Síndrome hemolítico urémico atípico posterior a trasplante renal: presentación de un caso y revisión de la literatura

Haemolytic uremic syndrome (HUS) is a clinical entity characterized by the appearance of non immune hemolytic anemia, thrombocytopenia and acute renal failure. It is a disease belonging to the group of thrombotic microangiopathy (MAT) which are part of thrombotic thrombocytopenic purpura also (PTT) and some other MAT associated with other medical conditions formerly known as secondary MAT.Moreover, the variety known as atypical HUS (aHUS) is an ultra-orphan disease that frequently progresses to chronic renal failure (CRF) and is associated with high morbidity and mortality if not properly treated. If a patient presents its first clinical manifestation of aHUS later receive a cadaveric renal transplant which not only makes it an even more exotic case but involves more complexity in their management is presented.El síndrome hemolítico urémico (SHU) es una entidad clínica caracterizada por la aparición de anemia hemolítica no inmune, trombocitopenia e insuficiencia renal aguda. Se trata de una enfermedad perteneciente al grupo de las microangiopatías trombóticas (MAT) de la que hacen parte también la purpura trombocitopénica trombótica (PTT) y algunas otras MAT asociadas a otras condiciones médicas antes conocidas como MAT secundarias. Por otra parte, la variedad conocida como SHU atípico (SHUa) es una patología ultra-huérfana que frecuentemente evoluciona a insuficiencia renal crónica (IRC) y se asocia con elevada morbimortalidad si no recibe el tratamiento adecuado. Se examina el caso de un paciente que presenta su primera manifestación clínica de síndrome hemolítico urémico atípico después de trasplante renal cadavérico lo cual no solo lo hace un caso aún más exótico, sino que implica mayor complejidad en su manejo. &nbsp

Psychosocial characteristics and cognitive function in community-dwelling adults over 50 years old from the Mexican Health and Aging Study (MHAS)

Aging is an inevitable process that can be associated with cognitive impairment. Evidence about the simultaneous evaluation of psychosocial variables that could be associated with cognitive function is crucial. We aimed to determine the association between psychosocial characteristics and cognition in adults over 50 years in Mexico. The fifth round of the Mexican Health and Aging Study (MHAS) (2018) provides the basis for this paper. The study is part of a longitudinal analysis, for which wave pasting 2012, 2015, and 2018 were performed. The final sample comprised 6,709 individuals. Ten psychosocial variables were measured through scales or specific questions. Cognition was assessed with the Cross-Cultural Cognitive Examination (CCCE). Confounders included sociodemographics, multimorbidity, and functionality. The analysis was performed by adjusting the regression model. Of the total sample, 2,761 (41.1%) were men; 3,948 (58.8%) were women. The mean age was 68.2 years (SD = 8.1). Cognition is significantly affected in people with higher age (β=-1.30, Cl 95% -1.54, -.1.06 p= 0.000), less schooling (β=.559, CI 95% .498, .621 p<0.001), depressive symptoms (β=-.066, CI 95% -.115, -.018 p=0.007), those who do not perform any volunteer service (β=-.057, CI 95% -.102, -.102 p=0.013), or do not participate in decision making (β=-.242, CI 95% -.295, -.189 p<0.001), low internal locus of control (β=-.012., CI 95% -.023, -.001 p=0.023), and poor economic perception (β=-.070., CI 95% -.115, -.024 p=0.002). When analyzing the cognitive function of older people, it is vital to consider the possible related psychosocial variables

