IntroductionExtremely low-frequency electromagnetic fields (ELF-EMFs) are ubiquitous in our daily life. They may have an impact not only on physical health but also on mental health.ObjectivesTo assess the impact of occupational exposure to the ELF-EMFs on sleep quality, depression, anxiety and stress among workers at the Tunisian Electricity and Gas Company (TEGC).MethodsThis was a cross-sectional study. The study population included two groups: an exposed group (EG), consisting of power plant employees, and a non-exposed group (NEG), consisting of administrative workers. Exposure to ELF-EMFs was assessed via spot measurements using a magnetometer. Sleep quality, depression, anxiety and stress were assessed by the French versions of the Pittsburgh Sleep Quality Index (PSQI) and the Depression, Anxiety and Stress Scale (DASS-21).ResultsSeventy-seven participants in the EG and 88 participants in the NEG were included in the study. The median value of the ELF-EMFs was 5.86 μT at the power plant [min 0.1, max 40.34 μT]. According to the PSQI global score, 64.9% of the EG had poor sleep quality versus 29.5% of the NEG. Depression was registered in 24.7% of EG and in 3.4% of NEG. Anxiety was noted in 23.4% of the EG and in none of the NEG. Stress was found in 46.8% of the EG and none of the NEG. After multivariate analysis, ELF-EMF exposure was significantly associated with poor sleep quality and depression.ConclusionThe present study revealed that ELF-EMFs can affect sleep and mental health. Further studies are needed to explain the mechanism involved.
Mental health in the time of polycrisis: geopolitical determinants and modern psychiatry
Psychiatry is increasingly being practised in environments affected by geopolitical instabilities, including economic fragmentation, democratic backsliding, and widening inequities. The confluence of these phenomena contributes to what has been described as a contemporary polycrisis, encompassing synchronous disruptions that reinforce one another and threaten collective wellbeing. Nevertheless, psychiatric research and clinical work have generally remained oriented towards immediate determinants and risk factors, overlooking the macro-level political and institutional dynamics that can condition stressor exposure and mental health disparities. Amidst, interconnected crises, this paper advances geopsychiatry as a framework for understanding how distal geopolitical determinants translate into psychiatric vulnerabilities across communities and societies. Focussing on armed conflicts, climate change, and forced migration as emblematic domains of polycrisis, it highlights how these compounding phenomena are generating direct mental health burdens and may amplify harms via secondary pathways. Moreover, it contends that the psychiatric consequences of polycrisis are unlikely to be ameliorated through patient-centred interventions alone, but also require innovative approaches responsive to structural inequalities and material forces that transcend borders. In this context, work from geopsychiatry can offer important implications for modern psychiatry, highlighting a need for a more globally representative evidence base, potential clinical adaptations, and policy engagement that better attends to the geopolitical determinants of mental health.
Transcriptomic and phenotypic convergence of neurodevelopmental disorder risk genes in vitro and in vivo
Nature Neuroscience, Published online: 24 April 2026; doi:10.1038/s41593-026-02247-7
By studying 23 neurodevelopmental disorder genes across model systems and brain cell types, the authors uncovered shared downstream effects that converge on synaptic biology, epigenetic regulation and mitochondrial function.
Building a Science-Driven Business: How National Institutes of Health Funding Enabled an Evidence-Based Approach to Maternal Mental Health Innovation
The digital mental health (DMH) industry has grown drastically over the last decade; yet, many DMH products have failed to demonstrate meaningful clinical outcomes, in large part due to lack of scientific evidence. This viewpoint paper highlights an example of how early-stage DMH companies can prioritize science as a strategic advantage. We discuss Moment for Parents, an artificial intelligence–driven maternal mental health app built entirely with support from the National Institutes of Health (NIH) Small Business Innovation Research (SBIR) program. We illustrate the advantages and challenges of building a science-backed product with federal funding. Benefits include credible evidence generation, independence in product development, and enhanced market differentiation. We also discuss the challenges of navigating the SBIR ecosystem, including grant writing and administrative demands, and aligning business objectives with federal research priorities. By showcasing both the promise and complexity of SBIR funding, this viewpoint paper offers actionable insights for founders and chief executive officers who aim to prioritize science in the DMH space.
