Electroconvulsive therapy (ECT) is efficacious for the treatment of major depressive disorder. However, its underlying mechanism of action remains debated and may involve neurogenesis, changes in axon/synaptic density or glial cells. Restriction spectrum imaging (RSI) is an MRI technique that can resolve subvoxel microstructure, separating intracellular water from extracellular water, which can be further partitioned into hindered and free water. This technique can be used to determine whether ECT affects cellular microstructure through changes in signal contributions. In the present study, we aim to determine how RSI metrics change following ECT and whether these metrics correlate with clinical improvement.

RSI analysis of the Perturbation of Depression Connectome (PDC 1.0) diffusion MRI data for healthy control and predominantly right unilateral ECT patients. MRI studies were undertaken at baseline, 1-week post-treatment and 3-months post ECT (follow-up).

Following ECT, right-sided RSI metrics changed in cortical and subcortical brain regions including right amygdala and right hippocampus between baseline and post-treatment. The hindered water compartment increased, the free water compartment decreased, and the intracellular compartment was unchanged. Post-treatment, volume increased in the amygdala and hippocampus, which partially resolved at follow-up, while right amygdala extracellular indices persisted. Changes in clinical symptoms did not clearly relate to volumetric or diffusion changes.

RSI provides imaging biomarkers that suggest extracellular water shifts with corresponding volume changes to be the predominant effect of ECT. Transient and longer-term changes in glymphatic flow may be one of the contributing, underlying mechanisms of action for ECT.

Schizophrenia spectrum disorders (SSDs) are chronic conditions associated with profound functional impairment, disability and economic burden. Traditional approaches to diagnosis and management often struggle to capture the heterogeneity of SSD. In recent years, machine learning (ML) has expanded rapidly in mental health research, offering ways to analyse complex datasets, uncover subtle patterns and generate predictive models that could inform more individualised interventions.

This scoping review aimed to map how ML has been applied to SSD research with emphasis on clinical and functional outcome trajectories.

Peer-reviewed studies investigating any use (eg, predictive tool, treatment algorithm) of ML in determining outcomes in SSD were eligible. Non-English language studies, grey literature, systematic reviews and conference abstracts were excluded.

Following Arksey and O'Malley's framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews, we searched three bibliographic databases (MEDLINE, APA PsycInfo, Embase) with input from a health sciences librarian. The initial search was completed on 28 July 2023 and re-run on 10 March 2025.

Two independent reviewers screened studies at title/abstract and full-text stages and extracted data using a standardised table in Covidence, capturing study design, ML techniques, data sources and outcomes. Methodological and reporting quality of included studies was appraised using the APPRAISE-AI instrument.

88 studies met inclusion criteria. For included studies, n=62 were cross-sectional, n=19 were cohort studies, n=5 were case-controls, n=1 was an evaluation study and n=1 carried out a secondary analysis of data from a randomised controlled trial. Overall, methodological and reporting quality were moderate (mean APPRAISE-AI score 47.4/100, range 31-65). ML applications were grouped into five domains: clinical, treatment, functional, behavioural and provider outcomes. Clinical outcome studies used electronic health records, clinical assessments, neuroimaging and mobile sensing to predict relapse, remission, symptom severity and suicide risk. Treatment studies modelled treatment response, resistance and adverse effects, with attention to clozapine and electroconvulsive therapy. Functional studies employed deep learning to analyse speech, social cognition and real-world functioning. Behavioural outcomes included prediction of aggression, self-harm and offending. Provider outcomes examined prescribing patterns using supervised learning. Across domains, integration of multimodal data and adoption of advanced approaches such as deep neural networks were evident.

ML in SSD research shows significant promise for advancing diagnosis, prognosis and treatment planning. However, methodological rigour, interpretability, fairness and clinical validation remain critical for future translation into practice.

Preclinical data suggest that fractionated stereotactic radiotherapy (FSRT) may sensitize immunologically-cold tumors to immune checkpoint inhibition by stimulating neoantigen release and that inhibition of interleukin-6 (IL-6) permits immunosuppressive tumor-associated macrophages to become immunostimulatory. This study aimed to test the safety and efficacy of the combination of tocilizumab (anti-IL6R), atezolizumab [anti-programmed death-ligand 1 (PD-L1)], and FSRT in patients with recurrent glioblastoma (rGBM).

