An answer for caregivers of people with Alzheimer’s disease

Trabajo fin de grado en EnfermeríaObjetivo: Cuidar a una persona con la enfermedad de Alzheimer supone un desafío para los cuidadores, muchos sufren depresión, ansiedad y sobrecarga, por lo que precisan una repuesta por parte de los profesionales de enfermería. Analizar la evidencia científica acerca de las demandas que plantean los familiares puede ser una primera aproximación para solventar este problema. Metodología: Revisión narrativa, llevada a cabo a través de una búsqueda bibliográfica sistemática en las bases de datos de ciencias de la salud: PubMed, Cuiden, CINAHL, Biblioteca Cochrane, Lilacs y el buscador Dialnet plus. Resultados: Se seleccionaron 21 artículos para su análisis que generó cuatro categorías: Información sobre la enfermedad, educación en las tareas de autocuidado, apoyo psicológico y emocional y recursos de apoyo y sociosanitarios. Conclusiones: Entre las expectativas que los cuidadores tienen de los profesionales de enfermería destacan la información, educación y el apoyo emocional para afrontar el cuidado diario y poder anticiparse a las necesidades futuras de su familiar.Objective: To take care of a person with Alzheimer disease could be challenging for their caregivers, lot of them suffer from depression, anxiety and burnout, because of that, they require an answer from nursing professionals. To analyze the analyze the scientific evidence about the demands expressed out by their relatives could be a first approach to solve this problem. Methodology: A narrative overview carried out through a bibliographic research in the Health Science Databases: PubMed, Cuiden, CINAHL, Cochrane Library, Lilacs and the searcher Dialnet plus. Results: 21 articles were selected for their revision, what generates four categories: information about the disease, self-care education program, psychological and emotional support, help from others family relatives and socio-health resources. Conclusions: Among the expectations that caregivers have on nursing professionals, they highlight the information, the education and the emotional support to face daily care and being able to anticipate the future needs of their family member

Validando la periodicidad de los incrementos de crecimiento en los otolitos de tres pequeños góbidos progenéticos

We determined the efficacy of marking the otoliths of three small-sized progenetic gobies to validate their increment periodicity. These small gobies have high mortalities and rearing difficulties, making direct validation difficult. The otoliths were marked by immersing the fish in a bath of alizarin red S. The fishes were euthanatized and the number of increments in their otoliths laid down after the fluorescent mark were counted and compared with the number of elapsed days. The results validated the daily periodicity of Aphia minuta and Pseudaphya ferreri. The high mortality hindered the validation of Crystallogobius linearis.Se determinó la eficacia en el marcado de los otolitos de tres góbidos progenéticos de pequeña talla con el fin de validar la periodicidad en la deposición de los incrementos de crecimiento. Estos pequeños góbidos presentan altas mortalidades y dificultades en su cría, por lo que la validación directa de su crecimiento es difícil. Los otolitos fueron marcados con rojo de alizarina S (ALR). Los peces fueron eutanasiados y el número de incrementos depositados después del marcaje con el colorante fluorescente se contó y comparó con el número de días transcurridos. Los resultados obtenidos validaron la periodicidad diaria para Aphia minuta y Pseudaphya ferreri. La elevada mortalidad impidió la validación en Crystallogobius linearis

De biblioteca a bibliotEKO

Póster presentado en las I Jornadas de Bibliotecas G9 sobre buenas prácticas en atención a espacios y usuarios, organizada por el Servicio de Bibliotecas de la UEx en Jarandilla de la Vera (Cáceres) los días 29 y 30 de septiembre de 2016Presentación de Ekoscan, de la Universidad del País Vasco. Es un gestor diseñado para aprovechar mejor los recursos de electricidad, agua y consumibles en la Biblioteca universitaria del Campus de Vizcaya.Ekoscan presentation of the Universidad del País Vasco. It is a manager designed to make better use of resources electricity, water and supplies in the library of Campus Universitario de Vizcay

Ekoscan, buena práctica solidaria con el medio ambiente

Comunicación presentada en las I Jornadas de Bibliotecas G9 sobre buenas prácticas en atención a espacios y usuarios, organizada por el Servicio de Bibliotecas de la UEx en Jarandilla de la Vera (Cáceres) los días 29 y 30 de septiembre de 2016Presentación de Ekoscan, de la Universidad del País Vasco. Es un gestor diseñado para aprovechar mejor los recursos de electricidad, agua y consumibles en la Biblioteca universitaria del Campus de Vizcaya.Ekoscan presentation of the Universidad del País Vasco. It is a manager designed to make better use of resources electricity, water and supplies in the library of Campus Universitario de Vizcay

Importance of immunometabolic markers for the classification of patients with major depressive disorder using machine learning

