Universidad Evangelica de El Salvador

BACKGROUND: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could predict new-onset AF from the resting 12-lead ECG and that this prediction may help identify those at risk of AF-related stroke. METHODS: We used 1.6 M resting 12-lead digital ECG traces from 430 000 patients collected from 1984 to 2019. Deep neural networks were trained to predict new-onset AF (within 1 year) in patients without a history of AF. Performance was evaluated using areas under the receiver operating characteristic curve and precision-recall curve. We performed an incidence-free survival analysis for a period of 30 years following the ECG stratified by model predictions. To simulate real-world deployment, we trained a separate model using all ECGs before 2010 and evaluated model performance on a test set of ECGs from 2010 through 2014 that were linked to our stroke registry. We identified the patients at risk for AF-related stroke among those predicted to be high risk for AF by the model at different prediction thresholds. RESULTS: The area under the receiver operating characteristic curve and area under the precision-recall curve were 0.85 and 0.22, respectively, for predicting new-onset AF within 1 year of an ECG. The hazard ratio for the predicted high- versus low-risk groups over a 30-year span was 7.2 (95% CI, 6.9-7.6). In a simulated deployment scenario, the model predicted new-onset AF at 1 year with a sensitivity of 69% and specificity of 81%. The number needed to screen to find 1 new case of AF was 9. This model predicted patients at high risk for new-onset AF in 62% of all patients who experienced an AF-related stroke within 3 years of the index ECG. CONCLUSIONS: Deep learning can predict new-onset AF from the 12-lead ECG in patients with no previous history of AF. This prediction may help identify patients at risk for AF-related strokes.

BACKGROUND: Resistance of Aedes aegypti to photostable pyrethroid insecticides is a major problem for disease-vector control programs. Pyrethroids target the voltage-gated sodium channel on the insects' neurons. Single amino acid substitutions in this channel associated with pyrethroid resistance are one of the main factors that cause knockdown resistance in insects. Although kdr has been observed in several mosquito species, point mutations in the para gene have not been fully characterized in Ae. aegypti populations in Vietnam. The aim of this study was to determine the types and frequencies of mutations in the para gene in Ae. aegypti collected from used tires in Vietnam. METHODS AND FINDINGS: Several point mutations were examined that cause insensitivity of the voltage-gated sodium channel in the insect nervous system due to the replacement of the amino acids L1014F, the most commonly found point mutation in several mosquitoes; I1011M (or V) and V1016G (or I), which have been reported to be associated to knockdown resistance in Ae. aegypti located in segment 6, domain II; and a recently found amino acid replacement in F1269 in Ae. aegypti, located in segment 6, domain III. Among 756 larvae from 70 locations, no I1011M or I1011V nor L1014F mutations were found, and only two heterozygous V1016G mosquitoes were detected. However, F1269C mutations on domain III were distributed widely and with high frequency in 269 individuals among 757 larvae (53 collection sites among 70 locations surveyed). F1269C frequencies were low in the middle to north part of Vietnam but were high in the areas neighboring big cities and in the south of Vietnam, with the exception of the southern mountainous areas located at an elevation of 500-1000 m. CONCLUSIONS: The overall percentage of homozygous F1269C seems to remain low (7.4%) in the present situation. However, extensive and uncontrolled frequent use of photostable pyrethroids might be a strong selection pressure for this mutation to cause serious problems in the control of dengue fever in Vietnam.

