Bipolar Disorder

Corrigendum to "Machine Learning Models for detecting Suicidal ideation in Chinese in-Patients with major depressive disorder: A Single-center retrospective study" [J. Affect. Disord. 406 (2026) 121679].

Discriminating bipolar depression from major depressive disorder using functional and microstructural signatures of inferior fronto-occipital fasciculus: Promising results from an MRI study.

BACKGROUND: Bipolar depression (BD-d) and major depressive disorder (MDD) share overlapping clinical symptoms, posing a significant diagnostic challenge. The inferior fronto-occipital fasciculus (IFOF) is a critical long-range tract that integrates cognitive and affective processes across brain networks. However, it remains unexplored whether bilateral IFOF abnormalities differ between BD-d and MD...

Hybrid physics-informed machine learning framework for calibration-free degradation prediction of lithium-ion batteries.

Lithium-ion battery degradation prediction traditionally requires chemistry-specific laboratory calibration, limiting scalability across diverse opera...

Methodological guidance on clinical prediction models in mental health research.

Clinical prediction models play a crucial role in advancing personalized care for mental health disorders, providing essential insights for diagnosis,...

AI-driven QSAR modelling and virtual screening in the discovery of selective dopamine D2 receptor ligands.

The dopamine D2 receptor (DRD2) is a key therapeutic target for several neuropsychiatric disorders, driving the need for new ligands with improved saf...

Progress of Non-Aqueous Liquid Electrolytes for High-Voltage Sodium-Ion Batteries.

Due to their significant resource-economic advantages, sodium-ion batteries are gradually becoming an important alternative to lithium-ion batteries i...

Exploration of comorbidity mechanisms between chronic pain and depression: Machine learning prediction models and SHAP interpretability analysis based on the CHARLS cohort.

INTRODUCTION: With rapid population aging in China, depression among middle-aged and older adults has become a major public health concern. Chronic pa...

Early Warning of Battery Thermal Runaway Enabled by Ag-Decorated SnO2 and Machine Learning: Diethyl Carbonate Recognition.

The safe utilization of lithium-ion batteries depends on the management of thermal runaway to avoid electrolyte leakage. It is crucial to make early w...

Developing and Testing a Brief Mindfulness Just-in-Time Adaptive Intervention to Reduce Stress Among Caregivers of People With Dementia: Quasi-Experimental Study.

BACKGROUND: Dementia caregiving entails chronic, fluctuating stress with downstream risks to caregivers' mental health and quality of care. Mindfulnes...

Exploratory eye movement characteristics to aid screening of schizophrenia and bipolar disorder: A cross-sectional outpatient study.

Current psychiatric diagnoses lack objective criteria, and this study aims to evaluate EEM as a potential tool for improving diagnostic objectivity ac...

Resting-state EEG microstates as biomarkers for major depressive disorder.

OBJECTIVES: To investigate the temporal dynamics of resting-state electroencephalography (EEG) microstates in patients with Major Depressive Disorder ...

Comparative study of granular computing and RKCCA-based feature fusion for early lithium-ion battery RUL estimation.

Efficient prediction of the remaining useful life (RUL) of lithium ion batteries is essential in ensuring safety, reliability and energy efficiency of...

Substance-induced manic psychosis in which delusions were corroborated by a chatbot - case report.

BACKGROUND: This case describes a substance-induced manic episode with psychotic features in which interaction with an AI (artificial intelligence) ch...

Estimation of battery SOC using a combined approach of temporal convolutional networks and Unscented Kalman Filter.

In recent years, power batteries have been widely used in electric vehicles, and the evaluation of state of charge (SOC) is an important parameter in ...

Week-ahead prediction of depressive episodes using wearable-derived circadian biomarkers: A multicenter deep learning study with risk-based operating thresholds.

Early detection of depressive episodes is important because shorter duration of untreated illness is associated with better outcomes, yet routine care...

An ab initio study and machine learning framework to capture the motional effects in solid-state NMR of lithium-ion conductors.

Solid-state NMR spectroscopy, when combined with first-principles density functional theory (DFT) calculations, offers a highly sensitive probe of ato...

Generative Artificial Intelligence in the Lives of Young Adults: Exploring Motivations and Mental Health.

PURPOSE: Young people have rapidly adopted generative artificial intelligence (genAI) technology, yet little is known about how genAI use relates to m...

Development and validation of a machine learning model predicting post-tonsillectomy hemorrhage.

PURPOSE: To develop and validate machine learning models to predict post-tonsillectomy hemorrhage. METHODS: This was a machine learning analysis of a ...

Advancing Lithium-Oxygen Batteries: Pioneering Cathode Catalyst Innovation and Artificial Intelligence-Driven Design Paradigms.

Lithium-oxygen batteries (LOBs) are regarded as one of the most promising next-generation energy storage systems, owing to their exceptionally high th...

Depression-Related Nutritional Risk and Physical Performance in Middle- to Older-Aged Adults: Cross-Sectional Secondary Analysis of Tree-Based and Regression Approaches.

BACKGROUND: Depressive symptoms are linked to nutritional vulnerability and functional decline in aging populations, but their relationships with nutr...