Diabetes mellitus en COVID-19: ¿factor de riesgo o factor pronóstico?

Antecedentes y propósito: la diabetes mellitus es una de las enfermedades crónicas no transmisibles de mayor prevalencia en el mundo. La frecuencia con la que se reporta en los pacientes con COVID-19 es alta. Sin embargo, no es claro si las personas que padecen diabetes mellitus tienen un mayor riesgo de infección o, si una vez infectados, tienen un peor pronóstico. Nuestro propósito fue revisar y analizar la información disponible de COVID-19 y diabetes mellitus e intentar entender mejor el riesgo al que están expuestas las personas con diabetes mellitus durante la pandemia por COVID-19. Métodos: se revisaron las bases de datos PubMed, Cochrane Database of Systematic Reviews, Google Scholar, Scopus y Epistemonikos en búsqueda de registros nacionales epidemiológicos y revisiones sistemáticas, utilizando los términos “Diabetes Mellitus” “COVID-19”, “Factores de riesgo”, “Pronostico”, “Cuidado Critico”, “Insuficiencia Respiratoria” y “Muerte”. Se seleccionaron para análisis las revisiones sistemáticas de las comorbilidades en pacientes con COVID-19, las que analizaban el curso de la enfermedad y los factores pronósticos en pacientes con COVID-19 y aquellas que incluían modelos de pronóstico. Resultados: la información disponible sugiere que la diabetes mellitus es una comorbilidad frecuente en las personas con COVID-19, pero es difícil diferenciar si esto es debido a la alta prevalencia de la diabetes mellitus o a un riesgo más alto de infección. Las personas con diabetes mellitus parecieran tener un riesgo más alto de presentar una forma grave o de morir a causa de la COVID-19

Manejo ambulatorio del paciente con diabetes en tiempos de COVID-19

El tratamiento ambulatorio de pacientes con enfermedades crónicas no transmisibles (ECNT) ofrece retos interesantes en el contexto de la pandemia por SARS-CoV-2 (COVID-19), que exigen un análisis individual de la diabetes. Es a lugar discutir elementos del tratamiento ambulatorio: desde implementación de modelos específicos, recomendaciones de cambios en el estilo de vida, orientación en comorbilidades, identificación de factores de riesgo adicionales, hasta sugerencias en cada uno de los grupos farmacológicos que acá competen. Todo lo anterior con el propósito de brindar el mejor soporte a los pacientes en el panorama no hospitalario

Non Sequential Recursive Pair Substitution: Some Rigorous Results

We present rigorous results on some open questions on NSRPS, non sequential recursive pairs substitution method (see Grassberger in \cite{G}). In particular, starting from the action of NSRPS on finite strings we define a corresponding natural action on measures and we prove that the iterated measure becomes asymptotically Markov. This certify the effectiveness of NSRPS as a tool for data compression and entropy estimation.Comment: 20 page

On the use of convolutional deep learning to predict shoreline change

The process of shoreline change is inherently complex, and reliable predictions of shoreline position remain a key challenge in coastal research. Predicting shoreline evolution could potentially benefit from deep learning (DL), which is a recently developed and widely successful data-driven methodology. However, so far its implementation for shoreline time series data has been limited. The aim of this contribution is to investigate the potential of DL algorithms to predict interannual shoreline position derived from camera system observations at a New Zealand study site. We investigate the application of convolutional neural networks (CNNs) and hybrid CNN-LSTM (Long Short-Term Memory) networks. We compare our results with two established models: a shoreline equilibrium model and a model that addresses timescales in shoreline drivers. Using a systematic search and different measures of fitness, we found DL models that outperformed the reference models when simulating the variability and distribution of the observations. Overall, these results indicate that DL models have potential to improve accuracy and reliability over current models.</p

Defects in the ferroxidase that participates in the reductive iron assimilation system results in hypervirulence in botrytis cinerea

Indexación: Scopus.Abstract The plant pathogen Botrytis cinerea is responsible for gray-mold disease, which infects a wide variety of species. The outcome of this host-pathogen interac-tion, a result of the interplay between plant defense and fungal virulence pathways, can be modulated by various environmental factors. Among these, iron availability and acquisition play a crucial role in diverse biological functions. How B. cinerea ob-tains iron, an essential micronutrient, during infection is unknown. We set out to deter-mine the role of the reductive iron assimilation (RIA) system during B. cinerea infection. This system comprises the BcFET1 ferroxidase, which belongs to the multicopper oxidase (MCO) family of proteins, and the BcFTR1 membrane-bound iron permease. Gene knockout and complementation studies revealed that, compared to the wild type, the bcfet1 mutant displays delayed conidiation, iron-dependent sclerotium pro-duction, and significantly reduced whole-cell iron content. Remarkably, this mutant exhibited a hypervirulence phenotype, whereas the bcftr1 mutant presents normal virulence and unaffected whole-cell iron levels and developmental programs. Inter-estingly, while in iron-starved plants wild-type B. cinerea produced slightly reduced necrotic lesions, the hypervirulence phenotype of the bcfet1 mutant is no longer observed in iron-deprived plants. This suggests that B. cinerea bcfet1 knockout mutants require plant-derived iron to achieve larger necrotic lesions, whereas in planta analyses of reactive oxygen species (ROS) revealed increased ROS levels only for infections caused by the bcfet1 mutant. These results suggest that increased ROS produc-tion, under an iron sufficiency environment, at least partly underlie the observed infection phenotype in this mutant. IMPORTANCE The plant-pathogenic fungus B. cinerea causes enormous economic losses, estimated at anywhere between $10 billion and$100 billion worldwide, under both pre-and postharvest conditions. Here, we present the characterization of a loss-of-function mutant in a component involved in iron acquisition that displays hyperviru-lence. While in different microbial systems iron uptake mechanisms appear to be critical to achieve full pathogenic potential, we found that the absence of the ferroxidase that is part of the reductive iron assimilation system leads to hypervirulence in this fungus. This is an unusual and rather underrepresented phenotype, which can be modulated by iron levels in the plant and provides an unexpected link between iron acquisition, reactive oxygen species (ROS) production, and pathogenesis in the Botrytis-plant interaction.https://journals.asm.org/doi/epdf/10.1128/mBio.01379-2

Harmonic Distortion Index for Stationary and Transient States

Abstract. For transient or aperiodic signals the Fourier analysis is unable to obtain accurate results and a joint timefrequency analysis must be used to provide simultaneous time and frequency information of transient intervals. A power quality index is proposed for evaluation of both the stationary and transient quality aspects of electrical signals. The widely used total harmonic distortion index (THD) is redefined in this paper to include harmonics, oscillatory transients, voltage sags and swells. The new index is defined between the 0-1 range