Exposicion economica al tipo de cambio de las sociedades anonimas chilenas

68 p.Las empresas chilenas, en este caso las sociedades anónimas abiertas, independientes de si tienen o no operaciones de comercio exterior tienen de una u otra forma dependencia de las variaciones en el tipo de cambio. Sin embargo existe una limitada gama de estudios relativos al tema en el ámbito local, y la evidencia disponible proviene en su mayoría de economías desarrolladas. En un reciente estudio, Chen et al. (2004) encuentra para Nueva Zelanda, economía pequeña y abierta, evidencia que indicaría que para este país los movimientos del tipo de cambio afectan el valor de las empresas transadas en bolsa. Basándose en la similitud con la economía Neo Zelandesa, nace la inquietud de replicar ese estudio en Chile. Esta investigación se justifica en el hecho de que a entender del autor no existen estudios relacionados con el tema en Chile, siendo relevante por al menos dos razones: la alta volatilidad que ha presentado el tipo de cambio desde que se adoptó la paridad flotante a mediados del año 1999 (ver apéndice 1) y por el poco desarrollo del mercado de instrumentos de cobertura. El propósito fundamental de esta memoria es determinar el nivel de exposición de las sociedades anónimas chilenas a variaciones en el tipo de cambio, y analizar algunos factores potenciales que influyen en la magnitud de esta exposición. Bajo la hipótesis de los mercados eficientes, el efecto del tipo de cambio debería ser reflejado en el precio de acciones. Las empresas que cumplen con los criterios de selección son 105, de las cuales alrededor del 40% presenta un nivel significativo de exposición al riesgo de cambio. El grado de exposición de las empresas en forma global es de carácter inverso, es decir, a medida que aumenta el valor del tipo de cambio, el valor de las empresas disminuye. Cabe destacar que la significancia económica es importante, esto se grafica en los montos que las empresas ganan o pierden de su capitalización bursátil ante una variación del dólar. A modo de ejemplo, la empresa menos afectada perdería aproximadamente 351 millones de pesos ante la subida de un 1% del valor de la divisa

Performance of prognostic index in severe Clostridium difficile-associated infection. Retrospective analysis in a university hospital

Indexación: ScieloIntroducción: Por consenso, la infección asociada a Clostridium difficile (IACD) grave es aquella que resulta en hospitalización en unidad de cuidados intensivos, colectomía o muerte dentro de 30 días. Múltiples índices pronósticos (IP) intentan predecir estos eventos adversos. Objetivo: evaluar el rendimiento de cuatro IP en la predicción de IACD grave. Metodología: pacientes hospitalizados ≥ 18 años con IACD fueron evaluados retrospectivamente. Se excluyeron pacientes con infección recurrente o cáncer hematológico. Se evaluaron cuatro IP: UPMC versión 1, Calgary versión 1, Hines VA y Calgary versión 2. Resultados: Siete de 81 pacientes (8,1%) presentaron una IACD grave. El valor predictor positivo (VPP) y valor predictor negativo (VPN) de los IP varió entre 20-75% y 91,3-95,7%, respectivamente. Sólo el índice de Hines VA tuvo un índice Kappa satisfactorio (0,74;IC 95% 0,46-1) con un VPP de 75% y un VPN de 95,7%. Sin embargo, por las variables incluidas en este IP, sólo pudo ser calculado en 32,6% de los pacientes. Conclusión: El índice de Hines VA presenta el mejor valor predictor y concordancia para descartar una IACD grave. Como otros IP, tiene la limitación de incluir variables difícilmente evaluables en todos los pacientes y tiende a sobreestimar un curso desfavorable.Introduction: By consensus severe, Clostridium difficile-associated infection (CDAI) is one that results in hospitalization in ICU, colectomy or death within 30 days. Multiple prognostic indices (IP) attempt to predict these adverse events. Objective: To evaluate the performance of 4 PI in predicting severe CDI. Methods: Hospitalized patients ≥ 18 years old with ICD were retrospectively evaluated. Patients with recurrent infection or hematological cancer were excluded. Four PI were evaluated: UPMC version 1, Calgary version 1, Hines VA and Calgary version 2. Results: Seven of 81 patients (8.1%) met the definition of severe CDI. Positive predicted value (PPV) and negative predicted value (NPV) of PI ranged from 20-75% and 91.3-95.7%, respectively. Only Hines VA index had a satisfactory Kappa index (0.74; 95% CI 0.41-1) with a PPV of 75% and NPV of 95,7%. However, because of the variables included, this PI could be calculated only in 32.6% of patients. Conclusion: Hines VA index has the best predicted value and agreement to rule out a severe CDI. Like others PI it has the limitation of including difficult variables to assess in all patients and tends to overestimate an unfavorable course.http://www.scielo.cl/pdf/rci/v31n6/art03.pd

