Why is Complexity Science valuable for reaching the goals of the UN 2030 Agenda?

Evaluation of Metabolic Dysfunction-Associated Fatty Liver Disease-Related Pathogenic Mechanisms in Human Steatotic Liver Cell-Based Model: Beneficial Effects of Prunus domestica L. subsp. syriaca Extract

Background/Objectives: Disrupted glucose uptake, oxidative stress, and increased de novo lipogenesis are some of the key features of metabolic dysfunction-associated fatty liver disease (MASLD). The modulation of these pathogenic mechanisms using extracts from natural and sustainable sources is a promising strategy to mitigate disease progression. This study aimed to evaluate the effects of Prunus domestica L. subsp. syriaca extract on these processes, taking advantage of a cell-based model of steatotic hepatocytes (HepG2-OA) that recapitulates some key pathophysiological features of MASLD. Methods: The HepG2-OA cell model was generated by treating cells for 7 days with 100 μM oleic acid (OA). The effect of different concentrations (0.01, 0.1, 0.5, and 1 mg/mL) of P. domestica extract was assessed through MTT assay (cell viability), flow cytometry (glucose uptake and reactive oxygen species, ROS, production), spectrophotometry (lipid accumulation), and qRT-PCR (expression of selected genes). Results: P. domestica extract exhibited no cytotoxicity at any tested concentration after 24 and 48 h in the HepG2-OA cells. The extract increased glucose uptake in a dose-dependent fashion after both 6 and 24 h. Additionally, the extract reduced lipid accumulation and downregulated the expression of key lipogenic genes (DGAT1 and FASN). Furthermore, in the HepG2-OA cells, P. domestica extract reduced ROS production and downregulated the expression of oxidative stress-related genes (SOD and CAT). Conclusions: P. domestica extract positively modulated some key molecular mechanisms associated with glucose metabolism, lipogenesis, and oxidative stress, supporting its potential as a nutraceutical candidate for MASLD management.

Determining Chemical Microheterogeneity from the Analysis of Absorption and Luminescence Transient Signals

The characterization of chemical microheterogeneity is compelling due to its relevant role in soft materials, high-entropy materials, and systems chemistry, to cite just a few instances. This work investigates the microheterogeneity of photochromic samples and metal oxide solid solutions by fitting time-resolved absorption and luminesce signals recorded after photoexcitation. The transient spectroscopic signals have been analyzed using polyexponential functions determined through the Maximum Entropy Method (MEM) and discrete exponential, Kohlrausch, and Becquerel functions through the Levenberg-Marquardt algorithm. The outputs of the different fitting functions and algorithms are compared and exploited to characterize chemical microheterogeneity quantitatively. The practical relevance of chemical microheterogeneity is supported by the demonstration that photochromic samples are transformed from binary to multistate systems, capable of encoding much more information, and that microheterogeneous photocatalysts are provided with several structural defects that guarantee the coexistence of many active sites and higher catalytic activity.

Living cells and biological mechanisms as prototypes for developing chemical artificial intelligence

Artificial Intelligence (AI) is having a revolutionary impact on our societies. It is helping humans in facing the global challenges of this century. Traditionally, AI is developed in software or through neuromorphic engineering in hardware. More recently, a brand-new strategy has been proposed. It is the so-called Chemical AI (CAI), which exploits molecular, supramolecular, and systems chemistry in wetware to mimic human intelligence. In this work, two promising approaches for boosting CAI are described. One regards designing and implementing neural surrogates that can communicate through optical or chemical signals and give rise to networks for computational purposes and to develop micro/nanorobotics. The other approach concerns "bottom-up synthetic cells" that can be exploited for applications in various scenarios, including future nano-medicine. Both topics are presented at a basic level, mainly to inform the broader audience of non-specialists, and so favour the rise of interest in these frontier subjects.

The Conformational Contribution to Molecular Complexity and Its Implications for Information Processing in Living Beings and Chemical Artificial Intelligence

This work highlights the relevant contribution of conformational stereoisomers to the complexity and functions of any molecular compound. Conformers have the same molecular and structural formulas but different orientations of the atoms in the three-dimensional space. Moving from one conformer to another is possible without breaking covalent bonds. The interconversion is usually feasible through the thermal energy available in ordinary conditions. The behavior of most biopolymers, such as enzymes, antibodies, RNA, and DNA, is understandable if we consider that each exists as an ensemble of conformers. Each conformational collection confers multi-functionality and adaptability to the single biopolymers. The conformational distribution of any biopolymer has the features of a fuzzy set. Hence, every compound that exists as an ensemble of conformers allows the molecular implementation of a fuzzy set. Since proteins, DNA, and RNA work as fuzzy sets, it is fair to say that life's logic is fuzzy. The power of processing fuzzy logic makes living beings capable of swift decisions in environments dominated by uncertainty and vagueness. These performances can be implemented in chemical robots, which are confined molecular assemblies mimicking unicellular organisms: they are supposed to help humans "colonise" the molecular world to defeat diseases in living beings and fight pollution in the environment.

