Depression

Targeting the MPO/LCN2/GMPPB axis in IBS-depression comorbidity: Integrated multi-omics and bidirectional network pharmacology for precision diagnostics and therapeutics.

BACKGROUND: Irritable Bowel Syndrome (IBS) and Major Depressive Disorder (MDD) exhibit high comorbidity, driven by dysregulation of gut-brain axis interactions. Despite evidence of shared pathophysiology, the core molecular mechanisms and therapeutic targets remain elusive, largely due to clinical heterogeneity and fragmented research approaches. METHODS: We established an integrated framework com...

Toward personalized classification and treatment in depression: A narrative review of digital phenotyping and artificial intelligence.

Major depressive disorder (MDD) remains a highly heterogeneous condition, presenting significant challenges for effective diagnosis and treatment. Traditional diagnostic systems often fail to capture the diverse clinical and biological phenotypes of MDD, limiting the efficacy and predictability of therapeutic interventions. The advent of wearable technology has enabled the continuous collection of...

Urban environmental determinants and their effects on mental health, physical function, and quality of life in older adults: a multidimensional study in Shiraz, Iran.

Urbanization and aging populations challenge public health in developing cities like Shiraz, where environmental factors significantly influence old a...

Changes in Phasic Heart Rate Variability Predict Clinical Outcomes Following Emotion Regulation Therapy for Generalized Anxiety Disorder and Comorbid Depression.

Generalized anxiety disorder (GAD) and major depressive disorder (MDD) frequently co-occur and are marked by greater chronicity, severity, and poorer ...

Identifying genome-by-childhood trauma interactions for depression using a forest-based approach in the UK Biobank and Adolescent Brain Cognitive Development Study.

Depression is shaped by both genetic and environmental factors, but genome-wide interaction studies (GWIS) often lack power to detect complex gene-env...

Profiling reasons for non-attendance in psychological assessments in adolescent suicide at-risk group using natural language processing.

BACKGROUND: Non-attendance at follow-up psychological evaluations is a major barrier to care for adolescents at risk of suicide. Identifying the facto...

Development of an explainable machine learning model for predicting depression in adults with type 2 diabetes mellitus: A cross-sectional SHAP-based analysis of NHANES 2009-2023.

Depression (DEP) is a common yet underdiagnosed comorbidity in adults with type 2 diabetes mellitus (T2DM), worsening glycemic control and increasing ...

Deep phenotyping of suicidal ideation after discharge from psychiatric inpatient care: study protocol for an interdisciplinary, multicentre prospective observational study in Psychiatric University Hospitals.

INTRODUCTION: Suicidal thoughts and behaviours (STB) are a critical public health concern, with 700 000 deaths by suicide each year. The period immedi...

Harnessing artificial intelligence to capture online mental health stigma: A scoping review.

BACKGROUND: Mental health stigma remains a major barrier to care and is increasingly expressed online. Artificial intelligence (AI) may offer scalable...

Iterative Large Language Model-Guided Sampling and Expert-Annotated Benchmark Corpus for Harmful Suicide Content Detection: Development and Validation Study.

BACKGROUND: Harmful suicide content on the internet poses significant risks, as it can induce suicidal thoughts and behaviors, particularly among vuln...

Tailored psychotherapy and AI-enhanced contingency management for co-occurring disorders in cannabis use disorder: a systematic review.

BACKGROUND: Cannabis use disorder (CUD) commonly co-occurs with depression, post-traumatic stress disorder (PTSD), anxiety, and attention-deficit/hype...

Digital Phenotyping for Adolescent Mental Health: Feasibility Study Using Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data.

BACKGROUND: Adolescents are particularly vulnerable to mental disorders, with over 75% of lifetime cases emerging before the age of 25 years. Yet most...

HappyMums mobile application study protocol: use of a smartphone application to gather data predictive of antenatal depression.

INTRODUCTION: Mobile health (mHealth) technologies have become increasingly popular for monitoring mental health symptoms and lifestyle behaviours, an...

Deep neural network-based analysis of voice biomarkers for monitoring treatment response in adolescent major depressive disorder.

BACKGROUND: In adolescents, identifying objective biomarkers for treatment response is crucial for the development of effective interventions. Voice-b...

Construction of a depression risk prediction model for hepatitis B patients based on machine learning strategy.

BACKGROUND: Hepatitis B (HBV) is a chronic viral infection that can lead to cirrhosis, liver failure, and liver cancer, and has a profound impact on t...

Characteristics of electroencephalographic changes induced by different hypnotics in elderly patients: a narrative review.

Aging is associated with widespread structural and functional changes in the brain including reduced neural plasticity, slower information processing,...

Predicting Infant Sleep Patterns From Postpartum Maternal Mental Health Measures: Machine Learning Approach.

BACKGROUND: Postpartum maternal mental health (MMH) symptoms, including depression, anxiety, and childbirth-related post-traumatic stress disorder, ar...

Predictive Accuracy of Natural Language Processing Extracted 3-Step Theory of Suicide Factor Scores Derived From Veterans' Clinical Progress Notes.

OBJECTIVES: To compare predictive accuracy of 3-step theory of suicide (3ST) factor scores derived from natural language processing of Veterans Health...

Neurocognitive and Social Cognitive Predictors of Adolescent Major Depressive Disorder: A Machine Learning Classification Study.

Background/aimThis study aimed to investigate whether machine learning (ML) algorithms could accurately differentiate adolescents with Major Depressiv...

Simplifying Depression Diagnosis: Single-Channel EEG and Deep Learning Approaches.

Major depressive disorder (MDD) or depression is a chronic mental illness that significantly impacts individuals' well-being and is often diagnosed at...