Anastomotic leak in colorectal cancer surgery: Contribution of gut microbiota and prediction approaches.

AIM: To monitor prospectively the occurrence of colorectal anastomotic leakage (CAL) in patients with colon cancer undergoing resectional surgery, characterizing the microbiota in both faeces and mucosal biopsies of anastomosis. In a second stage, we investigated the ability to predict CAL using machine learning models based on clinical data and microbiota composition. METHOD: A total of 111 patients were included, from whom a faecal sample was obtained, as well as biopsy samples from proximal and distal sites in the healthy margins of the tumour piece. The microorganisms present in the samples were investigated using microbial culture and 16S rDNA massive sequencing. Collagenase and protease production was determined, as well as the presence of genes responsible for expressing enzymes with these activities. Machine learning analyses were developed using clinical and microbiological data. RESULTS: The incidence of CAL was 9.0%, and CAL was associated with collagenase/protease-producing Enterococcus. Significant differences were found in the microbiota composition of proximal and distal biopsy samples, but not in faecal samples, among patients who developed CAL. Clinical predictors of CAL were 5-day C-reactive protein and heart disease, whereas 3-day C-reactive protein and diabetes were negative predictors. CONCLUSION: Biopsy samples from surgical margins, rather than faecal samples, are the most appropriate samples for exploring the contribution of the intestinal microbiota to CAL. Enterococci are only enriched in the anastomosis after surgery, and their collagenases and proteases are involved in the degradation of the anastomotic scar.

Prevalence of mutualism in a simple model of microbial coevolution.

Evolutionary transitions among ecological interactions are widely known, although their detailed dynamics remain absent for most population models. Adaptive dynamics has been used to illustrate how the parameters of population models might shift through evolution, but within an ecological regime. Here we use adaptive dynamics combined with a generalized logistic model of population dynamics to show that transitions of ecological interactions might appear as a consequence of evolution. To this purpose, we introduce a two-microbial toy model in which population parameters are determined by a bookkeeping of resources taken from (and excreted to) the environment, as well as from the byproducts of the other species. Despite its simplicity, this model exhibits all kinds of potential ecological transitions, some of which resemble those found in nature. Overall, the model shows a clear trend toward the emergence of mutualism.

Climate change conditions the selection of rust-resistant candidate wild lentil populations for in situ conservation.

Crop Wild Relatives (CWR) are a valuable source of genetic diversity that can be transferred to commercial crops, so their conservation will become a priority in the face of climate change. Bizarrely, in situ conserved CWR populations and the traits one might wish to preserve in them are themselves vulnerable to climate change. In this study, we used a quantitative machine learning predictive approach to project the resistance of CWR populations of lentils to a common disease, lentil rust, caused by fungus Uromyces viciae-fabae. Resistance is measured through a proxy quantitative value, DSr (Disease Severity relative), quite complex and expensive to get. Therefore, machine learning is a convenient tool to predict this magnitude using a well-curated georeferenced calibration set. Previous works have provided a binary outcome (resistant vs. non-resistant), but that approach is not fine enough to answer three practical questions: which variables are key to predict rust resistance, which CWR populations are resistant to rust under current environmental conditions, and which of them are likely to keep this trait under different climate change scenarios. We first predict rust resistance in present time for crop wild relatives that grow up inside protected areas. Then, we use the same models under future climate IPCC (Intergovernmental Panel on Climate Change) scenarios to predict future DSr values. Populations that are rust-resistant by now and under future conditions are optimal candidates for further evaluation and in situ conservation of this valuable trait. We have found that rust-resistance variation as a result of climate change is not uniform across the geographic scope of the study (the Mediterranean basin), and that candidate populations share some interesting common environmental conditions.

Fecal Metabolome and Bacterial Composition in Severe Obesity: Impact of Diet and Bariatric Surgery.