A multicenter, single-arm, phase 2 trial was conducted with a safety run-in phase followed by a Simon's two-stage design. Patients received the combination of tocilizumab and atezolizumab administered on 28-day treatment cycles until disease progression, unacceptable toxicity, or for up to 2 years. FSRT (24 Gy in 3 fractions) was initiated 3-7 days following cycle 1 of systemic therapy. The primary endpoint was objective response rate (ORR) by modified Response Assessment in Neuro-Oncology (mRANO) criteria. The window for assessment of the primary endpoint was 6 months from time of enrollment. The two-stage design was used for the primary endpoint analysis to discriminate between true response rates of no more than 10% (null hypothesis) and at least 30% (alternative hypothesis). Secondary endpoints included the rates and severity of adverse events (AEs), progression-free survival (PFS), and overall survival (OS).

Forty-one patients were eligible and evaluable. The safety run-in (n=12) established Dose Level 3 (combination of tocilizumab 8 mg/kg, FSRT, and atezolizumab 1680 mg) as the maximum tolerated dose, which was subsequently evaluated in the Simon's two-stage design (n=29). The study did not meet its primary endpoint (ORR 3.4%, 95% CI: 0.1% - 17.8%). Median PFS was 3.2 months (95% CI: 2.0 - 4.0), and median OS was 10.2 months (95% CI: 6.6 - 13.6).

These findings suggest that the combination of tocilizumab, atezolizumab, and FSRT was safe and well tolerated in patients with rGBM but did not achieve the pre-specified efficacy outcome.

Postpartum depression (PPD) affects 17.22% of women worldwide and represents a major public‑health concern. Cognitive-behavioral therapy (CBT) is a recommended intervention for PPD. However, evidence regarding its overall efficacy, additional clinical benefits, and real‑world performance across heterogeneous delivery formats remains insufficient. This meta‑analysis quantifies the efficacy of CBT compared with usual care, assesses the certainty of evidence, and descriptively explores intervention‑mode characteristics across included trials.

We systematically searched PubMed, Embase, Web of Science, the Cochrane Library, and Scopus from inception to December 13, 2025. Eligible randomized controlled trials (RCTs) were included in the meta-analysis, and all statistical analyses were performed using STATA 18.0 software (StataCorp LLC, College Station, TX, USA).

Eight RCTs (1811 PPD participants) were included. CBT significantly reduced Edinburgh Postnatal Depression Scale (EPDS) scores (SMD = -0.59, 95% CI: -0.77 to -0.41) and increased the EPDS‑defined clinical improvement rate (RR = 1.84, 95% CI: 1.59 to 2.12). It may improve perceived social support, mitigating infant‑focused maternal anxiety and generalized anxiety symptoms. No group difference was found for infant surgency temperament (IBQ‑R‑SUR, SMD = 0.17, 95% CI: -0.04 to 0.38).

Findings from this meta-analysis point to potential benefits of CBT for core PPD-related symptoms and secondary outcomes. The certainty of evidence ranges from very low to moderate. Future research should prioritize high‑quality, long‑term trials of non‑specialist‑delivered digital CBT models to develop accessible perinatal mental‑health services.

Translating genetic variations into clinically useful molecular interpretations in neuro-oncology remains difficult, requiring physicians to examine many databases and resources. We created NeuroGeno3D, a browser-based clinical decision support platform that combines structural bioinformatics, molecular pathology, and clinical evidence in a single interactive process. The platform integrates canonical protein sequence validation, interactive 3D WebGL visualization, and real-time AI-assisted structural prediction with ESMFold for protein sequences up to 500 amino acids. Detected variations are analyzed by a deterministic WHO CNS categorization support engine and linked to downstream signaling pathways using Reactome and KEGG databases. NeuroGeno3D also incorporates ClinVar annotations, PubMed literature, and appropriate clinical guidelines from NCCN, CNS, AANS, and EANO into a sequential pre-, intra-, and post-operative workflow. Predefined hotspot variations are evaluated in median 1.12 s (IQR: 1.02-1.26 s) under typical settings, while custom protein sequences take median 6.30 s (IQR: 5.70-7.15 s) to process. In retrospective clinical evaluation across 140 adult diffuse glioma cases, NeuroGeno3D achieved an overall diagnostic accuracy of 92.1 % (95 % CI: 86.4 %-96.0 %), supported by excellent usability scores (mean System Usability Scale score: 88.9 ± 4.8/100, Grade A + ). The platform also provides structured, evidence-based reports that bring together molecular discoveries, structural visualization, pathway context, and treatment relevance in a single interface. By combining these components, the platform enables rapid literature exploration for rare genomic variants, supporting research alongside clinical interpretation, and lays the groundwork for future potential real-time intraoperative molecular diagnostic and decision-support procedures.