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Expected Competencies and Personal Attributes of Digital Health Navigators to Support Digital Mental Health Care: Focus Group and Interview Study With Patients and Health Care Professionals
Background: Digital mental health apps (DMHAs), and in particular digital therapeutics (DTx), offer promising opportunities to support mental health care. However, their effective use in outpatient settings in Germany remains limited. To overcome this gap, the role of digital health navigators (DHNs) has been introduced. DHNs are trained individuals who support patients and health care professionals in selecting, using, and integrating DMHAs into care. Despite increasing interest in this role, there is limited evidence on the competencies, knowledge, and personal attributes required for DHNs to work effectively in mental health settings. Objective: The study aims to explore the expected competencies, knowledge areas, and personal attributes that DHNs need to effectively support the implementation and use of DTx in outpatient mental health care. Methods: As part of the prestudy of the Digital Navigators for Acceptance and Competence Development with Mental Health Apps (DigiNavi) study, a qualitative study was conducted involving 35 participants (7 general practitioners, 8 patients in general practice, 11 outpatient psychiatrists/psychologists, and 9 patients in psychiatric outpatient clinics) from different general practices and psychiatric outpatient clinics in Germany. A total of 17 semistructured interviews and 4 focus groups were conducted to explore expectations of DHNs. Data were analyzed using qualitative content analysis. Results: Participants emphasized that DHNs should combine strong interpersonal skills (empathy, patience, and sensitive communication) with technical and basic clinical competencies. Most favored DHNs as integrated clinical team members (eg, medical assistants), citing their existing patient relationships, but noted time and training constraints. Key expectations included the ability to support patients with DTx use, adapt communication to individual needs, and convey data privacy information clearly. Foundational knowledge of mental health conditions and sensitivity to crises were considered important for identifying warning signs and escalating concerns. While DHNs were seen as essential intermediaries between patients, health care professionals, and DTx, participants highlighted the necessity for clearly defined roles, structured training, and realistic expectations to prevent role overload and enable sustainable implementation in outpatient mental health care. Conclusions: DHNs require a specialized skill set that bridges clinical understanding, digital expertise, and interpersonal competence. Our results lay the groundwork for developing training curricula and implementation strategies that align with real-world expectations for the DHN role. Defining these core competencies is essential for supporting the sustainable and effective integration of DMHAs into mental health care. Trial Registration: German Clinical Trials Register DRKS00034327; https://drks.de/search/en/trial/DRKS00034327 and ClinicalTrials.gov NCT06575582; https://clinicaltrials.gov/study/NCT06575582 International Registered Report Identifier (IRRID): RR2-10.2196/67655
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Appearance-related attentional bias is associated with dysmorphic appearance concern in individuals with jaw deformity: an eye-tracking study
IntroductionAppearance-related attentional bias has been body dysmorphic disorder (BDD) and in individuals with elevated concern; however, it remains unclear whether similar attentional are observed in individuals with objectively verifiable jaw deformities. Appearance-related attentional bias has been observed across clinical populations with elevated appearance concern, highlighting its relevance as a general perceptual-cognitive mechanism associated with appearance monitoring.MethodsSixty patients with jaw deformities and 40 age-matched completed the Body Image Concern Inventory (BICI), a validated measure of dysmorphic appearance concern, and underwent eye-while viewing photographs of self and others’ faces. Gaze duration analyzed across six regions (forehead, eyes, nose, mouth, jaw, and Associations between gaze patterns and BICI scores were examined.ResultsPatients exhibited significantly higher BICI scores than controls. both self- and other-face viewing, patients showed prolonged gaze region. Furthermore, exploratory correlation analyses showed that gaze positively associated with BICI scores within the patient group and other’s faces.DiscussionIndividuals with jaw deformities exhibit elevated dysmorphic appearance concern and increased attention to perceptually salient features. These findings characterize appearance-related attentional individuals with jaw deformities and provide insight into perceptual-processes associated with dysmorphic appearance concern.