The aim of this study was to monitor the impact of a preoperative low-calorie diet and bariatric surgery on the bacterial gut microbiota composition and functionality in severe obesity and to compare sleeve gastrectomy (SG) versus Roux-en-Y gastric bypass (RYGB). The study also aimed to incorporate big data analysis for the omics results and machine learning by a Lasso-based analysis to detect the potential markers for excess weight loss. Forty patients who underwent bariatric surgery were recruited (14 underwent SG, and 26 underwent RYGB). Each participant contributed 4 fecal samples (baseline, post-diet, 1 month after surgery and 3 months after surgery). The bacterial composition was determined by 16S rDNA massive sequencing using MiSeq (Illumina). Metabolic signatures associated to fecal concentrations of short-chain fatty acids, amino acids, biogenic amines, gamma-aminobutyric acid and ammonium were determined by gas and liquid chromatography. Orange 3 software was employed to correlate the variables, and a Lasso analysis was employed to predict the weight loss at the baseline samples. A correlation between Bacillota (formerly Firmicutes) abundance and excess weight was observed only for the highest body mass indexes. The low-calorie diet had little impact on composition and targeted metabolic activity. RYGB had a deeper impact on bacterial composition and putrefactive metabolism than SG, although the excess weight loss was comparable in the two groups. Significantly higher ammonium concentrations were detected in the feces of the RYGB group. We detected individual signatures of composition and functionality, rather than a gut microbiota characteristic of severe obesity, with opposing tendencies for almost all measured variables in the two surgical approaches. The gut microbiota of the baseline samples was not useful for predicting excess weight loss after the bariatric process.

National Registry of Acute Coronary Syndrome in Paraguay (RENASCA-PY).

OBJECTIVE: To determine the initial management and in-hospital mortality of patients with acute coronary syndrome who attended referral hospitals in Paraguay. METHOD: Observational, multicenter study, in patients over 18 years with a confirmed diagnosis of acute coronary syndrome. RESULTS: 780 patients were included from May 2015 to February 2016; the mean age was 64.1 ± 12.3 years, 64.1% male. The clinical presentation was acute coronary syndrome with ST elevation in 40.1% and without elevation in 59.9%. In patients with ST elevation there is a high percentage of late attendance, more than 12 h of evolution in 49.8%; those with less than 12 h of evolution underwent reperfusion in 52.2% of the cases, received fibrinolytics in 36.3% of the cases, and primary percutaneous coronary intervention 15.9%. In-hospital mortality for acute coronary syndrome was 10.3%, with ST-segment elevation was 12.8%, and without ST-segment elevation was 8.6%. CONCLUSIONS: The management of acute coronary syndrome in Paraguay needs a comprehensive approach, which promotes earlier care, and increases the implementation of reperfusion therapies in the health services network, in order to improve the therapeutic response rates and decrease hospital mortality.

OBJETIVO: Determinar el tratamiento inicial y la mortalidad intrahospitalaria de pacientes con síndrome coronario agudo que acudieron a centros hospitalarios de referencia de Paraguay. MÉTODO: Estudio observacional y multicéntrico en pacientes mayores de 18 años con diagnóstico confirmado de síndrome coronario agudo. RESULTADOS: Se incluyó a 780 pacientes desde mayo de 2015 hasta febrero de 2016; la edad media fue de 64.1 ± 12.3 años y el género masculino representó el 64.1%. La presentación clínica fue la de síndrome coronario agudo con elevación del ST en 40.1% y sin elevación del ST en 59.9%. En pacientes con elevación del ST se observó un alto porcentaje de consultas tardías, mayor de 12 h de evolución en 49.8%; en aquéllos con menos de 12 h de evolución se indicó la reperfusión en 52.2%, el 36.3% recibió fibrinolíticos y 15.9% intervención coronaria percutánea primaria. La mortalidad hospitalaria del síndrome coronario agudo fue de 10.3%, con elevación del segmento ST en 12.8% y sin elevación del segmento ST en 8.6%. CONCLUSIONES: El tratamiento del síndrome coronario agudo en el Paraguay requiere un abordaje integral, que promueva consultas más tempranas y aumente la institución de tratamientos de reperfusión en la red de servicios de salud; el objetivo es mejorar los índices de respuesta terapéutica y disminuir la mortalidad hospitalaria.

Fine scale prediction of ecological community composition using a two-step sequential Machine Learning ensemble.

