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.

Rural communities face persistent health disparities and ongoing healthcare workforce shortages, especially in nursing. Despite national attention on strengthening the rural nursing pipeline, little synthesis exists on how rural health is integrated into nursing curricula. This narrative review provides an overview of the current landscape of rural health content within Bachelor of Science in Nursing (BSN) programs in the United States.

A comprehensive search of peer-reviewed and institutional sources was conducted using CINAHL, PubMed, ERIC, Scopus, and the Commission on Collegiate Nursing Education (CCNE) database. U.S.-based BSN programs with explicit rural health training or learning components were included. Data were extracted and synthesized using a narrative approach to identify recurring themes and institutional variations.

Sixty-three nursing programs met our inclusion criteria; however, many had various courses or initiatives for a total of 93 courses/initiatives. The majority of courses/initiatives were community-focused (n = 59), followed by underserved communities (n = 37). Few BSN programs had explicit rural health content (n = 20) that incorporated rural-specific courses or structured clinical experiences addressing rural practice.

Rural health remains underrepresented in BSN curricula. Findings underscore the need for systematic integration of rural concepts through coursework, simulation, and partnerships with rural healthcare facilities to strengthen the rural nursing workforce.

Criteria used by healthcare practitioners (HCPs) to set goals and assess when treatment was successful, met its goals, and/or should be ended has not been studied for men seeking treatment of premature ejaculation (PE). This study describes HCPs' priorities regarding treatment goals, assessment of success, and decisions to end treatment for men with PE, and examined whether HCP characteristics influenced these decisions. Overall, 240 medical and mental health professionals completed an online or in-person survey on treatment objectives and termination criteria in PE care. Results indicated that, according to HCP reports, patients' primary treatment goal was increased ejaculation latency, whereas HCPs themselves most strongly prioritized reducing distress and improving coping. For ejaculation latency, 44.7% of HCPs defined success according to the client's preferred level. Beyond latency, reduced stress and improved coping received the highest success rating, and 76.9% of HCPs reported moderate-to-high success in treating men with PE. Treatment termination was most often linked to increased sexual satisfaction and reduced distress. Professional training, identity, and PE treatment experience influenced goals, success assessment, and termination decisions. PE treatment requires explicit shared decision-making to align clinicians' biopsychosocial priorities with patients' symptom-specific expectations.

Killing in war is associated with multiple adverse outcomes, including posttraumatic stress disorder (PTSD), moral injury (MI), and suicide. Impact of killing (IOK) is an individual psychotherapy developed to treat the mental health impact of killing in war.

To determine whether IOK improves veterans' mental health symptoms and functioning at posttreatment and 6-month follow-up.

This multisite, superiority, randomized clinical trial was conducted from August 2018 to November 2023. Participants were veterans randomized to IOK or present-centered therapy (PCT). Diagnostic assessments were administered at screening and posttreatment by blinded evaluators. Self-report measures were collected at baseline, midtreatment, posttreatment, and 6-month follow-up at 3 Veterans Affairs health care systems in California, New York, and North Carolina via in-person and remote appointments. Participants were a sample of veterans with PTSD who reported distress related to killing in war or feeling responsible for the death of others. Primary exclusion criteria were a psychotic disorder diagnosis, moderate or severe alcohol or substance use disorder, untreated mania, or recent suicidal or homicidal behavior.

IOK, an individual psychotherapy that focuses on killing cognitions, acceptance, self-forgiveness, and moral repair, and PCT, which addresses current functional issues due to moral distress with related problem-solving.

The primary outcomes were psychosocial functioning assessed by the World Health Organization Quality of Life Scale-Brief (WHOQOL-BREF) and the Sheehan Disability Scale (SDS). Secondary outcomes included PTSD and MI measures.

A total of 100 individuals (98 male; mean [SD] age, 48.4 [13.7] years) were randomized. Linear mixed models revealed that psychosocial functioning improved for both groups at posttreatment (WHOQOL-BREF and SDS) and the IOK group had a significantly greater improvement than those in PCT on the SDS (-3.50; z score, -2.42; Cohen d, -0.51; P = .02; 95% CI, -5.65 to -0.59). There was no significant difference between treatments on the WHOQOL-BREF at posttreatment. Compared to PCT participants, IOK participants also reported significantly greater improvement in PTSD and MI symptoms.

Individuals in both treatments improved on measures of psychosocial functioning (WHOQOL-BREF and SDS). Those who received IOK evidenced greater improvements in work or school, social life, and home or family life (as measured by the SDS) compared to those in PCT at posttreatment. IOK participants also reported improved PTSD and MI symptoms. The findings suggest that IOK may improve several outcomes for those who experience distress due to killing in war.

ClinicalTrials.gov Identifier: NCT03764033.

Psychiatric diagnosis, prognosis, and management currently rely on subjective assessments. There is hope that peripheral (eg, blood) biomarkers or combinations thereof could transform clinical care by bringing objectivity and patient stratification while being accessible and scalable. The time is ripe to find molecular biomarkers: omics technologies can now profile hundreds to millions of molecules across thousands of individuals, while funders recognize the clinical need and have responded with ambition. However, this enthusiasm will likely be finite, and there is no guarantee that a biomarker will be found.

It is essential to design and perform studies that are most likely to deliver robust peripheral biomarkers with a clear path to translation. This narrative review provides recommendations to optimize peripheral psychiatric biomarker study design. The review first introduces conceptual grounding illustrating how biomarker study design must triangulate clinical utility, technological fit, and biological plausibility. Biomarker studies should optimize for signal-to-noise ratio via careful phenotype selection and minimization of biological and technical noise. However, feasibility issues-in collecting sufficient data on enough representative participants for an adequately powered and clinically translatable analysis-constrain study design.

Study design is the single most influential and controllable factor for future psychiatric biomarker discovery. This is because many years elapse between study design and data analysis (particularly for biobanked samples held for future use) and because no technological or analytical advances can answer questions that the dataset was not designed for, or overcome unmeasured confounding. Moreover, the direction of biomarker research is constrained by what is possible to design. Many clinical questions and contexts need biomarkers, but realistically, some are better primed for biomarker discovery than others. In this generation of peripheral psychiatric biomarker studies, designs most likely to demonstrate proof-of-principle should be prioritized to ensure the long-term sustainability of the field.