Adolescent marriage and early childbearing remain common in rural Pakistan, yet few interventions support young couples transitioning into parenthood. This study explores the perceived impact and implementation of preparing for parenthood (PPP), a life skills intervention designed for married adolescents and their families. A qualitative design explored participants' and delivery agents' experiences of PPP. Data were collected through 12 in-depth interviews and 4 focus group discussions with married adolescent women, spouses, family members and programme trainers. Thematic analysis was used. PPP was perceived as relevant, acceptable and empowering. Participants reported improved core life skills; communication, emotional regulation, problem-solving and decision-making that enhanced their confidence and preparedness for parenting. Home-based delivery by trusted female trainers and recruitment via community health workers supported engagement in mobility-restrictive, patriarchal settings. Challenges included limited male participation, family gatekeeping and the burden of domestic work. Participants suggested adaptations such as mobile content, male facilitators and media-based delivery to overcome these barriers. This study highlights the value of life skills interventions in equipping married adolescent women with socioemotional competencies necessary for effective parenting. Home-based, culturally grounded models paired with flexible, gender-sensitive approaches can foster empowerment and improve outcomes in low-resource, gender-restrictive settings.

Mental and behavioural health (MBH)-related visits account for a substantial portion of emergency department (ED) care. EDs have increasingly adopted technological interventions to support MBH patients and staff. This review examined ED-based technological interventions and their impact on patient safety and quality of care for MBH populations. An initial search on six databases led to identification of 37,432 articles and final selection of 34 peer-reviewed articles on technology interventions. Key findings indicate that screening interventions effectively identify underlying drug and alcohol use, depression, and suicidal ideation in patients presenting to the ED with non-psychiatric complaints; remote psychiatry consultations improve access to mental health care; crisis management intervention programs are well-accepted by patients while waiting in the ED; and documentation interventions enhance communication with providers, and LLMs show strong potential in identifying psychiatric indicators. This review delineates key implications, implementation challenges, and future research directions for ED MBH care delivery.

The relationship between retirement and mental health remains unclear, with mixed evidence likely driven by methodological challenges and heterogeneity across population subgroups. This study explores the relationship between retirement, time since retirement and mental health, focusing on differences between those living alone and cohabiting.

We used data from 10,698 participants (48,815 observations) in the Survey of Health, Ageing and Retirement in Europe, waves 1-8, spanning 20 countries and 16 years. Participants were included if they were employed at baseline and retired during follow-up. We applied a triangulated approach using (1) multilevel linear spline models to examine trajectories and (2) fixed-effects instrumental variable analyses (IV) using statutory retirement age as an instrument to establish causal effects. The outcome was depressive symptoms measured using the EURO-D scale (higher scores indicate more depressive symptoms).

Multilevel spline models showed minimal change in depressive symptoms before retirement. At retirement, a small decline in EURO-D scores was indicated (-0.07 points per year [95% CI: -0.13 to -0.0003]), followed by an increase starting two years post-retirement (0.09 per year, 95% CI: 0.02 to 0.16). IV analyses showed that retirement was associated with fewer depressive symptoms (-0.26, 95% CI: -0.45 to -0.07). Individuals living alone reported higher depressive symptoms, but cohabitation status did not moderate the relationship between retirement and mental health.

Retirement is associated with a short-term reduction in depressive symptoms, but the effect diminishes over time. While individuals living alone consistently report higher depressive symptoms, cohabitation status does not moderate the retirement-mental health relationship.

Schizophrenia is a chronic psychiatric disorder that significantly affects individuals' mental health. Sustained personal recovery in schizophrenia is a multidimensional process that extends beyond symptom reduction and may be significantly influenced by psychological resources and cognitive appraisals. While self-compassion and metacognitive beliefs are known to affect mental health outcomes independently, their interacting roles in predicting personal recovery remain unclear.

This study examined the mediating role of metacognitive beliefs in the relationship between self-compassion and recovery among individuals diagnosed with schizophrenia.

A cross-sectional correlational design was used. A purposive sample of 150 clinically stable adults diagnosed with schizophrenia (DSM-5 criteria) was recruited from psychiatric outpatient clinics at Helwan University Hospitals, Egypt, between April and September 2024. Data were collected through structured face-to-face interviews using the Arabic versions of the Metacognition Questionnaire-30, the Self-Compassion Scale-Short Form, and the Recovery Assessment Scale-Domains and Stages.