Prediction is one of the last frontiers in ecology. Indeed, predicting fine-scale species composition in natural systems is a complex challenge as multiple abiotic and biotic processes operate simultaneously to determine local species abundances. On the one hand, species intrinsic performance and their tolerance limits to different abiotic pressures modulate species abundances. On the other hand, there is growing recognition that species interactions play an equally important role in limiting or promoting such abundances within ecological communities. Here, we present a joint effort between ecologists and data scientists to use data-driven models to predict species abundances using reasonably easy to obtain data. We propose a sequential data-driven modeling approach that in a first step predicts the potential species abundances based on abiotic variables, and in a second step uses these predictions to model the realized abundances once accounting for species competition. Using a curated data set over five years we predict fine-scale species abundances in a highly diverse annual plant community. Our models show a remarkable spatial predictive accuracy using only easy-to-measure variables in the field, yet such predictive power is lost when temporal dynamics are taken into account. This result suggests that predicting future abundances requires longer time series analysis to capture enough variability. In addition, we show that these data-driven models can also suggest how to improve mechanistic models by adding missing variables that affect species performance such as particular soil conditions (e.g. carbonate availability in our case). Robust models for predicting fine-scale species composition informed by the mechanistic understanding of the underlying abiotic and biotic processes can be a pivotal tool for conservation, especially given the human-induced rapid environmental changes we are experiencing. This objective can be achieved by promoting the knowledge gained with classic modelling approaches in ecology and recently developed data-driven models.

Entropy of Badminton Strike Positions.

The aim of the current study was twofold: (i) to investigate the distribution of the strike positions of badminton players while quantifying the corresponding standard entropy and using an alternative metric (spatial entropy) related to winning and losing points and random positions; and (ii) to evaluate the standard entropy of the receiving positions. With the datasets of 259 badminton matches, we focused on the positions of players' strokes and the outcome of each point. First, we identified those regions of the court from which hits were most likely to be struck. Second, we computed the standard entropy of stroke positions, and then the spatial entropy, which also considers the order and clustering of the hitting locations in a two-dimensional Euclidean space. Both entropy quantifiers revealed high uncertainty in the striking position; however, specific court locations (i.e., the four corners) are preferred over the rest. When the outcome of each point was taken into account, we observed that the hitting patterns with lower entropy were associated with higher probabilities of winning points. On the contrary, players striking from more random positions were more prone to losing the points.

Improvement of Contact Tracing with Citizen's Distributed Risk Maps.

The rapid spread of COVID-19 has demonstrated the need for accurate information to contain its diffusion. Technological solutions are a complement that can help citizens to be informed about the risk in their environment. Although measures such as contact traceability have been successful in some countries, their use raises society's resistance. This paper proposes a variation of the consensus processes in directed networks to create a risk map of a determined area. The process shares information with trusted contacts: people we would notify in the case of being infected. When the process converges, each participant would have obtained the risk map for the selected zone. The results are compared with the pilot project's impact testing of the Spanish contact tracing app (RadarCOVID). The paper also depicts the results combining both strategies: contact tracing to detect potential infections and risk maps to avoid movements into conflictive areas. Although some works affirm that contact tracing apps need 60% of users to control the propagation, our results indicate that a 40% could be enough. On the other hand, the elaboration of risk maps could work with only 20% of active installations, but the effect is to delay the propagation instead of reducing the contagion. With both active strategies, this methodology is able to significantly reduce infected people with fewer participants.

Machine Learning Study in Caries Markers in Oral Microbiota from Monozygotic Twin Children.

In recent years, the etiology of caries has evolved from a simplistic infectious perspective based on Streptococcus mutans and/or Lactobacillus activity, to a multifactorial disease involving a complex oral microbiota, the human genetic background and the environment. The aim of this work was to identify bacterial markers associated with early caries using massive 16S rDNA. To minimize the other factors, the composition of the oral microbiota of twins in which only one of them had caries was compared with their healthy sibling. Twenty-one monozygotic twin pairs without a previous diagnosis of caries were recruited in the context of their orthodontic treatment and divided into two categories: (1) caries group in which only one of the twins had caries; and (2) control group in which neither of the twins had caries. Each participant contributed a single oral lavage sample in which the bacterial composition was determined by 16S rDNA amplification and further high-throughput sequencing. Data analysis included statistical comparison of alpha and beta diversity, as well as differential taxa abundance between groups. Our results show that twins of the control group have a closer bacterial composition than those from the caries group. However, statistical differences were not detected and we were unable to find any particular bacterial marker by 16S rDNA high-throughput sequencing that could be useful for prevention strategies. Although these results should be validated in a larger population, including children from other places or ethnicities, we conclude that the occurrence of caries is not related to the increase of any particular bacterial population.

