Obesity stands as a formidable 21st-century public health crisis, with its capacity to aggravate depressive symptoms gaining increasing clinical attention. Traditional treatment models often treat these two conditions separately. However, recent research evidence suggests a complex network linking obesity and depressive symptoms across metabolism, behavior, and mental health, with dietary patterns proposed as a key upstream modulator of both metabolic and psychological pathways. The specific mechanisms by which diet influences obesity and depressive symptoms remain unclear. Therefore, this narrative review focuses on analyzing molecular connections between diet, obesity, and depressive symptoms, including adipose tissue inflammation, the gut-brain axis, the hypothalamus-pituitary–adrenal axis, insulin and brain-derived neurotrophic factor levels, and neuroplasticity. We discuss the possible pathways and effects of different diet therapies in regulating metabolism and simultaneously impacting mental health, including calorie restriction diet, intermittent fasting, ketogenic diet, low glycemic index diet, plant-based diet, Mediterranean diet, Dietary Approaches to Stop Hypertension, among others. This review aims to provide a scientific basis for precision nutrition and personalized, sustainable diet therapies in clinical practice, promoting awareness and improving treatment strategies for depressive symptoms in obese patients.
Silent Twiddler syndrome despite dual-anchor fixation in deep brain stimulation: two case reports
BackgroundTwiddler syndrome (TS) is a rare hardware-related complication in which manipulation or excessive mobility of an implanted pacemaker or implantable pulse generator (IPG) results in device malfunction. Traditionally, TS is diagnosed only after lead coiling, fracture, displacement, or loss of therapeutic efficacy, suggesting that it represents a late-stage manifestation of the condition.CasesWe report 2 patients with deep brain stimulation (DBS) systems who presented with a subjective sensation of increased IPG mobility during changes in body position. Neither patient demonstrated mechanical failure, lead displacement, or deterioration of DBS clinical benefit, and both IPGs had been implanted with two anchoring sutures fixed to the fascia. Furthermore, neither patient reported intentional manipulation of the device. Because of persistent discomfort and abnormal mobility, revision surgery was performed. Intraoperative findings confirmed early-stage TS in both cases. The IPGs were reimplanted into tailored subpectoral pockets and firmly secured to the fascia. Following revision, both patients experienced complete resolution of symptoms and maintained sustained therapeutic benefit from DBS.ConclusionThese cases suggest that a subjective perception of increased IPG mobility may represent a distinct pre-Twiddler clinical syndrome that precedes the development of classic TS and overt hardware failure. Early recognition of this underrecognized presentation and timely surgical intervention through pocket revision and device immobilization may prevent progression to lead damage, device malfunction, and serious neurological adverse events, while ensuring uninterrupted neuromodulation therapy.
Evolution of brain-computer interface technologies for stroke rehabilitation: a bibliometric integration of neural decoding and functional recovery (2016–2025)
IntroductionBrain-computer interface (BCI) technology represents a critical frontier in neurorehabilitation. This study aims to systematically analyze the global research landscape, hotspot distribution, and evolving trends of BCI interventions for upper limb rehabilitation in stroke survivors between 2016 and 2025.MethodsBibliometric analysis and systematic mapping were conducted using data from the Web of Science Core Collection and PubMed. Literature was retrieved using terms related to “stroke,” “brain-computer interface,” and “upper limb rehabilitation.” Screening followed the PRISMA guidelines. Visualization and quantitative mapping were performed using CiteSpace (v.6.4.R2) and VOSviewer (v.1.6.20) to evaluate publication volume, international collaboration, and keyword co-occurrence clusters.ResultsAnnual publications increased steadily from 37 in 2016 to 104 in 2025, with 65.6% published since 2020. The United States (n = 144), China (n = 83), and Italy were the most productive countries. Keyword analysis revealed a paradigm shift from functional electrical stimulation toward robotics-assisted therapy, motor imagery, and AI-driven decoding. Significant burst strengths were observed for “closed-loop systems,” “generative AI,” and “multi-modal feedback,” indicating these as the current primary frontiers.DiscussionBCI research for post-stroke recovery is transitioning from experimental signal processing to intelligent, multi-modal, and personalized clinical systems. Bibliometric evidence confirms that integrating BCI with robotic-assisted rehabilitation or functional electrical stimulation (FES) has become the mainstream clinical trend. Future efforts must focus on improving EEG signal stability and developing user-friendly hardware to facilitate the transition of BCI from research settings to daily clinical practice. China has emerged as the second most productive country, though international cooperation with European institutions remains an area for further growth.