Social Anxiety Disorder (SAD) is a prevalent mental disorder characterized by altered processing of social stimuli. Intolerance of Uncertainty (IU) has been linked to social anxiety and influences the perception of social information. Yet, the neural correlates of IU in SAD during the processing of emotional stimuli remain insufficiently understood.

In this study, fMRI data were collected from 25 individuals with SAD and 25 healthy controls (HC), who completed a face processing task. Differential associations between the IU subdimensions prospective IU, inhibitory IU, and burden due to IU, brain activity, and functional connectivity were analyzed using full-factorial models. Functional connectivity measures focused on the insula.

For prospective IU, analysis of functional activity revealed an interaction effect in parietal regions and the insula (p_TFCE-FWE ≤ 0.031), driven by a positive association between IU and activation in the HC group. Concurrently, prospective IU was negatively associated with insular functional connectivity across both groups (p_TFCE-FWE ≤ 0.031), suggesting reduced insular integration independent of diagnosis. A distinct pattern emerged for burden due to IU, showing a negative association with precuneus and posterior cingulate gyrus functional activity across both groups (p_TFCE-FWE = 0.014). No significant effects were found for inhibitory IU.

These findings indicate both disorder-specific and shared neural associations of IU. In particular, prospective IU may hold clinical relevance in SAD, as it is linked to reduced insular connectivity across groups but to increased neural activity exclusively in HC, possibly pointing toward neural vulnerabilities that could inform future approaches to the diagnosis and treatment of SAD.

Neuroimaging studies in obsessive-compulsive disorder (OCD) have yielded heterogeneous findings, limiting efforts to localize OCD to specific brain regions or networks. This study applied coordinate network mapping (CNM) to test whether these distributed abnormalities converge onto a common brain network and to explore the biological substrates of this network through associations with neurotransmitter systems. We systematically reviewed and extracted coordinates of significant clusters from 66 Gy matter volume (GMV) and resting-state activity studies involving 3123 patients with OCD and 3116 healthy controls. Using CNM and functional connectome data from 1000 healthy individuals, we identified brain networks functionally connected to these abnormalities. Spatial correlations were examined between identified networks and 19 types of neurotransmitter maps acquired from over 1000 healthy subjects. The GMV and resting-state activity findings in OCD both converged to a common, functionally connected brain network involving the striatum, thalamus, cingulate, supplementary motor area, insula, and inferior frontal and superior temporal gyri. The identified networks derived from the two neuroimaging modalities were significantly correlated with the N-Methyl-D-Aspartate (NMDA) receptor distribution (r = 0.551 and r = 0.541, respectively; p < 0.001). Findings were robust across different methodological parameters and demonstrated good test-retest reliability. This study identifies a robust and reproducible brain network underlying the heterogeneous neuroimaging abnormalities in OCD, helping to interpret previous inconsistencies. Furthermore, the alignment of this network with the cortico-striato-thalamo-cortical circuitry and NMDA receptor distribution underscores its biological and clinical relevance, thereby illuminating potential targets for future neuromodulation and pharmacological interventions.

Gloabally, young people are experiencing increasing ill health. Since school health promotion often lacks an interprofessional language, this study strives to open up a global conversation by introducing Pedagogical Health in School (PHIS). This serves as a novel pedagogical lens to bridge individual-level well-being with school-level structures, providing a shared conceptual base for interprofessional collaboration.

