Bipolar Disorder

Optimal 24-Hour Sleep Duration and Depression Risk in Chinese Middle-Aged and Elderly: A Machine Learning and RCS Analysis of Nonlinear Associations.

OBJECTIVES: To investigate the relationship between sleep (nocturnal sleep and total 24-hour sleep) and depressive symptoms in middle-aged and elderly people using data from the China Health and Retirement Longitudinal Study (CHARLS). STUDY DESIGN: Cross-sectional study. METHODS: A total of 13,718 middle-aged and elderly people aged 45 and above from the China Health and Retirement Longitudinal St...

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 transitions is difficult due to infrequent clinical touchpoints. Digital health technologies, including wearables and smartphones, offer a unique opportunity to passively and continuously monitor behavior and physiology that could reflect underlying mo...

Measuring Depression Severity With Clinical Global Impression-Severity Scale Scores From Clinical Notes Using Large Language Models: Validation Study.

BACKGROUND: Real-world psychiatric care is marked by wide heterogeneity in clinical presentations and outcomes, underscoring the need for systematic a...

The diagnostic value of the circadian rhythm gene KLF10 in anxiety-depressive disorders and its neuroimmune regulatory mechanisms.

BACKGROUND: Anxiety-depressive disorder (ADD) is one of the important subtypes of depression, which has been shown to be closely related to neuroimmun...

Distinctive subcortical connectivity predicts treatment response in first-episode schizophrenia.

BACKGROUND: Subcortical regions are widely implicated in the pathological mechanisms and treatment of schizophrenia, and accumulating evidence, includ...

Depressive symptoms in the association between herpes zoster and heart disease in middle-aged and older adults: A longitudinal cohort study.

BACKGROUND: Herpes zoster (HZ) has been linked to elevated cardiovascular risk, but the mechanisms remain unclear. Depressive symptoms, particularly s...

Deep learning reveals a neurocomputational mechanism predicting depression risk in adolescents.

Early detection and prevention of psychiatric disorders, particularly depression, remain as major global health challenges, yet reliable tools for ide...

Decoding how macronutrients influence depressive symptoms: Combined insights from regression, nonlinear modeling, and machine learning: a cross-sectional study using NHANES data.

Dietary management is an essential component of comprehensive depression care;however, studies simultaneously evaluating multiple macronutrients and t...

Prediction of Clinically Meaningful Improvement After Internet-Delivered Cognitive Behavioral Therapy for Depression and Anxiety Disorders: Machine Learning-Based Predictive Model Development and Temporal Validation Study.

BACKGROUND: Up to 50% of patients treated with internet-delivered cognitive behavioral therapy (ICBT) for depression and anxiety disorders do not expe...

High-Entropy Engineering of Phosphate Based Polyanion Cathode Materials for Sodium-Ion Batteries: Advances and Perspectives.

Sodium-ion batteries (SIBs) are promising alternatives to lithium-ion batteries for large-scale energy storage due to their cost-effectiveness and res...

Identification of EIF4A1 as a candidate molecular indicator of autism spectrum disorder using integrative bioinformatic and biological methods.

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition with complex genetic and molecular mechanism. Identifying reliable mole...

Research on predicting the state of health of lithium-ion batteries via back propagation network based on multi-feature combination of electrochemical impedance spectroscopy.

Lithium-ion batteries are widely used in electric vehicles and portable electronic devices. Accurate estimation of the State of Health (SOH) is essent...

Psychological stress-associated ceramide and diacylglyceride lipotoxicity as contributors to first-episode depression pathophysiology: A neuroimmune-metabolic-oxidative stress (NIMETOX) perspective.

BACKGROUND: Aberrations in neuro-immune, metabolic, and oxidative stress (NIMETOX) pathways are implicated in major depressive disorder (MDD). First-e...

Resting-state fMRI-based machine learning for predicting SSRI treatment response in major depressive disorder.

BACKGROUND: Major Depressive Disorder (MDD) is a prevalent mental health condition with significant societal impact. Although prior research has highl...

From Synthesis to Failure: In Situ Characterization of Lithium-Ion Battery Cathodes.

Developing high-performance, long-life lithium-ion batteries requires an in-depth understanding of cathode material synthesis, lithium-ion (de)interca...

Can We Improve the Prediction of Early Onset Mania and Hypomania in the Community?

BACKGROUND: There is broad agreement that the onset of bipolar disorders (BD) can be predicted by using combined estimates of familial, genetic and cl...

The role of catastrophizing in anxiety and depression symptom scores among knee osteoarthritis patients: a multimodal assessment with EEG and machine learning.

To examine dimensional associations between anxiety- and depression-related symptom severity, pain catastrophizing, and resting-state EEG features in ...

Predicting Care Needs in Community-Dwelling Older Adults Using Explainable Machine Learning and a Multidimensional Approach Integrating Health, Social, and Environmental Factors: Cross-Sectional Study.

BACKGROUND: Rapid population aging and a worsening shortage of care workers necessitate the identification of older adults who require proactive inter...

A cross-domain deep learning framework for remaining useful life prediction in industrial applications.

Accurate prediction of Remaining Useful Life (RUL) is critical for predictive maintenance and minimizing downtime in industrial systems. This paper pr...

Metabolites stratify future major depressive disorder risk in obese population: a longitudinal machine learning analysis.

BACKGROUND: Obesity is a well-established risk factor for major depressive disorder (MDD), yet the risk is not uniform, highlighting the need for prec...