Latent profile analysis and associated factors of alexithymia among patients with chronic obstructive pulmonary disease
BackgroundAlexithymia is one of the most common psychological characteristics observed in patients with chronic obstructive pulmonary disease (COPD) and is associated with poorer health outcomes. The negative impact of alexithymia on physical illness is reflected in more severe clinical symptoms and poorer quality of life, which adversely affect clinical outcomes and further increase disease burden, potentially contributing to a cycle of worsening health status. Therefore, timely identification of the psychological characteristics of patients with COPD, along with early psychological support, may help improve the quality of life of patients with COPD.ObjectivesTo explore the latent profiles of alexithymia among patients with COPD and to analyze their associated factors.MethodsThis cross-sectional study recruited 312 patients with COPD using convenience sampling between October 2024 and March 2025. Sociodemographic characteristics, physiological and disease-related information, psychospiritual information, environmental factors, and alexithymia scores were collected. Latent profile analysis, univariate analysis, and multinomial logistic regression were conducted to identify alexithymia profiles and examine factors associated with profile membership.ResultsThe results of this study showed that patients with COPD were categorized into three alexithymia profiles: high (28.85%), moderate (63.46%), and low (7.69%). COPD duration, depression, activity intensity, self-perceived social support, psychological resilience, and anxiety were associated with different alexithymia profiles among patients with COPD.ConclusionThis single-center cross-sectional study identified three preliminary alexithymia profiles (high, moderate, and low) among patients with COPD. Due to potential selection bias resulting from convenience sampling, these findings require validation in multicenter studies before generalization. The observed associations among COPD duration, depression, activity intensity, social support, resilience, and anxiety provide empirical evidence to inform the development of tailored patient classification and targeted support strategies for early identification, with the goal of improving quality of life in this population.
Factors associated with unmet psychological care needs and development of a Neuman systems model-based risk prediction model in patients with hepatocellular carcinoma
BackgroundPatients with hepatocellular carcinoma (HCC) often face symptom distress, treatment decision-making, demands for family support, communication needs, and financial concerns during early hospitalization. This study examined factors associated with unmet psychological care needs and developed a risk prediction model, guided by the Neuman Systems Model, to support early nursing stratification.MethodsThis single-center retrospective study included 345 adults with HCC admitted between January 2020 and December 2024. Unmet psychological care needs were defined as a score of ≥3 on any item in the psychological domain of the Chinese Supportive Care Needs Survey Short Form-34 (SCNS-SF34) within 24–48 h after admission. Candidate predictors were classified as intrapersonal, interpersonal, or extrapersonal stressors. After outcome stratification, the cohort was randomly divided into training and validation sets (7:3). Multiple imputation, least absolute shrinkage and selection operator (LASSO) selection across 20 imputed datasets, and multivariable logistic regression were used to develop the nomogram. Discrimination, calibration, decision curve analysis, bootstrap internal validation, and sensitivity analyses were evaluated.ResultsUnmet psychological care needs were identified in 205 patients (59.4%). The final model retained pain numeric rating scale (NRS) score, sleep disturbance, Patient Health Questionnaire-9 (PHQ-9) score, Generalized Anxiety Disorder-7 (GAD-7) score, family support, cooperation in physician-patient communication, and perceived economic burden. Higher pain scores, sleep disturbance, higher depressive and anxiety symptom scores, poor family support, poor cooperation in physician-patient communication, and heavy perceived economic burden were independently associated with unmet psychological care needs. The areas under the receiver operating characteristic curves (AUCs) were 0.851 in the training set and 0.829 in the validation set; the bootstrap-corrected AUC was 0.836. Calibration-in-the-large, calibration slope, Brier scores, and decision curve analysis indicated acceptable apparent and internal-validation performance. Sensitivity analyses excluding PHQ-9 and GAD-7 and applying a stricter outcome definition showed consistent directions of association for the principal predictors.ConclusionsUnmet psychological care needs were common within 24–48 h after admission among patients with HCC. The internally validated nomogram may support early risk stratification, symptom management, psychosocial screening, family involvement, communication optimization, and referral to medical social work services. Independent external validation is required before routine clinical implementation.