Efectividad y seguridad del uso de alfa-cetoanálogos en el manejo de la enfermedad renal crónica avanzada: recomendaciones para la práctica clínica / Effectiveness and safety of the use of alpha-keto analogues in the management of advanced chronic kidney disease: clinical practice recommendations

Resumen Introducción: la dieta con restricción de proteínas parece tener un papel importante en la progresión de la enfermedad renal crónica (ERC) y la aparición anticipada de síntomas urémicos, además la suplementación de aminoácidos esenciales ofrece aparente seguridad para lograr restricciones agresivas de proteínas. Objetivo: realizar una revisión de la literatura para establecer las recomendaciones de uso práctico sobre la dieta con restricción de proteínas en la ERC avanzada. Materiales y métodos: se realizó una búsqueda estructurada rápida de la literatura en la que se incluyeron revisiones sistemáticas y metaanálisis, de los cuales se extrajeron las respuestas de las preguntas con estructura PICOT diseñadas a priori. Los resultados fueron sometidos a consenso para generar recomendaciones prácticas. Resultados: se incluyeron 6 revisiones sistemáticas de la literatura con una evaluación de calidad moderada. Según los hallazgos, una dieta muy baja en proteínas con suplementación de alfa-cetoanálogos beneficia a los pacientes que ingresan a diálisis o son sometidos a trasplante renal, además reduce la progresión de la enfermedad. No obstante, se requiere de ensayos clínicos con mejor calidad que consideren aspectos como la calidad de vida. Conclusiones: aunque la evidencia es de baja calidad, se establece que la dieta muy baja en proteínas y suplementada con alfa-cetoanálogos en pacientes adecuadamente seleccionados reduce el deterioro de la tasa de filtración glomerular y parece reducir el ingreso a diálisis. Por tanto, se recomienda hacer un seguimiento estricto y periódico en el que se vigilen las medidas antropométricas y el perfil de riesgo de desnutrición.

Abstract Introduction and objective: The protein-restricted diet appears to play an important role in the progression of chronic kidney disease and the early onset of uremic symptoms, the supplementation of essential amino acids offers apparent security in achieving aggressive protein restrictions. The objective of this document is to carry out a literature review to inform practical use recommendations on this behavior in advanced Renal Disease (CKD). Materials and methods: A quick structured search of the literature is carried out, with the selection of systematic reviews and meta-analyzes, from which the answers to the questions with a PICOT structure designed a priori are extracted. The results were submitted to consensus to generate practical recommendations. Results: six systematic reviews of the literature were included, with a moderate quality evaluation, the extraction of the information reports an apparent benefit of the very low protein diet with supplementation of alpha-keto-analogues on admission to dialysis or kidney transplantation and a consistent reduction of disease progression. Better clinical trials that integrate outcomes such as quality of life are required. Conclusions: With low quality of evidence, the very low protein diet, supplemented with alpha-keto analogues, in the properly selected patient, reduces the deterioration of the glomerular filtration rate, seems to reduce admission to dialysis. Regular strict monitoring is recommended, with monitoring of anthropometric measures and malnutrition risk profile.

