In vitro modeling of dysfunctional glial cells in neurodegenerative diseases using human pluripotent stem cells

Most neurodegenerative diseases are characterized by a complex and mostly still unresolved pathology. This fact, together with the lack of reliable models, have precluded the development of effective therapies counteracting the disease progression. In the past few years, several studies have evidenced that lack of proper functionality of glial cells (astrocytes, microglia and oligodendrocytes) has a key role in the pathology of several neurodegenerative conditions including Alzheimer´s disease, amyotrophic lateral sclerosis and multiple sclerosis among others. However, this glial dysfunction is poorly modelled by available animal models, and we hypothesize that patientderived cells can serve as a better platform where to study this glial dysfunction. In this sense, human pluripotent stem cells (hPSCs) has revolutionized the field allowing the generation of disease-relevant neural cell types that can be used for disease modelling, drug screening and, possibly, cell transplantation purposes. In the case of the generation of oligodendrocytes (OLs) from hPSCs, we have developed a fast and robust protocol to generate surface antigen O4-positive (O4+) and myelin basic protein-positive OLs from hPSCs in only 22 days, including from patients with multiple sclerosis or amyotrophic lateral sclerosis. The generated cells resemble primary human OLs at the transcriptome level and can myelinate neurons in vivo. Using in vitro OLneuron co-cultures, effective myelination of neurons can also be demonstrated. This platform is being translated as well to the generation of the other glial cell types, allowing the derivation of patient-specific glial cells where to model disease-specific dysfunction. This methodology can be used for elucidating pathogenic pathways associated with neurodegeneration and to identify therapeutic targets susceptible of drug modulation, contributing to the development of novel and effective drugs for these devastating disorders.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Supported by PI18/01557 (to AG) and P18/1556 (to JV) grants from ISCiii of Spain co-financed by FEDER funds from European Union, and PI-0276-2018 grant (to JAGL) from Consejeria de Salud of Junta de Andalucia. JAGL held a postdoctoral contract from the I Research Plan Propio of the University of Malaga. CV and KE were supported by IWT-SBO-150031-iPSCAF and the Thierry Lathran Foundation grant – ALS-OL, and KN by FWO1166518

Water Quality of the Poza Honda Dam and Other Water Points Down

The phenomenon of pollution of water basins is eliminating many potential water resources. Most of the pollution in Ecuador comes from household waste and agricultural chemicals, especially along the coast. One of the activities in the management of the water resource is the periodic monitoring of the bodies of water, being able to determine the different changes that occur and to influence through preventive actions that manage to reduce the pollution. The water resource is the articulating axis of all the activities in a territory and therefore of the populations that develop different productive activities that not only depend on the quantity and quality of this resource but also generate alterations to the natural state of the same. In the investigation, the monitoring of the quality of the water in different points of the Poza Honda dam and of the river Portoviejo is carried out. The study aims to manage the pollution processes that occur in the aquifer, due to the depositions of domestic, industrial and agricultural wastewater not controlled to be discharged

Dynamic mechanical behavior of starch-based scaffolds in dry and physiologically simulated conditions: effect of porosity and pore size

The three-dimensional scaffolds of a blend of starch and poly(L-lactic) acid, SPLA70, were produced using compression molding of polymer/salt mixture followed by leaching of salt. One series of scaffolds were prepared with varying polymer-to-salt ratio while keeping the salt size constant, and the other series of scaffolds were prepared with varying salt sizes while keeping the polymer-to-salt ratio constant. The X-ray microcomputed tomography and scanning electron microscopy assay were used to analyze the porous morphologies, porosity and distribution of porosity of the porous scaffolds. Salt-free and integrated SPLA70 scaffolds with porosities ranging from 74% to 82% and pore sizes of 125–250 to 500–1000 lm can be fabricated using the present fabrication technique. The water uptake of the SPLA70 scaffolds increases with increasing porosities and also with increasing pore size. In dry state, the storage modulus decreases with increasing porosity and also with increasing pore size. The normalized modulus values are related to normalized density of the scaffolds by a power-law function with an exponent between 2 and 3. For the immersed scaffolds under physiological conditions, the storage modulus was less dependent on porosity and pore size. However, the loss factor increased significantly compared with dry state measurements. The present study clearly shows that the mechanical performance of porous polymeric constructs in dry and in immersed state is completely different, and for comparison with biomechanical performance of tissues, the tests should ideally be performed in immersed state

RED DE “DETECTORES PASIVOS INFRARROJOS” ENLAZADOS POR RADIOFRECUENCIA, COMO SISTEMA DE ALARMA DE SEGURIDAD DE BAJO COSTO

La domótica (Casa robot) abarca todas las fases de la tecnología de hogarinteligente, incluidos los sensores altamente sofisticados y controles que automatizan la temperatura, iluminación, sistemas de seguridad y muchas otras funciones. El sistema de seguridad implementado para esta fase de “casa inteligente”, lo forma una red de sensores de movimiento PIR, enlazados por RF a un sistema de Hardware libre “Arduino”, para que este a su vez sirva como interfaz al entorno del hogar y también a la WEB

