Multidisciplinary approach to functional somatic syndromes:study protocol for a population-based prospective cohort study

Introduction Isfahan functional disorders (ISFUN) cohort study aims to describe the interplay of genetic and environmental factors in shaping the characteristics of functional somatic syndromes (FSS). This study is primarily intended to investigate the epidemiology, risk factors, course and prognosis of FSSs in a sample of adult Iranian population. The other aim is to develop a new delimitation of FSSs based on an integrated multidisciplinary approach comprising of phenotypic and multiomics data. Methods and analysis ISFUN is a population-based prospective cohort study designed to follow a population of randomly selected seemingly healthy adults (18-65 years) through annual visits during a 4-year observation period. Structured questionnaires are used for data collection and clinical assessment of the participants. Questionnaire-based diagnosis of FSSs are validated in a medical interview. Human DNA genotyping, microbial amplicon sequencing and urine analysis is under progress for genomics, microbiota and metabolomics profiling, respectively. Enrolment began in September 2017, and study completion is expected in 2022. A total number of 1943 participants were initially recruited. Ethics and dissemination Ethical approval for data collection was granted by the National Research Ethics Committee of the Iranian Ministry of Health and Medical Education and the Research Ethics Committee of Isfahan University of Medical Sciences (IR.MUI.REC.1395.1.149). Following the description of the study procedure, we obtained written informed consent from all study participants. Study findings will be disseminated through peer-reviewed publications and presentations at scientific meetings

Do “Dark” Personality Features Buffer Against Adversity? The Associations Between Cumulative Life Stress, the Dark Triad, and Mental Distress

Stressful life events have a major impact on adverse mental health outcomes, although not all individuals are equally affected. According to the buffering hypothesis, there may be personality traits that protect individuals against mental distress in the face of adversity, playing thus a moderating role between life stressors and mental distress. In the present online study (N = 574), Dark Triad of personality (i.e., Machiavellianism, narcissism, and psychopathy) were investigated as moderators between cumulative stressful life events and mental distress (i.e., psychosis, anxiety, and depression). Those who experienced more stressful events during lifetime, and scored higher in Machiavellianism, had higher scores on a psychosis instrument. Narcissism buffered the impact of stressful events on psychosis and depression. The results are discussed in terms of unique profiles associated with each of the traits</p

Human Resource Management in Emergency Situations

The dissertation examines the issues related to the human resource management in emergency situations and introduces the measures helping to solve these issues. The prime aim is to analyse complexly a human resource management, built environment resilience management life cycle and its stages for the purpose of creating an effective Human Resource Management in Emergency Situations Model and Intelligent System. This would help in accelerating resilience in every stage, managing personal stress and reducing disaster-related losses. The dissertation consists of an Introduction, three Chapters, the Conclusions, References, List of Author’s Publications and nine Appendices. The introduction discusses the research problem and the research relevance, outlines the research object, states the research aim and objectives, overviews the research methodology and the original contribution of the research, presents the practical value of the research results, and lists the defended propositions. The introduction concludes with an overview of the author’s publications and conference presentations on the topic of this dissertation. Chapter 1 introduces best practice in the field of disaster and resilience management in the built environment. It also analyses disaster and resilience management life cycle ant its stages, reviews different intelligent decision support systems, and investigates researches on application of physiological parameters and their dependence on stress. The chapter ends with conclusions and the explicit objectives of the dissertation. Chapter 2 of the dissertation introduces the conceptual model of human resource management in emergency situations. To implement multiple criteria analysis of the research object the methods of multiple criteria analysis and mahematics are proposed. They should be integrated with intelligent technologies. In Chapter 3 the model developed by the author and the methods of multiple criteria analysis are adopted by developing the Intelligent Decision Support System for a Human Resource Management in Emergency Situations consisting of four subsystems: Physiological Advisory Subsystem to Analyse a User’s Post-Disaster Stress Management; Text Analytics Subsystem; Recommender Thermometer for Measuring the Preparedness for Resilience and Subsystem of Integrated Virtual and Intelligent Technologies. The main statements of the thesis were published in eleven scientific articles: two in journals listed in the Thomson Reuters ISI Web of Science, one in a peer-reviewed scientific journal, four in peer-reviewed conference proceedings referenced in the Thomson Reuters ISI database, and three in peer-reviewed conference proceedings in Lithuania. Five presentations were given on the topic of the dissertation at conferences in Lithuania and other countries

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

ASSOCIATION OF THE GLUCOCORTICOID RECEPTOR GENE POLYMORPHISMS AND THEIR INTERACTION WITH STRESSFUL LIFE EVENTS IN POLISH ADOLESCENT GIRLS WITH ANOREXIA NERVOSA

Background: Disturbances in stress response mechanisms and hypothalamic-pituitary-adrenal axis (HPA) functioning are considered important factors involved in the pathophysiology of anorexia nervosa (AN). Thus, genetic variations in the end effector of HPA - glucocorticoid receptor gene and relationships to stressful life events (SLE) may be connected to a higher risk of illness. The aim of the study was examining the association between glucocorticoid receptor gene (NR3C1) polymorphisms and risk factors among stressful life events in AN patients. Subjects and methods: This study comprised 256 patients with AN and 167 control subjects. The questionnaires examining brief history of the mother’s pregnancy and long-acting stress factors, as well as life events checklist to assess stressful life events during the 6 months prior to hospitalization were used. The eight common SNPs (rs6198, rs6191, rs6196, rs258813, rs33388, rs41423247, rs56149945 and rs10052957) of NR3C1 gene were genotyped. Results: The association of five polymorphisms (rs6191, rs258813, rs33388, rs41423247 and rs10052957) and one complex allele (TCAGT) of NR3C1 gene with increased risk of AN were found. However, no significant correlations between early, longacting and predicting hospitalization SLE and any of the analyzed polymorphisms were observed. Conclusions: The results confirm that the NR3C1 gene is associated with AN risk regardless of the type of stressful triggering factors

The development of a risk index for depression (RID)

&nbsp;This thesis developed a novel methodology for a flexible and modular Risk Index for Depression (RID) that blended data mining and machine learning techniques with traditional statistical techniques. This RID shows great potential for future clinical use.<br /

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 373)

This bibliography lists 206 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Feb. 1993. Subject coverage includes: aerospace medicine and physiology, pharmacology, toxicology, environmental effect, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Applied Cognitive Sciences

Cognitive science is an interdisciplinary field in the study of the mind and intelligence. The term cognition refers to a variety of mental processes, including perception, problem solving, learning, decision making, language use, and emotional experience. The basis of the cognitive sciences is the contribution of philosophy and computing to the study of cognition. Computing is very important in the study of cognition because computer-aided research helps to develop mental processes, and computers are used to test scientific hypotheses about mental organization and functioning. This book provides a platform for reviewing these disciplines and presenting cognitive research as a separate discipline