Evolucion De La Epidemiologia De La Enfermedad Celiaca E Impacto De La Covid19. Resultados Preliminares Del Plan Nacional De Enfermedad Celiaca-Ciberehd

Introduccion: Se ha reportado que tanto la prevalencia como la incidencia de la enfermedad celiaca (EC) estan aumentando a nivel mundial. Este aumento podria ser atribuido a infecciones viricas como desencadenante de la EC, entre otros factores. La COVID-19 ha impactado de lleno en la realizacion del estudio de prevalencia del Plan Nacional de EC promovido por el CIBERHED. Se ha sugerido que SARS-CoV-2, que penetra por via digestiva, podria desencadenar brotes de EC. Objetivos: 1) Evaluar la prevalencia actual de EC en comparacion con la de la cohorte 2004-2009. 2) Evaluar el impacto de la COVID-19 en la prevalencia actual de EC. Metodos: Desde enero 2021 hasta la actualidad se han incluido 1886 individuos de entre 1 y 90 anos atendidos por cirugia menor en los 6 hospitales participantes (Hospitales Universitarios (HU) Mutua Terrassa- Sant Joan de Deu, HU Virgen del Rocio, HU Central de Asturias, HU Galdakao-Cruces y HU Fundacion Jimenez Diaz) (calculo muestral: 5.300). La inclusion se realiza ajustada por edad y sexo a la piramide poblacional. Se determinan Ac. antitransglutaminasa (tTGA) y en los casos positivos se confirma el diagnostico segun guias de consenso. Se reporta la prevalencia serologica (tTGA > 7 U/ml) mas los casos prevalentes previamente diagnosticados. La relacion entre COVID19, registrada mediante cuestionario, y los casos positivos (tTGA > 7 U/mL y tTGA zona gris 2-7 U/mL) se ha evaluado mediante prueba exacta de Fisher. Resultados: Se han detectado 11 sujetos con serologia positiva y 2 pacientes con diagnostico previo de EC;prevalencia 6,89 x 1.000, IC95% 3,68-11,76. En comparacion con la prevalencia de la cohorte (2004-2007), 5,67 x 1.000, no existen diferencias significativas. Se han detectado 3 casos con serologia tTGA positiva y COVID-19 sobre un total de 335 pacientes infectados y 8 casos con serologia tTGA positiva sin COVID-19 sobre un total de 1.547 individuos no infectados (p = 0,4247). Tampoco se ha encontrado relacion entre COVID-19 y valores de serologia en la zona gris. Conclusiones: No se detecta un incremento de la prevalencia de EC en las ultimas dos decadas en nuestro medio. La infeccion por COVID19 no ha tenido impacto en la aparicion de nuevos casos de la enfermedad.Copyright © 2023 Elsevier Espana, S.L.U. Todos los derechos reservados.

Geospatial analysis of COVID-19 distribution and its relation to public transportation services

COVID-19 has changed our lifestyle;nowadays, activities such as studying, working, and meetings, among others, have drastically changed from being face to face to being remote;however, there is still an activity that has not changed as quickly as needed because of its main purpose, i.e., transportation. In this approach, a complete COVID-19 geospatial analysis is conducted correlating official reported cases of COVID-19-infected individuals and those who died with the data of public transportation, focusing on specific areas and the subway service in Mexico City. The geospatial analysis allows identifying the importance of some subway stations and their influence on the rate of infected people and also allows visualizing the distribution of COVID-19 all over the geographic areas near the subway stations and understanding the distribution of COVID-19 in the city. Finally, the approach generates a visualization model of the distribution of COVID-19 and its relation to the subway service using geospatial intelligence. © 2022 Elsevier Inc. All rights reserved.

Research in teaching practice in non-face-to-face education

The present study work began in the face of the situation of the health emergency that is experienced worldwide in the face of covid19 and the non-face-to-face education that the Ministry of Education of Peru ordered;A qualitative research was carried out, taking into account the type of action research that allows generating pedagogical knowledge and allows achieving significant changes in the development of the learning sessions. Action research was implemented and put into practice to make an analysis and continuous improvement of my pedagogical practice, carrying out the following phases: the deconstruction of my pedagogical practice, followed by its reconstruction, the students wer'e considered as a study sample. 26 students of the artificial intelligence course. © 2021 IEEE.

