BackgroundSleep disorders represent a common and impactful complication following acute ischemic stroke (AIS). This study aimed to identify clinical risk factors and evaluate the predictive value of serum hypocretin (Hcrt), neurofilament heavy chain (NfH), and interleukin-1 beta (IL-1β) for post-stroke sleep disorders.MethodsWe conducted a retrospective observational study of 256 patients with AIS. Patients were classified into sleep disorder (n = 161) and non-sleep disorder (n = 95) groups based on their Pittsburgh Sleep Quality Index scores 7 days after stroke onset. Fasting serum levels of Hcrt, NfH, and IL-1β were measured upon admission. We utilized multivariate logistic regression and receiver operating characteristic (ROC) curves to evaluate predictive performance. The combined model was internally validated using 1,000 bootstrap resamples to assess optimism-corrected discriminative performance.ResultsSleep disorders were present in 62.9% of patients. Nine independent risk factors were identified: age ≥ 65 years (OR = 2.059), snoring history (OR = 1.980), prior stroke (OR = 2.036), lower ADL scores (OR = 1.839), higher HAMD (OR = 1.726) and NIHSS scores (OR = 1.677), decreased serum Hcrt (OR = 1.863), elevated NfH (OR = 2.020), and elevated IL-1β (OR = 1.793; all p < 0.05). Individual biomarker AUCs ranged from 0.742 to 0.781, whereas the combined three-biomarker model achieved a significantly superior AUC of 0.874 (sensitivity 88.82%, specificity 71.58%). Bootstrap internal validation yielded a mean optimism-corrected AUC of 0.861 (95% CI: 0.812–0.903), indicating robust model performance with minimal overfitting.ConclusionClinical variables alongside altered levels of Hcrt, NfH, and IL-1β serve as independent predictors of post-stroke sleep disorders. The combined three-biomarker panel, reflecting neuroendocrine dysregulation, axonal injury, and systemic inflammation, demonstrates substantially superior predictive accuracy over individual biomarkers and offers a clinically practical tool for early identification of high-risk patients.
Effect of pharyngeal musculature and genioglossus exercising on obstructive sleep apnea-hypopnea syndrome following uvulopalatopharyngoplasty
ObjectiveThis study aimed to evaluate the effectiveness of pharyngeal musculature and genioglossus exercising as a postoperative rehabilitation intervention for patients with obstructive sleep apnea-hypopnea syndrome (OSAHS) following uvulopalatopharyngoplasty (UPPP)—a setting with limited prior evidence.MethodsThis is a retrospective cohort study conducted in the first people’s Hospital of Linping District, Hangzhou. They included 120 patients of OSAHS who received UPPP between October 2022 and October 2024. Sixty patients who received pharyngeal and genioglossal muscle exercises were matched with the cohort who did not receive any exercise in a 1:1 ratio. The main outcome was the clinical efficacy 6 months after operation. The secondary outcomes were the changes of apnea hypopnea index (AHI), lowest oxygen saturation during sleep (LSaO2), Pittsburgh sleep quality index (PSQI), and the World Health Organisation Quality of Life tool (WHOQOL-BREF) score.ResultsSix months after operation, the clinical effective rate of the exercise group was significantly higher than that of the non-exercise group (p < 0.05). Before operation, there was no significant difference in AHI, LSaO2, PSQI and WHOQOL-BREF scores between the two groups (all p > 0.05). Six months after operation, the AHI,LSaO2, PSQI and WHOQOL-BREF scores of the two groups were significantly improved, and the AHI, LSaO2, PSQI, physical and psychological scores of the exercise group were better than those of the non-exercise group (all p < 0.05); However, there was no significant difference in the scores of environment and social domains between the two groups (all p > 0.05).ConclusionsPharyngeal musculature and genioglossus exercising may improve postoperative outcomes and quality of life in patients undergoing UPPP, and could be considered a promising rehabilitation strategy in clinical practice.
