Depression

Use of human data for risk assessment of pesticides: A review including an evaluation of trichlorfon as case study.

Pesticides are suspected to cause health effects in humans, but human-based data on their toxicity are often insufficient to establish associations and quantify risks. We reviewed literature on trichlorfon and investigated methodological aspects of risk assessment for pesticides based on human data. We provided an overview of epidemiology of pesticide toxicity, with focus on methodological feature...

Analysis of the influential factors of depression in the elderly based on CHARLS through partial least squares structural equation and random forest modeling.

OBJECTIVE: We aimed to explore how influential factors, such as chronic diseases, disability and social support, affect depressive symptoms in older adults, and to analyze the main influential factors on depression in different age groups. METHODS: Partial Least Squares Structural Equation Modeling (PLS-SEM) and Random Forest Modeling were used to explore the relationship between chronic diseases,...

Major depressive disorder and Hashimoto's thyroiditis: Shared immunometabolic signatures revealed by integrative transcriptomics.

Major depressive disorder (MDD) and Hashimoto's thyroiditis (HT) frequently co-occur, yet their shared molecular underpinnings remain unclear. We perf...

The engagement behaviors and treatment barriers for depressed patients in an online health community: a pre-/post-treatment comparison.

The treatment of depression involves numerous barriers, both before and after individuals seek professional care. Online health communities (OHCs) hav...

Curcumin reprograms metabolic pathways and MAPK signaling to exert antidepressant effects.

BACKGROUND: Depression is a prevalent and debilitating mental disorder with limited treatment options. Curcumin, a natural compound with neuroprotecti...

Detection of major depressive disorder in adolescents based on textual and acoustic features.

BACKGROUND: Adolescent major depressive disorder (MDD) has become a growing public health concern, with rising prevalence over the past decade and sig...

Artificial intelligence in depression diagnostics: A systematic review of methodologies and clinical applications.

The integration of artificial intelligence (AI) into the field of mental health diagnosis has garnered increasing scholarly and clinical attention, pa...

Graph-level contrastive learning with self-aware and cross-sample topology augmentation for brain disorder diagnosis using rs-fMRI.

Resting-state functional MRI (rs-fMRI) is widely used for diagnosing and analyzing brain disorders. However, existing fMRI studies have shown that lea...

Machine Learning Algorithms for Predicting Injurious Fall Risk Among Older Adults With Depression: A Prognostic Modeling Study.

BACKGROUND: Falls and related injuries (FRI) pose a large burden among older adults with depression. Proactively identifying individuals at high FRI r...

Delineating white matter phenotypes of sensori-/psychomotor functioning in large-scale cohorts of healthy individuals and patients with mental disorders across the lifespan (whiteSPAN): rationale and methods of an interdisciplinary bicentric project.

Aberrant sensori-/psychomotor functioning-including muscular hand weakness, sedentary behavior, psychomotor agitation, slowing, agitation, apathy, and...

Trajectories of affective disorders: neurobiological mechanisms during symptom change.

Effective treatment of affective disorders (AD) requires a deep understanding of the underlying neurobiological mechanisms. However, in machine-learni...

Machine learning identifies traumatic experiences as being associated with auditory verbal hallucinations in both a non-clinical population and individuals diagnosed with psychosis.

BACKGROUND: Auditory verbal hallucinations (AVHs) are a core symptom of psychosis but their prevalence in the general population ranges from 5-28 %. M...

Bridging tradition and innovation: Acupuncture for depression through clinical efficacy and neurobiological insights.

BACKGROUND: To elucidate the research progress on acupuncture therapy as a complementary and alternative medicine treatment for depression by reviewin...

Early identification of pediatric depression in western China: A multicenter, citywide evaluation of nine machine learning models.

BACKGROUND: Childhood depression is an emerging global concern, yet knowledge and early detection tools remain limited in low- and middle-income regio...

Identification of the core genes KLRB1 and RETN as potential shared diagnostic markers for major depressive disorder and systemic lupus erythematosus through bioinformatics and machine learning methodologies.

This study investigates the shared molecular mechanisms between major depressive disorder (MDD) and systemic lupus erythematosus (SLE) through integra...

Machine learning-assisted multicolor identification and quantification of antidepressant drugs by waste-derived fluorescent nanoprobes: Towards green AI-based electronic tongue.

Recently, the severe side effects related to the widespread consumption of antidepressants (ADs) have alarmingly created a global challenge for clinic...

Decoding depression with computer vision-assisted analysis of synchronized facial expressions.

BACKGROUND: Facial expressions convey nonverbal signals of internal emotional states and hold potential as important markers for affective disorders s...

Depression screening with textual and audio features based on large language models and machine learning.

BACKGROUND: Depression is a complex disorder that cannot be fully screened by textual features alone, as audio features capture additional psychomotor...

Suicidal thoughts and behaviours among construction workers: Identifying risks and protective factors.

BACKGROUND: The construction industry records concerningly high rates of suicide compared to other industries. This study aimed to (1) identify sub-gr...

A machine learning-based discriminative framework for mild cognitive impairment risk in middle-aged and elderly Chinese patients with depression: evidence from CHARLS and CLHLS.

OBJECTIVE: This study aimed to evaluate the application value of machine learning (ML) techniques in the discrimination of mild cognitive impairment (...