Influence of Educational Software 'Kindermat' in Learning of Geometric Shapes in Five-year-old Children

The pandemic caused by COVID-19 forced a mandatory confinement throughout the planet, thus, teachers and students distanced themselves and caused the migration from face-to-face to virtual education. This drastic change in education greatly affected Peru, as technological tools were required to support the student in the learning process. For this reason, the educational software KINDERMAT was built, which is a web application that supports the learning of geometric shapes in preschool children. The research aims to determine how KINDERMAT influences the conceptual and practical learning of geometric shapes in children 5 years old. To achieve this, students from an Educational Institution in Peru are taken as a sample, dividing them into two groups: experimental group (GE1) and control group (GE2). A post-test is applied to both groups following a quasi-experimental design. Finally, it was shown that the KINDERMAT educational software positively influences learning in the students in the sample, since there is a 15.5% improvement in the learning of children who used the KINDERMAT educational software compared to children who did not use it. © 2021 IEEE.

Influence of a Mobile Application With Augmented Reality ANATOMY-3D in Learning of Bone System

The pandemic caused by COVID-19, although it is considered a threat to humanity, also changed the life of the entire planet in different aspects, one of them is education because in a short time it converted the classic face-to-face stage into a virtual stage, a product of compulsory confinement in almost every country in the world. In Peru, this change has many negative consequences, since teachers do not have or know of virtual technological tools to support the student in the learning process. To alleviate this problem, an augmented reality mobile application called ANATOMY-3D was developed, to help students learn and recognize the bones of bone system in 3D. The influence of ANATOMY-3D on the learning of students from a school in the Apurimac region, in Peru, was evaluated, for this, 53 students were taken as a sample, dividing them into an experimental group (GE1) and a control group (G2). the experimental group used ANATOMY-3D and the control group used the traditional method. The results were analyzed in three dimensions: usability, motivation, and a knowledge questionnaire. The results showed that ANATOMY-3D has a good level of usability, promotes better motivation in students to use ANATOMY-3D compared to the traditional method. Finally, the questionnaire showed that ANATOMY-3D positively influences the learning and recognition of the bones of bone system, as it achieved better scores for the experimental group (G1), compared to the control group (G2), with a difference of 0.88 in the mean of the grades. © 2021 IEEE.

Micro-learning and Flipped Classroom to Improve Learning Motivation in Psychology Students

This research work shows the combination of micro-learning and flipped classroom to improve motivation in the learning process. The work was applied with 33 students from the professional school of Psychology of the Cognitive Psychology course of the National University of San Antonio Abad del Cusco in Peru. The applied methodology consisted of delivering podcast educational material before the synchronous session class;then, during the session class, they carry out a case study in groups of 4 to 5 students;then, the students created a video of very short time duration to be shared among other students. A questionnaire of 17 questions referring to student's satisfaction was applied to measure the satisfaction of the 33 participants, and the results show that 83.9% reached the positive evaluations of Agree+Totally Agree. © 2021 IEEE.

THE CRISIS AND SPATIALITY OF CARE IN A PANDEMIC: Housing and Neighborhoods in Santiago de Chile

The material conditions of the city offer evidence of socio-territorial segregation. When sanitary measures to control the spread of COVID-19 were applied, those conditions made the extent of segregation in Santiago evident. Thus, the city, neighborhood, and housing scales open possible points of analysis to better understand the care crisis at the territorial level, where segregation and mutual support have been its main characteristics. © 2021, Pontificia Universidad Catolica de Chile 1. All rights reserved.

CRISIS AND SPACE OF CARE IN PANDEMIA: housing and neighbourhood in Santiago de Chile

The material conditions of the city offer evidence of socio-territorial segregation. When sanitary measures were applied to control the spread of COVID-19, these conditions made the extent of segregation in Santiago. Thus, the city, neighborhood and housing scales offer inputs to understand the care crisis at a territorial level, where segregation and mutual support have been its main characteristics.

