Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning.

Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning (ML) models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required. In this work, we have explored a series of ML models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of ML models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of virtual reality exposure therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety.

Comparative Transcriptomics Reveals the Role of the Toll-Like Receptor Signaling Pathway in Fluoride-Induced Cardiotoxicity.

Many studies have shown that fluorosis due to long-term fluoride intake has damaging effects on the heart. However, the mechanisms underlying cardiac fluorosis have not been illuminated in detail. We performed high-throughput transcriptome sequencing (RNA-Seq) on rat cardiac tissue to explore the molecular effects of NaF exposure. In total, 372 and 254 differentially expressed genes (DEGs) were identified between a group given 30 mg/L NaF and control and between a group given 90 mg/L NaF and control, respectively. The transcript levels of most of these genes were significantly down-regulated and many were distributed in the Toll-like receptor signaling pathway. Transcriptome analysis revealed that herpes simplex infection, ECM-receptor interaction, influenza A, cytokine-cytokine receptor interaction, apoptosis, and Toll-like receptor signaling pathway were significantly affected. IL-6 and IL-10 may play a crucial role in the cardiac damage caused by NaF as external stimuli according to protein-protein interaction (PPI) network analysis. The results of qRT-PCR and Western blotting showed a marked decreased mRNA and protein levels of IL-1, IL-6, and IL-10 in the low concentration fluoride (LF) and high concentration fluoride (HF) groups, which was in agreement with RNA-Seq results. This is the first study to investigate NaF-induced cardiotoxicity at a transcriptome level.