Background: Although there is scientific evidence of the presence of immunometabolic alterations in major depression, not all patients present them. Recent studies point to the association between an inflammatory phenotype and certain clinical symptoms in patients with depression. The objective of our study was to classify major depression disorder patients using supervised learning algo-rithms or machine learning, based on immunometabolic and oxidative stress biomarkers and lifestyle habits.Methods: Taking into account a series of inflammatory and oxidative stress biomarkers (C-reactive protein (CRP), tumor necrosis factor (TNF), 4-hydroxynonenal (HNE) and glutathione), metabolic risk markers (blood pressure, waist circumference and glucose, triglyceride and cholesterol levels) and lifestyle habits of the participants (physical activity, smoking and alcohol consumption), a study was carried out using machine learning in a sample of 171 participants, 91 patients with depression (71.42% women, mean age = 50.64) and 80 healthy subjects (67.50% women, mean age = 49.12).The algorithm used was the support vector machine, performing cross validation, by which the subdivision of the sample in training (70%) and test (30%) was carried out in order to estimate the precision of the model. The prediction of belonging to the patient group (MDD patients versus control subjects), melancholic type (melancholic versus non-melancholic patients) or resistant depression group (treatment-resistant versus non -treatment-resistant) was based on the importance of each of the immunometabolic and lifestyle variables.Results: With the application of the algorithm, controls versus patients, such as patients with melancholic symptoms versus non-melancholic symptoms, and resistant versus non-resistant symptoms in the test phase were optimally classified.The variables that showed greater importance, according to the results of the area under the ROC curve, for the discrimination between healthy subjects and patients with depression were current alcohol consumption (AUC = 0.62), TNF-alpha levels (AUC = 0.61), glutathione redox status (AUC = 0.60) and the performance of both moderate (AUC = 0.59) and vigorous physical exercise (AUC = 0.58). On the other hand, the most important variables for classifying melancholic patients in relation to lifestyle habits were past (AUC = 0.65) and current (AUC = 0.60) tobacco habit, as well as walking routinely (AUC = 0.59) and in relation to immunometabolic markers were the levels of CRP (AUC = 0.62) and glucose (AUC = 0.58).In the analysis of the importance of the variables for the classification of treatment-resistant patients versus non-resistant patients, the systolic blood pressure (SBP) variable was shown to be the most relevant (AUC = 0.67). Other immunometabolic variables were also among the most important such as TNF-alpha (AUC = 0.65) and waist circumference (AUC = 0.64). In this case, sex (AUC = 0.59) was also relevant along with alcohol (AUC = 0.58) and tobacco (AUC = 0.56) consumption.Conclusions: The results obtained in our study show that it is possible to predict the diagnosis of depression and its clinical typology from immunometabolic markers and lifestyle habits, using machine learning techniques. The use of this type of methodology could facilitate the identification of patients at risk of presenting depression and could be very useful for managing clinical heterogeneity

Importance of immunometabolic markers for the classification of patients with major depressive disorder using machine learning

Background: Although there is scientific evidence of the presence of immunometabolic alterations in major depression, not all patients present them. Recent studies point to the association between an inflammatory phenotype and certain clinical symptoms in patients with depression. The objective of our study was to classify major depression disorder patients using supervised learning algorithms or machine learning, based on immunometabolic and oxidative stress biomarkers and lifestyle habits. Methods: Taking into account a series of inflammatory and oxidative stress biomarkers (C-reactive protein (CRP), tumor necrosis factor (TNF), 4-hydroxynonenal (HNE) and glutathione), metabolic risk markers (blood pressure, waist circumference and glucose, triglyceride and cholesterol levels) and lifestyle habits of the participants (physical activity, smoking and alcohol consumption), a study was carried out using machine learning in a sample of 171 participants, 91 patients with depression (71.42% women, mean age = 50.64) and 80 healthy subjects (67.50% women, mean age = 49.12). The algorithm used was the support vector machine, performing cross validation, by which the subdivision of the sample in training (70%) and test (30%) was carried out in order to estimate the precision of the model. The prediction of belonging to the patient group (MDD patients versus control subjects), melancholic type (melancholic versus non-melancholic patients) or resistant depression group (treatment-resistant versus non-treatment-resistant) was based on the importance of each of the immunometabolic and lifestyle variables. Results: With the application of the algorithm, controls versus patients, such as patients with melancholic symptoms versus non-melancholic symptoms, and resistant versus non-resistant symptoms in the test phase were optimally classified. The variables that showed greater importance, according to the results of the area under the ROC curve, for the discrimination between healthy subjects and patients with depression were current alcohol consumption (AUC = 0.62), TNF-α levels (AUC = 0.61), glutathione redox status (AUC = 0.60) and the performance of both moderate (AUC = 0.59) and vigorous physical exercise (AUC = 0.58). On the other hand, the most important variables for classifying melancholic patients in relation to lifestyle habits were past (AUC = 0.65) and current (AUC = 0.60) tobacco habit, as well as walking routinely (AUC = 0.59) and in relation to immunometabolic markers were the levels of CRP (AUC = 0.62) and glucose (AUC = 0.58). In the analysis of the importance of the variables for the classification of treatment-resistant patients versus non-resistant patients, the systolic blood pressure (SBP) variable was shown to be the most relevant (AUC = 0.67). Other immunometabolic variables were also among the most important such as TNF-α (AUC = 0.65) and waist circumference (AUC = 0.64). In this case, sex (AUC = 0.59) was also relevant along with alcohol (AUC = 0.58) and tobacco (AUC = 0.56) consumption. Conclusions: The results obtained in our study show that it is possible to predict the diagnosis of depression and its clinical typology from immunometabolic markers and lifestyle habits, using machine learning techniques. The use of this type of methodology could facilitate the identification of patients at risk of presenting depression and could be very useful for managing clinical heterogeneity.This study was supported in part by grants from the Carlos III Health Institute through the Ministry of Science, Innovation and Universities (PI15/00662, PI15/0039, PI15/00204, PI19/01040), co-funded by the European Regional Development Fund (ERDF) “A way to build Europe”, CIBERSAM, and the Catalan Agency for the Management of University and Research Grants (AGAUR 2017 SGR 1247). We also thank CERCA Programme/Generalitat de Catalunya for institutional support. Work partially supported by Biobank HUB-ICO-IDIBELL, integrated in the Spanish Biobank Network and funded by Instituto de Salud Carlos III (PT17/0015/0024) and by Xarxa Bancs de Tumors de Catalunya sponsored by Pla Director d’Oncologia de Catalunya (XBTC). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. YSC work is supported by the FPI predoctoral grant (FPI 2016/17) from Universidad Autonoma de Madrid. VS received an Intensification of the Research Activity Grant from the Instituto de Salud Carlos III (INT21/00055) during 202

