Psychiatry

Peripheral Metabolic–Redox Signaling as a Core Mechanism of Major Depressive Disorder: Evidence From Deep Metabolomic Phenotyping

Major depressive disorder (MDD) is a neuro-immune, oxidative, and nitrosative stress (NIMETOX) disorder, in which peripheral immune-redox pathways intersect with metabolic networks leading to neurotoxicity within the limbic-prefrontal affective circuits. Comprehensive metabolomics analysis in well-phenotyped patients is vital to elucidate their metabolic profile. To identify metabolic abnormalitie...

Natural language markers of drug context encoding track neural synchrony and treatment progression in heroin use disorder

Language transforms subjective internal states into observable behavior, enabling investigation of the neurocognitive dynamics central to psychiatric conditions. Intense emotional and physiological responses to evocative drug-related contexts precipitate craving and drug seeking in substance use disorders, yet sensitive, accessible behavioral markers of the underlying neural dynamics remain elusiv...

Personalization variables in digital mental health interventions for depression and anxiety in adolescents and youth: a scoping review.

INTRODUCTION: The impact of personalization on user engagement and adherence in digital mental health interventions (DMHIs) has been widely explored. ...

Large language models in oncology: a review.

Large language models (LLMs) have demonstrated emergent human-like capabilities in natural language processing, leading to enthusiasm about their inte...

The Hypothalamic Medial Preoptic Area-Paraventricular Nucleus Circuit Modulates Depressive-Like Behaviors in a Mouse Model of Postpartum Depression.

Estrogen fluctuations have been implicated in various mood disorders, including perimenopausal and postpartum depression (PPD), likely through complex...

Gray and white matter alterations in Obsessive-Compulsive Personality Disorder: a data fusion machine learning approach.

INTRODUCTION: Obsessive-Compulsive Personality Disorder (OCPD) is a complex mental condition marked by excessive perfectionism, orderliness, and rigid...

Employing large language models for emotion detection in psychotherapy transcripts.

PURPOSE: In the context of psychotherapy, emotions play an important role both through their association with symptom severity, as well as their effec...

A Clinical Risk Prediction Model for Depressive Disorders Based on Seven Machine Learning Algorithms.

OBJECTIVE: To develop a clinical risk prediction model for depressive disorders using seven machine learning algorithms based on routine blood test in...

A bibliometric analysis of the current state of research on family interventions for ASD.

UNLABELLED: In recent years, family intervention has become a hot research direction in the field of autism spectrum disorder (ASD) treatment. A large...

Using the Minnesota Multiphasic Personality Inventory-2 restructured form to predict functioning after treatment for borderline personality disorder: A machine learning approach.

Insight into predictors of functioning after treatment for borderline personality disorder (BPD) is limited, despite growing recognition that more foc...

Predicting Suicidal Ideation Among Youths With Autism Spectrum Disorder: An Advanced Machine Learning Study.

This study aimed to predict suicidal ideation among youth with autism spectrum disorder (ASD) by applying machine learning techniques. A cross-section...

Machine Learning Models to Identify Clinically Significant Anxiety in Short-Term Insomnia Using Accelerometers.

Clinically significant anxiety (CSA) is common in individuals with short-term insomnia. This study aims to explore the relationship between CSA and th...

From the -Factor to Cognitive Content: Detection and Discrimination of Psychopathologies Based on Explainable Artificial Intelligence.

Differentiating psychopathologies is challenging due to shared underlying mechanisms, such as the -factor. Nevertheless, recent methodological advanc...

Acoustic-based machine learning approaches for depression detection in Chinese university students.

BACKGROUND: Depression is major global public health problems among university students. Currently, the evaluation and monitoring of depression predom...

Acceptance of Using Artificial Intelligence and Digital Technology for Mental Health Interventions: The Development and Initial Validation of the UTAUT-AI-DMHI.

Digital health technologies are being increasingly integrated into mental healthcare. This means that patients have different treatment options, and c...

Artificial intelligence for contextual well-being: Protocol for an exploratory sequential mixed methods study with medical students as a social microcosm.

INTRODUCTION: AI-powered conversational agents have proven effective in alleviating psychological distress, however, concerns about autonomy and authe...

Assessing ML classification algorithms and NLP techniques for depression detection: An experimental case study.

CONTEXT AND BACKGROUND: Depression has affected millions of people worldwide and has become one of the most common mental disorders. Early mental diso...

Developing a machine learning algorithm to predict psychotropic drugs-induced weight gain and the effectiveness of anti-obesity drugs in patients with severe mental illness: Protocol for a prospective cohort study.

Obesity is a global public health concern, often co-occurring in patients with severe mental illnesses. The impact of psychotropic drugs-induced weigh...

Predicting depression severity using machine learning models: Insights from mitochondrial peptides and clinical factors.

Depression presents a significant challenge to global mental health, often intertwined with factors including oxidative stress. Although the precise r...

Brain-region specific autism prediction from electroencephalogram signals using graph convolution neural network.

BACKGROUND: Brain variations are responsible for developmental impairments, including autism spectrum disorder (ASD). EEG signals efficiently detect n...