Retrospective evaluation of transcranial magnetic stimulation for enhancing arousal in patients with minimally conscious state: a single-centre study in Inner Mongolia, China
ObjectiveThis study aimed to investigate the effect of high-frequency transcranial magnetic stimulation (TMS) targeting multiple brain regions on the recovery of consciousness in patients with minimally conscious state (MCS).MethodsA retrospective analysis was conducted on MCS patients between August 2022 and March 2024. Some patients received only conventional rehabilitation treatment, while others received additional TMS therapy. Clinical outcomes were assessed using the Glasgow Coma Scale (GCS) and the Coma Recovery Scale-Revised (CRS-R) at three time points: before treatment (T0), two weeks post-treatment (T1), and one month post-treatment (T2). Additional assessments included electroencephalogram (EEG), brainstem auditory evoked potential (BAEP), somatosensory evoked potentials (SEP), and serum levels of brain-derived neurotrophic factor (BDNF) and neuron-specific enolase (NSE).ResultsA total of 30 patients were included and divided equally into two groups. The GCS and CRS-R scores of the 15 patients who received TMS therapy demonstrated significant improvements at T1 and T2. Furthermore, these patients exhibited significant enhancements in EEG and BAEP grading at T1.ConclusionThe findings suggest that adjunctive multi-target high-frequency repetitive TMS may promote recovery of consciousness in MCS patients. These results underscore the potential of repetitive TMS as a therapeutic intervention for MCS and warrant further investigation in future studies.
At-Home Sleep Electroencephalography Assessment in Young and Older Adults Using a Novel Wireless Soft Electronics Sleep Monitoring System: Experimental Study
Background: Sleep quality declines with age and is a known contributor to multiple chronic health conditions, including Alzheimer disease. Emerging evidence suggests that certain electroencephalography (EEG) neural signatures measured during sleep may be predictive of cognitive decline in older adults. Sleep EEG signals are traditionally measured using bulky, rigid, and uncomfortable equipment in an unfamiliar laboratory setting, which can negatively impact sleep signals. Due to these limitations, sleep EEG data acquisition is typically limited to a single night. Objective: This study aimed to validate our recently developed portable, skin-like EEG monitoring patch for 7 nights in the home environment in a pilot sample of young and older adults by evaluating usability and acceptance, and replicating age-related differences in sleep architecture observed in the polysomnography literature. Methods: Eighteen young adults and 18 cognitively unimpaired older adults without sleep disorders were enrolled (data from 11 young adults and 12 older adults were included in the analyses) in a 7-night study during which they wore novel, gel-free, wireless, ultrathin, skin-conforming, sleep monitoring, fabric-based patches. These patches were self-applied to the forehead and face for optimal usability and comfort. The patches incorporate laser-cut mesh electrodes with low-profile electronics (including a rechargeable battery and amplifier) and transmit EEG signals to a participant-controlled, Bluetooth-enabled, tablet-based data acquisition app. An automated algorithm was used to stage sleep and assess microarchitecture features from the EEG commonly impacted for each participant. Averages across nights were computed for these sleep features for each participant. Results: Young and older adults reported that the sleep patch was easy to use and comfortable to wear. There was no loss of signal power over 7 nights of wear across participants (retained-data signal-to-noise ratio over the 7-d period: young adult, mean 20.69, SD 12.78, maximum 52.13, minimum 5.19; older adult, mean 22.10, SD 9.39, maximum 49.96, minimum 13.79). Most datasets not retained were lost due to poor reference electrode adhesion on the nose (75/101, 74% of lost datasets in young adults and 57/88, 65% in older adults). Trained sleep technologists verified that the retained datasets were of sufficient quality to be scored without difficulty. Expected age-group differences in sleep features were observed, including age-related reductions in stage N3 sleep (young adult, mean 18.55, SD 6.70; older adult, mean 10.40, SD 6.43; Mann-Whitney =42.0; =.01) and reduced sleep spindle density (young adult, mean 2.92, SD 2.24; older adult, mean 0.94, SD 1.33; Mann-Whitney =45.0; =.006). Conclusions: This study demonstrates that our novel, comfortable, wearable patch can reliably measure physiological sleep data over multiple nights at home in adults across the lifespan, thereby making multinight sleep assessment in cognitive aging studies and clinical research more accessible than traditional polysomnography. In future studies, the small, lightweight system, which is highly scalable, can be shipped inexpensively to participants’ homes, making this technology and research accessible to individuals who may have difficulty traveling or who are hesitant to travel to a laboratory or clinic.
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Tailoring AI solutions for health care needs
The AI market is full of big promises of grand transformation. Health care is a prime target for those promises, beset as it is by financial pressures, labor shortages, and the growing burden of caring for an aging population. AI developers are targeting functions that vary widely, from curing cancer and performing surgery to streamlining routine administrative tasks.
The opportunity is genuine, but execution can be difficult. Numerous software vendors have tried to “fix” health care challenges but failed because they misunderstood the environment. “Health care is very complex,” says Steve Bethke, vice president of the solution developer market for Mayo Clinic Platform, which supports the buildout and deployment of digital solutions for health care companies through data-based insights and expert validation. “Solution developers must have a deep focus on clinical and technical capabilities, and then align their solutions to the relevant business impacts. If they miss any dimension, the solution will not be adopted or drive value.”
