Tag: Sleep and arousal regulation

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The Download: whole-body rejuvenation drugs and five things to know about AI

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.

David Sinclair plans to test whole-body rejuvenation drugs in the XPrize competition

The outspoken longevity scientist David Sinclair has predicted that, one day, you’ll go to the doctor and get a prescription that will make you 10 years younger. MIT Technology Review has learned of his latest step toward this: human tests of a “reprogramming” drug.

Sinclair, a biologist at Harvard Medical School, plans to launch the tests in a $101 million competition organized by the XPrize Foundation. The winners will “restore” a person to an earlier apparent age, as measured by improvements in immune, cognitive, and muscle function.

The grand prize goes to any team able to show a 10-year (or greater) relative improvement after one year of treatment. 

Sinclair says he plans to give an oral drug mixture to volunteers, in a bid to seek “evidence for age restoration in humans.” Find out how he hopes to reverse ageing through chemical reprogramming.

—Antonio Regalado

Five things you need to know about AI

—Will Douglas Heaven

At SXSW London last week, I gave a talk called “Five things you need to know about AI,” in which I shared what I think are the biggest themes in AI right now.

I pulled a few things from our first AI10 list, an annual guide to the top trends in this buzzy world, but I also veered off on several tangents. In my half-hour slot, I tried to cover the key talking points that I think help to make sense of what’s going on in tech—and thus the economy—today.  

Five key thoughts emerged: AI is everywhere all at once, it’s getting scary, a backlash is growing, it’s becoming a big deal for science—and I didn’t even need to show up at the talk. Read the full story for all the details.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 OpenAI has confidentially filed for a US IPO
The listing could come as early as September. (Reuters $)
+ OpenAI is targeting a valuation of up to $1 trillion. (Financial Times $)
+ The IPO will test investor appetite for AI companies. (WSJ $)
+ The move follows IPO filings from Anthropic and SpaceX. (CNN)

2 The US claims BYD, Baidu, Alibaba, and others are aiding China’s military
The Pentagon added them to a list of military-linked companies. (WSJ $)
+ The designations limit their operations in the US. (BBC)
+ The new additions also include humanoid firm Unitree. (TechCrunch)
+ The Pentagon is adapting to China’s tech rise. (MIT Technology Review)

3 Apple’s long-awaited AI overhaul of Siri is finally here
“Siri AI” promises to be a more conversational assistant. (NYT $)
+ It includes a standalone app and screen-reading features. (Reuters $)
+ And arrives after two years of repeated delays. (Axios)

4 The White House and Congress are working to limit state AI laws
A new deal would curb state rules for federal legislation. (Axios)
+ AI regulation has divided US politicians. (MIT Technology Review)

5  Meta is launching a “workforce academy” for building data centers
The five-week program is free of charge and guarantees a job. (WSJ $)
+ It arrives shortly after Meta laid off 8,000 employees. (NPR)

6 Taiwan is mulling curbs on AI chip exports to China
The new controls would further align with US restrictions. (Bloomberg $)
+ Future AI chips could be built on glass. (MIT Technology Review)

7 Meta has quietly removed face-recognition code from its smart glasses app
The code identified by investigators has disappeared. (Wired $)

8 Humanoid robots are edging towards the battlefield
American and Chinese militaries are pursuing the tech. (BBC)

9 The world’s first wind-powered underwater data center has launched
It uses less power and water than land-based equivalents. (Guardian)

10 You could get some benefits of sleep without having to nod off
If new brain stimulation works as well on humans as on mice, that is. (New Scientist $)

Quote of the day

“You’re on the train, but you know that there’s no destination.”

—Clara Shih, a former top AI executive at Salesforce and Meta, tells the New York Times that AI training can’t keep up with the field’s advances.

One More Thing

biomilq concept illo

ILLUSTRATIONS BY AMRITA MARINO


Inside the race to make human sex cells in the lab

An embryo forms when sperm meets egg. But what if we could start with other cells—if a blood sample or skin biopsy could be transformed into “artificial” sperm and eggs? What if those were all you needed to make a baby?

That’s the promise of a radical approach to reproduction. Scientists have already created artificial eggs and sperm from mouse cells and used them to create mouse pups. Artificial human sex cells are next.

The advances could herald the end of infertility, but they raise major scientific and ethical challenges. 

