Objectives To study the differences in the levels of nitrogen metabolites, such as ammonia and nitric oxide and the correlations existing among them in both red blood cells (RBCs) and serum, as well as the possible differences by gender in healthy subjects and patients with type 2 Diabetes Mellitus (DM). Design and methods This cross-sectional study included 80 patients diagnosed with type 2 DM (40 female and 40 male patients) and their corresponding controls paired by gender (40 female and 40 male). We separated serum and RBC and determined metabolites mainly through colorimetric and spectrophotometric assays. We evaluated changes in the levels of the main catabolic by-products of blood nitrogen metabolism, nitric oxide (NO), and malondialdehyde (MDA). Results Healthy female and male controls showed a differential distribution of blood metabolites involved in NO metabolism and arginine metabolism for the ornithine and urea formation. Patients with DM had increased ammonia, citrulline, urea, uric acid, and ornithine, mainly in the RBCs, whereas the level of arginine was significantly lower in men with type 2 DM. These findings were associated with hyperglycemia, glycosylated hemoglobin (Hb A 1C ), and levels of RBC’s MDA. Furthermore, most of the DM-induced alterations in nitrogen-related metabolites appear to be associated with a difference in the RBC capacity for the release of these metabolites, thereby causing an abrogation of the gender-related differential management of nitrogen metabolites in healthy subjects. Conclusions We found evidence of a putative role of RBC as an extra-hepatic mechanism for controlling serum levels of nitrogen-related metabolites, which differs according to gender in healthy subjects. Type 2 DM promotes higher ammonia, citrulline, and MDA blood levels, which culminate in a loss of the differential management of nitrogen-related metabolites seen in healthy women and men.

The paper studies the properties of the productive structure of a region and national economies within the region. We use input-output data to calculate and compare input-output linkages and complex network measures of centrality to identify the most important sectors. We found that the most important sectors in each country are also the most important sectors in the European Union as a whole. Moreover, these sectors are the most intra-European Union traded goods and services. Finally, we computed the effect of a sectoral shock and its diffusion throughout the economy. We found that the most central sectors have the best diffusion of the effect of a shock and also a high aggregate impact in the economy. This gives evidence that centrality measures provided additional information that allowed to identified key sectors that have high linkages and good diffusion of effects. Therefore, the computation of centralities provides an alternative method to identify key sectors and formulate economic policies, complementing input-output analysis.

Economic systems have evolved through time thereby changing the structure that characterizes them. These changes respond to technological changes that transform economies into highly interconnected systems. The modifications in the norms that guide the behaviour of organizations and, therefore the functioning of the economy, are a first case of this transformation. The industrialization process, through the incorporation of increasing returns to scale in different sectors, and the introduction of service activities are other examples. Another form to represent structural change is the change of the values of the variables that characterize the state space of an economic system. This research article is an effort to put together and compare, from the complexity approach, different approaches for structural change and dynamics of economic systems. We start by briefly presenting the complexity approach in general and in economics. Then, we put forward three approaches highlighting structural change.

During the period of time between a new disease outbreaks and its vaccine is deployed, the health and the economic systems have to find a testing strategy for reopening activities. In particular, asymptomatic individuals, who transmit locally the COVID-19 indoors, have to be identified and isolated. We proposed a 2D cellular automaton based on the SI epidemic model for selecting the most desirable testing frequency and identifying the best fitting size of random trails on local urban environments to diagnose SARS-CoV-2 and isolate infected people. We used the complex systems approach to face the challenge of a large-scale test strategy based on urban interventions, starting with first responders and essential workers. We used the case of Mexico to exemplify a credible and intelligent intervention that reduces the virus transmission and detects economic and health costs. Findings suggest that controlling and stopping the virus transmission in a short period of time are possible if the frequency of testing is daily and the percentage of random samples to be tested is at least 90%. This combination of model parameters represents the least expensive intervention compared to others. Therefore, the key for a national testing-isolating strategy is local interventions.

We combine input–output analysis with a diffusion measure and complex network measures of centrality to propose a method to identify strategic or key sectors in an economy. We then apply our method to the case of Mexico for the years 2008 and 2012. Results for Mexico show that the ranking sectors according to the diffusion measure allows identifying strategic sectors with desirable properties such as high centrality and a high correlation to the aggregate effect of a shock. Furthermore, these sectors also have a good performance according to macroeconomic variables, such as exports and value-added. The disaggregation of the aggregate effect of a shock, together with our results on diffusion, suggests that one should focus on sectors with good diffusion to design policy recommendations.