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High-density lipoprotein (HDL) is associated with decreased incidence of cardiovascular events, and its functionality also influences prognosis. Exercise is an important tool to improve prognosis in the post-infarction (MI) population, but the role of exercise on HDL functionality is poorly understood. Sixty-two patients with acute MI were randomized in a supervised exercise program for 12–14 weeks (exercise group—EG) or a control group (CG). The main objective of the study was to analyze the role of exercise on esterified cholesterol (EC) and unesterified cholesterol (UC) transfer to HDL. For the total population, the baseline mean rate of EC transfer to HDL was 2.53 ± 0.83 and at the end of follow-up, it was 2.74 ± 0.64 (p = 0.03). The figures for UC were, respectively, 4.08 ± 1.2 and 4.4 ± 1.06 (p = 0.02). The difference (follow-up minus baseline) for EC was 0.15 ± 0.84 for the control group and 0.27 ± 0.69 for the exercise group (p = 0.53); for UC, the figures were 0.28 ± 1.14 and 0.35 ± 0.96 (p = 0.80), respectively, for the control and exercise groups. In post-MI patients, 12–14 weeks of supervised exercise did not improve HDL functionality. [ABSTRACT FROM AUTHOR]

Injectable calcium phosphate cements (CPCs) represent promising candidates for the regeneration of complex-shape bone defects, thanks to self-hardening ability, bioactive composition and nanostructure offering high specific surface area for cell attachment and conduction. Such features make CPCs also interesting for functionalization with various biomolecules, towards the generation of multifunctional devices with enhanced therapeutic ability. In particular, strontium-doped CPCs have been studied in the last years due to the intrinsic antiosteoporotic character of strontium. In this work, a SrCPC previously reported as osteointegrative and capable to modulate the fate of bone cells was enriched with hydroxyapatite nanoparticles (HA-NPs) functionalized with tetracycline (TC) to provide antibacterial activity. We found that HA-NPs functionalized with TC (NP-TC) can act as modulator of the drug release profile when embedded in SrCPCs, thus providing a sustained and tunable TC release. In vitro microbiological tests on Escherichia coli and Staphylococcus aureus strains proved effective bacteriostatic and bactericidal properties, especially for the NP-TC loaded SrCPC formulations. Overall, our results indicate that the addition of NP-TC on CPC acted as effective modulator towards a tunable drug release control in the treatment of bone infections or cancers.

Comprehensive policy measures are needed to close the emissions gap between Nationally Determined Contributions and emissions goals of the Paris Agreement. Here the authors present a Bridge scenario that may aid in closing the emissions gap by 2030.

Na Educa¸c˜ao B´asica o ensino de fun¸c˜oes exponenciais e logar´ıtmicas ´e um grande obst´aculo aos alunos e professores tanto pela complexidade do conte´udo como pela pouca habilidade no estudo de aplica¸c˜oes relacionadas com o tema, como empréstimos a taxa de juros compostos, crescimento de uma popula¸c˜ao, absor¸c˜ao de rem´edios, etc. Com a finalidade de apresentar o ensino de fun¸c˜oes exponenciais e logar´ıtmicas aos alunos da Educa¸c˜ao B´asica a partir de um problema contextualizado, escolhemos o estudo dos modelos de Malthus e Verhulst, a partir do software sobre Crescimento Populacional da cole¸c˜ao M3− Matem´atica Multim´ıdia, que tem como autores Samuel Rocha de Oliveira e Leonardo Barrichello (UNICAMP). Acrescentamos coment´arios que podem dar subs´ıdios ao professor, durante a execu¸c˜ao das atividades. E, em seguida, sugerimos uma atividade extra para ser trabalhada visando contemplar o conte´udo abordado pelo software e também colocar os alunos como protagonistas do aprendizado. Essa atividade pode ser adaptada para escolas que trabalham projetos ou Pr´e-Inicia¸c˜ao Cient´ıfica.

Abstract Intensifying climate change impacts can divert the economic resources away from emission reduction toward adaptation to reduce rising damages, jeopardizing temperature stabilization within safe levels. Indeed, the traditional static welfare‐maximizing climate policy design leads to a conflict between mitigation and adaptation, invalidating the recently established consistency of cost‐benefit analysis with the Paris Agreement's targets. Here, we show that this tension can be resolved by integrating multi‐objective optimization and feedback control in the Dynamic Integrated Climate Economy model to design self‐adaptive climate policies trading off welfare maximization with the Paris Agreement compliance. These policies allow adjusting against uncertainty as information on the socio‐climatic system accumulates, thus representing the policy‐making process more realistically. We show that, the costs being the same as in traditional methods, warming above 2°C and the probability of overshooting can be drastically reduced, emphasizing the need for integrating adaptation and mitigation strategies and the value of embracing a self‐adaptive, multi‐objective perspective.