BACKGROUND: Timely diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with echocardiography is impractical, ECG-based prediction models can help target high-risk patients. We developed a novel ECG-based machine learning approach to predict multiple structural heart conditions, hypothesizing that a composite model would yield higher prevalence and positive predictive values to facilitate meaningful recommendations for echocardiography. METHODS: Using 2 232 130 ECGs linked to electronic health records and echocardiography reports from 484 765 adults between 1984 to 2021, we trained machine learning models to predict the presence or absence of any of 7 echocardiography-confirmed diseases within 1 year. This composite label included the following: moderate or severe valvular disease (aortic/mitral stenosis or regurgitation, tricuspid regurgitation), reduced ejection fraction <50%, or interventricular septal thickness >15 mm. We tested various combinations of input features (demographics, laboratory values, structured ECG data, ECG traces) and evaluated model performance using 5-fold cross-validation, multisite validation trained on 1 site and tested on 10 independent sites, and simulated retrospective deployment trained on pre-2010 data and deployed in 2010. RESULTS: Our composite rECHOmmend model used age, sex, and ECG traces and had a 0.91 area under the receiver operating characteristic curve and a 42% positive predictive value at 90% sensitivity, with a composite label prevalence of 17.9%. Individual disease models had area under the receiver operating characteristic curves from 0.86 to 0.93 and lower positive predictive values from 1% to 31%. Area under the receiver operating characteristic curves for models using different input features ranged from 0.80 to 0.93, increasing with additional features. Multisite validation showed similar results to cross-validation, with an aggregate area under the receiver operating characteristic curve of 0.91 across our independent test set of 10 clinical sites after training on a separate site. Our simulated retrospective deployment showed that for ECGs acquired in patients without preexisting structural heart disease in the year 2010, 11% were classified as high risk and 41% (4.5% of total patients) developed true echocardiography-confirmed disease within 1 year. CONCLUSIONS: An ECG-based machine learning model using a composite end point can identify a high-risk population for having undiagnosed, clinically significant structural heart disease while outperforming single-disease models and improving practical utility with higher positive predictive values. This approach can facilitate targeted screening with echocardiography to improve underdiagnosis of structural heart disease.

The genetic association of interleukin-1 (IL-1) with periodontitis has been investigated in different populations. Failure to detect an association with IL-1 genotypes in European Caucasians with aggressive periodontitis (AGP) has recently been reported. No data from Central American Hispanics are available. The purpose of this explorative study was to study the association between IL-1 genotypes and AGP in two populations. Ninety-one subjects, 28 North European patients and 33 controls together with 16 Central American patients and 14 controls were included in the study according to validated radiographic and clinical criteria. Two polymorphisms, IL-1alpha G(+4845)-T and IL-1beta C(+3954)-T were analysed by means of polymerase chain reaction-restriction fragment length polymorphism. The association between presence of specific genotypes and disease status was estimated by the odds ratio. A logistic regression was also used in order to investigate whether the occurrence of the disease depends upon the combination of the IL-1A and IL-1B alleles in the population. A similar distribution of genotypes between patients and controls in both populations was detected. The frequency of allele 1 of the IL-1A gene was higher in patients of both populations compared with controls, however, no statistical significant differences were found between patients and controls.

Introduction. Tobacco use is one of the main risk factors for the development of non-communicable diseases, such as hypertension, diabetes mellitus, cancer, cardiovascular diseases and chronic kidney failure. Objective. To analyze the relationship between smoking and the diagnoses of hypertension, diabetes, kidney failure and cancer in users of the El Salvador health network, 2019. Methodology. An analytical cross-sectional study was carried out with a population of 63 061 users with a history of smoking and a diagnosis of hypertension, diabetes mellitus, kidney failure and / or cancer. Results. The prevalence of smoking at the country level was (1,2 %), with passive smoking predominating, followed by active smoking and exsmokers. Indirect smoking predominates in women, with a prevalence of (0,7 %), and in men, direct tobacco consumption predominates with a prevalence of (0,1 %), passive smoking predominates in people among the 25 to 59 years (48,1 %). Finally, a positive correlation was found between the prevalence of tobacco consumption with the diagnosis of arterial hypertension (0,4), as well as that of cancer (0,4), followed by diabetes mellitus (0,2) and insufficient chronic kidney (0,09). Conclusions. Smoking is related to the diagnosis of hypertension, diabetes, kidney failure and cancer in users of the El Salvador health network.

Congenital pathologies are those existing at or dating from birth. Occurrence of congenital cystic lesions in the oral cavity is uncommon in neonates. Eruption cyst (EC) is listed among these unusual lesions. It occurs within the mucosa overlying teeth that are about to erupt and, according to the current World Health Organization (WHO) classification of epithelial cysts of the jaws, EC is a separate entity. This paper presents a case of congenital EC successfully managed by close monitoring of the lesion, without any surgical procedure or tooth extraction. Eruption of the teeth involved, primary central incisors, occurred at the fourth month of age. During this time neither the child nor mother had any complication such as pain on sucking, refusal to feed, airway obstruction, or aspiration of fluids or teeth.

