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Life-threatening cardiomyopathy is a severe, but common, complication associated with severe trauma or sepsis. Several signaling pathways involved in apoptosis and necroptosis are linked to trauma- or sepsis-associated cardiomyopathy. However, the underling causative factors are still debatable. Heparan sulfate (HS) fragments belong to the class of danger/damage-associated molecular patterns liberated from endothelial-bound proteoglycans by heparanase during tissue injury associated with trauma or sepsis. We hypothesized that HS induces apoptosis or necroptosis in murine cardiomyocytes. By using a novel Medical-In silico approach that combines conventional cell culture experiments with machine learning algorithms, we aimed to reduce a significant part of the expensive and time-consuming cell culture experiments and data generation by using computational intelligence (refinement and replacement). Cardiomyocytes exposed to HS showed an activation of the intrinsic apoptosis signal pathway via cytochrome C and the activation of caspase 3 (both p < 0.001). Notably, the exposure of HS resulted in the induction of necroptosis by tumor necrosis factor α and receptor interaction protein 3 (p < 0.05; p < 0.01) and, hence, an increased level of necrotic cardiomyocytes. In conclusion, using this novel Medical-In silico approach, our data suggest (i) that HS induces necroptosis in cardiomyocytes by phosphorylation (activation) of receptor-interacting protein 3, (ii) that HS is a therapeutic target in trauma- or sepsis-associated cardiomyopathy, and (iii) indicate that this proof-of-concept is a first step toward simulating the extent of activated components in the pro-apoptotic pathway induced by HS with only a small data set gained from the in vitro experiments by using machine learning algorithms.
This paper analyzes some of the assumptions in which the varied use of technologies to confront the spread of the COVID-19 pandemic and protect people's health has impacted on the fundamental right to the protection of personal data; to do so, it starts from the premise that the use of these technologies cannot mean an affectation to the referred fundamental right, much less an indiscriminate treatment of such data without any minimum control whatsoever.
Integrated analysis on socio-economic metabolism could provide a basis for understanding and optimizing regional sustainability. The paper conducted socio-economic metabolism analysis by means of the emergy accounting method coupled with data envelopment analysis and decomposition analysis techniques to assess the sustainability of Qingyang city and its eight sub-region system, as well as to identify the major driving factors of performance change during 2000–2007, to serve as the basis for future policy scenarios. The results indicate that Qingyang greatly depended on non-renewable emergy flows and feedback (purchased) emergy flows, except the two sub-regions, named Huanxian and Huachi, which highly depended on renewable emergy flow. Zhenyuan, Huanxian and Qingcheng were identified as being relatively emergy efficient, and the other five sub-regions have potential to reduce natural resource inputs and waste output to achieve the goal of efficiency. The results of decomposition analysis show that the economic growth, as well as the increased emergy yield ratio and population not accompanied by a sufficient increase of resource utilization efficiency are the main drivers of the unsustainable economic model in Qingyang and call for polices to promote the efficiency of resource utilization and to optimize natural resource use.