Advances in computational modelling for personalised medicine after myocardial infarction

Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners

Hybrid statistical and mechanistic mathematical model guides mobile health intervention for chronic pain

Nearly a quarter of visits to the Emergency Department are for conditions that could have been managed via outpatient treatment; improvements that allow patients to quickly recognize and receive appropriate treatment are crucial. The growing popularity of mobile technology creates new opportunities for real-time adaptive medical intervention, and the simultaneous growth of big data sources allows for preparation of personalized recommendations. Here we focus on the reduction of chronic suffering in the sickle cell disease community. Sickle cell disease is a chronic blood disorder in which pain is the most frequent complication. There currently is no standard algorithm or analytical method for real-time adaptive treatment recommendations for pain. Furthermore, current state-of-the-art methods have difficulty in handling continuous-time decision optimization using big data. Facing these challenges, in this study we aim to develop new mathematical tools for incorporating mobile technology into personalized treatment plans for pain. We present a new hybrid model for the dynamics of subjective pain that consists of a dynamical systems approach using differential equations to predict future pain levels, as well as a statistical approach tying system parameters to patient data (both personal characteristics and medication response history). Pilot testing of our approach suggests that it has significant potential to predict pain dynamics given patients' reported pain levels and medication usages. With more abundant data, our hybrid approach should allow physicians to make personalized, data driven recommendations for treating chronic pain.Comment: 13 pages, 15 figures, 5 table

Estimating prognosis in patients with acute myocardial infarction using personalized computational heart models

Biomechanical computational models have potential prognostic utility in patients after an acute ST-segment–elevation myocardial infarction (STEMI). In a proof-of-concept study, we defined two groups (1) an acute STEMI group (n = 6, 83% male, age 54 ± 12 years) complicated by left ventricular (LV) systolic dysfunction; (2) an age- and sex- matched hyper-control group (n = 6, 83% male, age 46 ± 14 years), no prior history of cardiovascular disease and normal systolic blood pressure (SBP < 130 mmHg). Cardiac MRI was performed in the patients (2 days & 6 months post-STEMI) and the volunteers, and biomechanical heart models were synthesized for each subject. The candidate parameters included normalized active tension (ATnorm) and active tension at the resting sarcomere length (Treq, reflecting required contractility). Myocardial contractility was inversely determined from personalized heart models by matching CMR-imaged LV dynamics. Compared with controls, patients with recent STEMI exhibited increased LV wall active tension when normalized by SBP. We observed a linear relationship between Treq 2 days post-MI and global longitudinal strain 6 months later (r = 0.86; p = 0.03). Treq may be associated with changes in LV function in the longer term in STEMI patients complicated by LV dysfunction. Further studies seem warranted

Audio-tactile stimuli to improve health and well-being : a preliminary position paper

From literature and through common experience it is known that stimulation of the tactile (touch) sense or auditory (hearing) sense can be used to improve people's health and well-being. For example, to make people relax, feel better, sleep better or feel comforted. In this position paper we propose the concept of combined auditory-tactile stimulation and argue that it potentially has positive effects on human health and well-being through influencing a user's body and mental state. Such effects have, to date, not yet been fully explored in scientific research. The current relevant state of the art is briefly addressed and its limitations are indicated. Based on this, a vision is presented of how auditory-tactile stimulation could be used in healthcare and various other application domains. Three interesting research challenges in this field are identified: 1) identifying relevant mechanisms of human perception of combined auditory-tactile stimuli; 2) finding methods for automatic conversions between audio and tactile content; 3) using measurement and analysis of human bio-signals and behavior to adapt the stimulation in an optimal way to the user. Ideas and possible routes to address these challenges are presented

Aerospace Medicine and Biology: A continuing supplement 180, May 1978

This special bibliography lists 201 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1978

Practical Use of ChatGPT in Psychiatry for Treatment Plan and Psychoeducation

Artificial Intelligence (AI) has revolutionized various fields, including medicine and mental health support. One promising application is ChatGPT, an advanced conversational AI model that uses deep learning techniques to provide human-like responses. This review paper explores the potential impact of ChatGPT in psychiatry and its various applications, highlighting its role in therapy and counseling techniques, self-help and coping strategies, mindfulness and relaxation techniques, screening and monitoring, education and information dissemination, specialized support, group and family support, learning and training, expressive and artistic therapies, telepsychiatry and online support, and crisis management and prevention. While ChatGPT offers personalized, accessible, and scalable support, it is essential to emphasize that it should not replace the expertise and guidance of qualified mental health professionals. Ethical considerations, such as user privacy, data security, and human oversight, are also discussed. By examining the potential and challenges, this paper sheds light on the responsible integration of ChatGPT in psychiatric research and practice, fostering improved mental health outcomes

Health Coaching Case Report: Optimizing Employee Health and Wellbeing in Organizations

Abstract Health and wellbeing of employees has a direct correlation to organizational performance. It is essential that organizations and successful leaders prioritize the health and wellbeing of all employees – from the C-suite to entry level positions. As rates of stress, chronic illness, and unhealthy lifestyle choices continue to increase, it is imperative that organizations discover strategies that cultivate employee wellbeing. Employees with high wellbeing are more engaged, productive, and energized and directly affect a company’s bottom line; it is in the best interest of employers to invest in human capital and wellbeing of employees. Health and wellness coaching demonstrates encouraging potential as a cost-effective catalyst to optimize employee wellbeing. Rooted in science-based research with the foundation in relationships, communication, and connection, health coaches partner with employees as they build self-awareness around a holistic view of health. As employees build self-awareness, they increasingly recognize the importance of managing stress and self-care, connecting to their vision and values, taking active steps towards change, and addressing barriers and obstacles. With these strategies, individuals build resilience as they gain energy, empowerment, and work towards positive growth. This paper outlines the challenges that leaders and employees are facing, describes the intervention of health and wellness coaching, and provides a group coaching case study that demonstrates how health and wellness coaching can foster employee wellbeing. This case study provides evidence that health coaching shows promise as an intervention to optimize employee health and wellbeing. Keywords: employee health and wellbeing, wellness, stress management, health and wellness coaching, group coaching, leader wellbeing, self-awareness, case repor