Abstract. The San Salvador volcanic complex (El Salvador) and Nejapa-Chiltepe volcanic complex (Nicaragua) have been characterized by a significant variability in eruption style and vent location. Densely inhabited cities are built on them and their surroundings, including the metropolitan areas of San Salvador (∼2.4 million people) and Managua (∼1.4 million people), respectively. In this study we present novel vent opening probability maps for these volcanic complexes, which are based on a multi-model approach that relies on kernel density estimators. In particular, we present thematic vent opening maps, i.e., we consider different hazardous phenomena separately, including lava emission, small-scale pyroclastic density currents, ejection of ballistic projectiles, and low-intensity pyroclastic fallout. Our volcanological dataset includes: (1) the location of past vents, (2) the mapping of the main fault structures, and (3) the eruption styles of past events, obtained from critical analysis of the literature and/or inferred from volcanic deposits and morphological features observed remotely and in the field. To illustrate the effects of considering the expected eruption style in the construction of vent opening maps, we focus on the analysis of small-scale pyroclastic density currents derived from phreatomagmatic activity or from low-intensity magmatic volcanism. For the numerical simulation of these phenomena we adopted the recently developed branching energy cone model by using the program ECMapProb. Our results show that the implementation of thematic vent opening maps can produce significantly different hazard levels from those estimated with traditional, non-thematic maps.

Numerous studies about the role of assessment in English as a Foreign Language (EFL) and English as a Second Language (ESL) have been conducted in the last few decades. These studies describe a close connection between language assessment and language teaching. Evidence shows that the way students perceive their results after taking an exam strongly influences their motivation toward using the target language in real communication (Torrance, 2012). In compliance with such studies, this paper provides a general view of language assessment and its implications in the EFL/ESL classroom. The paper aims to provide a general background of language assessment, as well as a contrastive analysis of both summative and formative assessment. The paper approaches the topic from the point of view of both agents involved in the learning process; instructors and students. The findings suggest that students benefit more from formative assessment since it provides them with timely appropriate feedback that helps them shape the way they approach language learning (Huang, 2016). On the other hand, the study highlights that novice teachers usually opt for the traditional summative assessment in order to avoid challenges that formative assessment represents.

The author presents an innovative device to decompress odontogenic cystic lesions of the jawbones. Its use and characteristics are described.

El objetivo de este estudio fue comprender los patrones de policonsumo simultáneo de sustancias psicoactivas y sus implicaciones de género, legales y sociales, en estudiantes de primer y segundo año de las facultades de ciencias de la salud/ciencias médicas, en siete universidades de cinco países latinoamericanos, Colombia, Nicaragua, Chile, Brasil y El Salvador, y un país caribeño, Jamaica. El diseño fue un corte transversal. Las combinaciones de alcohol + tabaco y de alcohol + marihuana fueron las mas reportadas en todas las universidades, a excepción de alcohol + tabaco en Jamaica. Los factores asociados al policonsumo más referidos fueron "tener relaciones sexuales inesperada" en la universidad de Brasil, "tener relaciones sexuales sin protección" en las universidades de Chile, Colombia y Nicaragua, "tener problemas con su pareja sentimental" en la universidad de Jamaica, y "ausentarse de clases" en la universidad de El Salvador. Tres entornos se relacionaron, de manera positiva o negativa, con el policonsumo simultáneo de sustancias psicoactivas: estudiantil, familiar y de comportamiento sexual.

Methanol is a natural ingredient with major occurrence in fruit spirits, such as apple, pear, plum or cherry spirits, but also in spirits made from coffee pulp. The compound is formed during fermentation and the following mash storage by enzymatic hydrolysis of naturally present pectins. Methanol is toxic above certain threshold levels and legal limits have been set in most jurisdictions. Therefore, the methanol content needs to be mitigated and its level must be controlled. This article will review the several factors that influence the methanol content including the pH value of the mash, the addition of various yeast and enzyme preparations, fermentation temperature, mash storage, and most importantly the raw material quality and hygiene. From all these mitigation possibilities, lowering the pH value and the use of cultured yeasts when mashing fruit substances is already common as best practice today. Also a controlled yeast fermentation at acidic pH facilitates not only reduced methanol formation, but ultimately also leads to quality benefits of the distillate. Special care has to be observed in the case of spirits made from coffee by-products which are prone to spoilage with very high methanol contents reported in past studies.