Consenso colombiano de expertos sobre recomendaciones informadas en la evidencia para la prevención, el diagnóstico y el manejo de la lesión renal aguda por SARS-CoV-2/COVID-19 / Colombian consensus of experts on recommendations informed in the evidence for the prevention, diagnosis and management of acute kidney injury by SARS-CoV-2/COVID-19

resumen está disponible en el texto completo

Abstract Introduction: Acute kidney injury is a frequent complication in patients with COVID-19 and its occurrence is a potential indicator of multi-organ dysfunction and disease severity. Objective: Develop, through an expert consensus, evidence-based recommendations for the prevention, diagnosis, and management of acute kidney injury in patients with SARS CoV2 / COVID-19 infection. Materials and methods: Based on a rapid systematic review in Embase and Pubmed databases and documents from scientific societies, we made preliminary recommendations and consulted with an expert group through an online tool. Then we defined agreement after at least 70 % consensus approval. Quality evidence was evaluated according to the type of document included. The strength of the recommendations was graded as strong or weak. Results: Fifty clinical experts declared their conflict of interest; the consultation took place between May 2 and 29, 2020. The range of agreement ranged from 75.5 % to 100 %. Recommendations for prevention, diagnosis and management of acute kidney injury in patients with SARS CoV2 infection are presented. Conclusions: Although the good quality information available regarding acute kidney injury in patients with COVID-19 is scarce, the recommendations of clinical experts will guide clinical decision-making and strategies around patients with this complication, guaranteeing care focused on the people, with high quality standards, and the generation of safety, health and wellness policies for multidisciplinary care teams.

The role of intraspecific competition between plants in a nursery pollination system-Comments on Villacañas de Castro and Hoffmeister 2020.

We present comments on an article published by Villacañas de Castro and Hoffmeister (Ecology and Evolution, 10, 4220; 2020). The authors studied a tritrophic system composed of a plant, its pollinating seed predator, and a parasitoid of the latter. Their concern was whether the parasitoid modifies the interaction between the plant and its pollinator-herbivore along the mutualism-antagonism gradient, but they reduced their question to how the parasitoid impacts plant fitness. After showing that the parasitoid increases seed output of the plant by decreasing the amount of seeds consumed by the pollinating seed predator, they tested whether seed output is a good proxy for plant fitness. They argue that it is not by showing that the increased seed density has a negative impact on survival probability and flower production, likely due to plant intraspecific competition. The work presented shows careful experimentation and interesting results, but we do not share some of their conclusions. Most importantly, we believe that the net effect of the parasitoid on the plant-herbivore interaction cannot be adequately investigated by focusing on individual plant fitness. Thus, we first suggest considering the number of surviving plants up to adulthood as a proxy for population performance to address this question. Using this proxy, we show that the increase in seed output due to the parasitoid is beneficial to the plant population until its carrying capacity is achieved. Next, using a population dynamics model, we show under which particular conditions the negative effect of intraspecific competition outweighs the positive effect of seed density increase (due to parasitoid's defense). When these conditions do not hold, the role of plant intraspecific competition is basically limited to the prevention of unbounded population growth, while the parasitoid increases the plant's equilibrium density above its carrying capacity as measured when interacting only with the pollinating seed predator, thus making the system more stable.

A stochastic generative model of the World Trade Network.

The World Trade Network (WTN) is a network of exchange flows among countries whose topological and statistical properties are a valuable source of information. Degree and strength (weighted degree) are key magnitudes to understand its structure and generative mechanisms. In this work, we describe a stochastic model that yields synthetic networks that closely mimic the properties of annual empirical data. The model combines two popular mechanisms of network generation: preferential attachment and multiplicative process. Agreement between empirical and synthetic networks is checked using the available series from 1962 to 2017.

Pattern formation induced by intraspecific interactions in a predator-prey system.

Differential diffusion is a source of instability in population dynamics systems when species diffuse with different rates. Predator-prey systems show this instability only under certain specific conditions, usually requiring one to involve Holling-type functionals. Here we study the effects of intraspecific cooperation and competition on diffusion-driven instability in a predator-prey system with a different structure. We conduct the analysis on a generalized population dynamics that bounds intraspecific and interspecific interactions with Verhulst-type saturation terms instead of Holling-type functionals. We find that instability occurs due to the intraspecific saturation or intraspecific interactions, both cooperative and competitive. We present numerical simulations and show spatial patterns due to diffusion.

Individual Patterns of Complexity in Cystic Fibrosis Lung Microbiota, Including Predator Bacteria, over a 1-Year Period.

