Different Types of Training Lead to Different Levels of Cardiovascular Energy Dissipation: Young Soccer Players Versus Ballet Dancers.

INTRODUCTION: Arterial wall viscosity is a source of energy dissipation that takes place during mechanical transduction. In our previous studies, a "global" damping effect in endurance training athletes was introduced, verifying that endurance-athletes dissipate greater pulsatile energy in the circulation compared with healthy untrained subjects. OBJECTIVE: To investigate the wall energy dissipation in the vascular bed for each beat and within the conceptual framework of ventricular-arterial coupling, in order to elucidate if different types of training could lead to differentiated levels of cardiovascular energy dissipation. MATERIALS AND METHODS: Data from subjects with different kinds of training (soccer players and ballet dancers) have been collected noninvasively and compared with a control group of untrained individuals to analyse the differentiating characteristics of the subjects, especially in terms of Stroke Work Dissipation (WDIS). RESULTS: In the endurance-trained individuals, an enhanced WDIS has been observed compared to the untrained individuals (p<0.05). However, non-significant differences were found regarding ballet-dancers group. CONCLUSION: Changes in wall energy dissipation are developed under high intensity endurance training routines.

Open Data and transparency in artificial intelligence and machine learning: A new era of research.

Artificial Intelligence (AI) and machine learning are the current forefront of computer science and technology. AI and related sub-disciplines, including machine learning, are essential technologies which have enabled the widespread use of smart technology, such as smart phones, smart home appliances and even electric toothbrushes. It is AI that allows the devices used day-to-day across people's personal lives, working lives and in industry to better anticipate and respond to our needs. However, the use of AI technology comes with a range of ethical questions - including issues around privacy, security, reliability, copyright/plagiarism and whether AI is capable of independent, conscious thought. We have seen several issues related to racial and sexual bias in AI in the recent times, putting the reliability of AI in question. Many of these issues have been brought to the forefront of cultural awareness in late 2022, early 2023, with the rise of AI art programs (and the copyright issues arising from the deep-learning methods employed to train this AI), and the popularity of ChatGPT alongside its ability to be used to mimic human output, particularly in regard to academic work. In critical areas like healthcare, the errors of AI can be fatal. With the incorporation of AI in almost every sector of our everyday life, we need to keep asking ourselves- can we trust AI, and how much? This Editorial outlines the importance of openness and transparency in the development and applications of AI to allow all users to fully understand both the benefits and risks of this ubiquitous technology, and outlines how the Artificial Intelligence and Machine Learning  Gateway on F1000Research meets these needs.

Predictive Cardiometabolic Risk Profiling of Patients Using Vascular Age in Liver Transplantation.

Liver transplantation is the last therapeutic option in patients with end-stage liver diseases. The adequate clinical management of transplant-patients impacts their vital prognosis and decisions on many occasions are made from the interaction of multiple variables involved in the process. This work is based on the National Liver Transplantation Program in Uruguay. We performed predictive analysis of cardiometabolic diseases on the transplanted cohort between 2014 and 2019, considering vascular age as a key factor. This aims at classification of the cohort based on the vascular age of the evaluated patients before transplantation for risk-profiling. Predicted high-risk group of the patients showed substantial deterioration of post-transplant health-conditions, including higher mortality rate. In our knowledge, this is the first study in Latin America incorporating vascular age toward predictive analysis of cardiometabolic risk factors in liver transplantations. Predictive risk-modeling using vascular age in a pre-transplantation scenario provides significant opportunity for early prediction of post-transplant risk factors, leading to efficient treatment with anticipation.

Internet of Things and Artificial Intelligence in Healthcare During COVID-19 Pandemic-A South American Perspective.

The shudders of the COVID-19 pandemic have projected newer challenges in the healthcare domain across the world. In South American scenario, severe issues and difficulties have been noticed in areas like patient consultations, remote monitoring, medical resources, healthcare personnel etc. This work is aimed at providing a holistic view to the digital healthcare during the times of COVID-19 pandemic in South America. It includes different initiatives like mobile apps, web-platforms and intelligent analyses toward early detection and overall healthcare management. In addition to discussing briefly the key issues toward extensive implementation of eHealth paradigms, this work also sheds light on some key aspects of Artificial Intelligence and the Internet of Things along their potential applications like clinical decision support systems and predictive risk modeling, especially in the direction of combating the emergent challenges due to the COVID-19 pandemic.