IoT Computing for Monitoring NFT-I Cultivation Technique in Vegetable Production

This article compares the production and growth times of three types of lettuce and in three cultivation systems NFT-I, RF and soil with Worm Humus. Additionally, it describes the NFT-I cultivation system, which is a cultivation technique supported by the Internet of Things (IoT). NFT-I allows to measure and store the data of three parameters: ambient temperature, pH level and electrical conductivity;the advantage is that this system allows notifying the farmer about the current status of each variable and notifying through the social network Telegram (through bots). The methodology used was to start the planting process in the three systems on the same day, then the NFT-I system was saving data read by the sensors, and later measurements were made of the time and growth of each of the planted lettuces. The results show that this system can reduce electricity consumption by 91.6%;on the other hand, it helps farmers monitor plant growth. On the other hand, regarding the harvest time, it can be verified that the RF system, NFT-I and land were harvested in 61, 69 and 105 days respectively, which shows that RF is the most efficient;In terms of size, the number of leaves, length and width, RF is also of better size than the NFT-I crop and soil. Finally, in these times of confinement due to the coronavirus disease (COVID-19), in which the economy has slowed and the needs are multiple, this NFT-I system could help people create their vegetable growing system of quickly and cheaply. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Classification of Diseases Using Machine Learning Algorithms: A Comparative Study

Machine learning in the medical area has become a very important requirement. The healthcare professional needs useful tools to diagnose medical illnesses. Classifiers are important to provide tools that can be useful to the health professional for this purpose. However, questions arise: which classifier to use? What metrics are appropriate to measure the performance of the classifier? How to determine a good distribution of the data so that the classifier does not bias the medical patterns to be classified in a particular class? Then most important question: does a classifier perform well for a particular disease? This paper will present some answers to the questions mentioned above, making use of classification algorithms widely used in machine learning research with datasets relating to medical illnesses under the supervised learning scheme. In addition to state-of-the-art algorithms in pattern classification, we introduce a novelty: the use of meta-learning to determine, a priori, which classifier would be the ideal for a specific dataset. The results obtained show numerically and statistically that there are reliable classifiers to suggest medical diagnoses. In addition, we provide some insights about the expected performance of classifiers for such a task.

CovidStream: Interactive Visualization of Emotions Evolution Associated with Covid-19

Since the beginning of the pandemic caused by Covid-19, the emotions of humanity have evolved abruptly, mainly for policies adopted by the governments of countries. These policies, since they have a high impact on people’s health, need feedback on people’s emotional perception and their connections with entities directly related to emotions, to have relevant information for decision making. Given the global social isolation, emotions have been expressed with higher magnitude in comments on social networks, generating a large amount of data that is a source for various investigations. The objective of this work is to design and adapt an interactive visualization tool called CovidStream, for monitoring the evolution of emotions associated with Covid-19 in Peru, for which Visual Analytics, Deep learning, and Sentiment Analysis techniques are combined. This visualization tool allows showing the evolution of the emotions associated with the Covid-19 and its relationships with three entities: persons, places, and organizations, which have an impact on emotions, all in a temporal space dimension. For the visualization of entities and emotions, Peruvian tweets extracted between January and July 2020 were used, all of them with the hashtag #Covid-19. For the classification of emotions, a recurrent neural network model with LSTM architecture was implemented, taking as training and test data the one proposed by SemEval-2018 Task1, corresponding to Spanish tweets labeled with emotions: anger, fear, joy, and sadness. © 2021, Springer Nature Switzerland AG.

Identify sentiments in quarantine by covid-19 through lexical classifier and supervised learning

The Covid-19 began to affect Peru on March 6 of 2020, preventive measures were started to prevent the spread. On March 15 compulsory social isolation began throughout Peru, the people use Twitter to exchange various information about social isolation, this is important for authorities and the public because it helps to consider strategies to avoid contagion. The present work has the objective to classify the positive and negative sentiment that were expressed on Twitter through the proposal of the Lexical Word Classifier and the use of classifying algorithms. The result obtained was that the most frequent words are: Quarantine, Covid and Home. The positive words were Good and Win, the negative word was Strange. The sentiment classification model reached 91.5% accuracy using the Support Vector Machine algorithm and the Lexicon Word Classifier. © 2021, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

Positioning for post-COVID-19 health care upon return to the doctor s office

The recommendations for face-To-face medical attention in the post-pandemic COVID-19 should be taken up in a different reality. It should be borne in mind that the return of the medical doctor to consult after the COVID-19 pandemic supposedly ends, acquires a greater risk of becoming infected than if he continued to be confined at home. The decision of when to restart the medical consultation should be made in the context of local circumstances. Prioritizing patient safety will be essential and a great challenge. We must make many changes in our usual lifestyle, to adjust to a "different reality" that represents the return to medical activity in the office. We present some scenarios in the necessary measures that allow the prevention of contagion for the medical doctor, the health team, the staff and the patients. © 2020 Comunicaciones Cientificas Mexicanas S.A. de C.V.. All rights reserved.