Standardised description of health and social care: A systematic review of use of the ESMS/DESDE (European Service Mapping Schedule/Description and Evaluation of Services and DirectoriEs)

Background: Evidence-informed planning and interpretation of research results both require standardised description of local care delivery context. Such context analysis descriptions should be comparable across regions and countries to allow benchmarking and organizational learning, and for research findings to be interpreted in context. The European Service Mapping Schedule (ESMS) is a classification of adult mental health services that was later adapted for the assessment of health and social systems research (Description and Evaluation of Services and DirectoriEs - DESDE). The aim of the study was to review the diffusion and use of the ESMS/DESDE system in health and social care and its impact in health policy and decision-making. Method: We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (1997–2018). Results: Out of 155 papers mentioning ESMS/DESDE, 71 have used it for service research and planning. The classification has been translated into eight languages and has been used by seven international research networks. Since 2000, it has originated 11 instruments for health system research with extensive analysis of their metric properties. The ESMS/DESDE coding system has been used in 585 catchment areas in 34 countries for description of services delivery at local, regional and national levels. Conclusions: The ESMS/DESDE system provides a common terminology, a classification of care services, and a set of tools allowing a variety of aims to be addressed in healthcare and health systems research. It facilitates comparisons across and within countries for evidence-informed planning

Production performance, nutrient digestibility, and milk composition of dairy ewes supplemented with crushed sunflower seeds and sunflower seed silage in corn silage-based diets

This study determined production performance, nutrient digestibility, and milk composition of dairy ewes supplemented with crushed sunflower seeds (Helianthus annuus) and sunflower seed silage in corn silage-based diets. Six ewes were grouped in a double 3 × 3 Latin square design with three periods of 21 days. All treatments were based on ad libitum corn silage. Control diet was based on alfalfa hay (333 g/kg DM), sorghum grain (253 g/kg DM), triticale grain (200 g/kg DM), soybean meal (167 g /kg DM), and vitamin and mineral premix (47 g/kg DM). Sunflower seeds (SF) and sunflower seed silage (SFS) treatments consisted of alfalfa hay (333 g/kg DM), sorghum grain (267 g/kg DM), triticale grain (100 g/kg DM), soybean meal (167 g /kg DM), SF or SFS (87 g/kg DM) and vitamin and mineral premix (47 g/kg DM). Compared to control, SF and SFS increased intake and digestibility of fiber components, such as neutral detergent fiber (NDF) and acid detergent fiber (ADF). Body weight, nitrogen balance, milk yield, milk fat yield, milk protein yield, lactose yield and milk urea N were similar between treatments. Overall, results demonstrated that crushed sunflower seeds and ensiled seeds do not change significantly productive parameters of dairy sheep

A Two-Step Polynomial and Nonlinear Growth Approach for Modeling COVID-19 Cases in Mexico

Since December 2019, the novel coronavirus (SARS-CoV-2) and its associated illness COVID-19 have rapidly spread worldwide. The Mexican government has implemented public safety measures to minimize the spread of the virus. In this paper, we used statistical models in two stages to estimate the total number of coronavirus (COVID-19) cases per day at the state and national levels in Mexico. In this paper, we propose two types of models. First, a polynomial model of the growth for the first part of the outbreak until the inflection point of the pandemic curve and then a second nonlinear growth model used to estimate the middle and the end of the outbreak. Model selection was performed using Vuong’s test. The proposed models showed overall fit similar to predictive models (e.g., time series and machine learning); however, the interpretation of parameters is simpler for decisionmakers, and the residuals follow the expected distribution when fitting the models without autocorrelation being an issue

A study on multi-scale kernel optimisation via centered kernel-target alignment

Kernel mapping is one of the most widespread approaches to intrinsically deriving nonlinear classifiers. With the aim of better suiting a given dataset, different kernels have been proposed and different bounds and methodologies have been studied to optimise them. We focus on the optimisation of a multi-scale kernel, where a different width is chosen for each feature. This idea has been barely studied in the literature, although it has been shown to achieve better performance in the presence of heterogeneous attributes. The large number of parameters in multi-scale kernels makes it computationally unaffordable to optimise them by applying traditional cross-validation. Instead, an analytical measure known as centered kernel-target alignment (CKTA) can be used to align the kernel to the so-called ideal kernel matrix. This paper analyses and compares this and other alternatives, providing a review of the literature in kernel optimisation and some insights into the usefulness of multi-scale kernel optimisation via CKTA. When applied to the binary support vector machine paradigm (SVM), the results using 24 datasets show that CKTA with a multi-scale kernel leads to the construction of a well-defined feature space and simpler SVM models, provides an implicit filtering of non-informative features and achieves robust and comparable performance to other methods even when using random initialisations. Finally, we derive some considerations about when a multi-scale approach could be, in general, useful and propose a distance-based initialisation technique for the gradient-ascent method, which shows promising results