The International Journal of Psychiatry in Medicine

Few studies have simultaneously addressed self-management of cardiovascular disease and mental health in Black women at cardiovascular risk. This 24-week pilot prospective crossover randomized-controlled trial tested TEAM-Red, a 5-session, biweekly, nurse and peer-educator remotely delivered group self-management intervention for young Black women, compared to an enhanced waitlist control (eWL) group.

The TEAM-Red intervention, adapted from an evidence-based program and culturally tailored based on stakeholder input, enrolled 50 depressed Black women ages 18-49 with at least one risk factor for hypertension. Participants were randomized to TEAM-Red (n = 25) or eWL (n = 24) and assessed at screening, baseline, 12 weeks, and 24 weeks. Those in eWL crossed over to receive TEAM-Red at 12 weeks and all participants were followed to 24 weeks. The primary goal was acceptability and feasibility. The primary outcome was change in depression severity from baseline to 12 weeks assessed by the 9-item Patient Health Questionnaire (PHQ-9). Secondary outcomes were perceived stress, mental health quality of life, diet quality, energy expended, social support, hypertension knowledge, and alcohol use.

Among women depressed at baseline (N = 31), TEAM-Red participants had 17.1 times higher odds of depression remission at week 12 (OR = 17.14, 95% CI: 1.78, 164.97; P = .014). At 12 weeks, TEAM-Red participants also showed significantly lower PHQ-9 scores (P = .042), improved diet quality (P = .025), higher mental health quality of life (P = .032), and reduced perceived stress (P = .038) compared to those in the eWL. Treatment satisfaction and engagement in the intervention group was high (89% found it useful), with 22% attrition.

The TEAM-Red intervention demonstrated significant clinical benefits with a 17-fold higher odds of depression remission and meaningful improvements in mental health quality of life, diet quality, and perceived stress compared to controls. Despite limitations including small sample size and short follow-up period, this culturally tailored intervention showed promise for reducing cardiovascular risk and improving mental health outcomes in young Black women at risk for hypertension. These results need replication in a larger sample as part of a fully powered trial.

The present study investigated ADHD comorbidity and clinical features, including hyperfocus, in women with depressive or anxiety disorders, addressing the diagnostic complexities arising from symptom overlap and the underdiagnosis of ADHD in this population.

Female patients from outpatient psychiatric clinics (n = 170) were assessed using the Beck Depression Inventory and Beck Anxiety Inventory. Participants meeting threshold scores underwent further evaluation with the Wender-Utah Rating Scale, Hyperfocus Scale and Adult ADHD DSM-IV-Based Diagnostic Screening Scale, complemented by structured clinical interviews.

ADHD was identified in 19.6% (n = 33) of patients, 45.5% (n = 15) of whom were previously undiagnosed. ADHD was most prevalent in patients with social anxiety disorder (46.4%) and double depression (40.6%). In non-ADHD participants, (a) attention deficit scores correlated positively with depression (r = 0.236, p = 0.005), (b) hyperactivity with anxiety (r = 0.187, p = 0.029), and (c) hyperfocus with ADHD scores (r = 0.434, p < 0.001; r = 0.292, p = 0.001); no such correlations were seen in the ADHD group.

ADHD is common and frequently overlooked in women presenting with depression or anxiety. While hyperfocus is a relevant clinical feature, it is not diagnostic alone and correlates with ADHD symptoms in the absence of ADHD. Clinicians should conduct thorough screenings for ADHD and carefully interpret hyperfocus within the broader clinical context to ensure accurate diagnosis and timely, appropriate treatment.

Breast cancer patients undergoing radiotherapy frequently experience psychological distress that negatively impacts treatment outcomes and quality of life. Evidence for structured psychological interventions during radiotherapy remains limited. This study evaluated the longitudinal effects of a structured education plus entertainment therapy intervention on anxiety, depression, and quality of life among women with breast cancer in mainland China during and after radiotherapy using linear mixed models analyses.

This single-center, parallel-group, assessor-blinded randomized controlled trial enrolled 280 female breast cancer patients (aged 18-75 years) receiving adjuvant radiotherapy. Participants were randomized 1:1 to intervention (structured education plus entertainment therapy) or a control group (standard care). Primary outcomes were trajectories of change on the Self-rated Anxiety Scale (SAS) and Self-rated Depression Scale (SDS) from baseline (T0) to mid-radiotherapy (T1), to the end of radiotherapy (T2), and 1, 3, and 6 months post-radiotherapy (T3-T5). Secondary outcomes included quality of life (EORTC QLQ-C30/BR23 domains), acute toxicity (CTCAE v5.0), and adherence. Linear mixed models with group × time interaction assessed intervention effects.

