Empagliflozin reduces vascular damage and cognitive impairment in a mixed murine model of Alzheimer's disease and type 2 diabetes

Background Both Alzheimer's disease (AD) and type 2 diabetes (T2D) share common pathological features including inflammation, insulin signaling alterations, or vascular damage. AD has no successful treatment, and the close relationship between both diseases supports the study of antidiabetic drugs to limit or slow down brain pathology in AD. Empagliflozin (EMP) is a sodium-glucose co-transporter 2 inhibitor, the newest class of antidiabetic agents. EMP controls hyperglycemia and reduces cardiovascular comorbidities and deaths associated to T2D. Therefore, we have analyzed the role of EMP at the central level in a complex mouse model of AD-T2D. Methods We have treated AD-T2D mice (APP/PS1xdb/db mice) with EMP 10 mg/kg for 22 weeks. Glucose, insulin, and body weight were monthly assessed. We analyzed learning and memory in the Morris water maze and the new object discrimination test. Postmortem brain assessment was conducted to measure brain atrophy, senile plaques, and amyloid-beta levels. Tau phosphorylation, hemorrhage burden, and microglia were also measured in the brain after EMP treatment. Results EMP treatment helped to maintain insulin levels in diabetic mice. At the central level, EMP limited cortical thinning and reduced neuronal loss in treated mice. Hemorrhage and microglia burdens were also reduced in EMP-treated mice. Senile plaque burden was lower, and these effects were accompanied by an amelioration of cognitive deficits in APP/PS1xdb/db mice. Conclusions Altogether, our data support a feasible role for EMP to reduce brain complications associated to AD and T2D, including classical pathological features and vascular disease, and supporting further assessment of EMP at the central level

An elastic software architecture for extreme-scale big data analytics

This chapter describes a software architecture for processing big-data analytics considering the complete compute continuum, from the edge to the cloud. The new generation of smart systems requires processing a vast amount of diverse information from distributed data sources. The software architecture presented in this chapter addresses two main challenges. On the one hand, a new elasticity concept enables smart systems to satisfy the performance requirements of extreme-scale analytics workloads. By extending the elasticity concept (known at cloud side) across the compute continuum in a fog computing environment, combined with the usage of advanced heterogeneous hardware architectures at the edge side, the capabilities of the extreme-scale analytics can significantly increase, integrating both responsive data-in-motion and latent data-at-rest analytics into a single solution. On the other hand, the software architecture also focuses on the fulfilment of the non-functional properties inherited from smart systems, such as real-time, energy-efficiency, communication quality and security, that are of paramount importance for many application domains such as smart cities, smart mobility and smart manufacturing.The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the ELASTIC Project (www.elastic-project.eu), grant agreement No 825473.Peer ReviewedPostprint (published version

Alzheimer's Disease and Diabetes: Role of Diet, Microbiota and Inflammation in Preclinical Models

Alzheimer's disease (AD) is the most common cause of dementia. Epidemiological studies show the association between AD and type 2 diabetes (T2DM), although the mechanisms are not fully understood. Dietary habits and lifestyle, that are risk factors in both diseases, strongly modulate gut microbiota composition. Also, the brain-gut axis plays a relevant role in AD, diabetes and inflammation, through products of bacterial metabolism, like short-chain fatty acids. We provide a comprehensive review of current literature on the relation between dysbiosis, altered inflammatory cytokines profile and microglia in preclinical models of AD, T2DM and models that reproduce both diseases as commonly observed in the clinic. Increased proinflammatory cytokines, such as IL-1 beta and TNF-alpha, are widely detected. Microbiome analysis shows alterations in Actinobacteria, Bacteroidetes or Firmicutes phyla, among others. Altered alpha- and beta-diversity is observed in mice depending on genotype, gender and age; therefore, alterations in bacteria taxa highly depend on the models and approaches. We also review the use of pre- and probiotic supplements, that by favoring a healthy microbiome ameliorate AD and T2DM pathologies. Whereas extensive studies have been carried out, further research would be necessary to fully understand the relation between diet, microbiome and inflammation in AD and T2DM

Energy-aware optimum offloading strategies in fog-cloud architectures: a Lyapunov based scheme

We introduce offloading policies for fog-cloud architectures that consider different performance parameters. We design and develop a three-tier platform, using virtualization techniques, which can be used to deploy different scenarios, with nodes having distinct features, mimicking fog and cloud characteristics. We then exploit Lyapunov´s control theory to introduce offloading policies that balance energy consumption at fog nodes and monetary cost of using the cloud. The proposed scheme is able to find a trade-off between these two parameters, while ensuring system stability and so delay requirements. We compare our algorithm with baseline solutions (adapted round-robin), and the results evince that it is able to yield better performance, even under high loads and stringent energy requirements. By tweaking the algorithm operational parameters, we show that it is able to adapt its behavior to different goals, and we assess its performance under realistic configurations.This work was supported by the Spanish Government (Ministerio de Economía y Competitividad, Fondo Europeo de Desarrollo Regional, MINECO-FEDER) by means of the Project SITED: Semantically-enabled Interoperable Trustworthy Enriched Data-spaces under Grant PID2021-125725OB-I0

Contemporary use of cefazolin for MSSA infective endocarditis: analysis of a national prospective cohort

Objectives: This study aimed to assess the real use of cefazolin for methicillin-susceptible Staphylococcus aureus (MSSA) infective endocarditis (IE) in the Spanish National Endocarditis Database (GAMES) and to compare it with antistaphylococcal penicillin (ASP). Methods: Prospective cohort study with retrospective analysis of a cohort of MSSA IE treated with cloxacillin and/or cefazolin. Outcomes assessed were relapse; intra-hospital, overall, and endocarditis-related mortality; and adverse events. Risk of renal toxicity with each treatment was evaluated separately. Results: We included 631 IE episodes caused by MSSA treated with cloxacillin and/or cefazolin. Antibiotic treatment was cloxacillin, cefazolin, or both in 537 (85%), 57 (9%), and 37 (6%) episodes, respectively. Patients treated with cefazolin had significantly higher rates of comorbidities (median Charlson Index 7, P <0.01) and previous renal failure (57.9%, P <0.01). Patients treated with cloxacillin presented higher rates of septic shock (25%, P = 0.033) and new-onset or worsening renal failure (47.3%, P = 0.024) with significantly higher rates of in-hospital mortality (38.5%, P = 0.017). One-year IE-related mortality and rate of relapses were similar between treatment groups. None of the treatments were identified as risk or protective factors. Conclusion: Our results suggest that cefazolin is a valuable option for the treatment of MSSA IE, without differences in 1-year mortality or relapses compared with cloxacillin, and might be considered equally effective