Auricular vagus nerve stimulation (aVNS) is a neuromodulation technology that establishes balance in the autonomic nervous system and, in turn, provides therapy for numerous chronic ailments. Personalized aVNS adapts the stimulation parameters in accordance with the time-varying physiological state of the body, and is suggested to improve the therapeutic outcomes and reduce side effects. The physiological state is estimated via recorded biomarkers such as the electrocardiogram (ECG). aVNS can be delivered in synchrony with any phase of the cardiac cycle before and after the R-peak. This paper proposes the prediction of the duration of the next cardiac cycle after the detected R-peak for the realization of the personalized cardiac-gated closed-loop aVNS applied at any time point during the predicted cardiac cycle. We propose and explore the feasibility of four different prediction methods for predicting the duration of the next cardiac cycle. Two methods are respiration-insensitive, last value and averaging, and the other two are respiration-sensitive, extrapolation and interpolation. Offline recorded ECG waveforms were used to evaluate the different methods. Subsequently, three of the four methods (last value, averaging, and extrapolation) were implemented in real-time on a proprietary aVNS hardware setup, with the data acquisition performed across normal and paced deep breathing. Offline evaluation of the methods revealed that extrapolation and interpolation achieved lower prediction errors during deep breathing with the median absolute error (MdAE) of 32.09 ms (interquartile range 16.07–56.61 ms) and 31.71 ms (15.5–54.06 ms), respectively, as compared with the averaging and last-value methods with 88.75 ms (58.73–124.15 ms) and 40.85 ms (19.7–68.4 ms), respectively. During normal breathing, all evaluated methods yielded lower prediction errors relative to the averaging method 28.5 ms (15.2–43.7 ms). Real-time implementation validated these methods for closed-loop cardiac-gated aVNS, with the best performance achieved by the extrapolation method with 31.4 ms (15.17–55.9 ms) during paced deep breathing. During normal breathing, comparable performance across prediction methods favors the computationally simple last-value approach (MdAE: 31.6 ms). Proposed methods establish the potential of ECG-based R-peak prediction in real-time as a reliable and individual biomarker for the personalized cardiac-gated aVNS, creating a foundation for future clinical applications of aVNS.
DyAMNet: dynamic adversarial and contrastive network for EEG biometrics
IntroductionElectroencephalogram (EEG)-based biometric recognition for brain–computer interfaces faces challenges from domain shifts, temporal nonstationarity, and limited scalability.MethodsTo address these issues, we present DyAMNet, a framework that combines EEG microstate analysis with a hybrid attention mechanism. DyAMNet employs dynamic loss balancing to improve generalization and constructs a domain-invariant feature space that supports user expansion without catastrophic forgetting. We evaluated the model on three benchmark datasets (DEAP, THU-EP, and SEED).ResultsThe framework attains 87.2% accuracy in cross-dataset recognition and retains 84.0% accuracy when incrementally scaling to 60 users. The system also tolerates physiological artifacts and intersession signal drift, outperforming state-of-the-art models.DiscussionThese findings show that dynamic adversarial training coupled with contrastive feature learning reduces brain-signal variability and preserves scalability. The work establishes a robust basis for feasible identity authentication and supports deploying brain–computer interfaces in clinical and everyday settings. The code is available at: https://github.com/cangtianhaoxue/DyAMNet.git.
Module-specific diagnostic accuracy of ADOS-2 in real-world clinical referral populations: an updated systematic review and HSROC meta-analysis
BackgroundThe Autism Diagnostic Observation Schedule, Second Edition (ADOS-2), is widely used in the diagnostic evaluation of autism spectrum disorder (ASD); however, its diagnostic performance in real-world clinical referral populations remains heterogeneous, particularly across modules and clinical contexts. This systematic review and meta-analysis evaluated the module-specific diagnostic accuracy of ADOS-2 using hierarchical meta-analytic modeling and examined sources of heterogeneity in updated evidence clinical studies.MethodsA systematic search of PubMed/MEDLINE, Scopus, and Web of Science was conducted from January 2021 to February 2026, with additional screening of reference lists. Studies were included if they evaluated ADOS-2 diagnostic accuracy in real-world clinical referral populations, used DSM- or ICD-based clinical best-estimate diagnosis as the reference standard, and reported extractable 2×2 data. Diagnostic accuracy was pooled using a hierarchical summary receiver operating characteristic (HSROC) model with a bivariate random-effects approach. Module-specific analyses (Toddler Module, Modules 1–2, Module 3, Module 4) and meta-regression were performed to examine heterogeneity.ResultsTen studies were included in the qualitative synthesis, and six provided extractable 2×2 data for quantitative pooling. Overall pooled sensitivity was 0.88 (95% CI: 0.83–0.92) and pooled specificity was 0.74 (95% CI: 0.68–0.80), with an HSROC AUC of 0.86. The Toddler Module showed the highest diagnostic performance (sensitivity 0.92; specificity 0.88; AUC 0.94), whereas specificity decreased in Modules 3 and 4. Meta-regression identified module level, psychiatric referral setting, and adult samples as significant contributors to reduced specificity. No significant publication bias was detected.ConclusionsADOS-2 demonstrates high overall sensitivity but variable specificity across modules in real-world clinical referral populations. Reduced specificity was more commonly observed in higher ADOS-2 modules, which are typically administered to verbally fluent adolescents and adults with greater psychiatric complexity.