Cystic fibrosis (CF) lung microbiota composition has recently been redefined by the application of next-generation sequencing (NGS) tools, identifying, among others, previously undescribed anaerobic and uncultivable bacteria. In the present study, we monitored the fluctuations of this ecosystem in 15 CF patients during a 1-year follow-up period, describing for the first time, as far as we know, the presence of predator bacteria in the CF lung microbiome. In addition, a new computational model was developed to ascertain the hypothetical ecological repercussions of a prey-predator interaction in CF lung microbial communities. Fifteen adult CF patients, stratified according to their pulmonary function into mild (n = 5), moderate (n = 9), and severe (n = 1) disease, were recruited at the CF unit of the Ramón y Cajal University Hospital (Madrid, Spain). Each patient contributed three or four induced sputum samples during a 1-year follow-up period. Lung microbiota composition was determined by both cultivation and NGS techniques and was compared with the patients' clinical variables. Results revealed a particular microbiota composition for each patient that was maintained during the study period, although some fluctuations were detected without any clinical correlation. For the first time, Bdellovibrio and Vampirovibrio predator bacteria were shown in CF lung microbiota and reduced-genome bacterial parasites of the phylum Parcubacteria were also consistently detected. The newly designed computational model allows us to hypothesize that inoculation of predators into the pulmonary microbiome might contribute to the control of chronic colonization by CF pathogens in early colonization stages.IMPORTANCE The application of NGS to sequential samples of CF patients demonstrated the complexity of the organisms present in the lung (156 species) and the constancy of basic individual colonization patterns, although some differences between samples from the same patient were observed, probably related to sampling bias. Bdellovibrio and Vampirovibrio predator bacteria were found for the first time by NGS as part of the CF lung microbiota, although their ecological significance needs to be clarified. The newly designed computational model allows us to hypothesize that inoculation of predators into the lung microbiome can eradicate CF pathogens in early stages of the process. Our data strongly suggest that lower respiratory microbiome fluctuations are not necessarily related to the patient's clinical status.

Ranking of critical species to preserve the functionality of mutualistic networks using the k-core decomposition.

BACKGROUND: Network analysis has become a relevant approach to analyze cascading species extinctions resulting from perturbations on mutualistic interactions as a result of environmental change. In this context, it is essential to be able to point out key species, whose stability would prevent cascading extinctions, and the consequent loss of ecosystem function. In this study, we aim to explain how the k-core decomposition sheds light on the understanding the robustness of bipartite mutualistic networks. METHODS: We defined three k-magnitudes based on the k-core decomposition: k-radius, k-degree, and k-risk. The first one, k-radius, quantifies the distance from a node to the innermost shell of the partner guild, while k-degree provides a measure of centrality in the k-shell based decomposition. k-risk is a way to measure the vulnerability of a network to the loss of a particular species. Using these magnitudes we analyzed 89 mutualistic networks involving plant pollinators or seed dispersers. Two static extinction procedures were implemented in which k-degree and k-risk were compared against other commonly used ranking indexes, as for example MusRank, explained in detail in Material and Methods. RESULTS: When extinctions take place in both guilds, k-risk is the best ranking index if the goal is to identify the key species to preserve the giant component. When species are removed only in the primary class and cascading extinctions are measured in the secondary class, the most effective ranking index to identify the key species to preserve the giant component is k-degree. However, MusRank index was more effective when the goal is to identify the key species to preserve the greatest species richness in the second class. DISCUSSION: The k-core decomposition offers a new topological view of the structure of mutualistic networks. The new k-radius, k-degree and k-risk magnitudes take advantage of its properties and provide new insight into the structure of mutualistic networks. The k-risk and k-degree ranking indexes are especially effective approaches to identify key species to preserve when conservation practitioners focus on the preservation of ecosystem functionality over species richness.

Inducing self-organized criticality in a network toy model by neighborhood assortativity.

