Origen y distribución de los hidrocarburos policíclicos aromáticos en sedimentos actuales de la Laguna de El Hito (España central)

Se llevó a cabo la evaluación ambiental y el estudio del estado actual de la cuenca de la Laguna de El Hito referido a 18 hidrocarburos policíclicos aromáticos (PAHs) de 2 a 6 anillos bencénicos. Se determinó su origen a partir de diversos índices (%naftaleno, Fen/Ant y Flu/Pir), interpretándose tanto fuentes no antropogénicas (petrogénica) como antropogénicas (pirogénica). Se obtuvieron los mapas de distribución de las concentraciones de PAHs y de sus índices para localizar los puntos de concentraciones más elevadas. Ningún PAH superó las concentraciones marcadas por los Niveles Genéricos de Referencia (NGR) para la salud humana en los distintos usos del suelo del R.D.09/2005. Los PAHs con las mayores concentraciones fueron el naftaleno y el fenantreno.Environmental evaluation and analysis of the current state of El Hito Lake Basin referred to 18 polycyclic aromatic hydrocarbons (PAHs) with 2 to 6 benzene rings was carried out. Different indexes were used to determine the source of PAHs (% naftalene, Phe/Ant and Flu/Pyr). Both non anthropogenic (petrogenic) and anthropogenic (pyrogenic) sources were interpreted. Distribution maps for PAHs and indexes were plotted to locate the position of the higher concentrations and, therefore, their possible sources. None of these compounds showed concentrations above the Soil Screening Levels (SSL) for human health in the different uses of soil as is established in R.D.09/2005. The ones that reached the highest concentrations were naphthalene and phenanthrene

Origin and distribution of polycyclic aromatic hydrocarbons in recent sediments of El Hito Lake (Central Spain)

Se llevó a cabo la evaluación ambiental y el estudio del estado actual de la cuenca de la Laguna de El Hito referido a 18 hidrocarburos policíclicos aromáticos (PAHs) de 2 a 6 anillos bencénicos. Se determinó su origen a partir de diversos índices (%naftaleno, Fen/Ant y Flu/Pir), interpretándose tanto fuentes no antropogénicas (petrogénica) como antropogénicas (pirogénica). Se obtuvieron los mapas de distribución de las concentraciones de PAHs y de sus índices para localizar los puntos de concentraciones más elevadas. Ningún PAH superó las concentraciones marcadas por los Niveles Genéricos de Referencia (NGR) para la salud humana en los distintos usos del suelo del R.D.09/2005. Los PAHs con las mayores concentraciones fueron el naftaleno y el fenantrenoEnvironmental evaluation and analysis of the current state of El Hito Lake Basin referred to 18 polycyclic aromatic hydrocarbons (PAHs) with 2 to 6 benzene rings was carried out. Different indexes were used to determine the source of PAHs (% naftalene, Phe/Ant and Flu/Pyr). Both non anthropogenic (petrogenic) and anthropogenic (pyrogenic) sources were interpreted. Distribution maps for PAHs and indexes were plotted to locate the position of the higher concentrations and, therefore, their possible sources. None of these compounds showed concentrations above the Soil Screening Levels (SSL) for human health in the different uses of soil as is established in R.D.09/2005. The ones that reached the highest concentrations were naphthalene and phenanthren

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

Relationship between immunometabolic status and cognitive performance among major depression disorder patients