Tell me what you are like and I will tell you what you believe in: Social representations of COVID-19 in the Americas, Europe and Asia

This study analyzes the range and content of Social Representations (SRs) about the COVID-19 pandemic in 21 geographical zones from 17 countries in the Americas, Europe and Asia (N = 4430). Based on Social Representations Theory, as well as the psychosocial consequences of pandemics and crises, we evaluate the perceptions of severity and risks, the agreement with different SRs, and participants' Social Dominance Orientation (SDO) and Right-Wing Authoritarianism (RWA). Different sets of beliefs are discussed as SRs, together with their prevalence and association with contextual variables. Results show that severity and risk perceptions were associated with different SRs of the pandemic. Specifically, those focused on Emerging Externalizing zoonotic and ecological factors (the virus is due to Chinese unhygienic habits and the overexploitation of the planet), Polemic Conspiracies (the virus is a weapon), views of Elite and Mass Villains (the elites deceive us and profit with the pandemic), and Personal Responsibility (the neglectful deserves contagion) during the pandemic. Furthermore, most of the SRs are anchored in SDO and, more strongly, in RWA orientations. Additional meta-analyses and multi-level regressions show that the effects are replicated in most geographical areas and that risk perception was a consistent explanatory variable, even after controlling for demographics and `real risk' (i.e., actual numbers of contagion and death). Results suggest that, while coping with and making sense of the pandemic, authoritarian subjects agree with SR that feed a sense of social control and legitimize outgroup derogation, and support punishment of ingroup low-status deviants.

NFT-I technique using IoT to improve hydroponic cultivation of lettuce

The significant decrease in agricultural land and the rapid development of hydroponic system technology have brought a huge challenge to farmers. This paper describes the NFT-I (Nutrient Film Technique based on IoT) hydroponic system, and it is a variant of traditional NFT and Floating Root (RF) systems the system measures several parameters, such as temperature, water level, and acidity (pH) the system collects the information using sensors connected to Arduino microcontroller and Raspberry PI to store the collected data the results show that this system can reduce the electricity consumption by 91.6%;on the other hand, it helps farmers to increase the effectivity and efficiency on monitoring and controlling NFT-I Hydroponic Farm. Finally, in these times of confinement due to coronavirus disease (COVID-19), in which the economy has decreased, and the needs are multiple, this NFT-I system could help people to create their vegetable growing system quickly and cheaply. © 2020 IEEE.

Therapeutic approach in acute leukemia patients in the COVID-19 era

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already affected 180 countries. Older patients and patients with cancer or immunosuppression are at greater risk of severe forms of the disease. Patients with acute leukemia are challenging to manage during the pandemic. Recommendations for the management of these patients are based on expert opinion. This is a population in which polymerase chain reaction tests for SARS-CoV-2 must be performed routinely and cytotoxic chemotherapy should be deferred as far as possible in positive patients. On the other hand, some of the frequently used drugs such as corticosteroids, rituximab or asparaginase, can potentially complicate the course of COVID-19, so consideration should be given to deferring or adjusting them in higher-risk populations. In the same way, considering the particularities of each center, in certain cases it may be reasonable to give preference to outpatient regimens that also allow us to decrease the transfusion requirement. Finally, many of the patients with acute leukemia are candidates to receive allogeneic hematopoietic stem cell transplantation (alloHSCT). The limitation of the spaces in intensive care units must be considered, as well as the degree of immunosuppression derived from the transplant. The recommendation is not to defer alloHOCT in patients with an increased risk of relapse. Later, we will learn about the consequences on of the modifications in treatment on leukemia derived from the pandemic.