The more LDL receptors on liver cells, the more cholesterol gets cleared from the blood, which is why most cholesterol-lowering drugs, such as statins or PCSK9 inhibitors, work by preserving or increasing the number of these receptors. However, the team noted, such treatments have their limitations. “The molecular switches that coordinate LDLR trafficking and turnover in response to nutritional cues, including high dietary cholesterol, remain poorly defined.”
The new research, carried out in mice and in human cells, reveals a previously unknown mechanism that quietly works against the cholesterol removal process, slowly reducing the number of LDL receptors and contributing to high blood cholesterol. The team found that this process begins when a protein called Ral—which Saltiel has previously studied in fat cells—is activated by high dietary cholesterol. “We describe here a previously unrecognized role for Ral signaling in orchestrating LDLR cellular trafficking and lysosomal routing in hepatocytes under chronic cholesterol stress,” the team stated.
Their studies showed that the more Ral is activated, the fewer LDL receptors remain available to clear cholesterol from the blood. This depletion process ultimately relies on a lysosomal protease enzyme called cathepsin A (CTSA). They further explained, “Ral engages the endocytic RalBP1–REPS1 complex to promote LDLR internalization and lysosomal routing, where LDLR is degraded by the lysosomal protease cathepsin A (CTSA).”
The researchers also found that blocking CTSA with a selective small molecule inhibitor (SAR164653) was enough to stabilize LDL receptors and dramatically lower circulating LDL cholesterol in mice. “Pharmacological inhibition of CTSA activity increases hepatic LDLR function and improves cholesterol clearance, offering a potential new therapeutic strategy for hypercholesterolaemia and cardiovascular disease,” they stated.
“There’s still a real need for new cholesterol-lowering options, since some people can’t get to safe levels even with the drugs we have now,” said Saltiel. “This new pathway we discovered is completely separate from anything that existing drugs target, so it gives us a new opportunity to fill that gap.”
After a fundamental biological breakthrough, it typically takes significant additional research to find drugs that target it. However, in this case, a CTSA inhibitor has already been through the early stages of drug development, with the initial goal of treating heart failure. While it was eventually shelved for strategic reasons, the drug had previously advanced to a Phase I clinical trial, where it was successfully tested for safety.
This discovery suggests that the investigational drug is already ready for testing in a Phase II trial for high cholesterol. “Luckily, there’s an experimental drug sitting on the shelf that’s already been shown to be safe in humans,” said Saltiel. “We hope to test whether this might be effective by conducting a clinical trial, which could potentially bring a new treatment option to patients much sooner than would have been expected.”
The post Drug Targets LDL Receptor Pathway to Control Cholesterol appeared first on GEN – Genetic Engineering and Biotechnology News.
Coproduction Without Youth? Closing the Participation Gap in Digital Mental Health Research
Young people are among the most intensive users of digital and generative artificial intelligence (GenAI)–enabled mental health tools, yet they remain underrepresented in the research and design processes that shape these technologies. Although participatory approaches such as co-design and patient and public involvement are widely endorsed as best practices, youth involvement in digital youth mental health (DYMH) research is often inconsistent, superficial, or limited to late-stage consultation. This participation gap risks producing interventions that are misaligned with young people’s lived experiences, priorities, and vulnerabilities, particularly in the context of rapidly evolving and scalable GenAI systems. This Viewpoint aims to reexamine the underlying drivers of the participation gap in DYMH research; clarify how participation is conceptualized and implemented across disciplines; and propose concrete, actionable recommendations to support more meaningful and consistent youth involvement across the research life cycle. We draw on interdisciplinary literature from digital mental health, human-computer interaction, child-computer interaction, and health research policy. Our Viewpoint integrates conceptual frameworks (eg, Lundy’s model of participation), existing reviews of co-design practices, and emerging evidence on GenAI in mental health. We adopt a life cycle–oriented perspective to examine how youth participation is distributed across stages of research and development, including problem formulation, design, implementation, and evaluation. We identify 3 interrelated drivers of the participation gap. First, conceptual and linguistic fragmentation obscures what participation entails in practice, with terms such as co-design, participatory design, user-centered design, and patient and public involvement used inconsistently across disciplines. Second, youth involvement is uneven across the research life cycle, with participation often concentrated in early ideation or usability testing but largely absent from upstream decision-making and downstream evaluation. Third, institutional barriers—including ethics review processes, consent requirements, funding constraints, and adult-centric research norms—systematically limit meaningful youth partnership. These challenges are amplified in the context of GenAI, where opaque “black box” systems, simulated therapeutic interactions, and rapid deployment cycles introduce distinct risks if youth perspectives are not integrated. We propose a set of minimum expectations to address these gaps, including explicit specification of participatory models, life cycle mapping of youth involvement, reporting of youth influence on decisions, dedicated funding for participation, proportional ethics frameworks, and mechanisms for youth-informed governance of GenAI systems. Closing the participation gap in DYMH research is both an ethical imperative and a practical necessity. Moving beyond aspirational commitments requires embedding youth participation as a standard, resourced, and accountable component of research, design, and governance. In the context of rapidly evolving digital and GenAI technologies, failure to do so risks producing interventions that are scalable but not safe, credible, or responsive to the needs of young people.
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From Metrics to Meaning in Neurological Rehabilitation: Clinicians’ Perspectives on Digital Metrics of Upper Limb Functioning—A Focus Group Study
Background: Digital assessment technologies, such as optical motion capture and inertial measurement units, enable detailed kinematic analysis and continuous monitoring of upper limb activity in persons with neurological conditions. While such are increasingly recognized in research, their uptake in clinical neurorehabilitation is limited. It remains unclear which clinicians perceive as most meaningful and how these are integrated into patient-centered care. Understanding clinicians’ information needs and reasoning processes is a prerequisite for implementing digital assessment technology. Objective: This study aims to characterize how rehabilitation professionals perceive, prioritize, and integrate into clinical reasoning and to identify features that would support their routine use. Methods: Three 90-minute focus groups were conducted in 3 Swiss neurorehabilitation centers, involving 11 clinicians with diverse professional backgrounds (5 physiotherapists, 4 occupational therapists, 1 movement scientist, and 1 medical practitioner). Participants discussed essential parameter domains and individually rated the relevance and meaningfulness of 17 kinematic metrics for the well-studied drinking task and 10 established arm use performance metrics. Verbatim transcripts were analyzed using reflexive thematic analysis, and rating data were summarized descriptively. Results: Five main themes were identified. (1) (active/passive range of motion, strength, selective muscle control, and grasp) form the basis for interpreting movement. (2) (smoothness, efficiency, and compensatory movement) are valued when aligned with observable task execution. (3) (hourly activity profiles, arm-use symmetry, and functional workspace) represents the reference for patient-centered reasoning. (4) , including diagnosis-specific preferences, shapes assessment selection. (5) reflects clinicians’ reliance on visual judgment complemented by normative values. Intuitive metrics such as task duration, number of movement units, and range of motion were favored, whereas confidence was lower in more complex metrics (eg, jerk and interjoint coordination). Conclusions: Clinicians value intuitive when they are clearly linked to patient-centered outcomes and supported by normative references. The findings highlight the need for targeted educational strategies and digital competency training that help clinicians interpret digital metrics and integrate them with contextual information and clinical reasoning.
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