Complex networks are a recent type of framework used to study complex systems with many interacting elements, such as self-organized criticality (SOC). The network nodes' tendency to link to other nodes of similar type is characterized by assortative mixing. Real networks exhibit assortative mixing by vertex degree, however, typical random network models, such as the Erdos-Rényi or the Barabási-Albert model, show no assortative arrangements. In this paper we introduce the notion of neighborhood assortativity as the tendency of a node to belong to a community (its neighborhood) showing an average property similar to its own. Imposing neighborhood assortative mixing by degree in a network toy model, SOC dynamics can be found. These dynamics are driven only by the network topology. The long-range correlations resulting from criticality have been characterized by means of fluctuation analysis and show an anticorrelation in the node's activity. The model contains only one parameter and its statistics plots for different values of the parameter can be collapsed into a single curve. The simplicity of the model allows us to perform numerical simulations and also to study analytically the statistics for a specific value of the parameter, making use of the Markov chains.

Consenso Paraguayo de Hipertensión Arterial 2015

Este consenso fue ideado por el Director del Programa Nacional de Prevención Cardiovascular del M.S P. y B.S. (Paraguay) a raíz de que se necesitaba un documento actualizado que sirva como guía a los profesionales de la salud de todos los niveles de atención incluyendo a las diferentes especialidades, que tratan la hipertensión arterial y sus complicaciones agudas y crónicas. A través del convenio entre el Programa Nacional de Prevención Cardiovascular y la Sociedad Paraguaya de Cardiología de realizar actualizaciones de las Guías o documentos para difusión de manejo de patologías cardiovasculares y factores de riesgo asociados, se ha decidido elaborar y lanzar este material, que será utilizado como guía oficial en todo el territorio nacional. Con mesas de trabajo de varios días y la colaboración de representantes de sociedades científicas afines y asesores internacionales se ha logrado esta guía. Se presentan recomendaciones para facilitar el manejo de los pacientes con hipertensión arterial y sus complicaciones, con el propósito de disminuir la morbimortalidad cardiovascular. Han sido elaboradas según las evidencias científicas y conocimiento de expertos, con un enfoque práctico y sencillo. Todos los participantes aprobaron las decisiones finales. Los autores reconocen que la publicación y difusión de directrices serán siempre una guía práctica y que permitirá a los médicos ampliar los conocimientos con las recomendaciones propuestas, detectar precozmente el daño de órganos blancos y mejorar la calidad de vida de los pacientes hipertensos.

This consensus was conceived by the Director of the National Program of Cardiovascular Prevention of the M.S P. y B.S. (Ministry of Public Health and Social Welfare, Paraguay) because an updated document was necessary as a guide for health professional of all care levels including different specialties that treat arterial hypertension and its acute and chronic complications. Through this consensus between the National Program of Cardiovascular Prevention and the Paraguayan Cardiology Society of updating Guides or documents for the dissemination of the management of cardiovascular pathologies and associated risk factors it was decided to elaborate and launch this material that will be used as an official guide in the national territory. This guide was achieved after working tables held during several days and the collaboration of representatives of related scientific societies and international advisers. Recommendations for facilitating the management of patients with arterial hypertension and its complications are presented in order to reduce the cardiovascular morbidity and mortality. They have been elaborated according to scientific evidence and knowledge of experts with a practical and simple approach. All the participants approved the final decisions. The authors recognize that the publications and dissemination of guidelines will always be a practical guide that will allow physicians to increase their knowledge with the proposed recommendation in order to early detect the damage of target organs and improve the quality of life of the hypertensive patients.

Rethinking the logistic approach for population dynamics of mutualistic interactions.

Mutualistic communities have an internal structure that makes them resilient to external perturbations. Late research has focused on their stability and the topology of the relations between the different organisms to explain the reasons of the system robustness. Much less attention has been invested in analyzing the systems dynamics. The main population models in use are modifications of the r-K formulation of logistic equation with additional terms to account for the benefits produced by the interspecific interactions. These models have shortcomings as the so-called r-K formulation diverges under some conditions. In this work, we introduce a model for population dynamics under mutualism that preserves the original logistic formulation. It is mathematically simpler than the widely used type II models, although it shows similar complexity in terms of fixed points and stability of the dynamics. We perform an analytical stability analysis and numerical simulations to study the model behavior in general interaction scenarios including tests of the resilience of its dynamics under external perturbations. Despite its simplicity, our results indicate that the model dynamics shows an important richness that can be used to gain further insights in the dynamics of mutualistic communities.