Desarrollo e implementación de un canal para evaluar el comportamiento natatorio de peces en etapas tempranas de desarrollo aplicado a estudios de ecotoxicología

El progreso de nuestra sociedad se debe en gran medida a la generación de una ingente cantidad de sustancias químicas, muchas de las cuales tiene su destino final en los ecosistemas acuáticos continentales con el consiguiente impacto sobre su estructura y funcionamiento. En este escenario, los bioensayos ecotoxicológicos se presentan como unas herramientas esenciales para la evaluación del riesgo de sustancias químicas y donde los peces debido a su posición trófica y valor ecológico son bioindicadores ideales. Los bioensayos con peces se basan en el desarrollo de biomarcadores sensibles y ecológicamente relevantes entre los que la natación es, posiblemente, la cualidad más básica sobre la que se sostienen todos los demás tipos de comportamiento. Cualquier alteración en el comportamiento de la natación puede reflejar cambios sobre niveles de organización ecológicamente relevantes..

Efecto de la gestión forestal en la cantidad de carbono del suelo mineral en una masa de Pinus pinaster Ait.

El conocimiento del impacto de la gestión forestal sobre los diversos compartimentos de carbono existentes en una masa forestal es prioritario para la sostenibilidad del aprovechamiento y gestión de los sistemas forestales. Con la aplicación de tratamientos selvícolas hay una serie de transformaciones en la masa como la reducción de biomasa en pie, la modificación de la cantidad de residuos o la disminución de la incorporación de hojarasca, la alteración del microclima del suelo por mayor presencia de luz y su influencia en las condiciones de humedad y temperatura. Este cambio en las condiciones modifica el contenido de carbono en el suelo. En este trabajo se ha estudiado la influencia que tiene la gestión forestal en las cantidades de carbono y nitrógeno presentes en los 30 primeros cm del suelo mineral más la hojarasca y humus de una masa de Pinus pinaster Ait., a partir de la comparación entre los cantidades de carbono bajo tres intensidades de claras. A partir de los resultados obtenidos no se han encontrado diferencias significativas entre los tratamientos, tanto en la cantidad de carbono y nitrógeno almacenado en la capa orgánica como en la capa mineral del suelo, por lo cual la realización de claras fuertes no modifica sustancialmente las cantidades de carbono acumuladas

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

Ammonia levels in different kinds of sampling sites in the central Iberian Peninsula

Ponencia presentada en:2nd Iberian Meeting on Aerosol Science and Technology (RICTA 2014) celebrado en Tarragona del 7 al 9 de julio de 2014.Ammonia is the Secondary Inorganic Aerosol (SIC) gaseous precursor which has been studied to a lesser extent in the Madrid Metropolitan Area up to date. A study conducted in the city of Madrid with the aim of characterizing levels of ammonia took place in 2011. These campaigns formed part of a larger study conducted in 6 Spanish cities. A time series of weekly integrated ammonia measurements available at an EMEP rural site (Campisábalos) has been used to obtain information on the ammonia rural background in the region. The results point to traffic and waste treatment plants as the main ammonia sources in Madrid. Relevant seasonal differences have not been observed in the Metropolitan Area. The explanation can be related to the fall in the rural background levels during July 2011, which might conceal urban summer emission increases observed in other cities

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

Plitidepsin has a positive therapeutic index in adult patients with COVID-19 requiring hospitalization

Plitidepsin is a marine-derived cyclic-peptide that inhibits SARS-CoV-2 replication at low nanomolar concentrations by the targeting of host protein eEF1A (eukaryotic translation-elongation-factor-1A). We evaluated a model of intervention with plitidepsin in hospitalized COVID-19 adult patients where three doses were assessed (1.5, 2 and 2.5 mg/day for 3 days, as a 90-minute intravenous infusion) in 45 patients (15 per dose-cohort). Treatment was well tolerated, with only two Grade 3 treatment-related adverse events observed (hypersensitivity and diarrhea). The discharge rates by Days 8 and 15 were 56.8% and 81.8%, respectively, with data sustaining dose-effect. A mean 4.2 log10 viral load reduction was attained by Day 15. Improvement in inflammation markers was also noted in a seemingly dose-dependent manner. These results suggest that plitidepsin impacts the outcome of patients with COVID-19.This study has been funded by Pharmamar, S.A. (Madrid, Spain). This work was supported by grants from the Government of Spain (PIE_INTRAMURAL_ LINEA 1 - 202020E079; PIE_INTRAMURAL_CSIC-202020E043). The research of CBIG consortium (constituted by IRTA-CReSA, BSC, & IrsiCaixa) is supported by Grifols pharmaceutical. We also acknowledge the crowdfunding initiative #Yomecorono (https://www.yomecorono.com). N.I.U. has non-restrictive funding from PharmaMar to study the antiviral effect of Plitidepsin. N.J.K. was funded by grants from the National Institutes of Health (P50AI150476, U19AI135990, U19AI135972, R01AI143292, R01AI120694, and P01AI063302); by the Excellence in Research Award (ERA) from the Laboratory for Genomics Research (LGR), a collaboration between UCSF, UCB, and GSK (#133122P); by the Roddenberry Foundation, and gifts from QCRG philanthropic donors. This work was supported by the Defense Advanced Research Projects Agency (DARPA) under Cooperative Agreement #HR0011-19-2-0020. The views, opinions, and/or findings contained in this material are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. This research was partly funded by CRIP (Center for Research for Influenza Pathogenesis), a NIAID supported Center of Excellence for Influenza Research and Surveillance (CEIRS, contract # HHSN272201400008C), by DARPA grant HR0011-19-2-0020, by supplements to NIAID grants U19AI142733, U19AI135972 and DoD grant W81XWH-20-1-0270, and by the generous support of the JPB Foundation, the Open Philanthropy Project (research grant 2020-215611 (5384)), and anonymous donors to AG-S. S.Y. received funding from a Swiss National Foundation (SNF) Early Postdoc Mobility fellowship (P2GEP3_184202).N

