What is the Role of International Law in Global Health Governance on the Period of Covid-19

Rapid globalisation challenges many of the traditional assumptions about International law, which is linked to domestic law, especially the ways in which it is formed and the methods of its implementation. This phenomenon led governments to be more focused on international collaboration to achieve national public health purposes and succeed some audit over the cross-border powers that influence their populations. This essay will analyse the position on what is the role of international law in global health governance. Another significant result of this essay is that Global Actors should create a global health cooperation in order to implement the international law effectively on the period of Covid-19.

Metabolic syndrome in rheumatic diseases: epidemiology, pathophysiology, and clinical implications

Subjects with metabolic syndrome–a constellation of cardiovascular risk factors of which central obesity and insulin resistance are the most characteristic–are at increased risk for developing diabetes mellitus and cardiovascular disease. In these subjects, abdominal adipose tissue is a source of inflammatory cytokines such as tumor necrosis factor-alpha, known to promote insulin resistance. The presence of inflammatory cytokines together with the well-documented increased risk for cardiovascular diseases in patients with inflammatory arthritides and systemic lupus erythematosus has prompted studies to examine the prevalence of the metabolic syndrome in an effort to identify subjects at risk in addition to that conferred by traditional cardiovascular risk factors. These studies have documented a high prevalence of metabolic syndrome which correlates with disease activity and markers of atherosclerosis. The correlation of inflammatory disease activity with metabolic syndrome provides additional evidence for a link between inflammation and metabolic disturbances/vascular morbidity

AI and Big Data: A New Paradigm for Decision Making in Healthcare

The latest developments in artificial intelligence (AI) - a general-purpose technology impacting many industries - have been based on advancements in machine learning, which is recast as a quality-adjusted decline in forecasting ratio. The influence of Policy on AI and big data has impacted two key magnitudes which are known as diffusion and consequences. And these must be focused primarily on the context of AI and big data. First, in addition to the policies on subsidies and intellectual property (IP) that will affect the propagation of AI in ways close to their effect on other technologies, three policy categories - privacy, exchange, and liability - may have a specific impact on the diffusion of AI. The first step in the prohibition process is to identify the shortcomings of current hospital procedures, why we need acute care AI, and eventually how the direction of patient decision-making will shift with the introduction of AI-based research. The second step is to establish a plan to shift the direction of medical education in order to enable physicians to retain control of AI. Medical research would need to rely less on threshold decision-making and more on the prediction, interpretation, and pathophysiological context of contextual time cycles. This should be an early part of a medical student's education, and this is what their hospital aid (AI) ought to do. Effective contact between human and artificial intelligence includes a shared pattern of focused knowledge base. Human-to-human contact protection in hospitals should lead this professional transformation process

The contribution of CSR during the covid-19 period in Greece: A step forward

The spread of the Covid-19 brought global institutions, societies, states and economies in a critical position as they encounter a new worldwide multilevel crisis. At the same time, states have had to handle this crisis acquiring an interventionist role, protecting the social and economic cohesion, providing better health care services for their citizens and investing in scientific research, as a means to restrict this new pandemic. In order to handle that situation and its consequences, the use of all the available resources became necessary as well as the improvement of the cooperation between the private and the public sector. In Greece private sector has shown an unprecedented willingness for Greece’s CSR tradition, to contribute government’s efforts

BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations

Objective: The advent of High-Performance Computing (HPC) in recent years has led to its increasing use in brain study through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a single acceleration (or homogeneous) platform to effectively address the complete array of modeling requirements. Approach: In this paper we propose and build BrainFrame, a heterogeneous acceleration platform, incorporating three distinct acceleration technologies, a Dataflow Engine, a Xeon Phi and a GP-GPU. The PyNN framework is also integrated into the platform. As a challenging proof of concept, we analyze the performance of BrainFrame on different instances of a state-of-the-art neuron model, modeling the Inferior- Olivary Nucleus using a biophysically-meaningful, extended Hodgkin-Huxley representation. The model instances take into account not only the neuronal- network dimensions but also different network-connectivity circumstances that can drastically change application workload characteristics. Main results: The synthetic approach of three HPC technologies demonstrated that BrainFrame is better able to cope with the modeling diversity encountered. Our performance analysis shows clearly that the model directly affect performance and all three technologies are required to cope with all the model use cases.Comment: 16 pages, 18 figures, 5 table

EDEN: A high-performance, general-purpose, NeuroML-based neural simulator

Modern neuroscience employs in silico experimentation on ever-increasing and more detailed neural networks. The high modelling detail goes hand in hand with the need for high model reproducibility, reusability and transparency. Besides, the size of the models and the long timescales under study mandate the use of a simulation system with high computational performance, so as to provide an acceptable time to result. In this work, we present EDEN (Extensible Dynamics Engine for Networks), a new general-purpose, NeuroML-based neural simulator that achieves both high model flexibility and high computational performance, through an innovative model-analysis and code-generation technique. The simulator runs NeuroML v2 models directly, eliminating the need for users to learn yet another simulator-specific, model-specification language. EDEN's functional correctness and computational performance were assessed through NeuroML models available on the NeuroML-DB and Open Source Brain model repositories. In qualitative experiments, the results produced by EDEN were verified against the established NEURON simulator, for a wide range of models. At the same time, computational-performance benchmarks reveal that EDEN runs up to 2 orders-of-magnitude faster than NEURON on a typical desktop computer, and does so without additional effort from the user. Finally, and without added user effort, EDEN has been built from scratch to scale seamlessly over multiple CPUs and across computer clusters, when available.Comment: 29 pages, 9 figure

AI transforming Healthcare Management during Covid-19 pandemic

The dawn of artificial intelligence (AI) as a platform for improved health care provides unparalleled opportunity to enhance patient and clinical team performance, minimize costs, and reduce the health effects of the community. It provides a broad description of the legal and legislative context of the AI tools intended for the implementation of health care; highlights the need for equality, accessibility, the need for a human rights goal for the work; and identifies important factors for further advancement. AI framework describes the obstacles, drawbacks, and best practices for AI development, adoption, and management. It brings in a paradigm shift to healthcare, driven by rising clinical data access and rapid advancement in analytical techniques. Artificial Intelligence (AI) is going to revolutionize the practice of medicine and change the delivery of healthcare. This paper discusses the role of artificial intelligence in the advancement of health care and associated fields. It also discusses, the value of artificial intelligence in various healthcare sectors' transformation