Read the full story on the new recipes for sperm and eggs.

—Jessica Hamzelou

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.)

+ These chefs turn Pop-Tarts into the desserts that inspired them.
+ A choir has beautifully transformed System of a Down’s “Chop Suey!”
+ Scientists finally traced crabs’ sideways walk in this fascinating study of evolution.
+ This nostalgic essay on the family computer is a touching throwback to early internet life.

Top image credit: Stephanie Arnett/MIT Technology Review | Getty Images

Please send Pop-Tarts to hi@technologyreview.com. 

You can follow me on LinkedIn. Thanks for reading!

—Thomas

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SleepPathfinder: A Socratic Questioning and Self-Decision–Based Chatbot to Support User Engagement in Digital CBT-I: Usability and Feasibility Study

Background: Chronic insomnia is a highly prevalent sleep disorder that adversely affects quality of life and mental health. Cognitive behavioral therapy for insomnia (CBT-I) is internationally recommended as the first-line treatment, and digital CBT-I (dCBT-I) has been developed to improve accessibility and scalability. While existing dCBT-I systems effectively support structured behavioral training through standardized protocols, they provide relatively limited support for users’ cognitive exploration and meaning-making processes, particularly in helping users reflect on and internalize the rationale behind CBT-I practices in daily life. These limitations may contribute to challenges in sustained engagement and long-term adherence. Objective: This study aimed to examine the usability and feasibility of SleepPathfinder, a conversational CBT-I support chatbot that integrates Socratic questioning and a self-decision mechanism to support users’ understanding of and engagement with CBT-I practices. Methods: SleepPathfinder was designed around a 4-stage conversational flow: education on CBT-I techniques, Socratic cognitive exploration, self-decision, and advice provision. We conducted (1) a single-session pilot usability study (n=45) to assess system stability and user experience and (2) a 5-day condition-based comparative experiment (n=30) consisting of daily sessions, comparing an exploratory dialogue condition with a directive, protocol-guided dialogue condition. Quantitative measures assessed usability, cognitive appraisals related to sleep problems, autonomy-related experiences, and behavioral readiness, while qualitative feedback and conversational log analyses were used to examine interaction patterns and engagement characteristics. Results: In the comparative experiment, the exploratory dialogue condition showed a tendency toward reduced perceived threat and severity appraisal of sleep problems compared with the directive condition, accompanied by moderate effect sizes in cognitive perception measures. Autonomy-related experiences, including perceived choice and engagement, demonstrated suggestive upward trends in the exploratory condition. Behavioral intention changes were comparable across conditions, while overall readiness for change increased across participants. Conversational log analyses indicated that greater depth and volume of user self-narrative were associated with larger shifts in cognitive appraisals, whereas the frequency of chatbot questions alone was not. The pilot usability study indicated generally positive evaluations of system usability and content credibility, while identifying areas for improvement in emotional responsiveness and conversational naturalness. Conclusions: These findings suggest that a Socratic questioning–based and self-decision–based conversational structure is usable and feasible as a supportive interaction layer within dCBT-I systems. Rather than altering the directive behavioral structure of CBT-I, such an approach may complement existing protocols by facilitating cognitive exploration and supporting user-perceived autonomy. This study provides design-oriented evidence to inform the refinement of dialogue-supported digital CBT-I systems aimed at enhancing user engagement with CBT-I practices.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/53b6f3cffbd734291f43739e09584341" />

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Effects of motor imagery brain-computer interface task on quantitative EEG features in patients with prolonged disorders of consciousness