Tracing a new path in the field of AI and robotics: mimicking human intelligence through chemistry. Part II: systems chemistry

Inspired by some traits of human intelligence, it is proposed that wetware approaches based on molecular, supramolecular, and systems chemistry can provide valuable models and tools for novel forms of robotics and AI, being constituted by soft matter and fluid states as the human nervous system and, more generally, life, is. Bottom-up mimicries of intelligence range from the molecular world to the multicellular level, i.e., from the Ångström (10 - 10 meters) to the micrometer scales (10 - 6 meters), and allows the development of unconventional chemical robotics. Whereas conventional robotics lets humans explore and colonise otherwise inaccessible environments, such as the deep oceanic abysses and other solar system planets, chemical robots will permit us to inspect and control the microscopic molecular and cellular worlds. This article suggests that systems made of properly chosen molecular compounds can implement all those modules that are the fundamental ingredients of every living being: sensory, processing, actuating, and metabolic networks. Autonomous chemical robotics will be within reach when such modules are compartmentalised and assembled. The design of a strongly intertwined web of chemical robots, with or without the involvement of living matter, will give rise to collective forms of intelligence that will probably reproduce, on a minimal scale, some sophisticated performances of the human intellect and will implement forms of "general AI." These remarkable achievements will require a productive interdisciplinary collaboration among chemists, biotechnologists, computer scientists, engineers, physicists, neuroscientists, cognitive scientists, and philosophers to be achieved. The principal purpose of this paper is to spark this revolutionary collaborative scientific endeavour.

Tracing a new path in the field of AI and robotics: mimicking human intelligence through chemistry. Part I: molecular and supramolecular chemistry

Chemical Artificial Intelligence (CAI) is a brand-new research line that exploits molecular, supramolecular, and systems chemistry in wetware (i.e., in fluid solutions) to imitate some performances of human intelligence and promote unconventional robotics based on molecular assemblies, which act in the microscopic world, otherwise tough to be accessed by humans. It is undoubtedly worth spreading the news that AI researchers can rely on the help of chemists and biotechnologists to reach the ambitious goals of building intelligent systems from scratch. This article reports the first attempt at building a Chemical Artificial Intelligence knowledge map and describes the basic intelligent functions that can be implemented through molecular and supramolecular chemistry. Chemical Artificial Intelligence provides new tools and concepts to mimic human intelligence because it shares, with biological intelligence, the same principles and materials. It enables peculiar dynamics, possibly not accessible in software and hardware domains. Moreover, the development of Chemical Artificial Intelligence will contribute to a deeper understanding of the strict link between intelligence and life, which are two of the most remarkable emergent properties shown by the Complex Systems we call biological organisms.

The Versatile Photo-Thermal Behaviour of a 2-Hydroxyazobenzene

Photochromic compounds are employed in implementing neuron surrogates. They will boost the development of neuromorphic engineering in wetware. In this work, the photochromic behaviours of (E)-3,4,6-trichloro-2-(p-diazenil)-phenol (t-DZH) and its conjugated phenoxide base (t-DZ) have been investigated experimentally in three different media: (1) pure acetonitrile, (2) in water and acetonitrile mixed in a 1/1 volume ratio, and (3) in an aqueous micellar solution of 3-(N,N-Dimethylmyristylammonio)propanesulfonate (SB3-14). The analysis of the spectral and kinetic features of t-DZH and t-DZ has been supported by quantum-mechanical DFT calculations, the maximum entropy method, and the determination of their colourability (C). The versatility of t-DZH and t-DZ makes them promising molecular probes of micro-environments and potential ingredients of photochemical oscillators required for implementing pacemaker neurons capable of communicating through optical signals in wetware.

Establishing a New Link between Fuzzy Logic, Neuroscience, and Quantum Mechanics through Bayesian Probability: Perspectives in Artificial Intelligence and Unconventional Computing

Human interaction with the world is dominated by uncertainty. Probability theory is a valuable tool to face such uncertainty. According to the Bayesian definition, probabilities are personal beliefs. Experimental evidence supports the notion that human behavior is highly consistent with Bayesian probabilistic inference in both the sensory and motor and cognitive domain. All the higher-level psychophysical functions of our brain are believed to take the activities of interconnected and distributed networks of neurons in the neocortex as their physiological substrate. Neurons in the neocortex are organized in cortical columns that behave as fuzzy sets. Fuzzy sets theory has embraced uncertainty modeling when membership functions have been reinterpreted as possibility distributions. The terms of Bayes' formula are conceivable as fuzzy sets and Bayes' inference becomes a fuzzy inference. According to the QBism, quantum probabilities are also Bayesian. They are logical constructs rather than physical realities. It derives that the Born rule is nothing but a kind of Quantum Law of Total Probability. Wavefunctions and measurement operators are viewed epistemically. Both of them are similar to fuzzy sets. The new link that is established between fuzzy logic, neuroscience, and quantum mechanics through Bayesian probability could spark new ideas for the development of artificial intelligence and unconventional computing.

A network approach to rank countries chasing sustainable development

In 2015, the United Nations established the Agenda 2030 for sustainable development, addressing the major challenges the world faces and introducing the 17 Sustainable Development Goals (SDGs). How are countries performing in their challenge toward sustainable development? We address this question by treating countries and Goals as a complex bipartite network. While network science has been used to unveil the interconnections among the Goals, it has been poorly exploited to rank countries for their achievements. In this work, we show that the network representation of the countries-SDGs relations as a bipartite system allows one to recover aggregate scores of countries' capacity to cope with SDGs as the solutions of a network's centrality exercise. While the Goals are all equally important by definition, interesting differences self-emerge when non-standard centrality metrics, borrowed from economic complexity, are adopted. Innovation and Climate Action stand as contrasting Goals to be accomplished, with countries facing the well-known trade-offs between economic and environmental issues even in addressing the Agenda. In conclusion, the complexity of countries' paths toward sustainable development cannot be fully understood by resorting to a single, multipurpose ranking indicator, while multi-variable analyses shed new light on the present and future of sustainable development.