Anxiety & Stress

Identification of biomarker genes from multiple studies for abiotic stress in maize through machine learning.

Abiotic stresses are major limiting factors for maize growth. Therefore, exploration of the mechanisms underlying the response to abiotic stress in maize is of great interest. Toward this end, we performed integration of the feature selection method into the meta-analysis of microarray gene expression. Following extraction of raw data, normalization, and batch effect removal, the data were merged ...

Promoting Physical Activity Among Workers for Better Mental Health: An mHealth Intervention With Deep Learning.

There is clear scientific evidence that physical activity helps to prevent depression and anxiety. Utilizing mobile health (mHealth) technologies to enable physical activity is promising, but the evidence of the effectiveness of mHealth interventions on physical activity and mental health is inconsistent. We recently developed a native smartphone app to prevent depression and anxiety by promoting ...

Fuzzy-Based Bioengineering System for Predicting and Diagnosing Diseases of the Nervous System Triggered by the Interaction of Industrial Frequency Electromagnetic Fields.

The study aims to enhance the standard of medical care for individuals working in the electric power industry who are exposed to industrial frequency ...

The New Emerging Treatment Choice for Major Depressive Disorders: Digital Therapeutics.

The chapter provides an in-depth analysis of digital therapeutics (DTx) as a revolutionary approach to managing major depressive disorder (MDD). It di...

Single-cell dissection, hdWGCNA and deep learning reveal the role of oxidatively stressed plasma cells in ulcerative colitis.

Ulcerative colitis (UC) develops as a result of complex interactions between various cell types in the mucosal microenvironment. In this study, we aim...

[A preliminary prediction model of depression based on whole blood cell count by machine learning method].

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping diffe...

A New Perspective on Stress Detection: An Automated Approach for Detecting Eustress and Distress.

Previous studies have solely focused on establishing Machine Learning (ML) models for automated detection of stress arousal. However, these studies do...

Classification of Mental Stress Levels using EEG Connectivity and Convolutional Neural Networks.

Classifying mental stress is important as it helps in identifying the type and severity of stress, which can inform the most appropriate treatment or ...

Assessment of Driver's Stress using Multimodal Biosignals and Regularized Deep Kernel Learning.

In this work, we classify the stress state of car drivers using multimodal physiological signals and regularized deep kernel learning. Using a driving...

Wearable AI Reveals the Impact of Intermittent Fasting on Stress Levels in School Children During Ramadan.

Intermittent fasting has been practiced for centuries across many cultures globally. Recently many studies have reported intermittent fasting for its ...

A Transferable Machine Learning Framework for Predicting Transcriptional Responses of Genes Across Species.

Leveraging existing resources in studied species to predict gene functions has the potential to rapidly expand understanding of annotated genes in oth...

Influence of the stress level on the execution of the Grooved Pegboard Test.

BACKGROUND: The Grooved Pegboard Test (GPT) is a widely adopted test to evaluate manual dexterity. A factor that could influence the cognitive process...

Glucose trajectory prediction by deep learning for personal home care of type 2 diabetes mellitus: modelling and applying.

Glucose management for people with type 2 diabetes mellitus is essential but challenging due to the multi-factored and chronic disease nature of diabe...

Stress Inference from Abdominal Sounds using Machine Learning.

Stress is often considered the 21 century's epidemic, affecting more than a third of the globe's population. Long-term exposure to stress has signific...

Stress Detection from Surface Electromyography using Convolutional Neural Networks.

The study of stress and its implications has been the focus of interest in various fields of science. Many automated/semi-automated stress detection s...

Stressalyzer: Convolutional Neural Network Framework for Personalized Stress Classification.

Stress detection and monitoring is an active area of research with important implications for an individual's personal, professional, and social healt...

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection.

Stress has become a dangerous health problem in our life, especially in student education journey. Accordingly, previous methods have been conducted t...

The quantification and clinical analysis of depression and anxiety in patients undergoing Da Vinci robot-assisted radical gastrectomy and open radical gastrectomy.

The purpose of paper is to investigate the depression and anxiety as well as independent influential factors between patients who underwent Da Vinci r...

ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals.

Continuous and multimodal stress detection has been performed recently through wearable devices and machine learning algorithms. However, a well-known...

Accelerated Aging of the Amygdala in Alcohol Use Disorders: Relevance to the Dark Side of Addiction.

Here we assessed changes in subcortical volumes in alcohol use disorder (AUD). A simple morphometry-based classifier (MC) was developed to identify su...