Elevated NHHR at admission is independently associated with elevated post-stroke depressive symptoms
BackgroundElevated post-stroke depressive symptoms are common among acute ischemic stroke (AIS) survivors and are associated with poor functional recovery. However, readily available biomarkers reflecting lipid–inflammation status remain limited. The ratio of non–high-density lipoprotein cholesterol to high-density lipoprotein cholesterol (NHHR) may integrate atherogenic burden and inflammatory status and thus be associated with depressive symptom burden after stroke.MethodsWe retrospectively analyzed a cohort of 518 Chinese AIS patients. Admission NHHR was calculated from routine lipid panels. Depressive symptoms at 90 days were assessed using the Hamilton Depression Rating Scale (HAMD), and elevated post-stroke depressive symptoms were defined according to the prespecified HAMD threshold. Multivariable logistic regression, restricted cubic spline (RCS) analysis, and subgroup analyses were conducted to examine the association between NHHR and elevated depressive symptoms.ResultsAmong the 518 patients, 179 exhibited elevated post-stroke depressive symptoms at 90 days. Higher NHHR levels were independently associated with increased odds of elevated depressive symptoms after adjustment for demographics, stroke severity, cognitive function, inflammatory markers, and coagulation parameters (OR = 1.35, P = 0.029). Dose–response trends were observed, and RCS analysis suggested a linear relationship without significant nonlinearity. No significant interactions were found across sex, smoking, alcohol use, hypertension, or diabetes subgroups.ConclusionsElevated NHHR at admission was independently associated with elevated post-stroke depressive symptoms at 90 days in Chinese AIS patients. As a simple, cost-effective, and readily obtainable biomarker, NHHR may facilitate early risk stratification and guide individualized interventions.
Suicide and suicidal behavior in the gulf cooperation council countries: a Systematic Review of behavioral patterns, sociocultural determinants, and structural vulnerabilities
Suicide and suicidal behavior in the Gulf Cooperation Council (GCC) countries remain underrepresented in empirical research, largely due to cultural stigma, legal prohibitions, and systematic underreporting. The behavioral and sociocultural determinants of suicidal behavior in these contexts—where rapid modernization intersects with Islamic normative frameworks and large-scale labor migration—have received particularly limited systematic attention. This review synthesizes available evidence on the prevalence, behavioral patterns, risk factors, and structural determinants of suicidal behavior across Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates. Following PRISMA 2020 guidelines, we searched PubMed, Scopus, ScienceDirect, Web of Science, and Google Scholar for primary research published between 2000 and 2025. Fifty studies (34 GCC-specific and 16 contextual/comparative) met the inclusion criteria. Methodological quality was appraised using the Newcastle–Ottawa Scale, JBI checklists, the Mixed Methods Appraisal Tool, and the AACODS checklist. The review protocol was registered on the Open Science Framework (DOI: 10.17605/OSF.IO/RZXY7; URL: https://osf.io/rzxy7/). The findings were organized into six dimensions: (1) suicide prevalence, risk factors, and associated behaviors; (2) cultural and religious influences; (3) mental health of specific at-risk populations; (4) healthcare and support services; (5) migrant worker vulnerabilities; and (6) research trends. Reported suicide rates in the GCC range from 1.5 to 4.2 per 100,000 but likely underestimate the true prevalence due to forensic misclassification and stigma. Migrant workers constitute a high-risk subgroup, with suicidal ideation reaching 68% among those facing acute financial distress. Islamic religiosity is associated with lower suicide mortality but simultaneously deters help-seeking by stigmatizing mental distress. Interpreted through a Durkheimian lens, these findings position suicidal behavior in the GCC as a social fact shaped by weakened social integration and normative fragmentation under rapid modernization. Effective prevention requires culturally adapted behavioral interventions that integrate labor welfare reforms with community-based mental health strategies.
