Psychiatry

Bipolar Disorder

Latest AI and machine learning research in bipolar disorder for healthcare professionals.

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AI simulation models for diagnosing disabilities in smart electrical prosthetics using bipolar fuzzy decision making based on choquet integral.

The integration of AI simulation models within smart electrical prosthetic systems represents a sign...

Exposotypes in psychotic disorders.

Psychiatry lags in adopting etiological approaches to diagnosis, prognosis, and outcome prediction c...

Recent Advances and Strategies of Metal Sulfides for Accelerating Polysulfide Redox and Regulating Li Plating.

Metal sulfides are emerging as multifunctional mediators to address the shuttle effect and lithium d...

Machine learning-assisted design of cathode materials for lithium-sulfur batteries derived from a metal-organic framework.

Designing cathode materials is crucial for developing advanced Li-S batteries, but conventional tria...

A Study on the Synaptic Behavior of Al/ZrO/TiO/Al Electronic Bipolar Resistance Switching Memristor.

This study presents the Al/ZrO/TiO/Al (AZTA) memristor, a device based on a nonfilamentary mechanism...

Machine-Learning-Assisted Density Functional Theory Calculations: A New Approach to Screening Thermal Runaway Gas Sensors for Lithium-Ion Batteries.

Lithium-ion batteries face safety risks, such as spontaneous combustion and explosion, during long-t...

Determinants of depressive symptoms in multinational middle-aged and older adults.

This study harnesses machine learning to dissect the complex socioeconomic determinants of depressio...

AI-Driven Discovery and Optimization of Positive Allosteric Modulators for NMDA Receptors: Potential Applications in Depression.

-Methyl-d-aspartate receptors (NMDARs) are extensively distributed throughout the central nervous sy...

Revitalizing Micro-Sized Si-Based Anodes Through Advanced Structural Design and Interface Stabilization: A Review.

Silicon (Si) is recognized as a promising anode material for next-generation lithium-ion batteries o...

Trauma-predictive brain network connectivity adaptively responds to mild acute stress.

Past traumatic experiences shape neural responses to future stress, but the mechanisms underlying th...

AI-based prediction of depression symptomatology in first-episode psychosis patients: insights from the EUFEST and RAISE-ETP clinical trials.

BACKGROUND: Depressive symptoms are highly prevalent in first-episode psychosis (FEP) and worsen cli...

Diagnosis of Major Depressive Disorder Based on Multi-Granularity Brain Networks Fusion.

Major Depressive Disorder (MDD) is a common mental disorder, and making an early and accurate diagno...

Brain Oscillations in Bipolar Disorder: Insights from Quantitative EEG Studies.

IntroductionQuantitative electroencephalography (QEEG) is a neurophysiological tool that analyzes br...

Predicting depressive symptoms through social support: a machine learning approach in military populations.

Perceived Social support has been consistently shown to reduce depressive symptoms among military p...

Cyborg insect factory: automatic assembly for insect-computer hybrid robot via vision-guided robotic arm manipulation of custom bipolar electrodes.

Insect-computer hybrid robots offer strong potential for navigating complex terrains. This study ide...

Genetic predisposition to unwanted side effects under antidepressants and antipsychotics: a molecular-genetic study of 902 patients over 6 weeks.

This project aimed at (1) detailing the complex side effect patterns of 902 inpatients treated for m...

Machine Learning-Driven Prediction of Electrochemical Promotion in the Reverse Water Gas Shift Reaction.

Electrochemical promotion of catalysis (EPOC) provides an effective and versatile strategy to enhanc...

A machine learning-based approach to predict depression in Chinese older adults with subjective cognitive decline: a longitudinal study.

This study aims to identify depressive risks in elderly individuals with subjective cognitive declin...

Ambulatory physiological state dynamics predict proximal behavioral markers of affect regulation in everyday life.

Human physiology reflects the body's capacity for self-regulation that is crucial for flexible adapt...

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