The ECHO study: compassion-focused therapy for young voice hearers and their caregivers. A pilot study
BackgroundVoice hearing can occur in healthy people without a trigger, be caused by a traumatic event, or occur as part of a psychiatric disorder. There are still many gaps in our understanding of voice hearing, and research in this field can be challenging, maybe because it is associated with shame and stigma. Those who experience distress by voice hearing could benefit from a treatment, aimed directly at that. The ECHO manualized treatment is based on the principles of compassion-focused therapy (CFT).MethodsTreatment will take place on several different psychiatric hospitals in the Region of Southern Denmark. Treatment consists of a 10-session, manualized CFT for voice hearing. Caregivers participate in half of the sessions, and a recording of the voice content is made, which the caregivers are encouraged to listen to. The study is designed as a prospective single-arm pre–post intervention study using a mirror-image approach, in which participants serve as their own controls over time. The intervention group consists of (N = 60) voice-hearing adolescents aged 13–18 years. Additionally, a clinical comparison group (who also receive the intervention) of (N = 20) individuals aged 18–20 years with first-episode schizophrenia and voice hearing is included for descriptive and exploratory analyses. Data are collected 3 months prior to CFT treatment, immediately before the start of treatment, immediately after treatment is completed, and at 1 month follow-up. Data will be centered around social cognition, perceived social safeness, social connectedness, general well being, auditory hallucinations and compassion.DiscussionThe ECHO study addresses the wellbeing and thriving of an overlooked group of young people. The results of this study will provide information about who the voice hearers are, what challenges they face, and how they perceive them. The results will also provide information about the feasibility and efficacy of the treatment manual and, thus, its potential further implementability. Caregivers participate in half of the sessions, to facilitate a common language about mental states and voice hearing. To support their understanding, a recording of the voice content is made and the caregivers are encouraged to listen to it. We suggest that when caregivers understand the young voice hearers’ experiences, they can adjust their expectations and approach accordingly. We also suggest that by sharing the voice content, the young voice hearer will feel less isolated.Study protocol registrationhttps://clinicaltrials.gov/study/NCT07314515, identifier NCT07314515.
Engineered ADARs enable precision A-to-G base editing of DNA
Nature Biotechnology, Published online: 10 July 2026; doi:10.1038/s41587-026-03223-z
Bystander edits are minimized by engineering an adenosine deaminase acting on RNA for DNA base editing.
Efficient in vivo cytosine base editing using virus-like particles with uracil DNA glycosylase inhibition
Nature Biotechnology, Published online: 10 July 2026; doi:10.1038/s41587-026-03227-9
In vivo cytosine base editing is made efficient with potent glycosylase inhibition.
Anti-LAG-3 with or without anti-PD-1 in recurrent glioblastoma: a phase 1 trial
Nature Medicine, Published online: 10 July 2026; doi:10.1038/s41591-026-04475-7
In a non-randomized phase 1 trial, patients with recurrent glioblastoma received anti-LAG-3 relatlimab with or without anti-PD-1 nivolumab, showing that both monotherapy and combination therapy are safe and result in encouraging preliminary clinical response signals.
Health system learning enables generalist neuroimaging models
Nature Medicine, Published online: 10 July 2026; doi:10.1038/s41591-026-04497-1
NeuroVFM, trained on routine health system MRI and CT scans, learns general neuroimaging representations that improve diagnosis, report generation and triage, showing how private clinical data will power safer, more accurate medical AI.