Psychiatry

Postmarketing Safety of Transcranial Magnetic Stimulation: A 10-Year MAUDE Database Analysis of Adverse Events and Technological Advancements.

Background: Transcranial magnetic stimulation (TMS) is an FDA-cleared neuromodulation technique with expanding clinical applications beyond major depressive disorder. Despite increasing utilization, there has been no published, device-agnostic analysis of TMS-related adverse events (AEs) using the FDA's Manufacturer and User Facility Device Experience (MAUDE) database. Objective: To characterize t...

Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods.

The complexity of traditional Chinese medicine (TCM), with its numerous components, targets, and varying efficacy, presents challenges for current evaluation methods. Most existing methods rely on single, qualitative indicators, which provide limited insight into the overall quality. These methods fail to fully capture the intrinsic quality, efficacy, and safety of Chinese medicine, highlighting t...

Facing the Silent Crisis: Suicidal Ideation and Suicide in the Nursing Profession.

BACKGROUND: This manuscript explores the silent yet urgent crisis of suicidal ideation and suicide within the nursing profession. Despite being one of...

Inflammation at the crossroads of reproduction: SIRI as a prognostic signature of female infertility in hybrid regression-machine learning models.

BACKGROUND: Female infertility is a critical global health concern, with a rising prevalence and significant psychosocial consequences. This study aim...

Text-Based Depression Estimation Using Machine Learning With Standard Labels: Systematic Review and Meta-Analysis.

BACKGROUND: Depression affects people's daily lives and even leads to suicidal behavior. Text-based depression estimation using natural language proce...

The impact of AI anxiety on career decisions of college students.

The rapid advancement of artificial intelligence (AI) has reshaped the employment market, triggering widespread anxiety among college students about t...

Immune-related biomarkers for major depressive disorder identified via integrated bioinformatics and machine learning.

Background: Major depressive disorder (MDD) is a leading cause of disability worldwide, yet its early and objective diagnosis remains challenging due ...

Multimodal machine learning models for predicting remission in major depressive disorder using clinical data, blood biomarkers, and DNA methylation.

Major depressive disorder (MDD) is a leading global health burden, yet only one-third of patients achieve remission with initial antidepressant therap...

Comparative analysis of BORIS, Ethovision, DeepLabCut, and SimBA for quantifying autism spectrum disorder-like behaviors in the valproic acid mouse model.

Preclinical research often relies on animal observation and subsequent behavioral analysis to study brain function; however, traditional methods are c...

Network control dynamics and subjective reactivity in physiological responses to psychosocial stress.

The ability to endure psychosocial stressors is critical for mental and physical well-being. Clarifying mechanisms that differentiate high- from low-t...

Functional Inputs to the Subgenual Cingulate Cortex Differentiate Levels of Depressive Symptom Severity in Neurotypical Young Adults: Granger Causality and Clustering Analyses.

Extant research has implicated functional connectivity of the subgenual anterior cingulate cortex (sgACC) in major depressive disorders or depressive ...

Associations of AI-Based Facial Metrics With Patient-Reported Outcomes in Idiopathic Facial Paralysis.

OBJECTIVE: Conventional grading scales and static image assessments may not capture dynamic facial movement in facial paralysis. We developed a video-...

Improved schizophrenia detection using structural MRI with multi-scale transform and deep orthogonal features.

Artificial Intelligence to Support Human-Provided Mental Health Treatment.

Mental health disorders are some of the greatest contributors to the global disease burden, and healthcare systems are struggling to provide scalable ...

Enhancing graduate education assessment: a machine learning-based classification of academic performance in medical students.

BACKGROUND: Accurately predicting academic performance among medical postgraduate students is crucial for understanding educational outcomes and provi...

The use of the odds ratio product and self-reported data to detect comorbid insomnia and sleep apnea.

STUDY OBJECTIVES: To evaluate the utility of the odds ratio product (ORP) in differentiating comorbid insomnia and sleep apnea (COMISA) from obstructi...

Health-oriented sleep states: making sleep states reflect health conditions.

STUDY OBJECTIVES: The rich information in sleep offers insights into brain function and overall health. The current guidelines for sleep staging by th...

Predicting suicidal and self-harm ideation using ecological momentary assessment: deep learning analysis in a general population sample.

What's new in intravenous anaesthesia?

PURPOSE OF REVIEW: Advances in intravenous anaesthesia are driven by the need for agents with improved safety, enhanced hemodynamic stability, predict...

MS-STFNN: A multi-scale spatio-temporal fusion neural network for fMRI-based depression diagnosis.

Major depressive disorder (MDD) is a common mental disorder. Current clinical diagnosis primarily relies on subjective scales, underscoring an urgent ...