Anxiety & Stress

Intelligent Occlusion Stabilization Splint with Stress-Sensor System for Bruxism Diagnosis and Treatment.

Bruxism is a masticatory muscle activity characterized by high prevalence, widespread complications, and serious consequences but without specific guidelines for its diagnosis and treatment. Although occlusal force-based biofeedback therapy is proven to be safe, effective, and with few side effects in improving bruxism, its mechanism and key technologies remain unclear. The purpose of this study w...

StressGenePred: a twin prediction model architecture for classifying the stress types of samples and discovering stress-related genes in arabidopsis.

BACKGROUND: Recently, a number of studies have been conducted to investigate how plants respond to stress at the cellular molecular level by measuring gene expression profiles over time. As a result, a set of time-series gene expression data for the stress response are available in databases. With the data, an integrated analysis of multiple stresses is possible, which identifies stress-responsive...

Predicting cognitive behavioral therapy outcome in the outpatient sector based on clinical routine data: A machine learning approach.

The availability of large-scale datasets and sophisticated machine learning tools enables developing models that predict treatment outcomes for indivi...

Hyperspectral imaging combined with machine learning as a tool to obtain high-throughput plant salt-stress phenotyping.

The rapid selection of salinity-tolerant crops to increase food production in salinized lands is important for sustainable agriculture. Recently, high...

Computational modeling of the monoaminergic neurotransmitter and male neuroendocrine systems in an analysis of therapeutic neuroadaptation to chronic antidepressant.

Second-line depression treatment involves augmentation with one (rarely two) additional drugs, of chronic administration of a selective serotonin reup...

Developing algorithms to predict adult onset internalizing disorders: An ensemble learning approach.

A growing literature is utilizing machine learning methods to develop psychopathology risk algorithms that can be used to inform preventive interventi...

The diagnosticity of psychophysiological signatures: Can we disentangle mental workload from acute stress with ECG and fNIRS?

The ability to identify reliable and sensitive physiological signatures of psychological dimensions is key to developing intelligent adaptive systems ...

cytogenotoxic evaluation of sertraline.

Sertraline (SRT) is an antidepressant agent used as a neuronal selective serotonin-reuptake inhibitor (SSRI). SRT blocks serotonin reuptake and increa...

Predicting likelihood of psychological disorders in PlayerUnknown's Battlegrounds (PUBG) players from Asian countries using supervised machine learning.

Multiplayer Online Battle Arena (MOBA) has become one of the most popular genre of online video games played by gamers worldwide. Previous studies hav...

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis.

Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no ro...

Machine Learning to Understand the Immune-Inflammatory Pathways in Fibromyalgia.

Fibromyalgia (FM) is a chronic syndrome characterized by widespread musculoskeletal pain, and physical and emotional symptoms. Although its pathophysi...

Validation of oxidative stress assay for schizophrenia.

Accumulating evidence implicates oxidative stress in a range of diseases, yet no objective measurement has emerged that characterizes the global natur...

Differential gene expression and gene ontologies associated with increasing water-stress in leaf and root transcriptomes of perennial ryegrass (Lolium perenne).

Perennial ryegrass (Lolium perenne) is a forage and amenity grass species widely cultivated in temperate regions worldwide. As such, perennial ryegras...

Towards integrating personalized feedback research into clinical practice: Development of the Trier Treatment Navigator (TTN).

In this study, a computer-based feedback, decision and clinical problem-solving system for clinical practice will be described - the Trier Treatment N...

Application of machine learning to structural connectome to predict symptom reduction in depressed adolescents with cognitive behavioral therapy (CBT).

PURPOSE: Adolescent major depressive disorder (MDD) is a highly prevalent, incapacitating and costly illness. Many depressed teens do not improve with...

Novel Metric Using Laplacian Eigenmaps to Evaluate Ischemic Stress on the Torso Surface.

The underlying pathophysiology of myocardial ischemia is incompletely understood, resulting in persistent difficulty of diagnosis. This limited unders...

Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample.

BACKGROUND: The field of psychiatry has long sought biomarkers that can objectively diagnose patients, predict treatment response, or identify individ...

Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.

A psychological disorder is a mutilation state of the body that intervenes the imperative functioning of the mind or brain. In the last few years, the...

Pre-Treatment Biomarkers of Anti-Tumour Necrosis Factor Therapy Response in Crohn's Disease-A Systematic Review and Gene Ontology Analysis.

The most prominent treatment for the serious cases of Crohn's disease (CD) are biological tumour necrosis factor (TNF) inhibitors. Unfortunately, ther...

See your mental state from your walk: Recognizing anxiety and depression through Kinect-recorded gait data.

As the challenge of mental health problems such as anxiety and depression increasing today, more convenient, objective, real-time assessing techniques...