Anxiety & Stress

On-Chip Mental Stress Detection: Integrating a Wearable Behind-The-Ear EEG Device With Embedded Tiny Neural Network.

The study introduces an innovative approach to efficient mental stress detection by combining electroencephalography (EEG) analysis with on-chip neural networks, taking advantage of EEG's temporal resolution and the computational capabilities of embedded neural networks. The proposed system utilizes behind-the-ear (BTE) EEG signals and on-chip neural networks for mental stress detection. A wearabl...

Involvement of disulfidptosis in the pathophysiology of autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder, with oxidative stress recognized as a key pathogenic mechanisms. Oxidative stress disrupts intracellular dynamic- thiol/disulfide homeostasis (DTDH), potentially leading to disulfidptosis, a newly identified cell death mechanism. While studies suggest a link between DTDH and ASD, direct evidence implicating disulfidptosis in ...

Assessment of PTSD in military personnel via machine learning based on physiological habituation in a virtual immersive environment.

Posttraumatic stress disorder (PTSD) is a complex mental health condition triggered by exposure to traumatic events that leads to physical health prob...

Predictive stress analysis in simplified spinal disc model using physics-informed neural networks.

This study develops a physics-informed neural network (PINN) model to predict stress distribution in a simplified spinal disc structure. The model inc...

Automatic analysis of high, medium, and low activities of broilers with heat stress operations via image processing and machine learning.

Heat stress is a major welfare problem in the poultry industry, altering broilers' activity levels. Advancements in image processing and machine learn...

Using explainable machine learning to investigate the relationship between childhood maltreatment, positive psychological traits, and CPTSD symptoms.

The functional impairment resulting from CPTSD symptoms is enduring and far-reaching. Existing research has found that CPTSD symptoms are closely ass...

Predicting social anxiety disorder based on communication logs and social network data from a massively multiplayer online game: Using a graph neural network.

AIM: Social anxiety disorder (SAD) is a mental disorder that requires early detection and treatment. However, some individuals with SAD avoid face-to-...

Advanced computational approaches for predicting sunflower yield: Insights from ANN, ANFIS, and GEP in normal and salinity stress environments.

Prediction of crop yield is essential for decision-makers to ensure food security and provides valuable information to farmers about factors affecting...

Fast, smart, and adaptive: using machine learning to optimize mental health assessment and monitor change over time.

In mental health, accurate symptom assessment and precise measurement of patient conditions are crucial for clinical decision-making and effective tre...

Rapid wall shear stress prediction for aortic aneurysms using deep learning: a fast alternative to CFD.

Aortic aneurysms pose a significant risk of rupture. Previous research has shown that areas exposed to low wall shear stress (WSS) are more prone to r...

Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer.

Heart Rate Variability (HRV) serves as a vital marker of stress levels, with lower HRV indicating higher stress. It measures the variation in the time...

Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation.

BACKGROUND: The changes in DNA methylation patterns may reflect both physical and mental well-being, the latter being a relatively unexplored avenue i...

Effectiveness of social robots in improving psychological well-being of hospitalised children: A systematic review and meta-analysis.

AIM: To evaluate the effectiveness of social robots in reducing pain, stress, and anxiety among hospitalised children.

Stress hyperglycemia ratio and machine learning model for prediction of all-cause mortality in patients undergoing cardiac surgery.

BACKGROUND: The stress hyperglycemia ratio (SHR) was developed to reduce the effects of long-term chronic glycemic factors on stress hyperglycemia lev...

Efficacy and effectiveness of therapist-guided internet versus face-to-face cognitive behavioural therapy for depression via counterfactual inference using naturalistic registers and machine learning in Finland: a retrospective cohort study.

BACKGROUND: According to meta-analyses of randomised controlled trials (RCTs), therapist-guided internet-delivered cognitive behavioural therapy (iCBT...

Identifying Adolescent Depression and Anxiety Through Real-World Data and Social Determinants of Health: Machine Learning Model Development and Validation.

BACKGROUND: The prevalence of adolescent mental health conditions such as depression and anxiety has significantly increased. Despite the potential of...

Using natural language processing to identify patterns associated with depression, anxiety, and stress symptoms during the COVID-19 pandemic.

BACKGROUND: Combining data-driven natural language processing techniques with traditional methods using predefined word lists may offer greater insigh...

MechBERT: Language Models for Extracting Chemical and Property Relationships about Mechanical Stress and Strain.

Language models are transforming materials-aware natural-language processing by enabling the extraction of dynamic, context-rich information from unst...

Integrating deep learning algorithms for forecasting evapotranspiration and assessing crop water stress in agricultural water management.

The increasing impacts of climate change on global agriculture necessitate the development of advanced predictive models for efficient water managemen...

Exploring the role of oxidative stress in carotid atherosclerosis: insights from transcriptomic data and single-cell sequencing combined with machine learning.

BACKGROUND: Carotid atherosclerotic plaque is the primary cause of cardiovascular and cerebrovascular diseases. It is closely related to oxidative str...