Background: Alterations in cognitive performance have been described in patients with major depressive disorder (MDD). However, the specific risk factors of these changes are not yet known. This study aimed to explore whether inmunometabolic parameters are related to cognitive performance in MDD in comparison to healthy controls (HC) METHODS: Sample consisted of 84 MDD patients and 78 HC. Both groups were compared on the results of cognitive performance measured with the Cambridge Neuropsychological Test Automated Battery (CANTAB), the presence of metabolic syndrome (MetS) and an inflammatory/oxidative index calculated by a principal component analysis of peripheral biomarkers (tumor necrosis factor, C-reactive protein and 4-hydroxynonenal). A multiple linear regression was carried out, to study the relationship between inmunometabolic variables and the global cognitive performance, being the latter the dependent variable. Results: Significant differences were obtained in the inflammatory/oxidative index between both groups (F(1157)= 12.93; p < .001), also in cognitive performance (F(1157)= 56.75; p < .001). The inmunometabolic covariate regression model (i.e., condition (HC/MDD), sex, age and medication loading, MetS, inflammatory/oxidative index and the interaction between MetS and inflammatory/oxidative index) was statistically significant (F(7157)= 11.24; p < .01) and explained 31% of variance. The condition, being either MDD or HD, (B=-0.97; p < .001), age (B=-0.28; p < .001) and the interaction between inflammatory/oxidative index and MetS (B=-0.38; p = .02) were factors associated to cognitive performance. Limitations: Sample size was relatively small. The cross-sectional design of the study limits the possibilities of analysis. Conclusions: Our results provide evidence on the conjoint influence of metabolic and inflammatory dysregulation on cognitive dysfunction in MDD patients. In this way, our study opens a line of research in immunometabolic agents to deal with cognitive decline associated with MDD

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

Relación entre el estado inmunometabólico y el estado clínico y cognitivo en pacientes con Trastorno Depresivo Mayor

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Psiquiatría. Fecha de Lectura: 04-05-2023La investigación que conduce a estos resultados, el proyecto MARIDE, ha recibido financiación del Instituto de Salud Carlos III a través del Ministerio de Ciencia, Innovación y Universidades (PI15/00204), cofinanciado por el Fondo Europeo de Desarrollo Regional (FEDER) “Una forma de construir Europa

Age at illness onset and physical activity are associated with cognitive impairment in patients with current diagnosis of major depressive disorder

Background: Cognitive impairment has been reported in patients with Major Depressive Disorder (MDD). This study aims to explore the association between lifestyle habits and health-related factors and the presence of cognitive symptoms in MDD patients. Methods: Demographic, clinical, health-related variables and cognitive scores measured with the Cambridge Neuropsychological Test Automated Battery (CANTAB) were compared between 74 patients with current MDD and 68 healthy controls (HC). To test the hypothesis of associated factors to cognitive symptoms, multivariate backward stepwise linear regression models were run. Results: Significant neuropsychological deficits were evident in MDD compared with HC in the global cognitive index (F=8.29; df=1, 140; p=0.005). In the regression analysis performed on MDD and HC, years of schooling (β=-0.11; p=<0.001), job status (β=-0.50; p=0.016), physical activity (β=-0.25; p=0.04) and age at illness onset (β=0.17; p=0.017) were statistically significant factors associated to cognitive impairment. The regression model ran in HC showed that only years of schooling were significant (β=-0.07; p=<0.001) in this group. Limitations: Sample size was relatively small. Everyday cognitive skills were not evaluated. Conclusions: MDD patients have cognitive deficits. These deficits are linked with the years of education, job status, age of onset of the disease and the performance of physical activity. These results support the importance of the implementation of interventions targeting the cognitive reserve and lifestyle habits of MDD patients, in addition to the conventional therapeutic approach focused on symptoms control