Connections : safe spaces for women and youth in Latin America and The Caribbean

RESUMEN: Este libro se puede leer en muchos niveles. Uno de ellos puede no ser muy obvio para aquellos que están acostumbrados a leer sobre violencia e inseguridad en América Latina. Es el nivel que le da a este libro un estatus de originalidad y una contribución que va más allá de la región: el ser una forma de conocimiento destinada no solo a interpretar el mundo, sino a cambiarlo […], visibiliza la importancia de un proceso de investigación ajustado al tipo de conocimiento que produce. Aquí se conectan el proceso y el resultado, lo que debería propiciar un debate más amplio con respecto a cómo y qué sabemos de la naturaleza de la violencia y la agencia social para reducirla […]. Esta visión es particularmente relevante en contextos donde el Estado reproduce la violencia, con terribles impactos, en especial en periferias excluidas. […] El proceso de investigación abordado en este libro transgredió muchas fronteras. Hubo fronteras entre países, barreras lingüísticas, fronteras en torno a la educación, el conocimiento y la experiencia, y entre etnias, géneros y generaciones. […] este proceso reunió a académicos, activistas y líderes comunitarios de cinco países de América Latina y uno del Caribe, incluyendo comunidades indígenas en México y Guatemala […]. La violencia está en el tiempo y en el espacio y se reproduce entre las generaciones en diversos espacios de socialización. Este proceso de investigación que trasciende las fronteras, plantea una discusión que atraviesa los diferentes casos sobre cómo los déficits y las desigualdades materiales, las violencias estatales en nombre de la ‘seguridad’, las especificidades culturales, de género y generacionales de la experiencia y la comprensión de la violencia, así como las diversas formas de criminalidad, se cruzan y se reproducen a través del tiempo y el espacio. Jenny Pearce, investigadora y profesora en el Latin American and Caribbean Centre (LACC) de la London School of Economics and Political ScienceABSTRACT: This book can be read on many levels. One level may not be so obvious to those who are used to reading about violence and insecurity in Latin America. It is the level which gives this book a claim to true originality and a contribution beyond the region. This contribution is to form of scholarship aimed not only to interpret the world but to change it […], this text visibilizes the significance of the research process to the kind of knowledge that is produced. It connects process and outcome, and this should start a wider debate about how as well as what we know about the nature of violence and the social agency to reduce it […]. This is particularly relevant in contexts where the State reproduces violence, with terrible impacts on the margins. The research process discussed in this book transgressed many boundaries. There were intercountry borders, linguistic barriers, boundaries around education, knowledge and experience and between ethnicities, genders and generations. […] the research process brought together scholars and community activists and actors from five Latin American and one Caribbean country. And within Latin America there were indigenous communities in Mexico and Guatemala who participated […]. Violence is located in time and space. It is reproduced inter-generationally through varied socialisation spaces. The boundary crossing research process, raises cross case discussion about how material deficits and inequalities, state violences in the name of ‘security’, cultural, gender and generational specificities of experience and understanding of violence, and varied forms of criminality, intersect and reproduce through time and space. Professor Jenny Pearce. Latin American and Caribbean Centre (LACC), London School of Economics and Political Scienc

Comparing Populations in Data Involving Spatial Information.

Es común estudiar datos correspondientes a observaciones asociadas a unidades espaciales. Cuando se quiere ver si la distribución de una variable continua es la misma en un grupo de poblaciones pueden usarse diferentes métodos de acuerdo a las características de los datos. Puede ocurrir que las observaciones geográficas que se quieren analizar estén relacionadas entre sí porque pertenecen a una mism a unidad espacial, en este caso puede ser conveniente el uso de un modelo de medidas repetidas. Ya sea que se usen o no estos modelos, existen distintos métodos paramétricos y no paramétricos disponibles. Se analiza cómo medidas repetidas puede verse como un modelo lineal y la relación entre estos. Se ilustran los métodos en datos correspondientes a actividades económicas divididas en cinco sectores en regiones específicas de México en las cuales quiere verse si todos los sectores son igualmente relevantes. Se muestra además a través de simulaciones cómo puede ocurrir que al no seleccionar un modelo adecuado pueden obtenerse inferencias erróneas. Así mismo, en datos espaciales puede ocurrir que el supuesto de independencia que se asume en una ANOVA de un fac tor se viole, esto ocurre cuando la variable cambia espacialmente pues pudiera haber valores similares en unidades vecinas. Entonces, se requiere usar un modelo lineal que considere el aspecto espacial. Para ello se usa regresión geográficamente ponderada y se ilustra el método a través de datos correspondientes a ingreso en México. Se muestra que la falta de independencia se resuelve al usar este modelo espacial y se hace el análisis post hoc correspondiente