BACKGROUND: Antenatal corticosteroids administered to women at risk of preterm birth is an intervention which has been proved to reduce the risk of respiratory distress syndrome, intraventricular hemorrhage, and neonatal mortality. There is a significant gap in the literature regarding the prevalence of the use of antenatal corticosteroids in Latin American countries and the attitudes and opinions of providers regarding this practice. The aim of this study was to assess the knowledge, attitudes and practices of health care providers regarding the use of antenatal corticosteroids in women at risk of preterm birth in Latin America. METHODS: This was a multicenter, prospective, descriptive study conducted in maternity hospitals in Ecuador, El Salvador, Mexico and Uruguay. Physicians and midwives who provide prenatal care or intrapartum care for women delivering in the selected hospitals were approached using a self-administered questionnaire. Descriptive statistics was used. RESULTS: The percentage of use of ACT in threatened preterm labour (TPL) reported by providers varies from 70% in Mexico to 97% in Ecuador. However, 60% to 20% of the providers mentioned that they would not use this medication in women at risk and would limit its use when there was a threatened preterm labour. In only one country recommended regimens of antenatal corticosteroids are followed by around 90% of providers whereas in the other three countries recommended regimens are followed by only 21%, 61%, 69% of providers. Around 40% of providers mentioned that they would administer a new dose of corticosteroids again, regardless the patient already receiving an entire regimen. Between 11% and 35% of providers, according to the countries, mentioned that they do not have adequate information on the correct use of this medication. CONCLUSIONS: This study shows that the use of this intervention could be improved by increasing the knowledge of Latin American providers on its indications, benefits, and regimens.

OBJECTIVE: The objective of this study was to determine the psychometric properties of the 3-dimensional latent model of empathy on the Jefferson Scale of Physician Empathy instrument (version S), and to verify the existence of cutoff points capable of differentiating empathy measures classified as: "high," "medium," and "low" using data collected from observations of students from 11 dental faculties of 5 Central American and Caribbean countries (n = 3082) between 2015 and 2019. METHODS: This is an exploratory, "a posteriori," and non-experimental study. Factor structure and factor invariance by country and gender were analyzed. Hierarchical cluster analysis and bifactorial analysis were applied, and the data were normalized by cluster and by percentiles within them. RESULTS: Confirmatory factor analysis showed that the original model was replicable and fit the data, while multigroup analysis allowed assuming an invariant factor structure by country and by gender. There is reliability in the measurement made by the scale and its dimensions. CONCLUSIONS: The instrument has adequate psychometric properties, and cutoff values obtained allow people with lower or higher levels of empathy and its components to be classified. Therefore, these results solve the problem of comparing the scores and observed levels of empathy between dental schools within and between countries and between genders. Such comparisons were only possible since the original data of each study were made available for traditional statistical methods.

Introducción: El estudio examina los beneficios y limitaciones percibidos por docentes y estudiantes universitarios salvadoreños sobre el uso de inteligencia artificial (IA) en procesos de enseñanza-aprendizaje. Metodología: Se utilizó una metodología mixta con entrevistas a 5 docentes y cuestionarios a 673 estudiantes de 20 universidades salvadoreñas. Resultados: Los resultados indican que la mayoría tiene un conocimiento básico de herramientas de IA como ChatGPT y Copilot. Las percepciones son predominantemente positivas, aunque existen preocupaciones sobre la integridad ético-académica y la necesidad de capacitación. Discusión: Se resalta la necesidad de un enfoque equilibrado que maximice los beneficios de la IA y mitigue sus riesgos, sugiriendo futuras investigaciones para explorar mejoras en la educación superior. Conclusiones: La IA tiene un gran potencial, pero es fundamental abordar las limitaciones actuales y promover una implementación reflexiva y cuidadosa en la educación universitaria.

ABSTRACT This paper studies under which conditions the share of profit in value‐added, financial constraints on investment and capital shortage may foster unemployment and may limit the growth of capital and/or the growth of aggregate demand, in a stock‐flow consistent model. The efficiency of demand‐side versus supply‐side economic policies (decrease of the real interest rate and/or of the real wage, increase of the leverage ceiling constraint) depends on capital shortage and credit rationing, which are not necessarily simultaneous due to the effects of investment on aggregate demand and supply.