Benefit-cost analyses of climate policies have generated conflicting assessments; as social welfare is affected by regional heterogeneity. Here the authors show that economically optimal pathways are consistent with climate stabilization but are characterized by persistent economic inequalities due to climate damages.

We reflect on the development of digital twins of the Earth, which we associate with a reductionist view of nature as a machine. The projects of digital twins deviate from contemporary scientific paradigms in the treatment of complexity and uncertainty, and does not engage with critical and interpretative social sciences. We contest the utility of digital twins for addressing climate change issues and discuss societal risks associated with the concept, including the twins' potential to reinforce economicism and governance by numbers, emphasizing concerns about democratic accountability. We propose a more balanced alternative, advocating for independent institutions to develop diverse models, prioritize communication with simple heuristic‐based models, collect comprehensive data from various sources, including traditional knowledge, and shift focus away from physics‐centered variables to inform climate action. We argue that the advancement of digital twins should hinge on stringent controls, favoring a nuanced, interdisciplinary, and democratic approach that prioritizes societal well‐being over blind pursuit of computational sophistication. This article is categorized under:Climate Models and Modeling > Earth System ModelsClimate Models and Modeling > Knowledge Generation with ModelsClimate, History, Society, Culture > Disciplinary Perspectives [ABSTRACT FROM AUTHOR]

Understanding the dynamics and evolution of climate change and associated uncertainties is key for designing robust policy actions. Computer models are key tools in this scientific effort, which have now reached a high level of sophistication and complexity. Model auditing is needed in order to better understand their results, and to deal with the fact that such models are increasingly opaque with respect to their inner workings. Current techniques such as Global Sensitivity Analysis (GSA) are limited to dealing either with multivariate outputs, stochastic ones, or finite‐change inputs. This limits their applicability to time‐varying variables such as future pathways of greenhouse gases. To provide additional semantics in the analysis of a model ensemble, we provide an extension of GSA methodologies tackling the case of stochastic functional outputs with finite change inputs. To deal with finite change inputs and functional outputs, we propose an extension of currently available GSA methodologies while we deal with the stochastic part by introducing a novel, domain‐selective inferential technique for sensitivity indices. Our method is explored via a simulation study that shows its robustness and efficacy in detecting sensitivity patterns. We apply it to real‐world data, where its capabilities can provide to practitioners and policymakers additional information about the time dynamics of sensitivity patterns, as well as information about robustness. [ABSTRACT FROM AUTHOR]

In Marfan syndrome (MFS), dilation, dissection, and rupture of the aorta occur. Inflammation can be involved in the pathogenicity of aortic defects and can thus be a therapeutic target for MFS. Previously, we showed that the formulation of methotrexate (MTX) associated with lipid nanoparticles (LDE) has potent anti-inflammatory effects without toxicity. To investigate whether LDEMTX treatment can prevent the development of aortic lesions in the MFS murine model. MgΔloxPneo MFS (n = 40) and wild-type (WT, n = 60) mice were allocated to 6 groups weekly injected with IP solutions of: (1) only LDE; (2) commercial MTX; (3) LDEMTX (dose = 1mg/kg) between 3rd and 6th months of life. After 12 weeks of treatments, animals were examined by echocardiography and euthanatized for morphometric and molecular studies. MFS mice treated with LDEMTX showed narrower lumens in the aortic arch, as well as in the ascending and descending aorta. LDEMTX reduced fibrosis and the number of dissections in MFS but not the number of elastic fiber disruptions. In MFS mice, LDEMTX treatment lowered protein expression of pro-inflammatory factors macrophages (CD68), T-lymphocytes (CD3), tumor necrosis factor-α (TNF-α), apoptotic factor cleaved-caspase 3, and type 1 collagen and lowered the protein expression of the transforming growth factor-β (TGF-β), extracellular signal-regulated kinases ½ (ERK1/2), and SMAD3. Protein expression of CD68 and CD3 had a positive correlation with an area of aortic lumen (r2 = 0.36; p < 0.001), suggesting the importance of inflammation in the causative mechanisms of aortic dilation. Enhanced adenosine availability by LDEMTX was suggested by higher aortic expression of an anti-adenosine A2a receptor (A2a) and lower adenosine deaminase expression. Commercial MTX had negligible effects. LDEMTX prevented the development of MFS-associated aortic defects and can thus be a candidate for testing in clinical studies.