Of 280 randomized patients, 252 (90%) completed the T5 assessment. The Of 280 randomized patients, 252 (90%) completed the T5 assessment. The intervention group showed significantly improved SAS trajectory (group × time interaction β = −1.82, 95% CI = −3.14 to 0.50, P = 0.007) with estimated marginal mean difference of −3.45 points at T5. SDS trajectory improvement was marginally significant (β = −1.56, 95% CI = −3.12 to 0.01, P = 0.051). Minimal clinically important difference Of 280 randomized patients, 252 (90%) completed the T5 assessment. The intervention group showed significantly improved SAS trajectory (group × time interaction β = −1.82, 95% CI = −3.14 to 0.50, P = 0.007) with estimated marginal mean difference of −3.45 points at T5. SDS trajectory improvement was marginally significant (β = −1.56, 95% CI = −3.12 to 0.01, P = 0.051), indicating a minimal clinically important difference.

A structured education plus entertainment therapy intervention demonstrated small-to-moderate clinically meaningful improvements in anxiety, depression, and quality of life during radiotherapy, with effects persisting through 6- months of follow-up. Inclusion of this intervention as part of routine radiotherapy care of breast cancer patients in China appears feasible and effective.

This study explored health professionals' experiences of using Artificial Intelligence (AI) in healthcare with a particular focus on the potential benefits and challenges of using AI in clinical practice.

Using a qualitative research design, data were collected through in-depth interviews with a convenience sample of twenty-two health professionals from various medical specialties in northern Punjab, Pakistan. Thematic analysis identified recurring themes on AI adoption.

Health professionals recognized AI as a psychosocial modifier which increased efficiency and overall professional well-being. Participants expressed that AI should function as a supportive tool rather than a replacement for human judgment, empathy, and patient interaction. They also acknowledged a hesitation to incorporate AI due to a distrust of its accuracy, and also indicated that institutions have been slow to adopt AI due to ethical, technical, and institutional challenges.

AI adoption is increasingly reshaping clinical practice, but sustainable integration in Pakistan requires balancing innovation with ethical safeguards, trust, and empathy.

With the rapid adoption of artificial intelligence (AI) technologies by adolescents, the impact on their mental health is of critical concern. This article examines the emerging phenomenon of AI-related psychosis, which can be defined as new-onset or exacerbated psychotic experiences associated with generative AI platforms, such as chatbots, avatars, and virtual agents like Siri and Alexa.

Narrative review and perspective.

Adolescents are particularly susceptible due to ongoing neurodevelopmental immaturity, including underdeveloped prefrontal regulatory circuits and heightened limbic system reactivity, which may impair emotional regulation and reality testing. Combined with extensive digital engagement, these factors increase vulnerability to psychotic experiences in response to AI interactions. There are behavioral risk factors, online habits, and neurobiological susceptibilities that may predispose adolescents to such experiences. In addition, implications are outlined for healthcare providers, including an emphasis on proactive screening, digital literacy education, and early intervention strategies. Clinical approaches to recognize and manage AI-related mental health risks in adolescents are also proposed.

A multidisciplinary response by clinicians, educators, developers, and policymakers is needed to guide ethical AI design and safeguard the well-being of adolescents in today's digital environment.

This review systematizes knowledge about the use of artificial intelligence (AI) in neurobiological research of mental disorders and assesses its potential in identifying their causes.

A qualitative synthesis of scientific literature from the Scopus and Web of Science databases for 2020-2024 was conducted. A total of 50 sources were identified, including papers describing the use of AI in the analysis of neuroimaging, biomarkers, cognitive impairment, and genetics data. A thematic encoding was used to analyze methods, accuracy, and limitations.

Machine learning algorithms have accelerated the processing of large amounts of data, including magnetic resonance imaging, electroencephalogram, and genomic profiles, which has revealed new biomarkers and neural patterns associated with depression and schizophrenia. However, AI technologies face several limitations: low specificity, high computational complexity, and problems with reproducibility of results.

The integration of AI with neuroscience has significantly advanced the understanding of the etiology of mental disorders, revealing the complex relationships between genetic, neural, and behavioral factors. The practical significance of the research lies in the potential of AI to create personalized approaches to the diagnosis and treatment of mental disorders. This can improve the quality of life of patients and reduce the burden on healthcare systems.

To describe a case of probable autoimmune encephalitis initially misdiagnosed as a primary psychiatric disorder.

Review and description of probable autoimmune encephalitis presenting with catatonia in a young woman 6 months post-partum.

A young woman was initially admitted to an outside hospital and was diagnosed with a primary psychiatric condition. She was referred for psychiatric inpatient care, but was denied admission due to labile hypertension. After admission to the medical service (and ICU), she responded to a lorazepam challenge, and made a complete recovery after several weeks of combined treatment with IV methylprednisolone, IV immunoglobulin (IVIG), and rituximab, and was discharged with a diagnosis of autoimmune encephalitis.

Autoimmune encephalitis should be suspected in patients presenting with labile vital signs, family history of autoimmunity, and new psychosis without a prior history of psychiatric problems. Other important diagnostic considerations include neuroleptic malignant syndrome, substance-induced psychosis, or catatonia secondary to post-infectious immune-mediated encephalitis. While a first psychotic break should always be considered, the presence of autonomic instability, catatonia, seizures, or dyskinesias in a young woman postpartum should prompt a thorough medical and neurological work-up.