"Objective: To estimate empathy levels in general and empathic growth potential in dental students. Material and Methods: This is an exploratory, transversal study. The study population is made up of students from the first to fifth academic year of the career of dentistry of the Evangelical University of El Salvador (El Salvador) (148/240, 61.67% of the population studied). The participants were given the Jefferson Empathy Medical Scale, the Spanish version for medical students, validated in Chile and Mexico, and culturally adapted in El Salvador. A bifactorial variance analysis (model III) was applied to find differences in the means between the courses, between the genders and in the interaction between these two factors. The data were described using simple arithmetic graphs, processed with SPSS 22.0. Total growth potential was estimated. Results: Differences were found between academic years, but not in gender of empathy in general and in its components. The levels of empathy and its components are low in relation to other studies. The behavior of the levels of empathy agrees with the concept of empathic decline. The masculine gender presented levels of empathy, in absolute values, greater than the feminine. There is considerable potential for growth in empathy and that of its components. Conclusion: The behavior of the levels of empathy observed in this work does not agree with the concept of empathic decline. The differences observed between the genders were not consistent with those reported by other authors and it is possible that these findings constitute further evidence that empathy itself is not a female attribute."

OBJECTIVE: To determine the prevalence of the use of prenatal corticosteroids in women who delivered prematurely in 3 Latin American counties and to evaluate the maternal characteristics associated with use. METHODS: A multicenter, prospective, descriptive study was conducted in 4 hospitals in Ecuador, 5 in Uruguay, and 3 in El Salvador between 2004 and 2008. Women who had delivered between 24 and 34 weeks of pregnancy responded to a questionnaire assessing sociodemographic characteristics, obstetric history, prenatal care, women's attitudes to health services and knowledge of preterm risk factors, prenatal corticosteroid administration, and characteristics of the delivery and neonate. The association between the prenatal corticosteroid use and the study variables was evaluated through a logistic regression analysis based on a hierarchical model. RESULTS: A total of 1062 women who had a preterm birth were included in the study. Prenatal corticosteroid use was 34.8% (95% CI, 29.9%-39.9%) in Ecuador, 54.6% (95% CI, 49.6%-59.6%) in El Salvador, and 71.0% (95% CI, 65.3%-76.2%) in Uruguay. Hospital admission-to-delivery time was associated with the use of prenatal corticosteroids in all 3 countries. CONCLUSION: The study revealed a varied pattern of use of prenatal corticosteroids across the 3 countries, and a diversity of influencing factors.

This study is a multi-centric investigation on the role of family relations, spirituality and entertainment in moderating the relationship between peer influence and drug use (licit and illicit) among students from eight universities from five countries in Latin-America and three from the Caribbean. The sample was composed by 2198 university students from faculties of Social Sciences and Humanities/Health Sciences. Drug use was the dependent variable and the level of peer influence (number of friends who use drugs) was the independent variable. The results showed that problematic family relationship, high number of party-based entertainment and high number of friends on drugs use (peer influence) increased the odds of youths using illicit drugs. The only interaction statistically significant for licit and illicit drug use was party-based entertainment and friends on drugs. The use of licit drugs presented large variability among universities in the sample, potentially influenced by cultural/religious aspects.

A contagious disease can be transmitted very quickly from one person to another through direct contact or indirect contact. Today there are many doctors who become infected while monitoring their patients, this causes them to cease their work and even death. Therefore, this project has the objective of designing a robotic concept that allows remote monitoring of patients with contagious diseases, reducing direct contact between the doctor and the patient. For the design of this robot, the V methodology is proposed, which breakdown a complex prototype into a series of systems and subsystems. It is expected to obtain a robot with free obstacle locomotion applying the similarity law, thermal monitoring based on artificial vision, and facial recognition using Haar cascade. The concept was tested in solidworks motion. Finally, the proposed concept is simulated in ABS for the Housing with the capacity to withstand temperatures of up to 80 degrees Celsius and an aluminium structure with a security factor of 8.17.

e are physician-researchers who believe in the promise of computer assistance (especially machine learning) to improve medicine for both patients and providers. However, as believers, we live within a dichotomy of both promise and frustration. On the one hand, we read a dizzying amount of material promising that machine learning will accomplish the seemingly impossible task of transforming clinical practice by both improving patient outcomes and reducing health care costs. We buy into this promise, as we have seen first-hand the magic of deep learning, for example to identify clinically unrecognized evidence of disease (strongly linked to mortality) in seemingly normal 12-lead ECG traces. 1 Despite this promise, our clinical days are filled with increasing frustration as it seems not only that our smartphones are far smarter than the systems we use to both acquire and interpret clinical data but also that our colleagues keep asking for all the artificial intelligence solutions that they read about in countless review articles, Tweets, and opinion pieces; yet we have little to offer them but "Don't worry, the revolution is coming, we promise!"