Experiencias de innovación pedagógica y tecnológica en la implementación del Diplomado en Programación Pedagógica para la Docencia Universitaria por Competencias a través de la plataforma Moodle en la UAEM

Como parte de los proyectos de instrumentación para la ejecución del Modelo de Innovación Curricular en la UAEMéx, implementado en el 2003, se asumió a partir del 2010 por el Cuerpo Académico en Educación y Enseñanza de la Geografía, la propuesta de un programa de capacitación docente denominado “Diplomado en Programación Pedagógica para la Docencia Universitaria por Competencias”, una propuesta apoyada por la Dirección de Desarrollo del Personal Académico DIDEPA con la plataforma Moodle que se ha realizado en tres promociones de 2010 al 2013

Polimorfismo del gen del transportador de serotonina (5-HTT) y trastorno de depresión mayor en pacientes en bogotá, Colombia

Introduction: The 5-HTT short allele has been controversially associated with an increased risk of major depressive disorder. Objective: To determine the association of 5-HTT short allele with major depression in Bogotá, Colombia. Materials and methods: We carried out a study of cases (n=68) matched 1:1 with controls by gender and age (±5 years). Major depression was diagnosed using the Mini-International Neuropsychiatric Interview, and 5-HTT polymorphism using PCR. Results: Females were predominant (82.4%). The S (short) allele predominated in cases compared with controls (S: 72.1% vs. 63.2; L (long): 27.9% vs. 36.8%), and the SL genotype was more frequent in cases (SL: 45.6% vs. 36.8%; LL: 27.9% vs. 36.8%; SS: 26.5% vs. 26.5%), although not significantly. There were significant differences in those under age 37, with a predominance of the S allele in cases (p=0.038; OR=2.75; 95% CI: 0.88-8.64). Multivariate analysis, adjusted for comorbid anxiety disorders, showed a significant association of major depression with the SL genotype (p=0.049; OR=3.20; 95% CI: 1.00-10.23); the S allele was close to statistical significance (p=0.063; OR=2.94; 95% CI: 0.94-9.13), and it was statistically significant in cases under 37 years of age (p=0.026; OR=10.79; 95% CI: 1.32-80.36). Conclusions: The SL genotype was associated with major depressive disorder in patients of all ages. The S allele was significantly associated with major depressive disorder in patients under age 37, adjusted for comorbid anxiety disorders

Open bile duct exploration as a therapeutic solution for difficult to manage choledocholithiasis: a case report

The most common etiology of bile duct obstruction in patients with cholelithiasis is choledocholithiasis. The diagnosis of cholelithiasis is based on clinical suspicion and confirmed by ultrasound (US) of the liver and bile ducts. The management of bile duct lithiasis has evolved considerably and currently, ERCP is the most common and recommended technique. However, in cases of multiple lithiases, fragmentation of the lithiasis during extraction, excessive preoperative or transoperative handling of the ampullary region, previous stenosis of the ampullary region, juxtapapillary diverticula, primary bile duct stones, or residual intrahepatic stones, a large number of hospitals do not have sufficient resources to perform minimally invasive procedures and offer these therapeutic alternatives instead.

Current State of Teledentistry in Chile

Chile is a country where the geography and territorial distribution of the population make healthcare a constant challenge. Despite a reported improvement on oral health indicators, some levels of inequality are still noted in terms of access to healthcare services. In this context, teledentistry has been considered an effective tool to respond to the population’s healthcare needs. The aim of this paper is to present the current state of teledentistry in Chile. This paper describes the initiatives and programmes of teledentistry developed in Chile, the ethical and legal aspects, financing sources and pending challenges for its consolidation. It is expected that teledentistry will contribute toward an increase in coverage and access to specialists, improve the appropriateness of referrals and reduce costs of specialist care

Low-cost test measurement setup for real IoT BLE sensor device characterization

The methodology presented in this paper aims to characterize impairments shown by real devices which are usually neglected on standardized tests but that become very important in massive IoT scenarios. For instance, we have measured that real BLE scanners are not able to scan continuously even though they are configured to do so. Besides, we have also found and demonstrated that some manufacturers seem not to apply any backoff mechanism although it is mandatory. These two unexpected behaviors have a significant impact on the performance of massive wireless sensor networks based on BLE. So, it becomes necessary to characterize these and other impairments. The proposed tests are based on device current consumption measurements and their association with the information obtained from upper layers. We describe a new low-cost generic measurement setup and provide all the necessary data (configuration parameters, scripts, etc.) for applying the proposed methodology. As an example, we use it to profile the behavior of Bluetooth Low Energy devices. Furthermore, the proposed setup can also inspire researchers to characterize other wireless technology devices, like Wi-Fi, Zigbee, LoRa, etc

Experiencia de innovación pedagógica y tecnológica en la implementación del diplomado en programación pedagógica para la docencia universitaria por competencias a través de la plataforma Moodle en la UAEM

Como parte de los proyectos de instrumentación para la ejecución del Modelo de Innovación Curricular en la UAEMéx, implementado en el 2003, se asumió a partir del 2010 por el Cuerpo Académico en Educación y Enseñanza de la Geografía, la propuesta de un programa de capacitación docente denominado “Diplomado en Programación Pedagógica para la Docencia Universitaria por Competencias”, una propuesta apoyada por la Dirección de Desarrollo del Personal Académico DIDEPA con la plataforma Moodle que se ha realizado en tres promociones de 2010 al 2013.Sin Patrocinadore

Predicting emotional states using behavioral markers derived from passively sensed data: Data-driven machine learning approach

