A report in the FT suggests waiting list reductions attributed to the FDP are heavily driven by figures from a small number of NHS hospitals.
The Download: the first brain implant power user and South Korea’s AI obsession
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.
This man with ALS is the first “power user” of a brain implant that lets him speak
Casey Harrell has had a set of electrodes embedded in his brain for almost three years. Harrell, who has ALS and is paralyzed, first used his brain-computer interface (BCI) to “speak” in 2023. Since then, he’s clocked thousands of hours of use.
Harrell can now use the device largely independently. His team has added new features to it, and he also uses it to surf the web and perform his job. “Living with a disease like ALS, you are supposed to have diminished dreams. I do not,” Harrell told MIT Technology Review.
The team behind the device call Harrell “the first power user of a speech BCI.” They now plan to add further enhancements to the device.
Dive into the groundbreaking impact of Casey Harrell’s BCI.
—Jessica Hamzelou
Why do South Koreans love AI so much?
While a public backlash against AI brews across the US, South Koreans are optimistic. Only 16% say they are more concerned than excited about AI—the lowest of the 25 countries surveyed by the Pew Research Center—while 50% of Americans were more worried than excited.
South Koreans share a deep conviction that embracing technology is integral to modernizing the country and cementing its place in the global order. Their fascination with AI is just the latest incarnation of that ethos—and it’s making them anxious to stay ahead.
Read the full story on South Korea’s AI fervour.
—Michelle Kim
This story is from The Algorithm, our weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 The US says it restricted Anthropic AI over foreign intelligence risks
Commerce chief Lutnick said he acted over national security fears. (Reuters $)
+ Following the ban, Anthropic disabled access to its new models. (BBC)
+ Both sides are increasingly desperate for a resolution. (WSJ $)
2 DeepSeek just became China’s most valuable startup
It raised $7 billion, the largest-ever first-round funding for an AI startup. (The Information $)
+ The deal values DeepSeek at over $50 billion. (WSJ $)
+ Its unusual structure preserves founder control. (Reuters $)
+ DeepSeek’s new flagship model has caused a stir. (MIT Technology Review)
3 Alibaba has unveiled AI models for robots amid a shift from chatbots
It’s joined a global race to move AI into the physical world. (SCMP)
+ AI is learning to understand its surroundings. (MIT Technology Review)
4 Fox is buying streaming giant Roku for $22 billion
The deal creates the third-largest player in US TV by viewing share. (BBC)
+ Fox is making a big bet on free streaming. (Washington Post $)
5 EA has launched a new way to advertise “directly into gameplay”
EA Advertising allows brands to become part of the game itself. (CNBC)
+ Xbox’s new chief strategy officer is also eyeing in-game ads. (PC Gamer)
+ GenAI could reinvent what it means to play. (MIT Technology Review)
6 It’s trivially easy to use Reddit to manipulate AI search
A tiny snippet of text can trick ChatGPT and Google’s AI search. (404 Media)
+ AI search is being manipulated to generate dangerous biases. (BBC)
7 Sperm have been made magnetic to allow IVF inside the body
The technique enables remote guidance towards an egg. (New Scientist $)
+ Automation and AI are transforming IVF. (MIT Technology Review)
8 The world’s leading deepfake expert no longer trusts his own eyes
He’s struggling to prove what’s real before the internet decides. (NYT $)
9 Meta’s CTO admits its AI reorganisation was “atrocious”
He’s promised staff better communication—and snacks. (Wired $)
10 Silicon Valley billionaires are pretending to kill each other for fun
In a new game show from Peter Thiel’s Founders Fund. (WSJ $)
Quote of the day
“There was a speeding ticket, and they gave Fable the death penalty.”
—Alex Stamos, the former chief security officer of Facebook, tells the Washington Post that banning foreign access to Anthropic’s leading model is a disproportionate punishment.
One More Thing
VICTOR KERLOW
Inside effective altruism, where the far future counts a lot more than the present
Since its birth in the late 2000s, effective altruism has aimed to answer a deceptively simple question: “How can those with means have the greatest impact?”