Comparing Populations in Data Involving Spatial Information

Observations corresponding to spatial units are commonly studied. If we want to see whether a continuous variable has the same distribution in a group of populations, different methods can be used according to the characteristics of the data. It could occur that observations in geographical data are related because they correspond to the same spatial unit, in which case we can use a repeated measures model. Whether or not repeated measures are involved, parametric and non-parametric methods are available. We analyze how repeated measures can be seen as a linear model and their relationship. We illustrate all these methods using data concerning economical activity in five sectors in specific regions in Mexico, where we want to see if all sectors are equally relevant. We also show through simulated data how by not selecting an adequate model we can obtain wrong inferences. In data involving spatial units, the independence assumption associated with a one-factor ANOVA could be violated when a variable changes spatially so that there are similar values between neighbors. Then, an equivalent linear model involving that spatial information could be used. We use a geographically weighted regression and illustrate the method through data concerning income in Mexico. We also show how the lack of independence is solved through the spatial model and perform a post hoc analysis.Es común estudiar datos correspondientes a observaciones asociadas a unidades espaciales. Cuando se quiere ver si la distribución de una variable continua es la misma en un grupo de poblaciones pueden usarse diferentes métodos de acuerdo a las características de los datos. Puede ocurrir que las observaciones geográficas que se quieren analizar estén relacionadas entre sí porque pertenecen a una misma unidad espacial, en este caso puede ser conveniente el uso de un modelo de medidas repetidas. Ya sea que se usen o no estos modelos, existen distintos métodos paramétricos y no paramétricos disponibles. Se analiza cómo medidas repetidas puede verse como un modelo lineal y la relación entre estos. Se ilustran los métodos en datos correspondientes a actividades económicas divididas en cinco sectores en regiones específicas de México en las cuales quiere verse si todos los sectores son igualmente relevantes. Se muestra además a través de simulaciones cómo puede ocurrir que al no seleccionar un modelo adecuado pueden obtenerse inferencias erróneas. Así mismo, en datos espaciales puede ocurrir que el supuesto de independencia que se asume en una ANOVA de un factor se viole, esto ocurre cuando la variable cambia espacialmente pues pudiera haber valores similares en unidades vecinas. Entonces, se requiere usar un modelo lineal que considere el aspecto espacial. Para ello se usa regresión geográficamente ponderada y se ilustra el método a través de datos correspondientes a ingreso en México. Se muestra que la falta de independencia se resuelve al usar este modelo espacial y se hace el análisis post hoc correspondiente

Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level.

The Islamic Republic of Iran reported its first COVID-19 cases by 19th February 2020, since then it has become one of the most affected countries, with more than 73,000 cases and 4,585 deaths to this date. Spatial modeling could be used to approach an understanding of structural and sociodemographic factors that have impacted COVID-19 spread at a province-level in Iran. Therefore, in the present paper, we developed a spatial statistical approach to describe how COVID-19 cases are spatially distributed and to identify significant spatial clusters of cases and how socioeconomic and climatic features of Iranian provinces might predict the number of cases. The analyses are applied to cumulative cases of the disease from February 19th to March 18th. They correspond to obtaining maps associated with quartiles for rates of COVID-19 cases smoothed through a Bayesian technique and relative risks, the calculation of global (Moran's I) and local indicators of spatial autocorrelation (LISA), both univariate and bivariate, to derive significant clustering, and the fit of a multivariate spatial lag model considering a set of variables potentially affecting the presence of the disease. We identified a cluster of provinces with significantly higher rates of COVID-19 cases around Tehran (p-value< 0.05), indicating that the COVID-19 spread within Iran was spatially correlated. Urbanized, highly connected provinces with older population structures and higher average temperatures were the most susceptible to present a higher number of COVID-19 cases (p-value < 0.05). Interestingly, literacy is a factor that is associated with a decrease in the number of cases (p-value < 0.05), which might be directly related to health literacy and compliance with public health measures. These features indicate that social distancing, protecting older adults, and vulnerable populations, as well as promoting health literacy, might be useful to reduce SARS-CoV-2 spread in Iran. One limitation of our analysis is that the most updated information we found concerning socioeconomic and climatic features is not for 2020, or even for a same year, so that the obtained associations should be interpreted with caution. Our approach could be applied to model COVID-19 outbreaks in other countries with similar characteristics or in case of an upturn in COVID-19 within Iran