Bivariate analyses revealed a significant positive correlation between self-compassion and personal recovery (r = 0.374, p < 0.001). While total metacognitive belief scores correlated positively with recovery (r = 0.346, p < 0.001), subscale analysis indicated this was driven primarily by the Cognitive Self-Consciousness subscale (r = 0.598, p < 0.001). The association between self-compassion and total metacognitive beliefs was non-significant (r = 0.084, p = 0.304). Consequently, mediation analysis demonstrated that self-compassion showed a significant direct association with recovery (B = 0.5726, p < 0.001); however, the indirect pathway through metacognitive beliefs was not significant (B = 0.0442, 95% Bootstrap CI [-0.045, 0.147]).

Metacognitive beliefs were not found to mediate the association between self-compassion and recovery; instead, self-compassion was independently associated with multidimensional recovery. Clinically, these findings support the integration of self-compassion training into long-term schizophrenia treatment plans. Future longitudinal and intervention-based studies are warranted to clarify the temporal dynamics of these relationships.

Prenatal exposure to alcohol (PAE) and/or tobacco (PTE) may impact neurobiological and cognitive development years after such exposures have occurred. While such effects on cortical gray matter and cognition are widely researched, there is less research on the effects of PAE and PTE on white-matter development and cognition, particularly in non-clinical, community samples.

We analyzed white-matter microstructure and its cognitive correlates in four hundred and sixty five 8-12-year-olds in the urban Cape Town region of South Africa: 192 were prenatally exposed to alcohol and tobacco (PAE + PTE), 73 to tobacco only (PTE), 102 to alcohol only (PAE), and 98 to neither tobacco nor alcohol (CON). White-matter microstructure was quantified via neurite orientation and dendrite dispersion imaging (NODDI). The outcome variables were fractional anisotropy (FA), the neurite density index (NDI), and neurite orientation dispersion index (ODI) for 52 white-matter tracts. Cognition was measured using the Wechsler Intelligence Scale for Children-Fifth Edition (WISC-V).

PAE (but not PTE) was associated with NDI in 35 of 52 white-matter tracts analyzed. There were no false-discovery-rate (FDR) corrected main effects of PAE or PTE on FA or ODI. For 17 of the 35 tracts affected by PAE, the corresponding NDIs were positively associated with scores on the WISC-V Processing Speed Index.

Our previous research of neuroanatomical structure in this cohort suggested that there are stronger effects of PTE on cortical gray matter. In contrast, the present data suggest that PAE may have stronger associations with white-matter microstructure, with such effects potentially manifesting as downstream deficits in processing speed.

Overweight and obesity are characterized by detrimental effects on energy metabolism and an increase in adipose tissue, leading to chronic low-grade inflammation. This metaflammation may contribute to mental symptoms, such as reduced mood and motivation. Inflammatory processes are modulated by the cholinergic anti-inflammatory pathway (CAIP) that can be targeted with vagus nerve stimulation (VNS). However, the effects of non-invasive transcutaneous VNS (tVNS) on inflammation have predominantly been investigated in small feasibility studies in humans. Whereas acute anti-inflammatory effects of tVNS have been reported, no study has investigated the effect of daily home-based application on obesity-related metaflammation to date.

To assess tVNS-induced effects on inflammation, motivation, and mood, we are conducting a single-blind, randomized crossover study in participants with an elevated BMI (target N = 60, 27-35 kg/m², 18-40 y). Participants will self-administer high- (active) and low-intensity (sham) stimulation daily (≥ 30 min) for ~ 14 days each (≥ 7 d washout). We will assess peripheral (cytokine levels, circulating immune cells) and central (diffusion weighted imaging) inflammation, motivation (using an effort allocation task during functional MRI), and mood at baseline and after each stimulation phase. During each stimulation phase, participants will complete daily ecological momentary assessments of mood and food choices. We will use mixed-effects models to evaluate differences in slopes of high- vs. low-intensity tVNS.

