Treatment-resistant depression (TRD) remains a major clinical challenge. Intranasal esketamine, used adjunctively with an oral antidepressant, has been evaluated in randomized trials, but uncertainty persists regarding the magnitude and consistency of benefit, durability, and key harms. This systematic review and meta-analysis included randomized controlled trials comparing intranasal esketamine plus an oral antidepressant versus placebo nasal spray plus the same oral antidepressant in TRD. Acute induction (≈4 weeks) and maintenance randomized-withdrawal phases were analyzed separately. Depression outcomes were assessed primarily using the Montgomery–Åsberg Depression Rating Scale (MADRS), and functional outcomes using the Sheehan Disability Scale (SDS). Two reviewers independently screened studies, extracted data, and assessed risk of bias using RoB 2.0. Random-effects models pooled mean differences (MD) for continuous outcomes, risk ratios (RR) for binary outcomes, and hazard ratios (HR) for relapse prevention. Certainty of evidence was rated using GRADE. From 1,518 records, nine reports representing six unique RCTs (1,836 participants) were included. Four acute induction RCTs (n=937) showed greater symptom reduction at day 28 with esketamine (MADRS MD −2.99, 95% CI −5.10 to −0.89; I²=48.5%). Rapid improvement was evident by day 2 (MD −3.25, 95% CI −4.65 to −1.85). Esketamine increased day-28 response (RR 1.44, 95% CI 1.20–1.74) and remission (RR 1.52, 95% CI 1.20–1.92), corresponding to approximately +154 responders and +106 remitters per 1,000 patients, respectively, based on pooled control risks. Functioning improved (SDS MD −1.70, 95% CI −2.61 to −0.79). Two maintenance randomized-withdrawal RCTs (n=899) demonstrated reduced relapse risk with continued esketamine (HR 0.51, 95% CI 0.42–0.62; I²=0%). In acute induction, esketamine increased any treatment-emergent adverse event (TEAE) (RR 1.37, 95% CI 1.25–1.50) and discontinuation due to adverse events (RR 2.68, 95% CI 1.35–5.29), with notable increases in dissociation (RR 7.33, 95% CI 4.49–11.98) and blood pressure increased events (RR 3.96, 95% CI 2.24–7.01). Maintenance TEAE rates were similar between groups (RR 1.07, 95% CI 0.99–1.17). Intranasal esketamine plus an oral antidepressant provides rapid, modest acute improvement and reduces relapse risk during maintenance among stabilized responders/remitters, but increases acute adverse events, supporting use within supervised care and individualized benefit–risk assessment.
Exercise interventions are most consistently supported for depressive disorders: an umbrella review of diagnosed depressive and anxiety disorders
BackgroundExercise is increasingly discussed as part of lifestyle-based and multimodal care for mood and anxiety disorders, but review-level evidence often mixes formally diagnosed clinical populations with symptom-defined or medically mixed samples.MethodsWe conducted an umbrella review of systematic reviews, meta-analyses, and network meta-analyses of structured exercise interventions for adults with depressive or anxiety disorders. Six databases were searched from inception to 1 March 2026. Primary outcomes were depressive and anxiety symptom severity, remission, and response; secondary outcomes were acceptability and tolerability. Review quality was appraised with AMSTAR 2, and primary-study overlap was quantified with corrected covered area (CCA), including overall and symptom-cluster analyses. The synthesis was designed to summarize review-level credibility and clinical interpretability rather than to generate a second-order pooled efficacy estimate.ResultsNine reviews met eligibility criteria; four supplied directly extractable primary overall review-level estimates for core psychiatric symptom outcomes. AMSTAR 2 appraisal rated one review as high, three as low, and five as critically low. Recalculated overall overlap was slight (112 primary-study occurrences, 89 unique primary studies; CCA = 3.23%), although cluster-level analyses identified localized redundancy, particularly within anxiety-disorder-specific reviews. In major depressive disorder, one clinically focused review reported a large reduction in depressive symptoms for aerobic exercise versus non-exercise comparators (Hedges’ g = -0.79, 95% CI -1.00 to -0.57; I² = 21%). Across diagnosed depressive and/or anxiety disorders, broader review-level estimates also favored exercise for depressive symptoms (SMD = -0.97, 95% CI -1.28 to -0.66) and anxiety symptoms (SMD = -0.66, 95% CI -1.09 to -0.23), but heterogeneity was high. Anxiety-disorder-specific evidence was less secure: the primary DSM-IV anxiety-disorder pooled estimate showed no clear benefit over selected controls (SMD = 0.02, 95% CI -0.20 to 0.24). Acceptability estimates were close to null, and adverse-event reporting was too sparse to support confident safety conclusions.ConclusionExercise is best supported as an adjunctive, patient-centered component of care for depressive disorders. Anxiety-disorder-specific efficacy remains uncertain when comparator rigor, diagnostic heterogeneity, and localized overlap are considered, and safety reporting needs substantial improvement.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD420261364264.