Cardiovascular magnetic resonance findings in a pediatric population with isolated left ventricular non-compaction

<p>Abstract</p> <p>Background</p> <p>Isolated Left Ventricular Non-compaction (LVNC) is an uncommon disorder characterized by the presence of increased trabeculations and deep intertrabecular recesses. In adults, it has been found that Ejection Fraction (EF) decreases significantly as non-compaction severity increases. In children however, there are a few data describing the relation between anatomical characteristics of LVNC and ventricular function. We aimed to find correlations between morphological features and ventricular performance in children and young adolescents with LVNC using Cardiovascular Magnetic Resonance (CMR).</p> <p>Methods</p> <p>15 children with LVNC (10 males, mean age 9.7 y.o., range 0.6 - 17 y.o.), underwent a CMR scan. Different morphological measures such as the Compacted Myocardial Mass (CMM), Non-Compaction (NC) to the Compaction (C) distance ratio, Compacted Myocardial Area (CMA) and Non-Compacted Myocardial Area (NCMA), distribution of NC, and the assessment of ventricular wall motion abnormalities were performed to investigate correlations with ventricular performance. EF was considered normal over 53%.</p> <p>Results</p> <p>The distribution of non-compaction in children was similar to published adult data with a predilection for apical, mid-inferior and mid-lateral segments. Five patients had systolic dysfunction with decreased EF. The number of affected segments was the strongest predictor of systolic dysfunction, all five patients had greater than 9 affected segments. Basal segments were less commonly affected but they were affected only in these five severe cases.</p> <p>Conclusion</p> <p>The segmental pattern of involvement of non-compaction in children is similar to that seen in adults. Systolic dysfunction in children is closely related to the number of affected segments.</p

International lower limb collaborative (INTELLECT) study: a multicentre, international retrospective audit of lower extremity open fractures

Trauma remains a major cause of mortality and disability across the world1, with a higher burden in developing nations2. Open lower extremity injuries are devastating events from a physical3, mental health4, and socioeconomic5 standpoint. The potential sequelae, including risk of chronic infection and amputation, can lead to delayed recovery and major disability6. This international study aimed to describe global disparities, timely intervention, guideline-directed care, and economic aspects of open lower limb injuries

Evolving trends in the management of acute appendicitis during COVID-19 waves. The ACIE appy II study

Background: In 2020, ACIE Appy study showed that COVID-19 pandemic heavily affected the management of patients with acute appendicitis (AA) worldwide, with an increased rate of non-operative management (NOM) strategies and a trend toward open surgery due to concern of virus transmission by laparoscopy and controversial recommendations on this issue. The aim of this study was to survey again the same group of surgeons to assess if any difference in management attitudes of AA had occurred in the later stages of the outbreak. Methods: From August 15 to September 30, 2021, an online questionnaire was sent to all 709 participants of the ACIE Appy study. The questionnaire included questions on personal protective equipment (PPE), local policies and screening for SARS-CoV-2 infection, NOM, surgical approach and disease presentations in 2021. The results were compared with the results from the previous study. Results: A total of 476 answers were collected (response rate 67.1%). Screening policies were significatively improved with most patients screened regardless of symptoms (89.5% vs. 37.4%) with PCR and antigenic test as the preferred test (74.1% vs. 26.3%). More patients tested positive before surgery and commercial systems were the preferred ones to filter smoke plumes during laparoscopy. Laparoscopic appendicectomy was the first option in the treatment of AA, with a declined use of NOM. Conclusion: Management of AA has improved in the last waves of pandemic. Increased evidence regarding SARS-COV-2 infection along with a timely healthcare systems response has been translated into tailored attitudes and a better care for patients with AA worldwide

Auditory graphs from denoising real images using fully symmetric convolutional neural networks

Presented at the 26th International Conference on Auditory Display (ICAD 2021) 25-28 June 2021, Virtual conference.Auditory graphs are a very useful way to deliver numerical information to visually impaired users. Several tools have been proposed for chart data sonification, including audible spreadsheets, custom interfaces, interactive tools and automatic models. In the case of the latter, most of these models are aimed towards the extraction of contextual information and not many solutions have been proposed for the generation of an auditory graph directly from the pixels of an image by the automatic extraction of the underlying data. These kind of tools can dramatically augment the availability and usability of auditory graphs for the visually impaired community. We propose a deep learning-based approach for the generation of an automatic sonification of an image containing a bar or a line chart using only pixel information. In particular, we took a denoising approach to this problem, based on a fully symmetric convolutional neural network architecture. Our results show that this approach works as a basis for the automatic sonification of charts directly from the information contained in the pixels of an image.