Efficacy and predictors of cognitive stimulation therapy combined with pharmacotherapy for mild-to-moderate Alzheimer’s disease: a randomized controlled trial
IntroductionAlzheimer’s disease (AD) is associated with progressive cognitive decline, functional impairment, and reduced quality of life. Although pharmacological treatments such as cholinesterase inhibitors and memantine are commonly used, their clinical benefits remain limited and heterogeneous. Cognitive stimulation therapy (CST) may provide additional benefits when combined with standard pharmacotherapy. This randomized controlled trial (RCT) aimed to evaluate the clinical efficacy of modified CST combined with standard drug therapy on cognitive function, activities of daily living, and quality of life in patients with mild-to-moderate AD and to explore key predictors of CST efficacy using a multivariate regression model.MethodsThis evaluator-blinded randomized controlled trial enrolled 80 patients with mild-to-moderate Alzheimer’s disease (AD), who were randomly assigned in a 1:1 ratio to either the modified CST plus standard pharmacotherapy group (study group, n = 40) or the standard pharmacotherapy-alone group (control group, n = 40).The modified CST program comprised 14 weekly 45-minute sessions. The primary endpoint was the change in the Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-Cog) score from baseline to post-intervention. Secondary measures included the Activities of Daily Living (ADL) scale and the Quality of Life in Alzheimer’s disease (QOL-AD) questionnaire. Data were analyzed using an intention-to-treat (ITT) approach. Independent predictors of treatment efficacy were identified using a two-stage screening strategy (univariate screening and stepwise regression).ResultsA total of 75 patients completed the study, and 80 were included in the ITT analysis. After 14 weeks of intervention, baseline-adjusted ANCOVA showed that the study group had significantly better post-intervention ADAS-Cog scores than the control group. The adjusted mean difference in ADAS-Cog score was -3.28 points (95% CI: -3.72 to -2.83; P < 0.001), favoring the study group. Significant baseline-adjusted between-group differences were also observed for ADL (adjusted mean difference = -4.93, 95% CI: -8.39 to -1.47; P = 0.006) and QOL-AD (adjusted mean difference = 2.69, 95% CI: 1.01 to 4.37; P = 0.002), both favoring the study group. Higher years of education (β = -0.54, P < 0.001), regular physical activity (β = -0.28, P = 0.003), higher baseline Mini-Mental State Examination (MMSE) scores (β = -0.22, P = 0.001), and active hobbies (β = -0.20, P = 0.002) were significant independent predictors of CST efficacy.DiscussionModified CST combined with medication significantly delays cognitive decline and improves QOL-AD in patients with mild-to-moderate AD. Educational attainment, lifestyle factors, and cognitive reserve are key determinants of CST efficacy. Relevant institutions should develop targeted enhancement protocols for patients with lower educational levels or insufficient cognitive reserves when implementing CST.Trial RegistrationChinese Clinical Trial Registry, identifier ChiCTR2600118654, https://www.chictr.org.cn.
Editorial: Beyond abstinence: harm reduction and its impact on addiction disorders
Mapping the spatial landscape of extracellular vesicles in tissues with Spatial-EV-seq
Nature Biotechnology, Published online: 26 June 2026; doi:10.1038/s41587-026-03206-0
Spatial-EV-seq is a method for spatially resolved profiling of extracellular vesicles (EVs) in tissues while preserving EVs’ native distribution. By integrating EV mapping with spatial transcriptomics, Spatial-EV-seq enables location-specific analysis of EVs and their communication networks within the tissue microenvironment, establishing a new paradigm in the field of spatial EV biology.
Spatially resolved profiling of extracellular vesicles in tissues with Spatial-EV-seq
Nature Biotechnology, Published online: 26 June 2026; doi:10.1038/s41587-026-03192-3
Spatial-EV-seq reveals spatial patterns of extracellular vesicles in tissues.
Adaptive brain stimulation 2.0 for Parkinsonian gait
Nature Medicine, Published online: 26 June 2026; doi:10.1038/s41591-026-04489-1
Two studies show that adaptive neuromodulation can improve Parkinsonian gait by matching stimulation to the right circuit at the right moment, pointing toward strategies that decompose complex symptoms into targetable spatial and temporal components.