Health outcomes across socioeconomic strata B, C, and DE among Brazilian adults living in moderate social vulnerability
ObjectivesThis study examined whether socioeconomic status was associated with anxiety symptoms, depressive symptoms, BMI, waist-to-hip ratio, and quality of life among Brazilian adults living in areas of moderate social vulnerability. In addition, we described anxiety and depressive symptoms, BMI and waist-to-hip ratio, and quality of life in individuals living in moderate social vulnerability.MethodsThis is a cross-sectional study. In a socially vulnerable cohort, interviews captured demographics, comorbidities, medications, anxiety and depressive symptoms, and quality of life, followed by measurement of anthropometric characteristics.ResultsAmong 299 socially vulnerable adults, 8% had moderate–severe depressive symptoms and 7% had moderate–severe anxiety symptoms; ~50% showed increased risk of cardiometabolic diseases (i.e., waist-to-hip ratio greater or equal to 0.90 for men and 0.85 for women, respectively). Poor quality of life affected 4–12% across domains. Mental health, anthropometrics (waist-to-hip ratio, BMI), increased risk of cardiometabolic diseases and quality of life in physical, social and environmental domains did not differ by socioeconomic status (B, C, DE; all P>0.05). Poor psychological quality of life was more frequent among participants in higher socioeconomic status (B: 8%; C: 6%; DE: 4%, P = 0.0157). Linear regression analyses showed no statistically significant differences across socioeconomic status in depressive symptoms, anxiety symptoms, BMI, waist-to-hip ratio, or quality of life scores in any domain (all P>0.05).ConclusionsOur findings suggest that, among Brazilian adults living in moderate social vulnerability and classified within socioeconomic status B, C, and DE, mental health, BMI, waist-to-hip ratio, and quality-of-life indicators were similar across socioeconomic strata. However, these results should be interpreted as reflecting intra-group socioeconomic differences within a moderately vulnerable population and should not be generalized to individuals from the highest socioeconomic status.
Effects of Virtual Reality on Postoperative Pain Management Following Minimally Invasive Gynecologic Surgery: Randomized Controlled Trial
<strong>Background:</strong> Postoperative pain and anxiety remain common concerns after minimally invasive gynecologic surgery despite advances in surgical techniques and analgesic strategies. Virtual reality (VR) has been investigated as a potential nonpharmacological intervention for pain management; however, evidence in gynecologic postoperative settings is limited. <strong>Objective:</strong> This study aims to evaluate the efficacy and safety of VR technology compared with standard postoperative analgesia for pain and anxiety management in patients undergoing minimally invasive gynecologic surgery. <strong>Methods:</strong> This randomized controlled trial was conducted at Sun Yat-sen Memorial Hospital of Sun Yat-sen University in China. A total of 131 patients undergoing laparoscopy or combined hysteroscopy for benign gynecologic diseases were randomly assigned in a 1:1 ratio to either a VR group (n=68) or a control group (n=63). All patients received a standardized general anesthesia protocol intraoperatively. The control group received conventional analgesic therapy after surgery, and the VR group received a 20-minute VR intervention 6 hours postoperatively. The pain and anxiety levels were evaluated using a visual analog scale at 6 and 7 hours postoperatively. The primary outcome was the change in pain scores between 6 and 7 hours. Secondary outcomes included maximum pain score, anxiety score changes, length of hospital stay, hospitalization costs, and occurrence of adverse events. Analyses were performed according to the intention-to-treat principle. <strong>Results:</strong> There was no statistically significant difference in the primary outcome between the VR and control groups (mean difference 0.169, 95% CI −0.271 to 0.608; <i>P</i>=.45). Similarly, no significant differences were observed in the maximum pain score (mean difference 0.839, 95% CI −0.101 to 1.779; <i>P</i>=.08), and no improvement was observed in the anxiety score (mean difference 0.042, 95% CI −0.365 to 0.449; <i>P</i>=.84). No significant differences were found in length of hospital stay, hospitalization costs, or incidence of adverse events, including dizziness, nausea, and vomiting (all <i>P</i>>.05). <strong>Conclusions:</strong> A single 20-minute VR intervention did not provide additional analgesic or anxiolytic benefit compared with standard postoperative care after minimally invasive gynecologic surgery. VR was well tolerated, and its role in postoperative recovery requires further investigation. <strong>Trial Registration:</strong> Chinese Clinical Trial Registry ChiCTR2400091244; https://tinyurl.com/4b92a9td
From Alliance to Nexus: Rethinking Digital Therapeutic Relationships