Preclinical and randomized phase I studies of plitidepsin in adults hospitalized with COVID-19

Plitidepsin, a marine-derived cyclic-peptide, inhibits SARS-CoV-2 replication at nanomolar concentrations by targeting the host protein eukaryotic translation elongation factor 1A. Here, we show that plitidepsin distributes preferentially to lung over plasma, with similar potency against across several SARS-CoV-2 variants in preclinical studies. Simultaneously, in this randomized, parallel, open-label, proof-of-concept study (NCT04382066) conducted in 10 Spanish hospitals between May and November 2020, 46 adult hospitalized patients with confirmed SARS-CoV-2 infection received either 1.5 mg (n = 15), 2.0 mg (n = 16), or 2.5 mg (n = 15) plitidepsin once daily for 3 d. The primary objective was safety; viral load kinetics, mortality, need for increased respiratory support, and dose selection were secondary end points. One patient withdrew consent before starting procedures; 45 initiated treatment; one withdrew because of hypersensitivity. Two Grade 3 treatment-related adverse events were observed (hypersensitivity and diarrhea). Treatment-related adverse events affecting more than 5% of patients were nausea (42.2%), vomiting (15.6%), and diarrhea (6.7%). Mean viral load reductions from baseline were 1.35, 2.35, 3.25, and 3.85 log10 at days 4, 7, 15, and 31. Nonmechanical invasive ventilation was required in 8 of 44 evaluable patients (16.0%); six patients required intensive care support (13.6%), and three patients (6.7%) died (COVID-19-related). Plitidepsin has a favorable safety profile in patients with COVID-19.This work was supported by grants from the Government of Spain (PIE_INTRAMURAL_ LINEA 1 - 202020E079; PIE_INTRAMURAL_CSIC-202020E043). The research of CBIG consortium (constituted by IRTA-CReSA, BSC, & IrsiCaixa) is supported by Grifols pharmaceutical. We also acknowledge the crowdfunding initiative #Yomecorono (https://www.yomecorono.com). N Izquierdo-Useros has nonrestrictive funding from PharmaMar to study the antiviral effect of Plitidepsin. NJ Krogan was funded by grants from the National Institutes of Health (P50AI150476, U19AI135990, U19AI135972, R01AI143292, R01AI120694, and P01AI063302); by the Excellence in Research Award (ERA) from the Laboratory for Genomics Research (LGR), a collaboration between the University of California, San Francisco (UCSF), University of California, Berkley (UCB), and GlaxoSmithKline (GSK) (#133122P); by the Roddenberry Foundation, and gifts from QCRG philanthropic donors. This work was supported by the Defense Advanced Research Projects Agency (DARPA) under Cooperative Agreement #HR0011-19-2-0020. The views, opinions, and/or findings contained in this material are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. This research was partly funded by Center for Research for Influenza Pathogenesis and Transmission (CRIPT), a National Institute of Allergy and Infectious Diseases (NIAID) supported Center of Excellence for Influenza Research and Response (CEIRS, contract # 75N93021C00014), by DARPA grant HR0011-19-2-0020, by supplements to NIAID grants U19AI142733, U19AI135972, and DoD grant W81XWH-20-1-0270, and by the generous support of the JPB Foundation, the Open Philanthropy Project (research grant 2020-215611 (5384)), and anonymous donors to A García-Sastre. S Yildiz received funding from a Swiss National Foundation Early Postdoc Mobility fellowship (P2GEP3_184202).Peer reviewe

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Hydrodechlorination of chlorobenzene-tetrachloroethylene mixtures over a Pd/AL2O3 catalyst

Póste