Search Results (4)

Background: Chronic wounds are a significant health problem with devastating consequences for patients’ physical, social, and mental health, increasing healthcare systems’ costs. Their prolonged healing times, economic burden, diminished quality of life, increased infection risk, and impact on patients’ mobility and functionality make them a major concern for healthcare professionals. Purpose: This review offers a multi-perspective analysis of the medical literature focusing on chronic wound management. Methods used: We evaluated 48 articles from the last 21 years registered in the MEDLINE and Global Health databases. The articles included in our study had a minimum of 20 citations, patients > 18 years old, and focused on chronic, complex, and hard-to-heal wounds. Extracted data were summarized into a narrative synthesis using the same health-related quality of life instrument. Results: We evaluated the efficacy of existing wound care therapies from classical methods to modern concepts, and wound care products to regenerative medicine that uses a patient’s pluripotent stem cells and growth factors. Regenerative medicine and stem cell therapies, biologic dressings and scaffolds, negative pressure wound therapy (NPWT), electrical stimulation, topical growth factors and cytokines, hyperbaric oxygen therapy (HBOT), advanced wound dressings, artificial intelligence (AI), and digital wound management are all part of the new arsenal of wound healing. Conclusion: Periodic medical evaluation and proper use of modern wound care therapies, including the use of plasma-derived products [such as platelet-rich plasma (PRP) and platelet-rich fibrin (PRF)] combined with proper systemic support (adequate protein levels, blood sugar, vitamins involved in tissue regeneration, etc.) are the key to a faster wound healing, and, with the help of AI, can reach the fastest healing rate possible. Full article

In this perspective article, we show that a morphospace, based on information-theoretic measures, can be a useful construct for comparing biological agents with artificial intelligence (AI) systems. The axes of this space label three kinds of complexity: (i) autonomic, (ii) computational and (iii) social complexity. On this space, we map biological agents such as bacteria, bees, C. elegans, primates and humans; as well as AI technologies such as deep neural networks, multi-agent bots, social robots, Siri and Watson. A complexity-based conceptualization provides a useful framework for identifying defining features and classes of conscious and intelligent systems. Starting with cognitive and clinical metrics of consciousness that assess awareness and wakefulness, we ask how AI and synthetically engineered life-forms would measure on homologous metrics. We argue that awareness and wakefulness stem from computational and autonomic complexity. Furthermore, tapping insights from cognitive robotics, we examine the functional role of consciousness in the context of evolutionary games. This points to a third kind of complexity for describing consciousness, namely, social complexity. Based on these metrics, our morphospace suggests the possibility of additional types of consciousness other than biological; namely, synthetic, group-based and simulated. This space provides a common conceptual framework for comparing traits and highlighting design principles of minds and machines. Full article

This paper is our attempt, on the basis of physical theory, to bring more clarification on the question “What is life?” formulated in the well-known book of Schrödinger in 1944. According to Schrödinger, the main distinguishing feature of a biosystem’s functioning is the ability to preserve its order structure or, in mathematical terms, to prevent increasing of entropy. However, Schrödinger’s analysis shows that the classical theory is not able to adequately describe the order-stability in a biosystem. Schrödinger also appealed to the ambiguous notion of negative entropy. We apply quantum theory. As is well-known, behaviour of the quantum von Neumann entropy crucially differs from behaviour of classical entropy. We consider a complex biosystem S composed of many subsystems, say proteins, cells, or neural networks in the brain, that is, $S=\left(S_i\right).$ We study the following problem: whether the compound system S can maintain “global order” in the situation of an increase of local disorder and if S can preserve the low entropy while other $S_i$ increase their entropies (may be essentially). We show that the entropy of a system as a whole can be constant, while the entropies of its parts rising. For classical systems, this is impossible, because the entropy of S cannot be less than the entropy of its subsystem $S_i$. And if a subsystems’s entropy increases, then a system’s entropy should also increase, by at least the same amount. However, within the quantum information theory, the answer is positive. The significant role is played by the entanglement of a subsystems’ states. In the absence of entanglement, the increasing of local disorder implies an increasing disorder in the compound system S (as in the classical regime). In this note, we proceed within a quantum-like approach to mathematical modeling of information processing by biosystems—respecting the quantum laws need not be based on genuine quantum physical processes in biosystems. Recently, such modeling found numerous applications in molecular biology, genetics, evolution theory, cognition, psychology and decision making. The quantum-like model of order stability can be applied not only in biology, but also in social science and artificial intelligence. Full article

In a human society with emergent technology, the destructive actions of some pose a danger to the survival of all of humankind, increasing the need to maintain peace by overcoming universal conflicts. However, human society has not yet achieved complete global peacekeeping. Fortunately, a new possibility for peacekeeping among human societies using the appropriate interventions of an advanced system will be available in the near future. To achieve this goal, an artificial intelligence (AI) system must operate continuously and stably (condition 1) and have an intervention method for maintaining peace among human societies based on a common value (condition 2). However, as a premise, it is necessary to have a minimum common value upon which all of human society can agree (condition 3). In this study, an AI system to achieve condition 1 was investigated. This system was designed as a group of distributed intelligent agents (IAs) to ensure robust and rapid operation. Even if common goals are shared among all IAs, each autonomous IA acts on each local value to adapt quickly to each environment that it faces. Thus, conflicts between IAs are inevitable, and this situation sometimes interferes with the achievement of commonly shared goals. Even so, they can maintain peace within their own societies if all the dispersed IAs think that all other IAs aim for socially acceptable goals. However, communication channel problems, comprehension problems, and computational complexity problems are barriers to realization. This problem can be overcome by introducing an appropriate goal-management system in the case of computer-based IAs. Then, an IA society could achieve its goals peacefully, efficiently, and consistently. Therefore, condition 1 will be achievable. In contrast, humans are restricted by their biological nature and tend to interact with others similar to themselves, so the eradication of conflicts is more difficult. Full article