In traditional human psychotherapy, the therapeutic alliance (TA) is regarded as a fundamental factor that describes the client-therapist relationship, mainly due to strong evidence demonstrating its impact on treatment outcomes regardless of theoretical orientation. More recently, advances in artificial intelligence (AI) and other technologies have led to the emergence of the concept of digital TA, used to characterize the relationship between clients and AI-based therapeutic systems. This approach replicates human dynamics but overlooks key differences between human therapists and digital agents. Prematurely translating the concept of TA into the digital context fails to address issues such as the sycophantic tendencies of current systems and the inherent limitations of algorithmic interaction. We propose the digital therapeutic nexus, a framework that recognizes these differences and provides a set of structured criteria for categorizing digital interactions into 3 progressive levels. This Viewpoint argues that only at the highest level can parallels be drawn to the human TA and stratifies the main risks associated with each nexus level. Transitioning from the concept of alliance to that of a nexus offers a more precise conceptual basis for describing and evaluating digital therapeutic relationships, with implications for research, design, and the ethical development of AI-based mental health interventions.
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Prediction of Clinically Significant Depressive Symptoms at 2-Year Follow-Up in Older Adults: Machine Learning Study Using the English Longitudinal Study of Ageing
Background: Depression in older adults is often underdiagnosed due to atypical symptom presentation and generational stigma, leading to delayed intervention. Early identification of individuals at risk of developing elevated depressive symptoms is therefore critical, but traditional approaches show limited predictive accuracy. To date, no study has applied machine learning (ML) models to predict clinically significant depressive symptoms at 2-year follow-up in older adults in the United Kingdom using data from the English Longitudinal Study of Ageing (ELSA). Moreover, the impact of encoding strategies for categorical health care variables has not been examined. Objective: This study aimed to develop and evaluate ML models to predict the clinically significant depressive symptoms at 2-year follow-up in older adults using ELSA data. We further compared ordinal and one-hot encoding strategies across different ML architectures and identified key predictors of depressive symptoms at follow-up. Methods: Data were drawn from 4 consecutive waves of ELSA, including participants aged ≥50 years without significant depressive symptoms at the baseline wave (waves 6‐9). Clinically significant depressive symptoms were defined as 8-item Center for Epidemiologic Studies Depression Scale (CES-D 8) scores of ≥4 at the subsequent wave (waves 7‐10). Over 120 features spanning sociodemographic, psychological, and health-related domains were analyzed. Eight ML models were applied, including tree-based ensembles, deep learning architectures for tabular data, distance-based methods, probabilistic methods, and linear methods. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and -score. Model interpretability was examined using Shapley additive explanations (SHAP). Sensitivity analyses assessed the robustness of results across alternative CES-D 8 thresholds (≥3, ≥4, and ≥5) and encoding strategies. Results: Across waves, the best-performing models achieved mean AUROC scores of 0.72‐0.73, with a peak of 0.75 in the highest-performing wave. Ordinal encoding consistently outperformed one-hot encoding across all ML models, yielding improvements in AUROCs and -scores, with the greatest increase in tree-based methods. SHAP consistently identified loneliness, sleep disturbances, and low social engagement as strong predictors of elevated depressive symptoms at follow-up. Sensitivity analyses across CES-D 8 thresholds demonstrated robust feature importance, with AUROCs ranging from 0.67 to 0.82. Traditional ML models (random forest, extreme gradient boosting, and support vector machines) generally achieved higher performance than the deep learning models for this task. Conclusions: Our findings demonstrate the feasibility of predicting clinically significant depressive symptoms at 2-year follow-up in UK older adults, with moderate accuracy. Ordinal encoding demonstrates superior performance for health care datasets with inherently ordered categorical features. The identification of consistent risk factors highlights opportunities for developing targeted clinical screening tools and preventive interventions. This study provides new evidence on depressive symptom prediction in the UK context, leveraging longitudinal data from ELSA, and contributes to advancing digital mental health research for aging populations.