AI applications for health care are proliferating rapidly. The U.S. Food and Drug Administration has approved more than 1,300 AI-enabled medical devices, mostly for interpreting diagnostic images. More than half of these were approved in the past three years, with the earliest dating as far back as 1995. Non-radiological applications carry out tasks as diverse as tracking sleep apnea, analyzing heart rhythms, and planning orthopedic surgeries.
AI applications that do not count as medical devices— for example, those that handle scheduling and administrative tasks—are more difficult to track but are also rapidly increasing. AI can help coordinate complex tasks and workflows that are often conventionally managed by whiteboards and sticky notes. Such functions may well outstrip clinical uses in their impact on health systems. A recent survey of technology leaders found that 72% said their top priority for AI was reducing caregiver burden and improving caregiver satisfaction, while over half (53%) cited workflow efficiency and productivity.
Any health care-related application can potentially impact patient care, whether directly or indirectly, and AI apps that are poorly designed or inadequately trained and validated can put patients at risk. Providers recognize that risk: In the same survey, 77% said immature AI tools are a significant barrier to adoption. Regulators and lawmakers are also keeping an eye on the risks as development and adoption burgeon, though the U.S. regulatory picture is still in flux, as a 2024 report to Congress on AI in health care observes.
To tackle some of the technical challenges, many health care providers are partnering with application developers to build AI solutions. In a recent study, McKinsey found that 61% of health care organizations intend to pursue partnerships with third-party vendors to develop customized generative AI solutions as a primary strategy as opposed to building them in-house or buying off-the-shelf products.
But health care-specific AI applications must also be tailored to the nuanced clinical needs of medical providers as well as the complex business and regulatory considerations of the wider sector. This is where developers can benefit from working with a partner with a deep understanding of the health care environment to tailor applications to what providers want and need most. Doing so helps to position AI products for maximum impact and value, avoiding the pitfalls unique to the health care environment.
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.
Excessive Internet use and depressive symptom levels in adolescents with depressive disorders: chain mediation of social anxiety and sleep quality
BackgroundAdolescents with depressive disorders are at elevated risk for adverse mental health outcomes, and excessive Internet use has been increasingly linked to greater symptom severity. Therefore, this study aimed to examine the chain mediating roles of social anxiety and sleep quality in the association between excessive Internet use and depressive symptoms among adolescents with depressive disorders.MethodsA cross-sectional design was used. A total of 266 Chinese adolescents with clinically diagnosed depressive disorders (M = 15.79 years, SD = 1.85; 71.4% female) were assessed using the Internet Addiction Test, Zung Self-Rating Depression Scale, Social Anxiety Scale for Children, and Pittsburgh Sleep Quality Index. Correlation analyses and bootstrapping methods were conducted using SPSS and the PROCESS macro to examine the chain mediating effects of social anxiety and sleep quality.ResultsThe total indirect effect of excessive Internet use on depressive symptoms accounted for 65.66% of the total effect. Specifically, the indirect effects via social anxiety and sleep quality accounted for 24.10% and 26.51% of the total effect, respectively. In addition, the chain mediating effect of social anxiety and sleep quality was significant, accounting for 14.76% of the total effect.ConclusionExcessive Internet use was positively associated with more severe depressive symptoms among adolescents with depressive disorders, both directly and indirectly through the chain mediating effects of social anxiety and sleep quality. These findings highlight potential targets for preventing and intervening in excessive Internet use among this population.
Distinct sleep-disordered breathing phenotypes in elderly patients with depressive disorder: links to hypoxemia severity and inflammatory burden
ObjectiveTo identify sleep-disordered breathing phenotypes in older adults with depressive disorder and obstructive sleep apnea-hypopnea syndrome (OSAHS) and to evaluate their associations with systemic inflammation.MethodsElderly patients with depressive disorder and OSAHS were consecutively enrolled from January to December 2025. A Gower distance matrix was constructed and phenotypes were derived using partitioning around medoids (PAM; k-medoids), with k selected based on silhouette, elbow criteria, and clinical interpretability. Blood samples were collected the morning after PSG to measure serum high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), interleukin-1β (IL-1β), and tumor necrosis factor-α (TNF-α).ResultsAmong 198 participants, k = 2 was selected based on internal validity metrics (silhouette and elbow) and clinical interpretability. Compared with the lower-hypoxia/less-severe OSAHS phenotype (Cluster 1, n = 92), the high-hypoxia/severe OSAHS phenotype (Cluster 2, n = 106) had higher BMI, HAMD-17, and ESS, and more severe AHI/ODI/TS90 with a lower LSaO2. The high-hypoxia/severe OSAHS phenotype also showed higher hs-CRP, IL-6, IL-1β, TNF-α, WBC, neutrophils, and NLR. The inflammatory burden score was higher in the high-hypoxia/severe OSAHS phenotype (β = 1.10 SD unadjusted; β = 1.67 SD adjusted for age, sex, BMI, comorbidity, smoking, drinking, education, and MoCA; β = 1.45 SD further adjusted for HAMD-17 and ESS; all P < 0.001). In men (n = 135), PAM clustering similarly identified two phenotypes differentiated mainly by AHI/ODI, with selective elevations in IL-1β and neutrophil counts.ConclusionsThe high-hypoxia/severe OSAHS phenotype in older adults with depressive disorder is independently associated with a higher systemic inflammatory burden.