This study uses a 14-day home-based tVNS protocol (≥ 30 min daily) accompanied by daily ecologocial momentary assessments to investigate mid-term effects on chronic low-grade inflammation, mood, and motivation. The multimodal study design including a comprehensive panel of peripheral and central inflammatory markers enables the integration of inflammatory and behavioral outcomes and will provide important evidence regarding the efficacy of tVNS in chronic inflammation, based on a study with sufficient statistical power. In addition, the study could provide pathophysiological insights into the link between obesity and symptoms of depression via inflammatory processes. Ultimately, the study will facilitate future research on vagus nerve stimulation to complement the current set of therapies in overweight and obesity.

The study has been preregistered at ClinicalTrials.gov ( https://clinicaltrials.gov/study/NCT06954844 ) on March 21, 2025, Version 1.

Major Depressive Disorder (MDD) is a prevalent mental health condition with significant societal impact. Although prior research has highlighted the brain changes modulated by antidepressant therapy, their efficacy and effectiveness are debated. The low rates of treatment response still existed in the pharmacological therapy of MDD. Exploring an optimal neurological predictor of symptom improvement caused by pharmacotherapy is urgently needed for improving response to treatment. Our purpose is to develop a predictive model for MDD therapy using machine learning techniques based on resting-state fMRI metrics.

A total of 116 MDD patients underwent 3.0T resting-state magnetic resonance image scanning. Demographic data and the 24-item Hamilton Depression Rating Scale (HAMD-24) were collected from all participants. An additional independent cohort of 25 MDD patients was included for external model validation. Based on the reduction rate of HAMD-24 scores at different time points, the patients were divided into an early improvement group, an early response group, and a clinical response group: (1) Early improvement group: HAMD-24 reduction rate ≥ 20% after 1 week of treatment; (2) Early response group: HAMD-24 reduction rate ≥ 25% after 2 weeks of treatment; (3) Clinical response group: HAMD-24 reduction rate ≥ 50% after 4 weeks of treatment. For model construction, LASSO regression was applied for feature selection, integrating identified brain regions, HAMD-24 symptom clusters, and clinical variables. Five machine learning models were established based on features screened by the LASSO regression model to predict treatment response in MDD. All models were trained and assessed using 10-fold cross-validation, the most stable logistic regression model was selected for external validation in a temporally independent cohort (n = 25).

At 1 week, alterations were mainly observed in the frontal and sensorimotor regions. At 2 weeks, differences were found in the opercular, postcentral, orbitofrontal, temporal, and angular areas. At 4 weeks, additional abnormalities appeared in the frontal, occipital, and postcentral cortices. Moreover, right Postcentral gyru was found to be present in the differential brain regions observed at 1 week, 2 weeks, and 4 weeks comparisons. In addition, ten features related to the treatment response were identified using the LASSO regression model, including age, years of education, core depressive symptoms, and several brain region features primarily involving the postcentral gyrus. Among the constructed machine learning models, the Logistic Regression model based on LASSO-selected features (including age, education, core depressive symptoms, and the postcentral gyrus) demonstrated the most stable performance. It achieved an area under the curve (AUC) of 0.801 in the internal validation and maintained robust predictive performance in the independent external validation set, indicating promising generalizability. Following independent external validation, the results showed that the predictive performance of the Logistic regression model on the external validation set yielded an AUC value of 0.643 and an accuracy of 0.60. This indicates that the brain regions involved in the model-namely the right postcentral gyrus, right precentral gyrus, left superior frontal gyrus, left middle occipital gyrus, and the orbital part of the right inferior frontal gyrus-hold promise as exploratory neural correlates for predicting the efficacy of SSRIs in depression.

MDD exhibits early functional alterations in brain regions involved in emotion regulation, cognition, and sensorimotor processing. Integrating these multi-metric neural features with clinical variables via machine learning models suggests potential clinical utility for evaluating and predicting early SSRI treatment outcomes.

Affective disorders often co-occur with behavioral risk factors like smoking, and chronic metabolic disorders like obesity or type 2 diabetes. One possible regulation factor in these comorbidities could be resistin, an adipokine involved in inflammation and insulin resistance. Here, we investigate these factors in depressed patients, in a sex-sensitive approach.