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EFE-8c4, a polyamine from Elaeagnus multiflora, protects neuronal cells by regulating oxidative stress and apoptotic pathways
Neurological disorders, including depression, cognitive impairment, and neurodegenerative diseases, are closely associated with oxidative stress, apoptotic neuronal loss, and impaired neuronal differentiation. Polyamine derivatives from natural products have emerged as potential neuroprotective agents, although their mechanisms remain incompletely understood. In this study, three polyamine compounds isolated from Elaeagnus multiflora fruit were evaluated in SH-SY5Y cell models of oxidative and glucocorticoid-induced stress. Among these, EFE-8c4 exhibited the most pronounced neuroprotective activity, significantly restoring cell viability under corticosterone-induced stress and attenuating oxidative damage induced by hydrogen peroxide. Mechanistically, EFE-8c4 modulated apoptotic signaling by increasing Bcl-2 expression while suppressing Bax, caspase-8, and p53 activation, thereby restoring the balance between pro- and anti-apoptotic pathways. In addition, EFE-8c4 reduced intracellular reactive oxygen species accumulation and enhanced the expression of PCNA and βIII-tubulin, indicating improved cell survival capacity and neuronal phenotype maintenance. Furthermore, EFE-8c4 partially reduced apoptotic cell populations under corticosterone exposure. Collectively, these findings demonstrate that EFE-8c4 exerts multi-target neuroprotective effects through coordinated regulation of apoptosis, oxidative stress, and neuronal differentiation-related pathways, highlighting its potential as a candidate for the treatment of oxidative stress-associated neurological disorders.
Developing forensic patient-oriented research guidelines: a rapid review using an integrated knowledge translation approach
This paper reports findings from a rapid literature review that informed new guidelines for conducting patient-oriented research in forensic mental health settings. The project adopted an integrated knowledge translation approach at a mental health hospital in Ontario, Canada, engaging a project team that included current forensic patients, hospital staff, and members of an international community of practice. Sources were identified through nine academic databases and targeted grey literature searches, screened independently by two reviewers and extracted using a structured template guided by an a priori framework developed with patients and staff at a knowledge exchange event. Findings were iteratively refined through a patient advisory group, an implementation study, ethnographic observations, and related integrated knowledge translation activities conducted alongside the review. Together, 31 academic and grey literature sources informed a framework organized around five core dimensions: 1) Resourcing, orientation, and training; 2) Confidentiality, consent, and compensation; 3) Relationships, shared understanding, and support; 4) Levels of engagement; and 5) Evaluation and sustainability. Guided by cross-cutting principles common among participatory mental health research, such as dignity, trust, respect, and a commitment to redressing power and attending to forms of epistemic injustice, the guidelines respond to distinctive constraints of forensic environments while highlighting opportunities to promote authentic co-production and sustain patient involvement in research. Recommendations include dedicated resources and capacity-building for patients; relational, ongoing consent practices co-developed with patients; flexible patient researcher roles with fair, paid compensation; and sustained institutional support for participatory practices. We call on forensic hospitals and secure settings to adapt and evaluate these guidelines and to invest in expanding patient leadership to advance the field.