Directing money to evidence-based approaches is EA’s best-known technique. But as it’s expanded from an academic philosophy into a community and a movement, its ideas of the “best” way to change the world have evolved as well.
—Rebecca Ackermann
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.)
+ The humble table has been reimagined as an unconventional public artifact.
+ Take a visual tour of the weird, centuries-old history of architecture’s most gruesome gargoyles.
+ A colorful parakeet unseen for an entire century was triumphantly rediscovered in an unexplored Indonesian forest.
+ This shimmering Southern Lights timelapse filmed by an astronaut on the SpaceX Dragon is stunning.
<![CDATA[Clinicians weigh diagnosis clarity, side effects, and adherence when choosing antipsychotic formulations, often starting oral therapy then switching to long-acting injectables.]]>
How supplement use changed over decades
Get your daily dose of health and medicine every weekday with STAT’s free newsletter Morning Rounds. Sign up here.
Good morning. It’s a good time to be a soccer fan in the U.S. Still, I appreciate this take in Defector on the commercial hydration breaks.
Assessment of tenecteplase target-associated pathogenic mechanisms underlying depression in acute ischemic stroke patients: insights from artificial intelligence-driven multi-omics analysis and in vitro validation
BackgroundAs a first-line treatment for acute ischemic stroke (AIS), tenecteplase (TNK) can cause adverse effects, such as depression, in AIS patients.ObjectiveThis study aims to elucidate the TNK target-related pathogenic mechanisms underlying major depressive disorder (MDD) in AIS patients.MethodsBy analyzing six public peripheral blood bulk datasets from AIS and MDD patients using integrative bioinformatics methods (limma, non-negative matrix factorization (NMF), and machine learning), we identified TNK target-associated molecular subgroups and diagnostic models for MDD and AIS patients, respectively. Next, a hub gene involved in the pathogenesis of both MDD and AIS was identified, and its corresponding molecular characteristics were analyzed in the peripheral blood bulk profiles of MDD and AIS patients. In addition, to gain a deeper understanding of the molecular implications of the hub gene involved in the pathogenesis of MDD in AIS, we performed disease ontology (DO) analysis and virtual cell knockout (KO) of the hub gene using public AIS mouse brain single-cell datasets. Furthermore, a deep learning pipeline (DrugReflector) model and molecular docking were used to identify MDD-preventive therapeutic agents for AIS patients based on MDD and AIS public blood bulk data. Finally, the expression pattern of the hub gene was also evaluated in MDD and AIS cell models.ResultsMyeloperoxidase (MPO) can be considered an upregulated TNK target-associated gene involved in the pathogenesis of MDD in AIS patients, and BRD-K11973162 can be considered an MDD-preventive therapeutic candidate for AIS patients after TNK treatment.ConclusionOur study is the first to identify MDD-associated diagnostic and therapeutic candidates for AIS patients after TNK treatment, providing a novel strategy for their clinical management.
Screening the optimal rTSMS frequency to orchestrate immune-fibrotic remodeling for adult spinal cord repair
IntroductionThe clinical translation of magnetic stimulation for central nervous system trauma is severely hindered by “parameter ambiguity”—the lack of evidence-based screen of stimulation protocol. Repetitive trans-spinal magnetic stimulation (rTSMS) holds therapeutic promise, yet its frequency-dependent effects on the complex spinal microenvironment remain poorly understood. To address this gap, this study aimed to decode the frequency-response relationship of rTSMS and establish an optimal, evidence-based frequency standard to orchestrate immune-fibrotic remodeling and promote functional recovery following spinal cord injury (SCI).MethodsUtilizing novel in vivo label-free second-harmonic generation (SHG) imaging to visualize the real-time microglia activation, we performed a high-fidelity close-loop screen of various rTSMS frequencies (10 Hz, 40 Hz, and 80 Hz). In an adult mice SCI model, we integrated transcriptomic profiling, morphological analysis, electrophysiological recordings, and behavioral assessments to comprehensively evaluate the neuroregenerative potential.ResultsWe identified 40 Hz as a privileged therapeutic frequency that specifically modulates microglia and the extracellular matrix. Unlike 10 Hz or 80 Hz regimens, 40 Hz rTSMS uniquely reprogrammed the transcriptomic landscape, driving microglia toward a restorative M2 phenotype, and most importantly, suppressing collagen fibrillogenesis. This targeted modulation effectively attenuated fibrotic scarring and preserved mitochondrial dynamics and axonal integrity. Consequently, the 40 Hz protocol significantly enhanced corticospinal tract conduction and drove robust, long term sensorimotor recovery.ConclusionOur findings define 40 Hz as the critical therapeutic standard for coupling immune modulation with fibrotic remodeling in SCI. By overcoming the barrier of inconsistent parameters, this study provides a precise, clinically translatable framework for the application of rTSMS in neurorestorative medicine.