Understanding Frailty: Probabilistic Causality between Components and Their Relationship with Death through a Bayesian Network and Evidence Propagation

Identifying relationships between components of an index helps to gain a better understanding of the condition they define. The Frailty Index (FI) measures the global health of individuals and can be used to predict outcomes as mortality. Previously, we modelled the relationship between the FI components (deficits) and death through an undirected graphical model and a social network analysis framework. Here, we model the FI components and death through an averaged Bayesian network obtained through a structural learning process and resampling, in order to understand how the FI components and death are causally related. We identified that components are not similarly related between them and that deficits are related according to their type. Two deficits were the most relevant in terms of their connections, and two others were directly associated with death. We obtained the strength of the relationships in order to identify the most plausible, identifying clusters of deficits. Finally, we propagated evidence and studied how FI components predict mortality, obtaining a correct assignation of almost 74% and a true positive rate (TPR) of 56%. Values were obtained after changing the model threshold (via Youden’s Index maximization) whose possible values are represented in a Receiving Operating Characteristic (ROC) curve (TPR vs. 1-True Negative Rate). The greater number of deficits included for the evidence, the best performances; nevertheless, the FI does not seem to be quite efficient to correctly differentiate between dead and living people

Structural and Pharmacological Network Analysis of miRNAs Involved in Acute Ischemic Stroke: A Systematic Review

Acute ischemic stroke (AIS) is among the main causes of mortality worldwide. A rapid and opportune diagnosis is crucial to improve a patient&rsquo;s outcomes; despite the current advanced image technologies for diagnosis, their implementation is challenging. MicroRNAs have been recognized as useful as biomarkers since they are specific and stable for characterization of AIS. However, there is still a lack of consensus over the primary miRNAs implicated in AIS. Here, we performed a systematic review of the literature covering from 2015&ndash;2021 regarding miRNAs expression during AIS and built structural networks to analyze and identify the most common miRNAs expressed during AIS and shared pathways, genes, and compounds that seem to influence their expression. We identified two sets of miRNAs: on one side, a set that was independent of geographical location and tissue (miR-124, miR-107, miR-221, miR-223, miR-140, miR-151a, miR-181a, miR-320b, and miR-484); and on the other side, a set that was connected (hubs) in biological networks (miR-27b-3p, miR-26b-5p, miR-124-3p, miR-570-3p, miR-19a-3p, miR-101-3p and miR-25-3p), which altered FOXO3, FOXO4, and EP300 genes. Interestingly, such genes are involved in cell death, FOXO-mediated transcription, and brain-derived neurotrophic factor signaling pathways. Finally, our pharmacological network analysis depicted a set of toxicants and drugs related to AIS for the first time

Mortality and associated risk factors for older adults admitted to the emergency department: a hospital cohort

Abstract Background Older emergency department patients are more vulnerable than younger patients, yet many risk factors that contribute to the mortality of older patients remain unclear and under investigation. This study endeavored to determine mortality and factors associated with mortality in patients over 60 years of age who were admitted to the emergency departments of two general hospitals in Mexico City. Methods This is a hospital cohort study involving adults over 60 years of age admitted to the emergency department and who are beneficiaries of the Mexican Institute of Social Security and residents of Mexico City. All causes of mortality from the time of emergency department admission until a follow-up home visit after discharge were measured. Included risk factors were: socio-demographic, health-care related, mental and physical variables, and in-hospital care-related. Survival functions were estimated using Kaplan-Meier curves. Hazard ratios (HR) were derived from Cox regression models in a multivariate analysis. Results From the 1406 older adults who participated in this study, 306 (21.8%) did not survive. Independent mortality risk factors found in the last Cox model were age (HR = 1.02, 95% CI, 1.005–1.04; p = 0.01), length of stay in the ED (HR = 1.003, 95% CI = 0.99, 1.04; p = 0.006), geriatric care trained residents model in Hospital A (protective factor) (HR = 0.66, 95% CI = 0.46, 0.96; p = 0.031), and the FRAIL scale (HR of 1.34 95% CI, 1.02–1.76; p = 0.033). Conclusions Risk factors for mortality in patients treated at Mexican emergency departments are length of stay and variables related to frailty status