Depression

Plasma Lyso-PE 22:6 and Lyso-PE 20:4 are associated with development of mild to moderate depression revealed by metabolomics: a pilot study.

BACKGROUND: Mild to moderate depression (MMD), as an early stage of depression, has a high incidence and may progress to severe depression, even leading to suicide. The lack of effective screening and treatment is due to the unknown metabolic changes and processes in patients with MMD. Here, we performed metabolite profiling to investigate the key metabolites associated with MMD in plasma.

An interpretable machine learning model for predicting depression in middle-aged and elderly cancer patients in China: a study based on the CHARLS cohort.

BACKGROUND: Depression is very common in middle-aged and elderly cancer patients, which will seriously damage the quality of life and treatment effect of patients. This study aims to use machine learning methods to develop a predictive model to identify depression risk. However, since the traditional machine learning models have 'black box nature', Shapley Additive exPlanation is used to determine...

Suicide Risk Screening in Jails: Protocol for a Pilot Study Leveraging the Mental Health Research Network Algorithm and Health Care Data.

BACKGROUND: Suicide in local jails occurs at a higher rate than in the general population, requiring improvements to risk screening methods. Current s...

Alterations in Gut Microbiota-Brain Axis in Major Depressive Disorder as Identified by Machine Learning.

Major depressive disorder (MDD) is a complex mental health condition whose causes may extend beyond purely biological explanations and are increasingl...

Multimodal Depression Recognition via Mutual Information Maximization Joint with Multi-task Learning.

Depression is a serious mental health disorder with a potential hazard for individuals and society characterized by persistent sadness and hopelessnes...

Sleep disturbances and PTSD: identifying baseline predictors of insomnia response in an intensive treatment programme.

This study examined whether baseline demographic and clinical variables could predict clinically significant reductions in insomnia symptoms among ve...

AI in Medical Questionnaires: Innovations, Diagnosis, and Implications.

This systematic review aimed to explore the current applications, potential benefits, and issues of artificial intelligence (AI) in medical questionna...

Machine Learning Model for Predicting Sertraline-like Activities and Its Impact on Cancer Chemosensitization.

Selective serotonin reuptake inhibitors (SSRIs) like sertraline are crucial in treating depression and anxiety disorders, and studies indicate their p...

Development of the treatment prediction model in the artificial intelligence in depression - medication enhancement study.

We introduce an artificial intelligence model to personalize treatment in major depression, which was deployed in the Artificial Intelligence in Depre...

Leveraging machine learning to uncover the hidden links between trusting behavior and biological markers.

Understanding the decision-making mechanisms underlying trust is essential, particularly for individuals with mental disorders who often experience di...

Use of childhood adversity and mental health admission patterns to predict suicide in young people.

Dougall et al found that mental health admissions are a strong predictor of suicide risk in young people. The findings can improve machine learning mo...

Development of prediction models for screening depression and anxiety using smartphone and wearable-based digital phenotyping: protocol for the Smartphone and Wearable Assessment for Real-Time Screening of Depression and Anxiety (SWARTS-DA) observational study in Korea.

INTRODUCTION: Depression and anxiety are highly prevalent mental health conditions that significantly affect quality of life and cause societal burden...

Applying language models for suicide prevention: evaluating news article adherence to WHO reporting guidelines.

The responsible reporting of suicide in media is crucial for public health, as irresponsible coverage can potentially promote suicidal behaviors. This...

Functional connectivity alterations of the pregenual anterior cingulate cortex by ketamine and the modulation by lamotrigine.

BACKGROUND: Neuroimaging studies have linked the beneficial effects of subanaesthetic ketamine doses in psychiatric conditions characterized by chroni...

Prospective prediction of first onset of major depressive disorder in midlife using machine learning.

PURPOSE: In this paper we leverage machine learning (ML) models to prospectively predict the first onset of Major Depressive Disorder (MDD), one of th...

Comparative cytokine signatures and cognitive deficits in early-onset schizophrenia and adolescent major depression: Toward refined diagnostic classification frameworks.

BACKGROUND: This study analyzed plasma cytokine patterns in individuals with schizophrenia (SCZ), major depressive disorder (MDD), and healthy control...

Psychedelics, entactogens and psychoplastogens for depression and related disorders.

Currently, the most actively investigated rapidly acting antidepressants, anxiolytics and/or anti PTSD agents, include psychedelics e.g. psilocybin, L...

Real-world effectiveness of cariprazine in major depressive disorder and bipolar I disorder in the United States.

AIMS: The efficacy of cariprazine for major depressive disorder (MDD) (adjunctive therapy) and bipolar I (BP-I) depression has been demonstrated in cl...

Patterns in Mental Health Symptoms, Substance Use, and Viral Suppression in People with HIV: A Clustering Analysis.

Mental health conditions and substance use are prevalent among people with HIV (PWH), are correlated with one another, and associate with viral non-su...

Multivariate brain morphological patterns across mood disorders: key roles of frontotemporal and cerebellar areas.

BACKGROUND: Differentiating major depressive disorder (MDD) from bipolar disorder (BD) remains a significant clinical challenge, as both disorders exh...