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Objective: To analyse the overall effectiveness and cost-efficiency of a mobile application (APP) as a community health asset (HA) with recommendations and recovery exercises created bearing in mind the main symptoms presented by patients in order to improve their quality of life, as well as other secondary variables, such as the number and severity of ongoing symptoms, physical and cognitive functions, affective state, and sleep quality., Methods: The first step was to design and develop the technologic community resource, the APP, following the steps involved in the process of recommending health assets (RHA). After this, a protocol of a randomised clinical trial for analysing its effectiveness and cost-efficiency as a HA was developed. The participants will be assigned to: (1st) usual treatment by the primary care practitioner (TAU), as a control group; and (2nd) TAU + use of the APP as a HA and adjuvant treatment in their recovery + three motivational interviews (MI), as an interventional group. An evaluation will be carried out at baseline with further assessments three and six months following the end of the intervention., Discussion: Although research and care for these patients are still in their initial stages, it is necessary to equip patients and health care practitioners with tools to assist in their recovery. Furthermore, enhanced motivation can be achieved through telerehabilitation (TR).

The aim of this study was to explore the immunomodulatory effects of the natural enantiomer ( R )-Sulforaphane (SFN) and the possible signaling pathways involved in an ex vivo model of LPS-stimulated murine peritoneal macrophages. Furthermore, we studied the epigenetic changes induced by ( R )-SFN as well as the post-translational modifications of histone H3 (H3K9me3 and H3K18ac) in relation to the production of cytokines in murine splenocytes after LPS stimulation. ( R )-SFN was able to modulate the inflammatory response and oxidative stress induced by LPS stimulation in murine peritoneal macrophages through the inhibition of reactive oxygen species (ROS), nitric oxide (NO) and cytokine (IL-1β, IL-6, IL-17, IL-18 and TNF-α) production by down-regulating the expression of pro-inflammatory enzymes (iNOS, COX-2 and mPGES-1). We also found that activation of the Nrf-2/HO-1 axis and inhibition of the JAK2/STAT-3, MAPK, canonical and non-canonical inflammasome signaling pathways could have been responsible for the immunomodulatory effects of ( R )-SFN. Furthermore, ( R )-SFN modulated epigenetic modifications through histone methylation (H3K9me3) and deacetylation (H3K18ac) in LPS-activated spleen cells. Collectively, our results suggest that ( R )-SFN could be a promising epinutraceutical compound for the management of immunoinflammatory diseases.

The antiproliferative effects played by benzothiazoles in different cancers have aroused the interest for these molecules as promising antitumor agents. In this work, a library of phenylacetamide derivatives containing the benzothiazole nucleus was synthesized and compounds were tested for their antiproliferative activity in paraganglioma and pancreatic cancer cell lines. The novel synthesized compounds induced a marked viability reduction at low micromolar concentrations both in paraganglioma and pancreatic cancer cells. Derivative 4l showed a greater antiproliferative effect and higher selectivity index against cancer cells, as compared to other compounds. Notably, combinations of derivative 4l with gemcitabine at low concentrations induced enhanced and synergistic effects on pancreatic cancer cell viability, thus supporting the relevance of compound 4l in the perspective of clinical translation. A target prediction analysis was also carried out on 4l by using multiple computational tools, identifying cannabinoid receptors and sentrin-specific proteases as putative targets contributing to the observed antiproliferative activity.

The prevalence of pediatric respiratory diseases in Spain is 23%. Inhalation is the preferred route of administration but there are frequent errors in the performance of the inhalation technique leading a poor control of the disease. The aim of this research was to detect errors in the execution of the inhalation technique at a Pediatric Pulmonology Unit in a hospital of Aragón (Spain). In order to improve the administration of inhaled medication, an educational intervention for 1 year by nursing was conducted. This interventional study, including children aged 1 to 15 years with an inhalation therapy and who attended the Pediatric Pulmonology Unit, was conducted between September 2017 and September 2018. Logistic Regression models were conducted in SPSS. This study involved 393 children (61.1% boys). Before the intervention, 39.4% achieved a correct inhalation technique increasing up to 62.1% after the intervention. Those who had their first visit to the Unit, young children and girls had a higher risk of incorrect performance than those with subsequent visits, older children, and boys, respectively. The most common errors in the inhalation technique were not performing adequate apnoea after inhaling and not rinsing the mouth at the end of the procedure. The education given by nurses to pediatric patients improved the inhalation technique, achieving better control of the disease and use of the health system.

It is essential to understand the impact of social inequalities on the risk of COVID-19 infection in order to mitigate the social consequences of the pandemic. With this aim, the objective of our study was to analyze the effect of socioeconomic inequalities, both at the individual and area of residence levels, on the probability of COVID-19 confirmed infection, and its variations across three pandemic waves. We conducted a retrospective cohort study and included data from all individuals tested for COVID-19 during the three waves of the pandemic, from March to December 2020 (357,989 individuals) in Aragón (Spain). We studied the effect of inequalities on the risk of having a COVID-19 confirmed diagnosis after being tested using multilevel analyses with two levels of aggregation: individuals and basic healthcare area of residence (deprivation level and type of zone). Inequalities in the risk of COVID-19 confirmed infection were observed at both the individual and area level. There was a predominance of low-paid employees living in deprived areas. Workers with low salaries, unemployed and people on minimum integration income or who no longer receive the unemployment allowance, had a higher probability of COVID-19 infection than workers with salaries ≥ €18,000 per year. Inequalities were greater in women and in the second wave. The deprivation level of areas of residence influenced the risk of COVID-19 infection, especially in the second wave. It is necessary to develop individual and area coordinated measures by areas in the control, diagnosis and treatment of the epidemic, in order to avoid an increase in the already existing inequalities.

Radar high resolution range profiles are widely used among the target recognition community for the detection and identification of flying targets. In this paper, singular value decomposition is applied to extract the relevant information and to model each aircraft as a subspace. The identification algorithm is based on angle between subspaces and takes place in a transformed domain. In order to have a wide database of radar signatures and evaluate the performance, simulated range profiles are used as the recognition database while the test samples comprise data of actual range profiles collected in a measurement campaign. Thanks to the modeling of aircraft as subspaces only the valuable information of each target is used in the recognition process. Thus, one of the main advantages of using singular value decomposition, is that it helps to overcome the notable dissimilarities found in the shape and signal-to-noise ratio between actual and simulated profiles due to their difference in nature. Despite these differences, the recognition rates obtained with the algorithm are quite promising.

This paper presents a methodology for high resolution radar image generation and automatic target recognition emphasizing the computational cost involved in the process. In order to obtain focused inverse synthetic aperture radar (ISAR) images certain signal processing algorithms must be applied to the information sensed by the radar. From actual data collected by radar the stages and algorithms needed to obtain ISAR images are revised, including high resolution range profile generation, motion compensation and ISAR formation. Target recognition is achieved by comparing the generated set of actual ISAR images with a database of ISAR images generated by electromagnetic software. High resolution radar image generation and target recognition processes are burdensome and time consuming, so to determine the most suitable implementation platform the analysis of the computational complexity is of great interest. To this end and since target identification must be completed in real time, computational burden of both processes the generation and comparison with a database is explained separately. Conclusions are drawn about implementation platforms and calculation efficiency in order to reduce time consumption in a possible future implementation.