Ensuring food security requires food production and distribution systems function throughout disruptions. Understanding the factors that contribute to the global food system's ability to respond and adapt to such disruptions (i.e. resilience) is critical for understanding the long-term sustainability of human populations. Variable impacts of production shocks on food supply between countries indicate a need for national-scale resilience indicators that can provide global comparisons. However, methods for tracking changes in resilience have had limited application to food systems. We developed an indicator-based analysis of food systems resilience for the years 1992-2011. Our approach is based on three dimensions of resilience: socio-economic access to food in terms of income of the poorest quintile relative to food prices, biophysical capacity to intensify or extensify food production, and the magnitude and diversity of current domestic food production. The socio-economic indicator has large variability, but with low values concentrated in Africa and Asia. The biophysical capacity indicator is highest in Africa and Eastern Europe, in part because of high potential for extensification of cropland and for yield gap closure in cultivated areas. However, the biophysical capacity indicator has declined globally in recent years. The production diversity indicator has increased slightly, with a relatively even geographic distribution. Few countries had exclusively high or low values for all indicators. Collectively, these results are the basis for global comparisons of resilience between nations, and provide necessary context for developing generalizations about the resilience in the global food system.

Spatially diverse trends in population growth, climate change, industrialization, urbanization and economic development are expected to change future food supply and demand. These changes may affect the suitability of land for food production, implying elevated risks especially for resourceconstrained, food-importing countries.Wepresent the evolution of biophysical redundancy for agricultural production at country level, from 1992 to 2012. Biophysical redundancy, defined as unused biotic and abiotic environmental resources, is represented by the potential food production of 'spare land', available water resources (i.e., not already used for human activities), as well as production increases through yield gap closure on cultivated areas and potential agricultural areas. In 2012, the biophysical redundancy of 75 (48) countries, mainly in North Africa, Western Europe, the Middle East and Asia, was insufficient to produce the caloric nutritional needs for at least 50% (25%) of their population during a year. Biophysical redundancy has decreased in the last two decades in 102 out of 155 countries, 11 of these went from high to limited redundancy, and nine of these from limited to very low redundancy. Although the variability of the drivers of change across different countries is high, improvements in yield and population growth have a clear impact on the decreases of redundancy towards the very low redundancy category.Wetook a more detailed look at countries classified as 'Low Income Economies (LIEs)' since they are particularly vulnerable to domestic or external food supply changes, due to their limited capacity to offset for food supply decreases with higher purchasing power on the international market. Currently, nine LIEs have limited or very low biophysical redundancy. Many of these showed a decrease in redundancy over the last two decades, which is not always linked with improvements in per capita food availability.

Abstract Background Systemic Sclerosis (SSc) is a chronic autoimmune disease with a complex pathogenesis that includes vascular injury, abnormal immune activation, and tissue fibrosis. We provided a complete epidemiological characterization of SSc in the Tuscany region (Italy), considering prevalence and incidence, survival, comorbidities and drug prescriptions, by using a multi-database population-based approach. Cases of SSc diagnosed between 1st January 2003 and 31st December 2017 among residents in Tuscany were collected from the population-based Rare Diseases Registry of Tuscany. All cases were linked to regional health and demographic databases to obtain information about vital statistics, principal causes of hospitalization, complications and comorbidities, and drug prescriptions. Results The prevalence of SSc in Tuscany population resulted to be 22.2 per 100,000, with the highest prevalence observed for the cases aged ≥ 65 years (33.2 per 100,000, CI 95% 29.6–37.3). In females, SSc was predominant (86.7% on the total) with an overall sex ratio F/M of 6.5. Nevertheless, males presented a more severe disease, with a lower survival and significant differences in respiratory complications and metabolic comorbidities. Complications and comorbidities such as pulmonary involvement (HR = 1.66, CI 95% 1.17–2.35), congestive heart failure (HR = 2.76, CI 95% 1.80–4.25), subarachnoid and intracerebral haemorrhage (HR = 2.33, CI 95% 1.21–4.48) and malignant neoplasms (HR = 1.63, CI 95% 1.06–2.52), were significantly associated to a lower survival, also after adjustment for age, sex and other SSc-related complications. Disease-modifying antirheumatic drugs, endothelin receptor antagonists, and phosphodiesterase-5 inhibitors were the drugs with the more increasing prevalence of use in the 2008–2017 period. Conclusions The multi-database approach is important in the investigation of rare diseases where it is often difficult to provide accurate epidemiological indicators. A population-based registry can be exploited in synergy with health databases, to provide evidence related to disease outcomes and therapies and to assess the burden of disease, relying on a large cohort of cases. Building an integrated archive of data from multiple databases linking a cohort of patients to their comorbidities, clinical outcomes and survival, is important both in terms of treatment and prevention.