CEST MRI reveals nicotine-induced alterations in glutamate-associated molecular connectivity in the mouse brain
IntroductionUnderstanding how neurotransmitter systems organize into large-scale networks is essential for elucidating the mechanisms through which drugs, diseases, and behavioral states alter brain function. Existing imaging modalities such as functional MRI (fMRI) and positron emission tomography (PET) provide measures of hemodynamic and metabolic connectivity, but cannot noninvasively map neurotransmitter-associated networks with high spatial resolution. Herein, we introduce a chemical exchange saturation transfer (CEST) MRI-based framework for mapping glutamate-associated molecular connectivity and apply it to characterize nicotine-induced network reorganization in the mouse brain.MethodsMale C57BL/6 mice underwent dynamic glutamate-weighted CEST (gluCEST) MRI before and after seven days of nicotine exposure. Regional glutamate-weighted CEST time series were extracted from 51 brain regions, and connectivity was evaluated using within-subject temporal correlation and inter-subject covariance analyses.ResultsGraph theory analyses identified four baseline glutamate-associated modules involving frontal-sensorimotor, cortico-hippocampal, intra-hippocampal, and cortico-striatal circuits. Nicotine exposure attenuated these baseline networks in analyses performed without global signal regression (GSR) and with conditional GSR, whereas full GSR reduced the apparent magnitude of these effects. Nicotine also reduced nodal strength in the CA1 and insular cortex. In contrast, nicotine selectively strengthened a thalamo-striato-motor circuit involving the motor cortex, mediodorsal and ventral thalamic nuclei, and caudoputamen. This pattern remained evident even under full GSR. Subject-level covariance analysis confirmed widespread nicotine-induced attenuation of glutamate-associated connectivity and revealed a distinct sensory-limbic module involving the lateral geniculate nucleus, amygdala, and piriform cortex that was selectively disrupted following nicotine exposure.DiscussionThese results demonstrate the feasibility of dynamic gluCEST MRI for mapping glutamate-associated molecular connectivity in vivo and detecting pharmacologically induced network remodeling. This approach provides a noninvasive platform for investigating glutamatergic dysregulation in addiction, neuropsychiatric disorders, and therapeutic response.
Brain protein burden is related to intravoxel incoherent motion: PET-MR imaging study
IntroductionDysfunction in brain protein clearance mechanisms is thought to contribute to many neurodegenerative diseases, yet non-invasive assessment of these mechanisms in humans remains challenging. This study is the first to examine whether intravoxel incoherent motion (IVIM) diffusion MRI metrics, measures of water diffusion and fluid dynamics, are associated with pathological protein accumulation and cognition in aging individuals, and hence whether they serve as a proxy for brain waste clearance function.MethodsWe analyzed data from 94 participants (n = 45 β-amyloid positive) who underwent simultaneous PET/MRI scans to calculate three key IVIM metrics: D (true diffusion coefficient), D* (pseudo-diffusion coefficient reflecting perfusion), and f (perfusion fraction) within 98 regions of interest. A machine learning model was trained to identify the most informative IVIM features for predicting β-amyloid (Aβ) status. Selected features were then evaluated for correlations with protein burden (Aβ and tau) and cognitive performance.ResultsThe model identified a subset of 25 key features that effectively predicted Aβ status, achieving a predictive accuracy of 80.0% on unseen data. Regions with important IVIM features aligned with previously identified Aβ-affected regions and showed significant correlations with Aβ burden (r = 0.53, p < 0.0001) and tau burden (r = 0.61, p < 0.0001). A significant negative correlation was observed between IVIM features and cognitive decline (r = −0.60, p < 0.0001). When stratified by Aβ status, this correlation remained significant only in the Aβ-positive group (r = −0.61, p < 0.0001), but not in the Aβ-negative group.ConclusionIVIM-derived metrics (D, D*, and f), which measure water diffusion and perfusion dynamics in the brain, may be valuable non-invasive biomarkers of protein accumulation and associated cognitive decline in the aging human brain.