Auditory processing and communication in autism: exploring verbal abilities and vocal affective cues
This study examined the role of auditory processing in autism spectrum disorder, focusing on its association with verbal and non-verbal vocal communication skills in children and adolescents. A total of 97 English-speaking autistic participants (ages 7.9–17.4 years, mean = 12.3) and 44 neurotypical peers (ages 8.4-16.8, mean= 12.3) completed assessments of auditory processing and communication skills. We analyzed the relationships between scores on the SCAN-3 Tests for Auditory Processing Disorders time-compressed sentences, auditory figure-ground (+ 8dB), gap detection, and competing words-free recall subtests, the Clinical Evaluation of Language Fundamentals-Fifth Edition (CELF-5) expressive and receptive language indices, the Goldman-Fristoe Test of Articulation-3rd Edition (GFTA-3) Sounds-In-Words subtest, and the Diagnostic Analysis of Nonverbal Accuracy-2nd Edition (DANVA-2) paralanguage subtests. Measures of auditory processing were associated with both verbal and non-verbal communication skills in the autistic participants. Specifically, we found that SCAN-3 time-compressed sentence and gap detection scores were associated with expressive and receptive language skills, receptive vocabulary scores, and ability to recognize vocal emotional cues. Gap detection abilities additionally correlated positively with expressive and receptive language skills, while auditory figure-ground task performance was related to articulation. In conclusion, this study suggests specific aspects of auditory processing may be important for development of specific aspects of auditory communication skills in children on the spectrum. Specifically, spectral aspects of auditory processing abilities were associated with articulation accuracy whereas temporal components of auditory processing may impact broader verbal and nonverbal communication skills. Further research is needed to better understand the underlying mechanisms of these associations and potential directions of causality to inform development of interventions that target auditory processing and auditory communication skills in ASD.
Cyclothymic and anxious affective temperament in perinatal depression: findings from an exploratory cross-sectional study
IntroductionThe perinatal period represents a vulnerable period in which women may experience high psychic distress due to psychological, biological and social changes. The prevalence of perinatal depression (PND) is estimated around 15%-20% during pregnancy and 16%-18% after childbirth. Although several risk factors have been investigated in the PND development, few studies explored the role of affective temperaments, well known to exert a role in any mood disorders. The aim of our study was to explore which is the most represented affective temperamental profile in PND as well as which is its role in the development and severity of depressive symptoms during perinatal period.MethodsAll pregnant women admitted at the Perinatal Mental Health Outpatient Service, Unit of Clinical Psychiatry, University Hospital of Marche, Polytechnic University of Marche, Ancona, Italy, between April 2021 and July 2025, were screened for PND through Edinburgh Postnatal Depression Scale (EPDS) and a semi-structured clinical interview (SCID-5-CV). Temperament Evaluation of Memphis, Pisa, Paris and San Diego (TEMPS-M) was administered to all pregnant women. ResultsThe PND prevalence was 33.1%. PND was significantly associated with higher cyclothymic (B = 0.356, p = 0.001) and anxious TEMPS-M scores (B = 0.247, p = 0.026) and a positive psychiatric history (B = 5.245, p < 0.001) (R = 0.6, R2 = 0.36, F(3,129) = 24.189, p < 0.001). Logistic regression indicated that cyclothymic (Exp(B)=1.118, p=0.008), hyperthymic (Exp(B)=0.911, p=0.049), anxious temperaments (Exp(B)=1.109, p=0.029), presence of medical comorbidities (Exp(B)=0.224, p=0.003) and psychiatric history (Exp(B)=5.144, p=0.001) were independent predictors of PND.DiscussionAffective temperaments, particularly cyclothymic and anxious profiles, and prior psychiatric history are predictors of perinatal depression. Incorporating temperament assessment alongside standard screening tools such as the EPDS may improve early identification of women at risk, supporting tailored preventive and therapeutic strategies.
Autism Spectrum Disorder (ASD) Neurodevelopmental Disorder With Issues Social Behavior, Communication Issues, GI Dysfunction. Study is Multimodal Interventions Targeting These Pathways With cSVF, Stored MSCs, FMT and Diet Modification. Role of Autoimmunity, Gut-brain Issues, & Issues Examined.