ObjectiveTo analyze quantitative electroencephalographic (EEG) characteristics during Motor Imagery Brain-Computer Interface (MI-BCI) task in patients with prolonged disorders of consciousness (pDoC).MethodsForty-three patients with pDoC due to various brain injuries were enrolled. Based on modified Coma Recovery Scale-Revised (CRS-R) assessments, the patients were divided into 19 in the unresponsive wakefulness syndrome (UWS) group and 24 in the minimally conscious state (MCS) group. All patients underwent 5 min of resting-state (RS) EEG followed by 5 min of MI-BCI task. Relative power, DTABR, and average brain engagement (BE) during MI-BCI were analyzed across resting and MI-BCI states using Fast Fourier Transform (FFT) spectra.ResultsMixed-design ANOVA showed significant main effects of condition and group across all EEG frequency bands, indicating clear differences between the RS and MI-BCI conditions and between UWS and MCS patients. Significant group × condition interactions were found in the delta, beta, and gamma bands, as well as in DTABR. Simple effects analysis showed that delta power was higher in RS than in MI-BCI in both groups, with UWS consistently exhibiting higher delta power than MCS under both conditions. In contrast, beta and gamma power were higher in MI-BCI than in RS in both groups. For beta power, UWS was higher than MCS under RS, whereas MCS was higher than UWS under MI-BCI, showing a reversal of the interaction pattern. For gamma power, MCS showed higher values than UWS under both conditions, with a larger between-group difference during MI-BCI. DTABR was significantly higher in RS than in MI-BCI in both groups; however, MCS exhibited higher DTABR than UWS under RS, whereas the opposite pattern was observed under MI-BCI. In addition, during MI-BCI tasks, the MCS group showed greater average BE than the UWS group.ConclusionMI-BCI shows potential as a diagnostic or assessment tool for evaluating the level of consciousness in patients with pDoC.
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A hierarchical machine learning model for predicting self-harm and suicidal behaviour in hospitalised patients with schizophrenia using clinical history and nursing observations

ObjectiveThis study aimed to develop and evaluate a two-layered machine learning framework that combines admission clinical information with longitudinal nursing observations to identify schizophrenia inpatients at high risk of self-harm or suicidal acts.MethodsWe retrospectively reviewed the records of 477 patients with schizophrenia hospitalised in Liaoning Province between July 2021 and July 2024. According to whether at least one self-injurious or suicidal episode was documented during the index admission, 159 individuals were assigned to a high-risk group and 318 to a non-high-risk group. At admission, 18 baseline variables (including age, sex, history of self-harm, hopelessness/depression, and educational attainment) were extracted from electronic medical records, and 39 nurse-rated behavioural items were scored weekly using the Psychiatric Patient Nursing Observation Scale. Static and dynamic feature sets were used to train six classifiers [regularized logistic regression (LR), support vector machine (SVM), extreme gradient boosting, random forest, multi-layer perceptron, and K-nearest neighbours]. The best static model (regularized LR) and the best dynamic model (SVM) were combined through probability-level weighted fusion to generate a hierarchical risk score.ResultsMultivariable analysis of admission features showed that previous self-harm [odds ratio (OR) = 4.323], hopelessness/depression (OR = 3.090), younger age (OR = 0.938), and higher educational level (OR = 1.357) were independent predictors of self-harm/suicidal behaviour. Among dynamic indicators, negative self-evaluation (OR = 2.303), self-reported depression (OR = 1.812), insomnia (OR = 1.768), talking to oneself (OR = 1.733), crying (OR = 1.700), and reduced conversation with others (OR = 1.422) remained significant. The optimised static LR model achieved an area under the curve (AUC) of 0.7564, and the dynamic SVM model reached an AUC of 0.8531. Their fusion further improved performance (AUC = 0.9048; sensitivity 0.8542; specificity 0.7789; accuracy 0.8042). This hierarchical model outperformed the best flat combined-feature model (SVM; AUC = 0.9022) in sensitivity (0.8542 vs. 0.6667), indicating a more clinically appropriate detection of high-risk patients.ConclusionA hierarchical machine learning approach that integrates baseline clinical history with repeated nursing assessments can effectively flag schizophrenia inpatients at high risk for self-harm and suicidal behaviour, supporting timely and individualised preventive strategies in psychiatric wards.
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[Comment] From policy to practice: implementing China’s measures to strengthen student mental health

In October 2025, China’s Ministry of Education issued ten national measures to strengthen mental health work in primary and secondary schools.1 These measures target major school-linked stressors such as academic pressure, physical activity, sleep, and internet use, and they call for whole-staff responsibility and cross-department collaboration. The policy signals a shift from episodic crisis response towards a public mental health agenda spanning prevention, early identification, supportive school environments, and referral pathways.

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Wireless Stress Detector Offers Multiple Medical Uses

A next-generation device that detects signs of stress could have wide-ranging applications, from investigating sleep disorders to detecting signs of sepsis.

The polygraph detector, described in Science Advances, is worn on the chest and can even sense when a person is lying.