Age-stratified multimodal MRI and machine learning to explore autism-related brain characteristics in youth
PurposeAutism is a common neurodevelopmental condition (NDC) that is characterized by restricted, repetitive behaviors and social communication differences that can impact the daily functioning of individuals. The clinical diagnosis of autism can be challenging, mainly due to its behavioral variability and frequent co-occurrence with other NDCs. This study investigates the ability of machine learning-based classification models trained using multimodal neuroimaging data combined with feature-importance analyses to identify development-specific brain characteristics associated with autism.ApproachA total of 144 participants aged 5 to 18 years with structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rs-fMRI) data available were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. Radiomic features were extracted from each MRI data modality and used to train support vector machine (SVM) classifiers to identify neuroimaging patterns associated with autism. Single MRI modality classifiers, as well as one combining all three modalities, were trained for comparison purposes. To investigate age-specific effects, the same approach was followed for three age sub-groups: younger children (5–11 years), adolescents (12–18 years), and the entire 5–18 years age cohort. Model performance was evaluated using leave-one-out cross-validation across 30 diagnosis-balanced data splits. Feature-importance analyses were conducted to identify the most important neuroimaging features for classification.ResultsThe classification accuracies of the unimodal models ranged from 68.3% to 75.3% for sMRI, from 69.3% to 77.6% for dMRI, and from 66.3% to 69.9% for rs-fMRI data across age groups. Among all single imaging modalities and age groups, dMRI showed the highest performance with a 77.6% accuracy in younger children (5–11 years). The multimodal approach improved classification performance when compared to the unimodal models in all age groups, achieving accuracies of 78.9%, 76.7%, and 70.5% in the younger, adolescent, and entire age cohorts, respectively. Our findings indicate that multimodal classifiers integrating complementary structural, microstructural, and functional imaging features result in a more comprehensive representation of brain features that strengthens model performance. The most informative brain regions for classification differed between children and adolescents while several diffusion-derived features significantly correlated with social responsiveness scores, emphasizing the clinical importance of studying white and gray matter microstructure in autism.ConclusionsThis study demonstrates the potential of multimodal neuroimaging-based machine learning models to identify development-specific biomarkers associated with autism. The results highlight the value of integrating age-stratified analyses of multimodal neuroimaging to better capture autism-associated developmental brain characteristics. The framework adopted in this study could be extended to explore other NDCs in the future.
Enhancing psychiatry education: effectiveness of a psychodynamic psychotherapy module for borderline personality disorder for psychiatry residents
BackgroundPsychodynamic psychotherapy is the treatment of choice for borderline personality disorder (BPD); however, psychiatric residents frequently report difficulty in applying it, partly due to the lack of structured training models. This study developed and evaluated the effectiveness of psychodynamic psychotherapy learning modules for BPD among Indonesian psychiatry residents.MethodsA quasi-experimental pre-/post-test control group study using mixed methods was conducted across nine psychiatric residency programs in Indonesia. Thirty-four residents were recruited, of whom 33 completed the study. Learning outcomes were assessed using multiple-choice questions and the Psychodynamic Formulation Competency Assessment Scale (PF-CAS) and Practical Competency Assessment Scale (PC-CAS). The module program was evaluated by the participants using the Indonesian version of the Kirkpatrick Level 1 questionnaire.ResultsThe intervention group showed significantly greater improvement in psychodynamic formulation skills (PF-CAS) than the control group (p < 0.001). The multiple-choice scores improved in both groups, with no significant between-group differences. The intervention group showed a numerically greater improvement in Practical Skills (PC-CAS) than the control group, although the difference was not statistically significant. Participants’ feedback was highly positive, emphasizing the usefulness of psychodynamic formulation training, psychotherapy protocols, and supervision.ConclusionImplementation of the psychodynamic psychotherapy for BPD Learning Module enhanced competencies in the cognitive and affective domains and showed promising trends in practical skills. This positive reception highlights its feasibility and potential benefits as part of the residency training curriculum.