Relationship between HLB Number and Predominant Destabilization Process in Microfluidized Nanoemulsions Formulated with Lemon Essential Oil

Lemon essential oil (LEO) is associated with a multitude of health benefits due to its anticancer, antioxidant, antiviral, anti-inflammatory and bactericidal properties. Its drawback is that it is very sensitive to oxidation by heat. For this reason, researchers are increasingly investigating the use of LEO in nanoemulsions. In this work, we used laser diffraction, rheology and multiple light scattering techniques to study the effects of different HLB numbers (indicating different mixtures of Tween 80 and Span 20) on the physical stability of nanoemulsions formulated with LEO. We found that different HLB numbers induced different destabilization mechanisms in these emulsions. An HLB number lower than 12 resulted in an Ostwald ripening effect; an HLB number higher than 12 resulted in coalescence. In addition, all the developed nanoemulsions exhibited Newtonian behavior, which could favor the mechanism of creaming. All emulsions exhibited not only a growth in droplet size, but also a creaming with aging time. These findings highlight the importance of selecting the right surfactant to stabilize nanoemulsions, with potential applications in the food industry

Clasificación y pronóstico del nivel de satisfacción de egresados de programas de salud en el contexto de una metodología de aprendizaje automático: un análisis de caso orientado a posgrados online de una institución educativa iberoamericana

The purpose of this research article was to perform a classification based on neural networks to predict the level of satisfaction of a sample of graduates, corresponding to different graduate programs in the health area of a Latin American educational institution under an e-learning methodology. To this end, a Likert scale questionnaire model was instrumented which, after being validated, had a reliability of 0.791. Likewise, the average global satisfaction index of the graduates was 2.66/4, witha better score in the section on logistics of materials and in the management and technical support of the virtual campus, while the lowest scores referred to aspects related to extra-center communication and the facilities offered by the institution for the improvement of the participant's economic and social context. Finally, the probabilistic classification and prediction algorithm of the neural network obtained an accuracy of 96.8%, indicating an excellent degree of model fit. The methodology followed and the rigor in determining the validity and reliability of the instrument, as well as the subsequent analysis of the results, endorsed by the review of the documented information, suggest that the instrument can be applied to other multidisciplinary programs for decision making with guarantees in the educational field.El propósito de este artículo de investigación fue realizar una clasificación basada en redes neuronales, para pronosticar el nivel de satisfacción de una muestra de egresados, correspondiente a diferentes programas de posgrado del área de salud de una institución educativa latinoamericana bajo una metodología e-learning. Con este fin, se instrumentalizó un modelo en un cuestionario de escala de Likert que, tras ser validado, resultó con una confiabilidad de 0.791. Asimismo, el índice global medio de satisfacción de los egresados fue de 2.66/4, observando una mejor puntuación en el apartado de logística de materiales y en el manejo y soporte técnico del campus virtual, mientras que las puntuaciones más bajas se refirieron a aspectos relacionados con la comunicación extra-centro y las facilidades ofrecidas por la institución para la mejora del contexto económico y social del participante. Finalmente, el algoritmo de clasificación y predicción probabilística de la red neuronal obtuvo una precisión del 96.8%, lo que indicó un excelente grado de ajuste del modelo. La metodología seguida y el rigor en la determinación de la validez y confiabilidad del instrumento, así como el posterior análisis de resultados, refrendado con la revisión de la información documentada, hace presuponer la aplicación del instrumento a otros programas multidisciplinares para la toma de decisiones con garantías en el ámbito educativo

Thyroid disease prediction using selective features and machine learning techniques

Producción CientíficaSimple Summary: The study presents a thyroid disease prediction approach which utilizes random forest-based features to obtain high accuracy. The approach can obtain a 0.99 accuracy to predict ten thyroid diseases.Thyroid disease prediction has emerged as an important task recently. Despite existing approaches for its diagnosis, often the target is binary classification, the used datasets are small-sized and results are not validated either. Predominantly, existing approaches focus on model optimization and the feature engineering part is less investigated. To overcome these limitations, this study presents an approach that investigates feature engineering for machine learning and deep learning models. Forward feature selection, backward feature elimination, bidirectional feature elimination, and machine learning-based feature selection using extra tree classifiers are adopted. The proposed approach can predict Hashimoto’s thyroiditis (primary hypothyroid), binding protein (increased binding protein), autoimmune thyroiditis (compensated hypothyroid), and non-thyroidal syndrome (NTIS) (concurrent non-thyroidal illness). Extensive experiments show that the extra tree classifier-based selected feature yields the best results with 0.99 accuracy and an F1 score when used with the random forest classifier. Results suggest that the machine learning models are a better choice for thyroid disease detection regarding the provided accuracy and the computational complexity. K-fold cross-validation and performance comparison with existing studies corroborate the superior performance of the proposed approach