Background: Mental health disorders affect multiple aspects of patients’ lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient’s mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues. Objective: This study aims to present a machine learning–based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous time-series data with a large percentage of missing observations. Methods: Passively sensed behavior and self-reported emotional state data from a cohort of 943 individuals (outpatients recruited from community clinics) were available for analysis. All patients had at least 30 days’ worth of naturally occurring behavior observations, including information about physical activity, geolocation, sleep, and smartphone app use. These regularly sampled but frequently missing and heterogeneous time series were analyzed with the following probabilistic latent variable models for data averaging and feature extraction: mixture model (MM) and hidden Markov model (HMM). The extracted features were then combined with a classifier to predict emotional state. A variety of classical machine learning methods and recurrent neural networks were compared. Finally, a personalized Bayesian model was proposed to improve performance by considering the individual differences in the data and applying a different classifier bias term for each patient. Results: Probabilistic generative models proved to be good preprocessing and feature extractor tools for data with large percentages of missing observations. Models that took into account the posterior probabilities of the MM and HMM latent states outperformed those that did not by more than 20%, suggesting that the underlying behavioral patterns identified were meaningful for individuals’ overall emotional state. The best performing generalized models achieved a 0.81 area under the curve of the receiver operating characteristic and 0.71 area under the precision-recall curve when predicting self-reported emotional valence from behavior in held-out test data. Moreover, the proposed personalized models demonstrated that accounting for individual differences through a simple hierarchical model can substantially improve emotional state prediction performance without relying on previous days’ data. Conclusions: These findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients’ mood states.This project has received funding from the European Union's Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie grant agreement number 813533. This work was partly supported by the Spanish government (Ministerio de Ciencia e Innovación) under grants TEC2017-92552-EXP and RTI2018-099655-B-100; the Comunidad de Madrid under grants IND2017/TIC-7618, IND2018/TIC-9649, IND2020/TIC-17372, and Y2018/TCS-4705; the BBVA Foundation under the Domain Alignment and Data Wrangling with Deep Generative Models (Deep-DARWiN) project; and the European Union (European Regional Development Fund and the European Research Council) through the European Union's Horizon 2020 Research and Innovation Program under grant 714161. The authors thank Enrique Baca-Garcia for providing demographic and clinical data and assisting in interpreting and summarizing the data

The prevalence of war-related post-traumatic stress disorder in children from Cundinamarca, Colombia

Objetivos Determinar en tres municipios de Cundinamarca la prevalencia del Trastorno por Estrés Postraumático (TEPT) infantil y factores asociados, según tipo de exposición a la guerra. Métodos Estudio de corte transversal. Muestra representativa de 493 escolares de 5 a 14 años de edad. 167 niños en La Palma, con exposición crónica; 164 en Arbeláez, con un hostigamiento armado reciente y 162 en Sopó, sin exposición. Por medio de entrevistas psiquiátricas semi-estructuradas y la Escala para Trastorno de Estrés Postraumático Administrada por el Clínico se determinó la prevalencia de TEPT y factores de vulnerabilidad, la razón de desventaja (RD) y Ji-cuadrada. Se hizo análisis multivariado con regresión logística. Los niños afectados requirieron intervención de salud mental. Resultados Prevalencia de TEPT bélico: La Palma 16,8 %, Arbeláez 23,2 %, y Sopó 1,2 %. Al comparar los municipios expuestos con Sopó: RD 19,9 (IC 4,7, 119,2), Ji-cuadrada 30,4 P=0,000. La regresión logística identificó que la proximidad geográfica y la alteración emocional intensa con el evento estresante incrementaron el TEPT. Los factores de vulnerabilidad predominaron en los municipios expuestos a la guerra. Los indicadores de pobreza, baja escolaridad paterna y maltrato infantil predominaron en La Palma. En Arbeláez predominó el déficit de atención y los trastornos psicosomáticos. Conclusiones La Guerra afecta la salud mental infantil. Los niños de los municipios expuestos tuvieron 19 veces mayor probabilidad de sufrir TEPT bélico que los no expuestos. Intervenir tempranamente es prioridad en salud pública. Los resultados son útiles para países con conflicto bélico o terrorismo.OBJECTIVE: Determining the prevalence of post-traumatic stress disorder (PTSD) related to the type of war exposure and associated factors in school-aged children from three Colombian towns. METHODS: Cross-sectional epidemiological study. Representative randomised sample of 493 children aged 5-14. The children were evaluated during 2002 using semi-structured psychiatric interviews and the clinician administered PTSD scale. 167 children were evaluated in La Palma who had been chronically exposed to war, 164 in Arbeláez who had had recent war-exposure and 162 in Sopó who had not been exposed to war. War-related PTSD prevalence was calculated in each municipality. Odds ratio (OR) and chi-square were used for evaluating the association between exposure to war and PTSD and the related risk. Multivariate analysis used the logistic regression model. The affected children required specialised mental health counselling. RESULTS: The prevalence of PTSD resulting from war was 16,8 % in La Palma, 23,2 % in Arbeláez and 1.2% in Sopó. A 19.9 OR (CI 4.7, 119.2), 30,5 Chi-square and p = 0.000 revealed war-related PTSD association and risk for children when comparing the exposed towns to Sopó. The logistic regression showed that geographical closeness to war zone and intense emotional reaction to war increased the probability of war-related PTSD. Vulnerability factors were predominant in war-exposed towns. Poverty, parents' low educational level and child abuse predominated in La Palma. Attention-deficit and psychosomatic disorders were more prevalent in Arbeláez. CONCLUSIONS: War affects children's mental health; the children from the exposed towns had 19 times greater probability of war-related PTSD than those from a non-exposed town. Early therapeutic intervention is a public health priority. The results are useful for countries suffering from war, internal conflict and/or terrorism