Bipolar Disorder

Digital phenotyping of depression: A multi-modal passive sensing approach to identifying novel behavioral and physiological markers of treatment response.

BACKGROUND: Despite advances in treatment approaches for Major Depressive Disorder (MDD), significant challenges persist in predicting individual treatment responses. Digital phenotyping-the passive collection of behavioral and physiological data through personal digital devices-offers promising opportunities for identifying objective markers of depression severity and treatment response. METHODS:...

Emerging Strategies for Sustainable Nitrogen Activation to Ammonia.

Activating inert nitrogen (N2) under mild conditions remains a grand challenge in chemistry, constrained by strong N≡N bonding and high kinetic stability. While the Haber-Bosch process has long dominated ammonia (NH3) production, its energy-intensive nature and heavy carbon footprint highlight the urgent need for alternative, low-carbon strategies. Recent advances have redefined N2 fixation by byp...

Urinary steroid metabolome shows adrenal, gonadal, and neuroactive steroid dysregulation in adolescents with depressive symptoms.

BACKGROUND: Steroid hormone profiles in affective disorders suggest hypothalamic-pituitary-adrenal (HPA) axis dysregulation and may reveal novel thera...

Enhancing the circular economy of lithium-ion batteries: a data-driven optimisation of leaching efficiency using kernel ridge regression.

The increasing demand for lithium-ion batteries (LIBs) and the consequential accumulation of waste from these batteries necessitate its appropriate di...

Impact of an autonomous delivery robot on sample turnaround time in a clinical laboratory: an early evaluation of first implementation.

OBJECTIVES: The aim of this study was to evaluate the impact of introducing an autonomous courier in a clinical laboratory, focusing on specimen turna...

Mental disorders & self-injurious thoughts and behaviors predict high risk of role impairment among university students - results from the world mental health international college student initiative.

OBJECTIVE: We examined psychopathological conditions accounting for the highest risk of role impairment among university students. METHOD: Cross-secti...

Analysis of the influential factors of depression in the elderly based on CHARLS through partial least squares structural equation and random forest modeling.

OBJECTIVE: We aimed to explore how influential factors, such as chronic diseases, disability and social support, affect depressive symptoms in older a...

Development of a machine learning-based prediction model for bipolar disorder relapse via integration of 1H-NMR metabolomics and clinical features.

BACKGROUND: Bipolar disorder (BD) is a chronic psychiatric illness characterized by recurrent episodes of depression and mania, with a global lifetime...

Major depressive disorder and Hashimoto's thyroiditis: Shared immunometabolic signatures revealed by integrative transcriptomics.

Major depressive disorder (MDD) and Hashimoto's thyroiditis (HT) frequently co-occur, yet their shared molecular underpinnings remain unclear. We perf...

Detection of major depressive disorder in adolescents based on textual and acoustic features.

BACKGROUND: Adolescent major depressive disorder (MDD) has become a growing public health concern, with rising prevalence over the past decade and sig...

Decoding Lithium Metal Battery Degradation with Symmetric-Cell Artificial Intelligence Diagnostics (SAID).

While the underlying crosstalk effect between cathode and anode complicates the understanding of lithium metal degradation utilizing asymmetric (full)...

Graph-level contrastive learning with self-aware and cross-sample topology augmentation for brain disorder diagnosis using rs-fMRI.

Resting-state functional MRI (rs-fMRI) is widely used for diagnosing and analyzing brain disorders. However, existing fMRI studies have shown that lea...

Trajectories of affective disorders-the central structures of CRC/TRR 393.

The recurrent and often unpredictable course of affective disorders poses a critical challenge for long-term patient care. The CRC/TRR 393 consortium ...

Battery SOH estimation based on thermodynamic parameters from an electrochemical fractional-order model and LSTM.

Accurate state of health (SOH) estimation is vital for lithium-ion battery safety and performance. Since SOH cannot be directly measured, traditional ...

Accurate prediction of NCM batteries recovery process under machine learning: Mechanism analysis and industrial application.

Effective recycling of spent LiNixCoyMn1-x-yO2 (NCM) battery is crucial to ensure sustainability of the lithium-ion battery industry. However, recycli...

Delineating white matter phenotypes of sensori-/psychomotor functioning in large-scale cohorts of healthy individuals and patients with mental disorders across the lifespan (whiteSPAN): rationale and methods of an interdisciplinary bicentric project.

Aberrant sensori-/psychomotor functioning-including muscular hand weakness, sedentary behavior, psychomotor agitation, slowing, agitation, apathy, and...

Linking speech patterns to brain structure in affective and psychotic disorders: an integrative natural language processing approach.

Language disturbances are central features of serious mental illnesses, yet traditional clinical assessments often rely on subjective evaluation that ...

Trajectories of affective disorders: neurobiological mechanisms during symptom change.

Effective treatment of affective disorders (AD) requires a deep understanding of the underlying neurobiological mechanisms. However, in machine-learni...

Machine learning identifies traumatic experiences as being associated with auditory verbal hallucinations in both a non-clinical population and individuals diagnosed with psychosis.

BACKGROUND: Auditory verbal hallucinations (AVHs) are a core symptom of psychosis but their prevalence in the general population ranges from 5-28 %. M...

Early identification of pediatric depression in western China: A multicenter, citywide evaluation of nine machine learning models.

BACKGROUND: Childhood depression is an emerging global concern, yet knowledge and early detection tools remain limited in low- and middle-income regio...