Risk factors for suicidal behaviour in late-life depression: A systematic review

Background: Suicide is a leading cause of preventable death worldwide, with its peak of maximum incidence in later life. Depression often puts an individual at higher risk for suicidal behaviour. In turn, depression deserves particular interest in old age due to its high prevalence and dramatic impact on health and wellbeing. Aim: To gather integrated evidence on the potential risk factors for suicide behaviour development in depressive older adults, and to examine the effects of depression treatment to tackle suicide behaviour in this population. Methods: A systematic review of empirical studies, published from 2000 onwards, was conducted. Suicidal behaviour was addressed considering its varying forms (i.e., wish to die, ideation, attempt, and completed suicide). Results: Thirty-five papers were selected for review, comprising both clinical and epidemiological studies. Most of studies focused on suicidal ideation (60%). The studies consistently pointed out that the risk was related to depressive episode severity, psychiatric comorbidity (anxiety or substance use disorders), poorer health status, and loss of functionality. Reduced social support and loneliness were also associated with suicide behaviour in depressive older adults. Finally, the intervention studies showed that suicidal behaviour was a robust predictor of depression treatment response. Reductions in suicidal ideation were moderated by reductions in risk factors for suicide symptoms. Conclusion: To sum up, common and age-specific risk factors seem to be involved in suicide development in depressive older adults. A major effort should be made to tackle this serious public health concern so as to promote older people to age healthily and well.Depto. de Medicina Legal, Psiquiatría y PatologíaFac. de MedicinaTRUEpu

Incorporación de nuevas metodologías para la dinamización de la biblioteca escolar

Se adjunta una guía de libros de lecturaRealizado en el Colegio Público Teresa Revilla, por 15 profesores del centro, que han llevado a cabo 23 sesiones con su acta de reunión. Los destinatarios han sido los 116 alumnos del Centro desde Infantil hasta el primer ciclo de ESO. La puesta en marcha del proyecto ha resultado muy positiva para la marcha del centro a lo largo del curso ya que ha posibilitado: una mayor utilización de la biblioteca escolar y de los recursos con que cuente, se ha optimizado el aula de informática en los aspectos relacionados con la lectura, ha mejorado el hábito lector y el gusto por la lectura, se ha mejorado en el conocimiento de las herramientas informáticas, y se ha mejorado el trabajo en equipo. Los materiales realizados han sido 2 CD-ROM con unidades didácticas de cuentos y relatos.Junta de Castilla y León. Consejería de Educación y CulturaCastilla y LeónJunta de Castilla y León. Consejería de Educación y Cultura; Monasterio de Nuestra Señora de Prado. Autovía Puente Colgante, s. n.; 47071 Valladolid; Tel. +34983411881; Fax +34983411939; [email protected]

Relationship between immunometabolic status and cognitive performance among major depression disorder patients

Background: Alterations in cognitive performance have been described in patients with major depressive disorder (MDD). However, the specific risk factors of these changes are not yet known. This study aimed to explore whether inmunometabolic parameters are related to cognitive performance in MDD in comparison to healthy controls (HC) Methods: Sample consisted of 84 MDD patients and 78 HC. Both groups were compared on the results of cognitive performance measured with the Cambridge Neuropsychological Test Automated Battery (CANTAB), the presence of metabolic syndrome (MetS) and an inflammatory/oxidative index calculated by a principal component analysis of peripheral biomarkers (tumor necrosis factor, C-reactive protein and 4-hydroxynonenal). A multiple linear regression was carried out, to study the relationship between inmunometabolic variables and the global cognitive performance, being the latter the dependent variable. Results: Significant differences were obtained in the inflammatory/oxidative index between both groups (F= 12.93; p <.001), also in cognitive performance (F= 56.75; p <.001). The inmunometabolic covariate regression model (i.e., condition (HC/MDD), sex, age and medication loading, MetS, inflammatory/oxidative index and the interaction between MetS and inflammatory/oxidative index) was statistically significant (F= 11.24; p <.01) and explained 31% of variance. The condition, being either MDD or HD, (B=˗0.97; p <.001), age (B=˗0.28; p <.001) and the interaction between inflammatory/oxidative index and MetS (B=˗0.38; p =.02) were factors associated to cognitive performance. Limitations: Sample size was relatively small. The cross-sectional design of the study limits the possibilities of analysis. Conclusions: Our results provide evidence on the conjoint influence of metabolic and inflammatory dysregulation on cognitive dysfunction in MDD patients. In this way, our study opens a line of research in immunometabolic agents to deal with cognitive decline associated with MDD