Conditions: Autism Spectrum Disorder; Autism
Interventions: Procedure: Autologous MSC isolation, concentration, and cryopreservation of adult mesenchymal stem cells; Dietary Supplement: Fecal Biomicrome Transplantation (FMT); Dietary Supplement: Ketogenic RESET Diet Protocol; Behavioral: Trial combines use of cSVF and MSCs (cryopres) with diet modification and FMT (fecal material Transplant in adult ASD patients; Dietary Supplement: FMT (Fecal Material Tranplantation); Biological: Use autologous cSVF + MSCs (cryo) for management of autoimmune component, anti-inflammatory and immune modulation
Sponsors: Black Tie Medical, Inc.
Enrolling by invitation
Interventions: Procedure: Autologous MSC isolation, concentration, and cryopreservation of adult mesenchymal stem cells; Dietary Supplement: Fecal Biomicrome Transplantation (FMT); Dietary Supplement: Ketogenic RESET Diet Protocol; Behavioral: Trial combines use of cSVF and MSCs (cryopres) with diet modification and FMT (fecal material Transplant in adult ASD patients; Dietary Supplement: FMT (Fecal Material Tranplantation); Biological: Use autologous cSVF + MSCs (cryo) for management of autoimmune component, anti-inflammatory and immune modulation
Sponsors: Black Tie Medical, Inc.
Enrolling by invitation
Effectiveness of a Fully Automated Mobile Therapeutic Versus a General Chatbot in Reducing Depression and Anxiety and Improving Well-Being: Feasibility Randomized Controlled Trial
Background: Given the increasing prevalence of depression and anxiety disorders and enduring barriers to care, there is a critical need for alternative treatment options. Generative artificial intelligence (AI) chatbots show promise for increasing access to mental health care, though more direct research is needed to establish their efficacy. Objective: This pilot study aimed to test the efficacy of a generative mental health chatbot rooted in solution-focused therapy compared to the general-purpose ChatGPT and an assessment-only control (AOC) group on depression, anxiety, and well-being. Methods: A total of 185 English-speaking adults were recruited online and randomly assigned to one of three groups: AI therapy, ChatGPT, or AOC. Of these, 147 eligible participants filled out a pretreatment assessment. Over a 3-week period, the AI therapy group (n=44) was instructed to complete 3 structured, fully automated app-based sessions per week (9 total), while the ChatGPT group (n=60) was instructed to engage in 9 unstructured conversations with ChatGPT (GPT-4o–based models). The control group (n=43) received no intervention. In the AI therapy group, 39% (n=17) completed all sessions, as did 62% (n=38) of those in the ChatGPT group. Primary outcome measures, self-assessed online at baseline and postintervention, included the Patient Health Questionnaire-9 (PHQ-9), Overall Depression Severity and Impairment Scale (ODSIS) (depression), 7-item Generalized Anxiety Disorder Scale (anxiety), and World Health Organization Well-Being Index (5-item version) (well-being). Linear mixed effects models were used for data analysis. Results: Compared to AOC, both the AI therapy group (=−0.47; =.01) and the ChatGPT group (=−0.44; =.02) demonstrated significant reductions in depression scores measured by PHQ-9. The AI therapy group showed nonsignificant reductions in anxiety (=−0.37; =.11) and ODSIS depression scores (=−0.25; =.22) and an increase in well-being (=0.12; =.53) compared to AOC. Similarly, a nonsignificant reduction in anxiety (=−0.27; =.22) and ODSIS depression scores (=−0.12; =.53) and an increase in well-being (=0.20; =.29) were observed in the ChatGPT group compared to AOC. The AI therapy group did not significantly outperform the ChatGPT group on any outcomes (PHQ-9: =−0.19; =0.03; =.87; 7-item Generalized Anxiety Disorder Scale: =−0.57; =−0.11; =.62; ODSIS: =−0.59; =−0.13; =.50; and WHO: =−0.38; =−0.07; =.69). Conclusions: Both the structured generative AI chatbot and ChatGPT showed a significant reduction in depression scores compared to the control group. No significant effects were observed across other outcomes, although descriptive trends indicated improvements in anxiety. While the AI therapy group showed descriptively better outcomes for depression and anxiety, differences between groups were not significant. A larger sample and longer intervention may be needed for the emerging trends to yield clinically meaningful effect sizes. Trial Registration: OSF Registries osf.io/r76ef;
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