Data-Driven Tool Identifies Individuals at Highest Risk of Obesity-Related Disease
A new clinical risk model may transform how obesity is managed, by identifying which individuals are most likely to develop serious complications, regardless of their body mass index (BMI).
Developed by researchers at Queen Mary University of London and the Berlin Institute of Health, the tool, called OBSCORE, uses just 20 routinely collected clinical variables to predict the future risk of 18 obesity-related conditions, ranging from type 2 diabetes to cardiovascular disease.
Published in Nature Medicine, the study challenges the long-standing reliance on BMI as the primary metric for assessing obesity-related health risk.
Moving beyond BMI
BMI has long served as a simple proxy for obesity, but it fails to capture the biological heterogeneity of patients. Two individuals with similar BMI can have vastly different risks of developing complications.
The new model addresses this limitation directly. As described in the study, it “provides information beyond BMI” by integrating multiple dimensions of health into a unified risk score.
These include demographic data, clinical biomarkers, disease history, and lifestyle factors, variables already commonly available in healthcare settings.
The findings show that BMI alone is a poor discriminator of risk. The model consistently outperformed BMI-based approaches across all tested outcomes.
Large-scale data enables precision risk prediction
To build the model, researchers analyzed health data from nearly 200,000 individuals with overweight or obesity from the UK Biobank.
Using an interpretable machine learning framework, they screened more than 2,000 potential predictors and distilled them into a core set of 20 features that best predicted long-term health outcomes.
The resulting OBSCORE model estimates the 10-year risk of developing 18 conditions, including cardiovascular disease, kidney disease, sleep apnea, and metabolic disorders.
The model demonstrated strong predictive performance, with median concordance indices around 0.75 across outcomes, indicating robust discrimination between high- and low-risk individuals.
Hidden high-risk individuals
One of the most striking findings is that high-risk individuals are not always those with the highest BMI.
A substantial proportion of individuals classified as high risk fell into the “overweight” category (BMI 27–30 kg/m²), rather than obesity. In some outcomes, up to ~40% of those in the highest risk group had BMI below the obesity threshold.
This reveals a critical gap in current clinical practice: individuals who may benefit from intervention could be overlooked simply because they do not meet BMI-based criteria.
On the other hand, some individuals with obesity may have relatively low risk and may not require intensive intervention.
Strong risk stratification across diseases
Beyond prediction, the scientists believe that OBSCORE enables meaningful risk stratification. Individuals in the highest risk group showed dramatically higher rates of disease compared to those in the lowest group.
For example, the study reports:
- Up to 89-fold higher risk for chronic kidney disease
- 42-fold higher risk for type 2 diabetes
- 47-fold higher risk for cardiovascular mortality
These differences exceed those observed when comparing individuals based solely on BMI categories, underscoring the added value of multidimensional risk assessment.
Clinical and healthcare implications
The implications of these findings are significant, particularly in the context of emerging obesity therapies.
Highly effective drugs such as GLP-1 receptor agonists and dual incretin therapies have transformed treatment options, but their high cost and limited availability make patient prioritization essential.
As the authors note, current systems lack robust frameworks to identify which patients should receive treatment.
OBSCORE offers a potential solution by enabling risk-based allocation of interventions, ensuring that treatment is directed toward those most likely to benefit.
This could improve clinical outcomes while optimizing healthcare resource use.
Toward implementation in clinical practice
One of the key strengths of OBSCORE is its practicality. Unlike many predictive models, it relies on a small number of variables that are already routinely collected, making it suitable for integration into electronic health records.
The researchers envision the model being used as a decision-support tool in clinical settings, complementing rather than replacing existing frameworks.
External validation in independent cohorts—including populations of different ancestry, demonstrated strong generalizability, further supporting its potential for real-world deployment.
