Psychiatry

Baseline metabolic signature predicts cognitive improvement and longitudinal glycerophospholipid shifts track recovery in first-episode drug-naïve schizophrenia.

BACKGROUND: Cognitive impairment is a core yet treatment-resistant feature of schizophrenia (SZ), and reliable biomarkers predicting cognitive improvement remain scarce. This study identified a baseline plasma metabolic signature for predicting antipsychotic-induced cognitive improvement and elucidated the preliminary evidence for early cognitive improvement in first-episode drug-naïve SZ patients...

Dysregulated glucocorticoid-responsive immune genes in peripheral blood mononuclear cells as a shared molecular signature of autism spectrum disorder and irritable bowel syndrome.

BACKGROUND: Autism spectrum disorder (ASD) is frequently accompanied by gastrointestinal (GI) disturbances resembling irritable bowel syndrome (IBS). While dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis and impaired glucocorticoid-responsive immune (GRI) signaling are proposed links between these disorders, the precise molecular mechanisms remain poorly understood. METHODS: We perf...

Enhancing the Effectiveness of Attention-Deficit/Hyperactivity Disorder Screening Using the SNAP-IV: A Deep Learning Approach.

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition requiring early and accurate identification to optimize outco...

Predicting Psychiatric Readmission of Children and Adolescents Using Machine Learning.

INTRODUCTION: Recurrent psychiatric hospitalizations in youth are costly and burdensome to patients and families. While postdischarge planning rarely ...

Debate: Conversational AI and young people's mental health - friend or foe? - the illusion of a safe space for youth mental health.

Artificially intelligent (AI) chatbots are increasingly used for mental health support, yet their safety guidelines and ethical structures remain uncl...

Neuropsychological and metabolic interconnectivity in obesity, anorexia and bulimia nervosa - an integrative literature review.

A dysfunctional bi-directional signalling of plural neural networks expresses distinct metabolic disruption with mental health consequences in obesity...

Validation of EEG mental workload markers using integrated statistical and machine learning analyses.

AIM: Attentional and working-memory processes can be monitored noninvasively using electroencephalography (EEG), which provides physiological indices ...

Machine learning-driven correction of handgrip strength: a novel biomarker for neurological and health outcomes in the UK Biobank.

Handgrip strength (HGS) is a significant biomarker for overall health, offering a simple, cost-effective method for assessing muscle function. Lower H...

Explainable machine learning revealing the impact of mental and physical health on arthritis.

OBJECTIVES: To develop a robust and interpretable machine learning framework for arthritis risk prediction and to identify important risk factors asso...

Cross-attention-guided subject-adaptive graph learning for multimodal autism classification: integrating structural and functional MRI data.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition marked by structural atypicality and abnormal functional connectivity. It rem...

Remaining Human in the Age of Artificial Intelligence: Spiritual and Psychiatric Reflections on Magnifica Humanitas.

Artificial intelligence (AI) is increasingly shaping the ways in which psychiatry understands, predicts, communicates, and responds to mental sufferin...

Bioinformatics analysis reveals the characteristics of immune microenvironment in major depressive disorder and vitiligo.

BACKGROUND: Major depressive disorder (MDD) and vitiligo often occur together, worsening patient outcomes. However, the shared pathogenic mechanisms r...

Neurological roots of emotional ambivalence: from brain networks to clinical implications.

Introduction: Emotional ambivalence - the coexistence of conflicting positive and negative feelings toward the same object, event, or person - has bee...

Deep Learning Reveals Cross-Modal Neural Representations of Auditory and Visual Mental Imagery in MEG.

Mental imagery provides a unique window into the brain's ability to internally simulate sensory experiences, offering valuable insights for both cogni...

The role and application of task-state fNIRS in identifying biomarkers in children and adolescents with ADHD: a systematic review.

Attention Deficit Hyperactivity Disorder (ADHD), a prevalent neurodevelopmental condition, urgently requires objective biomarkers to improve diagnosti...

Understanding Drivers of Physical Activity Through Explainable Machine Learning: The Role of Disability and Social Determinants.

BACKGROUND: Physical activity among adults with disabilities is influenced by functional limitations, health status, and socioeconomic conditions; yet...

Capturing omega sign in the clinical assessment of depression by deep learning.

The omega sign is a melancholic facial expression, resembling the Greek letter omega "Ω". To study static omega sign (visible full-blown Ω-shaped or v...

Psychosomatic Susceptibility Profile (PSSP): a machine-learning-derived eight-item instrument integrating personality and coping for psychosomatic risk stratification.

Psychological, behavioral, and physical factors jointly contribute to heterogeneity in health-related outcomes, yet existing instruments often assess ...

A multicenter study on the prediction model for chronic low back pain after lumbar decompression surgery in patients with diabetes mellitus: integration of metabolic and paraspinal muscle features.

OBJECTIVE: Based on multicenter clinical data, this study aimed to develop and validate a predictive model for chronic low back pain (CLBP) after lumb...

Suicide risk prediction and analysis of associated factors among high school students in four provinces of China based on explainable machine learning.

BACKGROUND: Suicidal ideation (SI) among adolescents is a severe global public health issue; however, accurate identification and mechanistic explanat...