El Parkinson no me detiene campaña de concientización de la enfermedad de Parkinson

Parkinson's Disease, after Alzheimer’s, is the second most common neurodegenerative disorder to appear. This disease is commonly associated with people from 65 years or older, however, recent studies have revealed the existence of Juvenile Parkinson’s presented in patients from 35 years. To date, Ecuador hasn’t developed studies to get official statistics on the incidence of Parkinson's. The following work consists in a research plan and an communicational initiative to encourage Parkinson's patients to remain active and to develop an equal society.La Enfermedad de Parkinson es el segundo trastorno neurodegenerativo más común en aparecer luego del Alzheimer. Hasta la fecha sus causas no han sido determinadas; sin embargo, estudios recientes han revelado la existencia del Parkinson Juvenil que se presenta a partir de los 35 años de edad. El Ecuador carece de cifras oficiales sobre la incidencia del Parkinson en la población, por lo que el trabajo presentado a continuación consiste en un plan de investigación e iniciativa comunicacional de impulso para que los pacientes con Parkinson sigan siendo entes activos de la sociedad y para que exista una sociedad más proactiva e informada

Bayesian analysis of the species-specific lengthening of the growing season in two European countries and the influence of an insect pest

A recent lengthening of the growing season in mid and higher latitudes of the northern hemisphere is reported as a clear indicator for climate change impacts. Using data from Germany (1951–2003) and Slovenia (1961–2004), we study whether changes in the start, end, and length of the growing season differ among four deciduous broad-leaved tree species and countries, how the changes are related to temperature changes, and what might be the confounding effects of an insect attack. The functional behaviour of the phenological and climatological time series and their trends are not analysed by linear regression, but by a new Bayesian approach taking into account different models for the functional description (one change-point, linear, constant models). We find advanced leaf unfolding in both countries with the same species order (oak > horse chestnut, beech, and birch). However, this advance is non linear over time and more apparent in Germany with clear change-points in the late 1970s, followed by marked advances (on average 3.67 days decade−1 in the 2000s). In Slovenia, we find a more gradual advance of onset dates (on average 0.8 days decade−1 in the 2000s). Leaf colouring of birch, beech, and oak has been slightly delayed in the last 3 decades, especially in Germany, however with no clear functional behaviour. Abrupt changes in leaf colouring dates of horse chestnut with recent advancing onset dates can be linked across countries to damage by a newly emerging pest, the horse chestnut leaf-miner (Cameraria ohridella). The lengthening of the growing season, more distinct in Germany than in Slovenia (on average 4.2 and 1.0 days decade−1 in the 2000s, respectively), exhibits the same species order in both countries (oak > birch > beech). Damage by horse chestnut leaf-miner leads to reduced lengthening (Germany) and drastic shortening (Slovenia) of the horse chestnut growing season (-12 days decade−1 in the 2000s). Advanced spring leaf unfolding and lengthening of the growing season of oak, beech and birch are highly significantly related to increasing March temperatures in both countries. Only beech and oak leaf unfolding in Germany, which is generally observed later in the year than that of the other two species, is more closely correlated with April temperatures, which comparably exhibit marked change-points at the end of the 1970s

Stratiform and convective rain classification using machine learning models and micro rain radar

Rain type classification into convective and stratiform is an essential step required to improve quantitative precipitation estimations by remote sensing instruments. Previous studies with Micro Rain Radar (MRR) measurements and subjective rules have been performed to classify rain events. However, automating this process by using machine learning (ML) models provides the advantages of fast and reliable classification with the possibility to classify rain minute by minute. A total of 20,979 min of rain data measured by an MRR at Das in northeast Spain were used to build seven types of ML models for stratiform and convective rain type classification. The proposed classification models use a set of 22 parameters that summarize the reflectivity, the Doppler velocity, and the spectral width (SW) above and below the so-called separation level (SL). This level is defined as the level with the highest increase in Doppler velocity and corresponds with the bright band in stratiform rain. A pre-classification of the rain type for each minute based on the rain microstructure provided by the collocated disdrometer was performed. Our results indicate that complex ML models, particularly tree-based ensembles such as xgboost and random forest which capture the interactions of different features, perform better than simpler models. Applying methods from the field of interpretable ML, we identified reflectivity at the lowest layer and the average spectral width in the layers below SL as the most important features. High reflectivity and low SW values indicate a higher probability of convective rainPostprint (published version