Limitations and next steps
Despite its promise, the model requires further validation in broader populations, including younger individuals and more diverse healthcare settings.
Additionally, while OBSCORE effectively stratifies risk, translating these predictions into actionable treatment thresholds will require clinical consensus and cost-effectiveness analyses.
The authors also emphasize that the model identifies predictive, not necessarily causal, factors, and should be interpreted accordingly.
Taken together, the findings mark a shift toward precision medicine in obesity, moving from simplistic metrics like BMI to data-driven, individualized risk assessment.
By capturing the complex interplay of metabolic, clinical, and behavioral factors, OBSCORE could enable earlier intervention, better targeting of therapies, and improved long-term outcomes for patients living with overweight and obesity.
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Examining circadian rhythm dysregulation using actigraphy among treatment-seeking individuals with alcohol use disorder
BackgroundIdentifying factors predictive of relapse in patients with alcohol use disorder (AUD) following a period of abstinence and/or treatment is essential to discover effective treatment plans for this disease. Previous evidence found that individuals with AUD who relapsed had lower sleep regularity scores than those who did not relapse. This analysis aimed to extend previous work to explore the relationship between circadian rhythms and relapse.MethodsTreatment-seeking individuals with AUD (n = 126) were admitted to an inpatient treatment program for approximately 28 days and, upon discharge, wore Philips Respironics Actiwatches® for the subsequent 4 weeks during an unprotected environment. A subset of these participants wore the devices prior to discharge for up to 7 days inpatient (n = 36). Relapse status was assigned if a participant consumed any alcohol during the outpatient period of data collection. Inpatient and outpatient circadian rhythm nonparametric statistics were calculated for all participants including weekly interdaily stability (IS) and intradaily variability (IV), and daily most active 10 h (M10), least active 5 h of the day (L5), relative amplitude (RA), and the wake time. Linear and logistic generalized mixed models were fitted to estimate the effect of discharge on circadian rhythms, the effect of preceding circadian rhythms on the probability of relapse, and the effect of relapse on circadian rhythms. All analyses accounted for within-patient repeated measurements.ResultsThe final cohort size was n = 103 for the outpatient subset and n = 36 for the inpatient subset after actigraphy data filtering. Participants were 48.6 ± 11.3 years of age, and 32% were female. A total of 26 (25.2%) participants relapsed. There were significant decreases in IV and L5 and increases in M10, RA, and wake time between inpatient and outpatient settings, whereas IS did not substantially differ between the two settings. Following relapse, there was a moderate decrease in hourly IV and an increase in (later) wake time of ~1 h; no other circadian variables were significantly predictive of relapse.ConclusionOverall, circadian rhythms shifted after discharge but were not predictive of relapse. Instead, relapse was followed by a delayed average wake time and a moderate reduction of daily activity patterns. These results highlight the potential value of monitoring circadian changes as indicators of relapse occurrence rather than relapse risk.
Tryptophan modulates the impact of prolactin on insomnia in perimenopausal women: a cross-sectional study
BackgroundInsomnia is highly prevalent among perimenopausal women and exerts detrimental effects on physical health, psychological well-being, and overall quality of life. However, its underlying mechanisms remain incompletely understood. This cross-sectional study aimed to identify factors associated with insomnia in perimenopausal women.MethodsA total of 187 perimenopausal women aged 45–55 years were enrolled. Insomnia, anxiety, and depression severity were assessed using the Insomnia Severity Index (ISI), Generalized Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-9 (PHQ-9), respectively. Serum levels of relevant amino acids and hormones were measured. Spearman correlation and linear regression analyses were performed to examine the associations among prolactin levels, tryptophan levels, insomnia, anxiety, and depression. Moderation analysis was further conducted to evaluate the potential moderating role of tryptophan in these relationships.ResultsSerum prolactin levels were positively associated with scores of ISI, GAD-7, and PHQ-9. Furthermore, prolactin levels were positively correlated with the severity of sleep-onset difficulties, sleep maintenance problems, noticeability of impairment, and sleep-related distress. Of note, serum tryptophan levels significantly moderated the association between prolactin levels and ISI scores (β = 0.227, 95% CI = 0.04–0.41, p = 0.0148). To wit, he positive relationship between prolactin levels and insomnia severity was stronger in perimenopausal women with higher serum tryptophan levels compared with those with lower levels.ConclusionsThe moderating effect of serum tryptophan on the relationship between prolactin levels and insomnia in perimenopausal women helps us understand the neuroendocrine mechanisms underlying perimenopausal insomnia and may inform future research on targeted preventive and therapeutic strategies.