ADHD/ADD

Converging Representations of Attention-Deficit/Hyperactivity Disorder and Autism on Social Media: Linguistic and Topic Analysis of Trends in Reddit Data.

BACKGROUND: Social media platforms have witnessed a substantial increase in mental health-related discussions, with particular attention focused on attention-deficit/hyperactivity disorder (ADHD) and autism. This heightened interest coincides with growing neurodiversity advocacy. The impact of these changes in the conceptualization of ADHD and autism, and the relationship between the 2 conditions,...

In pursuit of software solutions for pharmaceutical regulatory affairs: Insights and trends.

With rapid upsurge in technology and digital tools, the existing systems including the healthcare systems, especially the pharmaceutical sector is experiencing the revolution in the flow and management of data. Use of digital tools in pharmaceutical regulatory framework and compliance has led to development of a more harmonized system globally. This has facilitated growth in pharmaceutical sector ...

Automated Depression Detection from Text and Audio: A Systematic Review.

Depression is a prevalent mental health disorder that presents significant challenges for timely diagnosis and intervention. Automated Depression Dete...

Learning temporal granularity with quadruplet networks for temporal knowledge graph completion.

Temporal Knowledge Graphs (TKGs) capture the dynamic nature of real-world facts by incorporating temporal dimensions that reflect their evolving state...

Detection of Cortical Arousals in Sleep Using Multimodal Wearable Sensors and Machine Learning.

Cortical arousals are brief brain activations that disrupt sleep continuity and contribute to cardiovascular, cognitive, and behavioral impairments. A...

MC-ASFF-ShipYOLO: Improved Algorithm for Small-Target and Multi-Scale Ship Detection for Synthetic Aperture Radar (SAR) Images.

Synthetic aperture radar (SAR) ship detection holds significant application value in maritime monitoring, marine traffic management, and safety mainte...

The evolution and future of integrated evidence planning.

INTRODUCTION: Integrated Evidence Planning (IEP) is a strategic approach that optimizes drug development and market access by ensuring evidence genera...

Machine learning approach to single cell transcriptomic analysis of Sjogren's disease reveals altered activation states of B and T lymphocytes.

Sjogren's Disease (SjD) is an autoimmune disorder characterized by salivary and lacrimal gland dysfunction and immune cell infiltration leading to gla...

Insulin resistance in type 1 diabetes is a key modulator of platelet hyperreactivity.

AIMS/HYPOTHESIS: Individuals with type 1 diabetes are at increased cardiovascular risk, particularly in the presence of insulin resistance. A prothrom...

Topology-Guided Graph Masked Autoencoder Learning for Population-Based Neurodevelopmental Disorder Diagnosis.

Exploring the pathogenic mechanisms of brain disorders within population is an important research in the field of neuroscience. Existing methods eithe...

Monitoring Amphetamine and Methamphetamine Mixtures Based on Deep Learning Involves Colorimetric Sensing.

Precise recognition and discrimination of highly similar analytes (either in structure or property) with distinguishable sensing responses are challen...

Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review.

Attention deficit/hyperactivity disorder is a common neuropsychiatric disorder that affects around 5%-7% of children worldwide. Artificial intelligenc...

Cerebrospinal fluid inflammatory cytokines as prognostic indicators for cognitive decline across Alzheimer's disease spectrum.

BackgroundNeuroinflammation actively contributes to the pathophysiology of Alzheimer's disease (AD); however, the value of neuroinflammatory biomarker...

Functional connectivity anomalies in medication-naive children with ADHD: Diagnostic potential, symptoms interpretation, and a mediation model.

OBJECTIVE: To identify reliable electroencephalography (EEG) biomarkers for attention deficit/hyperactivity disorder (ADHD) by investigating anomalous...

Enhancing brain age estimation under uncertainty: A spectral-normalized neural gaussian process approach utilizing 2.5D slicing.

Brain age gap, the difference between estimated brain age and chronological age via magnetic resonance imaging, has emerged as a pivotal biomarker in ...

Recurrent and convolutional neural networks in classification of EEG signal for guided imagery and mental workload detection.

The Guided Imagery technique is reported to be used by therapists all over the world in order to increase the comfort of patients suffering from a var...

ZnO nanoflower-mediated paper-based electrochemical biosensor for perfect classification of cardiac biomarkers with physics-informed machine learning.

The widespread exposure of acute myocardial infarction globally demands an ultrasensitive, rapid, and cost-effective biosensor for troponin-I and T in...

Automated ADHD detection using dual-modal sensory data and machine learning.

This study explores using dual-modal sensory data and machine learning to objectively identify Attention-Deficit/Hyperactivity Disorder (ADHD), a neur...

Explainability Enhanced Machine Learning Model for Classifying Intellectual Disability and Attention-Deficit/Hyperactivity Disorder With Psychological Test Reports.

BACKGROUND: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability ...

Narrative Search Engine for Case Series Assessment Supported by Artificial Intelligence Query Suggestions.

INTRODUCTION: Manual identification of case narratives with specific relevant information can be challenging when working with large numbers of advers...