Multimodal behavioral phenotyping for depressive-spectrum classification and severity estimation using eye tracking, facial behavior, and transcript-derived language
IntroductionDepression assessment remains largely dependent on symptom reports and clinician judgment, while objective tools for depressive-spectrum stratification and severity estimation remain limited. Existing digital and multimodal depression-detection studies often focus on binary case-control classification, handle missing modalities incompletely, provide limited calibration assessment, and rarely combine depressive-spectrum classification with continuous symptom-severity estimation. We therefore developed a quality-aware multimodal framework integrating eye tracking, facial behavior, and transcript-derived language for classification across normal control (NC), subthreshold depression (SD), and major depressive disorder (MDD), together with prediction of 17-item Hamilton Depression Rating Scale (HAMD-17) severity.MethodsA total of 186 participants completed a controlled task battery including interview, emotional reading, free viewing with verbal description, fixation, gaze orienting, smooth pursuit, prosaccade, and antisaccade tasks. Eye-tracking, facial-video, and transcript-derived language data were converted into modality-specific features. Baseline-3 combined modality-specific encoders, quality-aware gated fusion, and joint classification-regression learning under a nested repeated-resampling framework with explicit missing-modality handling. Baseline-3+ further incorporated Transformer-based cross-modal interaction and uncertainty-based dynamic task weighting. Performance was evaluated on held-out outer-loop test sets after temperature scaling. Interpretability analyses included gate profiling, selective prediction, SHAP, Integrated Gradients, and counterfactual analysis.ResultsBaseline-3+ showed the most favorable classification and calibration profile, with accuracy, balanced accuracy, and F1-macro approaching 0.90 across both classification routes and lower expected calibration error than Baseline-3. For severity estimation, the improvement was route-dependent and mainly reduced the regression disadvantage observed under the hierarchical route. Misclassification was concentrated near the SD boundary. Interpretability analyses showed stable quality-aware modality reweighting, with facial features providing the dominant signal, complemented by eye tracking and smaller but meaningful language contributions.DiscussionThis framework addresses key limitations of prior binary and incompletely calibrated depression-detection models by jointly supporting depressive-spectrum classification, severity estimation, missing-modality handling, calibrated prediction, and individual-level interpretation. Its most plausible role is to augment clinical assessment, particularly for boundary states such as SD.
Generative AI as interactional infrastructure for meaning-centered care in later life
Generative artificial intelligence (GenAI) and large language models are rapidly entering mental health research and service delivery, yet their dominant use remains symptom-centric, emphasizing screening, classification, triage, and risk detection. For older adults, mental health is often inseparable from existential concerns: loss of social role, disrupted continuity of self, loneliness, diminished dignity, and questions of legacy. This perspective argues that GenAI should not be conceptualized as an autonomous substitute for clinicians, nurses, social workers, or family caregivers. Instead, it may be better understood as an interactional infrastructure for meaning-centered care in later life. Drawing on meaning-centered psychotherapy, dignity therapy, life review, gerotranscendence theory, care ethics, and implementation science, we propose a Sensing-Narrating-Connecting-Governing framework. In this model, multimodal AI systems help detect existential and relational cues, support life-review conversations, co-construct dignity-preserving narratives, connect older adults with human care networks, and operate under explicit safeguards for privacy, hallucination, dependency, crisis escalation, and cultural adaptation. The proposed framework shifts evaluation from model performance alone toward existential well-being, dignity, continuity of self, therapeutic alliance, equity, and workflow integration. We conclude that GenAI may contribute to public mental health only when deployed as a bounded, human-supervised, culturally responsive layer of relational augmentation rather than as a replacement for human presence.