Psychiatry

Understanding the Experiences of Young People Living With Haemophilia-A UK Social Media Study.

INTRODUCTION: Haemophilia is a rare congenital bleeding disorder that presents significant physical and psychological challenges, especially for young people, with limited real-world evidence about their experiences in the UK. AIM: This study sought to understand the perspectives and challenges of young people living with haemophilia in the UK by analysing social media conversations. METHODS: A re...

Graph theory identifies altered prefrontal microcircuit organization in Shank3 mice, a mouse Model of autism.

Graph theory provides unique tools to assess complex networks. It has been previously used with functional magnetic resonance imaging (fMRI) datasets to quantify macroscopic-scale connections among different brain regions, readily capturing brain network changes in subjects with Alzheimer's disease. Here, we apply graph theory to miniscope calcium imaging data recorded from the prefrontal cortex o...

Health Care Professionals' Perspectives on Conversational Mental Health Chatbots: Protocol for a Systematic Review.

BACKGROUND: Mental health disorders (MHDs) represent a growing global challenge and pose a significant risk to public health. Alongside developments i...

Predicting six-year depression trajectories in adulthood: A super learner ensemble approach using the Add Health dataset.

BACKGROUND: Depression is a leading global cause of disability, and much focus has been directed toward identifying population-wide predictors of adul...

A multi-metric profiling of OFC subregion connectivity identifies system-level alterations in major depressive disorder.

BACKGROUND: Orbitofrontal cortex (OFC) dysfunction contributes to major depressive disorder (MDD), particularly through disrupted network interactions...

Data diversity vs. model complexity in the prediction of pediatric bipolar disorder: Evidence from academic and community clinical samples.

Pediatric bipolar disorder is challenging to diagnose accurately due to symptom heterogeneity. More standardized and data-driven approaches are needed...

A Novel EEG-Based Topographic Brain Map-Driven Deep Learning Method for Autism Spectrum Disorder Detection in Children.

Autism Spectrum Disorder (ASD) is a neurological and developmental condition that affects children's social and cognitive skills, leading to repetitiv...

Digital Markers for Passive Remote Monitoring of Bipolar Disorder: Systematic Review.

BACKGROUND: Bipolar disorder (BD) features episodic shifts among mania, hypomania, depression, mixed states, and euthymia. Timely detection of mood tr...

Prediction in routine care: a naturalistic comparison of clinicians and a machine learning model for patients receiving treatment for generalised anxiety disorder.

OBJECTIVE: Approximately half of patients with generalised anxiety disorder (GAD) do not recover following psychological treatment. Accurate prognosti...

Exploring Applications of AI in the Crisis Line Sector: Protocol for a Scoping Review.

BACKGROUND: Crisis helplines are a vital component of a robust public health approach to suicide prevention as they are often free, accessible, and pr...

Using AI to determine optimal cost-effective diagnostic pathways for chronic breathlessness.

Breathlessness is a common symptom in clinical practice, yet evidence for cost-effective strategies to diagnose the underlying health conditions causi...

The case for near-term artificial intelligence risks to be considered a public health problem.

AI is being rapidly integrated across nearly all sectors, including within healthcare and health systems. The prevailing dominant narrative frames AI'...

Bridging fibrosis and function: an integrated mechanistic and therapeutic framework for Peyronie's disease and related erectile dysfunction.

INTRODUCTION: Peyronie's disease (PD) and erectile dysfunction (ED) are prevalent and frequently coexisting male sexual disorders that share endotheli...

Disseminating brief video-based mental health interventions on Instagram: Reach, engagement, and real-world implementation.

OBJECTIVE: Childhood trauma (CT) profoundly impacts mental health. Negative attitudes and stigma surrounding mental health and trauma often deter surv...

Predicting US Army Soldier Suicide-Related Behaviors at the Time of Periodic Health Assessments.

IMPORTANCE: Suicide is the leading cause of death among active-duty US Army soldiers. Evidence-based preventive interventions exist but need to be tar...

Correlates of muscle strengthening exercise in adolescents: A machine learning based analysis.

BACKGROUND: Muscle-strengthening exercise (MSE) is a critical component of adolescent health, yet its correlates remain less understood than those of ...

Scalable prediction of suicidal risk in university students: a three steps machine learning approach in university settings.

Suicide is a leading cause of death among young adults, with university students representing a particularly vulnerable subgroup. Although prevention ...

Artificial Intelligence in Psychiatry: Five Decades of Progress and Persistent Translational Challenges.

OBJECTIVE: This review examined the historical development of artificial intelligence (AI) in psychiatry from 1972 to 2025 and identified persistent b...

The use of large language models in automated depression detection.

BACKGROUND: Large language models have been evaluated on many healthcare tasks, including depression screening. However, it is unclear whether estimat...

Multidimensional thermal anomalies and suicide mortality: Acute risks, delayed effects, and the operational utility of early warning systems.

The rising frequency of extreme temperature events due to climate change presents an urgent public health challenge, particularly regarding their comp...