We assessed depression symptoms in a sex-balanced cohort using the Beck Depression Inventory-II and the Male Depression Risk Scale. We further evaluated smoking behavior using self-report and the Fagerström Test for Nicotine Dependence. Resistin levels were measured in blood samples.

Higher resistin levels were associated with increased risk of major depressive disorder (MDD) in a sex- and smoking-dependent manner, with the strongest effect observed in female smokers and ex-smokers. No association was found between resistin and depression severity, while alcohol use emerged as the strongest predictor of masculine depression symptoms.

This study demonstrates that resistin interacts with smoking and sex to increase MDD risk, particularly in female smokers/ex-smokers, highlighting the importance of preventive strategies targeting depression risk in women with comorbid risk behaviors such as smoking.

German Clinical Trials Register ID DRKS00015291, registration date 2020-01-06, retrospectively registered.

Women experience a disproportionate burden of affective and stress-related disorders, with symptom variability shaped by endocrine transitions across the menstrual cycle, pregnancy, postpartum, and menopause. Although psychedelic-assisted therapies are increasingly investigated for these conditions, hormonal state represents a largely unaccounted determinant of variability in exposure, response, and tolerability. This narrative review synthesizes clinical, preclinical, and neuroimaging literature to examine how estradiol and progesterone modulate psychedelic pharmacokinetics, pharmacodynamics, and large-scale brain network dynamics implicated in psychiatric disorders. At the pharmacokinetic level, sex hormones influence gastrointestinal physiology (including gastric pH and transit time), tissue distribution via body composition and fluid balance, and hepatic metabolism through modulation of CYP450 enzymes, particularly CYP3A4 and CYP2D6, which are key pathways for LSD and psilocin biotransformation. Renal elimination plays a comparatively minor role. These processes may collectively alter onset, peak concentration, and systemic exposure across hormonal states, although direct human data for psychedelics remain absent. At the pharmacodynamic level, estradiol and progesterone regulate serotonergic signaling, including dynamic modulation of 5-HT2A receptor density and binding across the menstrual cycle and reproductive lifespan. Platelet, PET, and SPECT studies indicate higher 5-HT2A availability in low-progesterone states and reduced availability in progesterone-dominant or hypoestrogenic states. Beyond 5-HT2A, psychedelics engage broader serotonergic, glutamatergic, and GABAergic systems that converge on prefrontal-limbic, default mode, and salience networks, circuits that are also dysregulated across depression, anxiety, and trauma-related disorders. Hormonal fluctuations may further influence subjective intensity and tolerability, with potential sensitivity peaks in the early-mid follicular and early luteal phases, although direct evidence remains limited. Together, these findings suggest sex hormones as a mechanistically plausible, clinically relevant but under-characterized source of variability in psychedelic treatment response.

Smartphone delivery of contingency management (CM) could overcome barriers to dissemination of this effective treatment for methamphetamine use. We quantified help-seeking intentions for CM and preferences for smartphone versus in-person delivery of CM amongst people who used methamphetamine.

An open web-based cross-sectional survey of 220 Australian residents who had used methamphetamine weekly or more often in the past year was conducted. Help-seeking intentions for CM were compared to other available treatment options using the General Help Seeking Questionnaire. Preferences for three CM models: in-person voucher-based CM, in-person prize-based CM and a smartphone-delivered model of CM, were sought, along with preferences for other aspects of CM delivery.

Help-seeking intentions were similar for CM and other treatment options (82% vs. 76%-81% likely to seek help). Smartphone delivery of CM was preferred by 43% of participants (cf. 27% for in-person voucher CM and 30% for in-person prize draw CM). Overall, other preferences were for a fixed (predictable) reinforcement schedule (78%), cash (53%) or gift card (40%) rewards rather than merchandise (7%), and adjunctive support (78%) - mostly counselling (50%) and withdrawal management (43%). Participants wanted a median minimum payment of $69 (interquartile range $58-$77) to start CM, and a minimal median potential earnings of $4000 (interquartile range $3600-$4500) over a 12-week program.

We found strong potential demand for smartphone-delivered CM, although adjunctive counselling and withdrawal management would be needed and incentives may need to be larger than those used in conventional CM models.