Influence of the Seasonal Thermocline on the Vertical Distribution of Larval Fish Assemblages Associated with Atlantic Bluefin Tuna Spawning Grounds

Temperature is often an important variable influencing the vertical position of fish larvae in the water column. The same species may show different vertical distributions in areas with a strong near-surface seasonal thermocline compared to isothermal near-surface regions. In areas with a strong surface thermocline, tuna larvae show a significant preference for the near-surface warmer layers. Little is known regarding larval tuna vertical distribution in isothermal waters and on the vertical distribution of the associated larval fish assemblages. We conducted vertical stratified sampling using the same methodology and fishing device (MOCNESS) in the two major spawning areas of Atlantic bluefin tuna (BFT): western Mediterranean Sea (MED), characterized by a surface thermocline, and the Gulf of Mexico (GOM) which lacks thermal stratification. Tuna larvae occupied the upper 30 m in both areas, but the average larval depth distribution was consistently deeper in the GOM. In the MED, vertical distribution of larval fish assemblages was explained by temperature, and species such as BFT, Thunnus alalunga, and Ceratoscopelus maderensis, among others, coexist above the thermocline and are separated from species such as Cyclothone braueri and Hygophum spp. (found below the thermocline). In the GOM, the environmental correlates of the vertical distribution of the larvae were salinity and fluorescence. Mesopelagic taxa such as Ceratoscopelus spp. and Cyclothone spp., among others, had a shallower average distribution than Lampanyctus spp., Hygophum spp., and Myctophum spp.Versión del edito

HGF, IL-1α, and IL-27 are robust biomarkers in early severity stratification of COVID-19 patients

© 2021 by the authors.Pneumonia is the leading cause of hospital admission and mortality in coronavirus disease 2019 (COVID-19). We aimed to identify the cytokines responsible for lung damage and mortality. We prospectively recruited 108 COVID-19 patients between March and April 2020 and divided them into four groups according to the severity of respiratory symptoms. Twenty-eight healthy volunteers were used for normalization of the results. Multiple cytokines showed statistically significant differences between mild and critical patients. High HGF levels were associated with the critical group (OR = 3.51; p < 0.001; 95%CI = 1.95–6.33). Moreover, high IL-1α (OR = 1.36; p = 0.01; 95%CI = 1.07–1.73) and low IL-27 (OR = 0.58; p < 0.005; 95%CI = 0.39–0.85) greatly increased the risk of ending up in the severe group. This model was especially sensitive in order to predict critical status (AUC = 0.794; specificity = 69.74%; sensitivity = 81.25%). Furthermore, high levels of HGF and IL-1α showed significant results in the survival analysis (p = 0.033 and p = 0.011, respectively). HGF, IL-1α, and IL 27 at hospital admission were strongly associated with severe/critical COVID-19 patients and therefore are excellent predictors of bad prognosis. HGF and IL-1α were also mortality biomarkers.This work was supported by the Carlos III Health Institute (Grant COV20/00491)