Análisis cuantitativo de variables hemodinámicas de la aorta obtenidas de 4D flow Quantitative analysis of hemodynamic variables of the aorta by 4D flow MRI

Objetivo: Los parámetros hemodinámicos son de gran utilidad para realizar un adecuado diagnóstico. Sin embargo, debido a la gran cantidad de variables que pueden obtenerse, el análisis global de todas ellas puede ser complejo. Para facilitar esta tarea, nosotros proponemos crear un modelo que permita clasificar distintas variables hemodinámicas entre las pertenecientes a un individuo sano o a uno patológico. Para ello, usaremos técnicas de minería de datos que permitan identificar y encontrar relaciones entre distintos parámetros hemodinámicos de la aorta obtenidos a través de flujo multidimensional (4D flow) por resonancia magnética. Método: Una secuencia 4D flow de todo el corazón y los grandes vasos fue adquirida utilizando resonancia magnética en 19 voluntarios sanos y 2 pacientes (uno con una coartación aórtica y otro con una coartación aórtica reparada). Retrospectivamente, los datos fueron reformateados a lo largo de la aorta, originándose 3 cortes en los voluntarios y 30 cortes en cada paciente. En cada corte la aorta fue segmentada y distintos parámetros fueron cuantificados: área, velocidad máxima, velocidad mínima, flujo y volumen, calculándose en los cuatro últimos su valor máximo, promedio, desviación estándar, curtosis, sesgo, proporción de tiempo en alcanzar el valor máximo, entre otros. Teniendo un total de 26 variables por cada corte. Se aplicó la técnica de árboles de decisión tipo CART (por sus siglas en inglés) para clasificar los datos. Para validar el modelo, 2 cortes extras fueron generados por cada voluntario y 20 cortes por cada paciente. Resultados: La técnica CART, mediante la utilización de sólo 7 variables, puede clasificar las imágenes de los voluntarios y pacientes con una tasa de error del 14,1%, una sensibilidad de 82,5% y una especificidad de 89.4%. Conclusiones: 4D flow provee una gran cantidad de datos hemodinámicos que son difíciles de analizar. En este trabajo demostramos que al utilizar minería de datos se pueden clasificar imágenes a partir de parámetros hemodinámicos relevantes y sus relaciones para apoyar el diagnóstico de alteraciones cardiovasculares.Objective: Hemodynamic parameters are critical to perform a proper diagnosis. However, due to the large number of variables that can be obtained, overall analysis may represent a complex task. To facilitate this, we propose to create a model for classifying different hemodynamic variables between those belonging to a healthy individual and to a pathological patient. For this purpose, we employed data mining techniques to identify relationships among various aortic hemodynamic parameters obtained through multi-dimensional (4D flow) MR imaging. Method: A 4D flow sequence of whole heart and great vessels was acquired using MRI in 19 healthy volunteers and 2 patients (one with aortic coarctation and one with repaired coarctation of the aorta). Retrospectively, data were reformatted along the aorta; three MRI acquisitions were performed for volunteers and 30 sequences for each patient. In each slice the aorta was segmented and various parameters were quantified: area, maximum velocity, minimum velocity, flow and volumen, with following values being calculated for last four parameters: maximum, average, standard deviation, kurtosis, skewness, proportion of time to reach the maximum value, among others. A total of 26 variables for each acquisition were obtained. In order to classify data, the CART Technique (Classification and Regression Trees) was applied. To validate the model, two extra projections were generated per each volunteer and 20 slice per each patient. Results: By using only 7 variables, the CART Technique allows discrimination between images performed either on volunteers or patients with an error rate of 14.1%, a sensitivity of 82.5%, and a specificity of 89.4%. Conclusions: 4D flow MR imaging provides a wealth of hemodynamic data that can be difficult to analyze. In this paper we demonstrate that by using data mining techniques it is possible to classify images from relevant hemodynamic parameters and their relationships in order to support the diagnosis of cardiovascular disorders