Consortium framework using blockchain for asthma healthcare in pandemics

Producción CientíficaAsthma is a deadly disease that affects the lungs and air supply of the human body. Coronavirus and its variants also affect the airways of the lungs. Asthma patients approach hospitals mostly in a critical condition and require emergency treatment, which creates a burden on health institutions during pandemics. The similar symptoms of asthma and coronavirus create confusion for health workers during patient handling and treatment of disease. The unavailability of patient history to physicians causes complications in proper diagnostics and treatments. Many asthma patient deaths have been reported especially during pandemics, which necessitates an efficient framework for asthma patients. In this article, we have proposed a blockchain consortium healthcare framework for asthma patients. The proposed framework helps in managing asthma healthcare units, coronavirus patient records and vaccination centers, insurance companies, and government agencies, which are connected through the secure blockchain network. The proposed framework increases data security and scalability as it stores encrypted patient data on the Interplanetary File System (IPFS) and keeps data hash values on the blockchain. The patient data are traceable and accessible to physicians and stakeholders, which helps in accurate diagnostics, timely treatment, and the management of patients. The smart contract ensures the execution of all business rules. The patient profile generation mechanism is also discussed. The experiment results revealed that the proposed framework has better transaction throughput, query delay, and security than existing solutions

¡Pensá, para la prevención!

This article has an objective to develop the capacity of critical thinking in dealing with cyberbullying. It addresses a population composed of high school students (2nd grade) from a private school, taking into account the following case studies dimensions: analysis of information, inferences and implications, alternatives of solutions and position of argument. Through the  application called “Critical thinking test” relevant information for the study can be collected. Furthermore, the intervention program involves the participation of affected students, aggressors and observers as well as parents, teachers and educational agents.El presente artículo tiene como objetivo desarrollar la capacidad del pensamiento crítico frente al ciberbullying. Atiende a una población compuesta por estudiantes de segundo grado de secundaria de una institución educativa particular, teniendo en cuenta las dimensiones de estudio: análisis de información, inferencias de implicancias,  propuestas de alternativas de solución y argumento de posición. Por medio de la aplicación del instrumento denominado “Test de pensamiento crítico” se recoge información relevante para el estudio. También, el  programa de intervención involucra la participación conjunta de los estudiantes afectados, agresores y observadores, así como los docentes, padres y agentes educativos

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies

Mapping genomic loci prioritises genes and implicates synaptic biology in schizophrenia

Schizophrenia has a heritability of 60–80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies

Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants with Treatment Resistance in Schizophrenia

Importance: About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts. Objective: To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples. Design, Setting, and Participants: Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n = 10501) and individuals with non-TRS (n = 20325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]). Main Outcomes and Measures: GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition. Results: The study included a total of 85490 participants (48635 [56.9%] male) in its GWAS stage and 1380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41-0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (r2 = 2.03%; P =.001), which was replicated in the STRATA-G incidence sample (r2 = 1.09%; P =.04). Conclusions and Relevance: In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance

An artificial neural network model for water quality and water consumption prediction

Producción CientíficaWith rapid urbanization, high rates of industrialization, and inappropriate waste disposal, water quality has been substantially degraded during the past decade. So, water quality prediction, an essential element for a healthy society, has become a task of great significance to protecting the water environment. Existing approaches focus predominantly on either water quality or water consumption prediction, utilizing complex algorithms that reduce the accuracy of imbalanced datasets and increase computational complexity. This study proposes a simple architecture of neural networks which is more efficient and accurate and can work for predicting both water quality and water consumption. An artificial neural network (ANN) consisting of one hidden layer and a couple of dropout and activation layers is utilized in this regard. The approach is tested using two datasets for predicting water quality and water consumption. Results show a 0.96 accuracy for water quality prediction which is better than existing studies. A 0.99 R2 score is obtained for water consumption prediction which is superior to existing state-of-the-art approaches