It allows psychophysiological states to be continuously monitored through a combination of multimodal sensing and wireless data transmission.

The gadget offers an alternative to current approaches such as such as polygraphy and polysomnography (PSG), which involve cumbersome wired sensors that limit their practicality.

“By uncovering mechanistic links between autonomic imbalance, stress reactivity, and health outcomes, these devices have the potential to transform diagnostic workflows, optimize educational programs, and enable personalized therapeutic monitoring across stress medicine, pediatrics, and behavioral health,” reported Sun Hong Kim, PhD, from the University of Seoul in South Korea, and co-workers.

Subtle physiological variations in cardiac, respiratory, electrodermal, and thermal activity often serve as indicators of compromised health or heightened stress responses.

These can be reflected in many scenarios, from pediatric sleep disorders that disrupt neurodevelopment to the psychological strain experienced in high-stakes clinical settings or during polygraph examinations.

Accurate monitoring of psychophysiological states is therefore essential for understanding how stress and autonomic dysfunction manifest across a wide spectrum of medical conditions.

However, most existing devices monitor only one or two parameters or rely on electrochemical sensors that detect sweat biomarkers, thereby failing to reflect the complex and dynamic interplay between multiple physiological systems.

Wearable polygraph device in the palm of a hand for scale. [John A. Rogers/Northwestern University]

Kim and co-workers therefore designed a single platform to enable comprehensive assessment of autonomic and stress-related physiology in real time.

The device continuously measures changes in heartbeat, skin temperature, and breathing, which are then converted using machine learning into measures of psychological strain.

The device had high fidelity with gold standard systems in quantifying the complex psychological stress induced by polygraph interviews and complex cognitive load tasks as well as the physical stress caused by repeatedly putting a hand in an iced water.

During overnight monitoring of children, it reliably identified arousals, hypopnea, and apnea while revealing disease-specific autonomic signatures among infants with Down syndrome.

Real-world deployment during emergency simulation training showed that multimodal stress signatures correlate inversely with performance, reflecting its value for medical education.

Machine learning analyses across all studies confirmed that multimodal features outperformed single-signal approaches in detecting stress and clinical events with high sensitivity and specificity.

“A particularly notable contribution lies in pediatric sleep medicine,” the authors noted.

“Simultaneous comparison with PSG confirms the ability to detect arousals, hypopnea, and apnea while also providing mechanistic insights into autonomic regulation.

“In infants with Down syndrome, multimodal analysis reveals attenuated sympathetic responsiveness and parasympathetic dominance, consistent with known vulnerabilities in airway patency and autonomic control.

“Such disease-specific autonomic signatures may serve as valuable biomarkers for risk stratification, early diagnosis, and targeted intervention in neurodevelopmental disorders.”

The post Wireless Stress Detector Offers Multiple Medical Uses appeared first on Inside Precision Medicine.

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Disrupted sleep-wake cycles and circadian rhythms in a Drosophila model of C9orf72-FTD

Frontotemporal dementia (FTD) is a neurodegenerative disorder that affects behavior, personality, motor activity, speech, cognition, and sleeping patterns. Previous findings support the idea that disruption of sleep and circadian systems may not only be affected by this disease but also work to actively shape the clinical phenotype of FTD. Thus, understanding how sleep-wake cycles are altered may provide insight into mechanisms that influence both disease progression and quality of life. We studied an established Drosophila model of FTD to investigate changes in the sleep-wake cycle of both young and aging flies. A C9orf72-associated FTD model was chosen, as the most common genetic cause of sporadic and hereditary FTD is a hexanucleotide repeat expansion in intron 1 of the C9orf72 gene. We performed behavioral assays to measure locomotor activity in both a 12 h:12 h light/dark (LD) cycle and complete darkness (free running). From this data, we were able to analyze changes in sleep and activity patterns, as well as circadian rhythms in flies modeling C9orf72-FTD. Our data suggests that these flies have increased nighttime activity and decreased sleep at night, which becomes more significant as they age. Older flies also displayed decreased sleep pressure during both day and night and lost rhythmicity. Of specific interest, young flies modeling C9orf72-FTD demonstrated altered day and night sleep latency, decreased sleep depth at night, and reduced rhythmicity in constant darkness. This suggests that changes in their sleep-wake cycle occur early in disease progression and provide an avenue for potential intervention and early diagnostic markers.