Bipolar Disorder

The most important features in generalized additive models might be groups of features.

While analyzing the importance of features has become ubiquitous in interpretable machine learning, the joint signal from a group of related features is sometimes overlooked or inadvertently excluded. Neglecting the joint signal could bypass a critical insight: in many instances, the most significant predictors are not isolated features, but rather the combined effect of groups of features. This c...

Data-driven prediction of ionic conductivity in solid-state electrolytes with machine learning and large language models.

Solid-state electrolytes (SSEs) are attractive for next-generation lithium-ion batteries due to improved safety and stability, but their low room-temperature ionic conductivity hinders practical application. Experimental synthesis and testing of new SSEs remain time-consuming and resource-intensive. Machine learning offers an accelerated route for SSE discovery; however, composition-only models ne...

Attitudes toward artificial intelligence among individuals with anxiety, depression, bipolar disorders, and schizophrenia.

BACKGROUND: Artificial intelligence (AI) increasingly supports medical diagnosis, interventions, and clinical decision-making. In various domains of h...

Synergistic Effect of a Combined 10-MDP/γ-MPTS Primer on Glass-Ceramic Bonding: A Multimodal Study.

INTRODUCTION AND AIMS: This study investigated the synergistic effect of combining 10-methacryloyloxydecyl dihydrogen phosphate (10-MDP) and γ-methacr...

Distinct Biotypes of Visual Perception in Major Depressive Disorder.

Previous studies showed abnormalities in both visual motion perception (VMP) and occipital cortex activity in subjects suffering from major depressive...

A decade-long shift in use of energy devices for BABA robotic thyroidectomy: automated video analysis by deep learning.

Bilateral axillo-breast approach robotic thyroidectomy (BABA RT) is a well-established minimally invasive surgical option; however, shifts in preferen...

Lithium battery fault diagnosis by integrating improved EMD decomposition algorithm and 2DCNN.

With the development of science and technology, lithium batteries, as important energy storage devices, have become a research hotspot for fault diagn...

Joint effects of physical activity and diet quality on depressive symptoms and all-cause mortality in adults with diabetes: NHANES 2007-2018.

Depressive symptoms are common among adults with diabetes and are associated with adverse clinical outcomes, including mortality. Evidence from genera...

Utilizing stimulation-evoked hemodynamic activity to predict antidepressant response to intermittent theta-burst stimulation in adults with major depression.

BACKGROUND: The instantaneous neural response to prefrontal theta burst stimulation (TBS) may serve as predictive marker for antidepressant treatment ...

Predicting lithium-associated thyroid dysfunction through dimension reduction to establish a practical machine learning model: A retrospective multicenter study.

BACKGROUND: Thyroid dysfunction is a prevalent side effect among patients using lithium and links to refractory mood disorders. Existing predictive mo...

From Spent Batteries to Green Hydrogen: Catalytic Upcycling of Lithium-Ion Battery Cathodes for Water Electrolysis.

The accelerating global "dual-carbon" transition and the rapid proliferation of electric vehicles are driving an unprecedented surge in spent lithium-...

Unravelling the Catalytic Activity of Dual-Metal Doped N6-Graphene for Sulfur Reduction via Machine Learning-Accelerated First-Principles Calculations.

Mitigating polysulfide shuttling and sluggish redox kinetics is crucial for the practical utilization of lithium-sulfur batteries (LSBs). Using first-...

In Search of Lithium Intermetallics with Channel Structures: Prediction and Discovery of Ternary Silicides Li-RE-Si (RE = Pr, Nd, Tm, Lu).

Lithium intermetallics with channel structures are of interest for energy storage applications. As a major subset of these intermetallics, ternary tet...

Enhancing Predictive Accuracy of Mood Symptoms Using Wearable Devices and Machine Learning in Bipolar Disorder.

Multimodal machine learning for major depressive disorder: Integrating EEG functional connectivity and clinical variables to enhance diagnostic accuracy.

The diagnosis of Major Depressive Disorder (MDD) relies heavily on subjective clinical assessments. This study evaluated various machine learning mode...

Classification of functional near-infrared spectroscopy (fNIRS) signals in schizophrenia and bipolar disorder using deep learning methods.

BACKGROUND: Schizophrenia (SCZ) and Bipolar Disorder (BD) are prevalent mental disorders. This study uses functional near-infrared spectroscopy (fNIRS...

The Performance of Wearable Device-Based Artificial Intelligence in Detecting Depression: Systematic Review and Meta-Analysis.

BACKGROUND: In recent years, advances in wearable sensor technology and artificial intelligence (AI) have provided new possibilities for detecting and...

Functional brain network modulation following conventional and optimized HD-tDCS in major depressive disorder: A machine learning prediction of treatment response.

BACKGROUND: Major depressive disorder (MDD) is a prevalent and disabling condition that remains inadequately treated in many patients. Transcranial di...

Predicting Electrolyte Stability in Lithium and Post-Lithium Metal Batteries: The Failure of Linear Single Descriptor-Based Models.

A stable solid electrolyte interphase (SEI), formed by the reductive decomposition of electrolytes at the anode surface, is crucial for ensuring the s...

Deep Brain Stimulation Induces Antidepressant Effects by Restoring High-Fidelity Communication in the BNST-NAc Circuit.

Deep brain stimulation (DBS) for treatment-resistant depression (TRD) is challenged by significant individual variability in efficacy and unclear neur...