Drawing on qualitative data from preschool through upper secondary school, the study explores the umbrella concept of pedagogical health, encompassing four health foundations: mental, physical, social, and existential health, and offering a holistic perspective on children's and students' well-being in school settings.

The primary contribution is the identification of 52 candidate indicators manifesting across the four foundations in practice. These candidate indicators translate broad health concepts into observable phenomena, offering school staff a concrete, shared basis for interprofessional dialogue and action.

The article proposes pedagogically grounded definitions for each foundation to support coordinated health-promoting work. PHIS represents a significant advancement in the field of school health promotion by providing a holistic, pedagogically grounded perspective that aligns practice with Sustainable Development Goals (SDG 3 and 4). By integrating well-being into pedagogical settings, PHIS strengthens schools' capacity for proactive and sustainable health promotion.

Review literature on the frequency of sleep disorders and receipt of sleep-related healthcare among individuals with muscular dystrophy.

Included: Population-Duchenne/Becker (DBMD), congenital (CMD), facioscapulohumeral (FSHD), limb-girdle (LGMD), or myotonic (DM) muscular dystrophy; Outcomes-frequency of sleep evaluations, disorders, or treatments; Language-English; Study type-original research, systematic review. Excluded: Sample <10, duplicate reports.

PubMed and EBSCO, last searched March 7, 2023. Risk of bias assessment : Recruitment source or study inclusion selected for or against sleep symptoms or disorders.

Range and weighted average (low risk studies only) of outcome.

We included 95 studies of sleep evaluations (7), disorders (94) or treatments (13) with a total of 7,964 participants. Studies reported on DM-61, DBMD-20, FSHD-9, CMD-1, LGMD-1, and multiple MD-3. One-half of people with DBMD (51%, 2 studies, 85 individuals) and over 60% of people with DM1 (65%, 2 studies, 327 individuals) had a sleep study. Measured by questionnaire, excessive daytime sleepiness frequency among adults with DM was 39% (DM2, 10, 284) to 65% (any DM, 6, 1195), 9% among controls (5, 199), and 6% to 13% among other MDs. Sleep apnea frequency was 11% (LGMD, 1, 18) to 65% (CMD, 1, 20). Assisted ventilation use was 5%-16% (DM).

Regular sleep evaluations may improve identification and treatment of sleep disorders and the physical and mental health of individuals with MD.

Funding-US Centers for Disease Control and Prevention. Not registered.

Tardive dyskinesia (TD) is a neurological syndrome of involuntary repetitive movements which results from treatment with antipsychotic medication. The pathoetiology of TD is not well understood but a possible mechanism involves dopaminergic overactivity in the nigrostriatal pathway. If this theory is correct, then levels of neuromelanin (a long-term marker of dopaminergic activity) should be higher in people with TD than those without TD.

The aim of the study was to test the hypothesis that neuromelanin levels are higher in patients with TD relative to those without TD.

Data from 27 participants (TD: n = 13; non-TD n = 14) with a diagnosis of schizophrenia, all taking antipsychotic drugs, were used. Neuromelanin was measured in the midbrain via Magnetic Resonance Imaging (MRI) and compared between the groups. Movement symptoms were measured using the Abnormal Involuntary Movement Scale (AIMS), and antipsychotic dose recorded.

MRI-measured neuromelanin levels were significantly higher in patients with TD, as compared to those without (mean = 0.223; t(17.27) = 3.817, p = 0.001; g = 1.386, 95% CI=[0.559, 2.213]). This remained significant after controlling for age, sex, substantia nigra/ventral tegmental area volume and antipsychotic dose (ANCOVA: F(1,20) = 12.08, p = 0.0024; adjusted β = 0.0301, 95% CI [0.0120, 0.0481]). The most pronounced difference was seen in the ventral substantia nigra.

The finding of higher midbrain neuromelanin in patients with TD aligns with the dopamine overactivity hypothesis of TD aetiology. It also supports a neurobiological basis for treatment of TD with drugs that target presynaptic dopamine activity, such as VMAT2 inhibitors. Additionally, it identifies the ventral substantia nigra as a key locus. Future longitudinal studies are needed to delineate if dopamine overactivity develops in response to antipsychotic treatment or is a trait vulnerability marker for risk of TD.