Why do not pregnant women want to get vaccinated against the flu? : a scoping review

Fundamentos: Las mujeres embarazadas, los neonatos y los recién nacidos tienen mayor riesgo de complicaciones por la gri- pe estacional. La vacunación es efectiva y segura, pero hay baja adherencia en embarazadas. El objetivo de este trabajo fue iden- tificar los motivos que llevan a las embarazadas a no vacunarse contra la gripe estacional. Métodos: Scoping Review, en la que su utilizaron como tér- minos de búsqueda: DeCS “Mujeres Embarazadas”, “Vacunas contra la Influenza”. MeSH “Pregnant Women”, “Influenza Vac- cines”. Las bases de datos en las que se realizaron las búsquedas fueron: Medline, BVS, Scielo, CUIDEN. Se utilizó el modelo PRISMA y herramienta del Instituto Joanna Briggs para ordenar la búsqueda y sintetizar los resultados. Se identificaron los mo- tivos de no vacunación en cada estudio y se ordenaron según su frecuencia de aparición. Resultados: 16 estudios que identifican 15 motivos para no vacunarse. Los más frecuentes: Preocupación por los efectos secundarios y/o seguridad de la vacuna y falta de información/ recomendación por parte del personal sanitario. Conclusiones: La decisión para no vacunarse parece ser mul- tifactorial. En algunos motivos hallados el papel del profesional puede jugar un papel fundamental en la adherencia. Estos resul- tados podrían ser útiles para futuras investigaciones y pueden ser- vir de ejemplo para discusiones internas entre los profesionales sanitarios con el objetivo de promover la vacunación antigripal en embarazadas.Background: Pregnant women and newborns are at in- creased risk of complications from seasonal flu. Vaccination is effective and safe but there is low adherence in pregnant women. Objective: to identify the reasons that lead pregnant women not to be vaccinated against seasonal influenza. Methods: Scoping Review in which we used as search terms. DeCS “Pregnant Women”, “Vaccines against Influenza”. MeSH “Pregnant Women”, “Influenza Vaccines”, united by AND. Data- bases: Medline, VHL, Scielo, CUIDEN. The PRISMA model and the Joanna Briggs Institute tool were used to sort the search and synthesize the results. Motives were identified in each study and ordered according to frequency of appearance. Results: 16 studies were found that identify 15 reasons for not being vaccinated. The most frequent were: Concern about side effects and / or vaccine safety and lack of information / re- commendation from health professionals. Conclusions: The decision of not to be vaccinated seems to be multifactorial. In some cases, health professionals can play a fundamental role in adherence. These results could be useful for future research

Changes to Airborne Pollen Counts across Europe

A progressive global increase in the burden of allergic diseases has affected the industrialized world over the last half century and has been reported in the literature. The clinical evidence reveals a general increase in both incidence and prevalence of respiratory diseases, such as allergic rhinitis (common hay fever) and asthma. Such phenomena may be related not only to air pollution and changes in lifestyle, but also to an actual increase in airborne quantities of allergenic pollen. Experimental enhancements of carbon dioxide (CO2) have demonstrated changes in pollen amount and allergenicity, but this has rarely been shown in the wider environment. The present analysis of a continental-scale pollen data set reveals an increasing trend in the yearly amount of airborne pollen for many taxa in Europe, which is more pronounced in urban than semi-rural/rural areas. Climate change may contribute to these changes, however increased temperatures do not appear to be a major influencing factor. Instead, we suggest the anthropogenic rise of atmospheric CO2 levels may be influentia

A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing

Purpose Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the “ClinVar low-hanging fruit” reanalysis, reasons for the failure of previous analyses, and lessons learned. Methods Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. Results We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). Conclusion The “ClinVar low-hanging fruit” analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock