Guevara Maldonado, César Byron
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Guevara Maldonado, César Byron
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Quito
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cesarguevara@uti.edu.ec
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56452706300

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Analysis of the Spread and Evolution of COVID-19 Mutations in Ecuador Using Open Data

2024 , Guevara Maldonado, César Byron , Dennys Coronel , Byron Salazar , Jorge Salazar , Arias Flores, Hugo Patricio

Currently, the analyses of and prediction using COVID-19-related data extracted from patient information repositories compiled by hospitals and health organizations are of paramount importance. These efforts significantly contribute to vaccine development and the formulation of contingency techniques, providing essential tools to prevent resurgence and to effectively manage the spread of the disease. In this context, the present research focuses on analyzing the biological information of the SARS-CoV-2 viral gene sequences and the clinical data of COVID-19-affected patients using publicly accessible data from Ecuador. This involves considering variables such as age, gender, and geographical location to understand the evolution of mutations and their distributions across Ecuadorian provinces. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology is applied for data analysis. Various data preprocessing and statistical analysis techniques are employed, including Pearson correlation, the chi-square test, and analysis of variance (ANOVA). Statistical diagrams and charts are used to facilitate a better visualization of the results. The results illuminate the genetic diversity of the virus and its correlation with clinical variables, offering a comprehensive understanding of the dynamics of COVID-19 spread in Ecuador. Critical variables influencing population vulnerability are highlighted, and the findings underscore the significance of mutation monitoring and indicate a need for global expansion of the research area.

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COVID-19 spread algorithm in the international airport network-DetArpds

2023 , Guevara Maldonado, César Byron , Coronel D. , Maldonado B.E.S. , Flores J.E.S.

Due to COVID-19, the spread of diseases through air transport has become an important issue for public health in countries globally. Moreover, mass transportation (such as air travel) was a fundamental reason why infections spread to all countries within weeks. In the last 2 years in this research area, many studies have applied machine learning methods to predict the spread of COVID-19 in different environments with optimal results. These studies have implemented algorithms, methods, techniques, and other statistical models to analyze the information in accuracy form. Accordingly, this study focuses on analyzing the spread of COVID-19 in the international airport network. Initially, we conducted a review of the technical literature on algorithms, techniques, and theorems for generating routes between two points, comprising an analysis of 80 scientific papers that were published in indexed journals between 2017 and 2021. Subsequently, we analyzed the international airport database and information on the spread of COVID-19 from 2020 to 2022 to develop an algorithm for determining airport routes and the prevention of disease spread (DetARPDS). The main objective of this computational algorithm is to generate the routes taken by people infected with COVID-19 who transited the international airport network. The DetARPDS algorithm uses graph theory to map the international airport network using geographic allocations to position each terminal (vertex), while the distance between terminals was calculated with the Euclidian distance. Additionally, the proposed algorithm employs the Dijkstra algorithm to generate route simulations from a starting point to a destination air terminal. The generated routes are then compared with chronological contagion information to determine whether they meet the temporality in the spread of the virus. Finally, the obtained results are presented achieving a high probability of 93.46% accuracy for determining the entire route of how the disease spreads. Above all, the results of the algorithm proposed improved different computational aspects, such as time processing and detection of airports with a high rate of infection concentration, in comparison with other similar studies shown in the literature review. © 2023 Guevara et al.

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Topological Analysis Techniques for Improving Neural Network Performance in COVID-19 Detection Using Persistent Homology

2024 , Israel Reyes , Karen Cáceres-Benítez , Ana Marcillo , Andre Vera , Carolina Cadena-Morejón , Fernando Villalba-Meneses , Guevara Maldonado, César Byron , Paulina Vizcaíno-Imacaña , Diego Almeida-Galárraga , Andrés Tirado-Espín

In this study, we employ topological data analysis techniques on neural networks applied in COVID-19 detection, aiming to improve their predictive power. Leveraging the power of persistent homology, a mathematical tool for extracting topological features from intricate datasets, we turned chest X-ray images into a representation of the topological features. This representation was used to train and test the ability of neural networks to learn topological properties from images. We examine neural networks trained on chest radiographs containing both COVID-19 positive and negative cases. Our results suggest that, by identifying specific topological features correlated with COVID-19 detection, we may enhance the performance of the neural network models and analyze the underlying factors contributing to high accuracy rate of detection. The findings from this study contribute as exploratory advance in the field of medical imaging analysis and disease detection, showcasing the potential of topological analysis within neural networks.

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Development of a Convolutional Neural Network for Detection of Ovarian Cancer Based on Computed Tomography Images

2024 , Gabriela Narvaez-Chunillo , Ronny Ordoñez-Sanchez , Lizbeth Ortiz-Vinueza , Diego Almeida-Galárraga , Fernando Villalba-Meneses , Roberto Bravo-Freire , Andrés Tirado-Espín , Carolina Cadena-Morejón , Paulina Vizcaíno-Imacaña , Guevara Maldonado, César Byron

Ovarian cancer is one of the most frequent gynecologic malignancies in women, but it is often detected in late stage, leaving patients with little time to follow a successful therapy. Specialists have opted to use computer-aided diagnosis (CAD) for the detection of ovarian cancer through the analysis of computed tomography (CT) images, in which the professional examines the size, shape and different characteristics that enable a precise diagnosis in the ovary. This present project purposes a Convolutional Neural Network (CNN) which consist on four convolutional layers; including two pooling layer and two fully-connected layer. The cancerous ovaries images is selected from the Cancer Imaging Achive dataset for training and validation of the model. Moreover, the training of the CNN contain filters to ensure that all of the images are the same dimensions and pixel size. The testing results from the training of the images showed that the proposed model obtained a range of accuracy that goes from 90.0% to the best of the cases 98.85%. The variables obtained like the data of the pressure and loss of the training were compared with those of the validation, allowing for the determination of a successful CNN training.

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Unlocking the puzzle: non-defining mutations in SARS-CoV-2 proteome may affect vaccine effectiveness

2024 , Eugenia Ulzurrun , Ana Grande-Pérez , Daniel del Hoyo , Guevara Maldonado, César Byron , Carmen Gil , Carlos Oscar Sorzano , Nuria E. Campillo

Introduction: SARS-CoV-2 variants are defined by specific genome-wide mutations compared to the Wuhan genome. However, non-clade-defining mutations may also impact protein structure and function, potentially leading to reduced vaccine effectiveness. Our objective is to identify mutations across the entire viral genome rather than focus on individual mutations that may be associated with vaccine failure and to examine the physicochemical properties of the resulting amino acid changes. Materials and methods: Whole-genome consensus sequences of SARS-CoV-2 from COVID-19 patients were retrieved from the GISAID database. Analysis focused on Dataset_1 (7,154 genomes from Italy) and Dataset_2 (8,819 sequences from Spain). Bioinformatic tools identified amino acid changes resulting from codon mutations with frequencies of 10% or higher, and sequences were organized into sets based on identical amino acid combinations. Results: Non-defining mutations in SARS-CoV-2 genomes belonging to clades 21 L (Omicron), 22B/22E (Omicron), 22F/23A (Omicron) and 21J (Delta) were associated with vaccine failure. Four sets of sequences from Dataset_1 were significantly linked to low vaccine coverage: one from clade 21L with mutations L3201F (ORF1a), A27- (S) and G30- (N); two sets shared by clades 22B and 22E with changes A27- (S), I68- (S), R346T (S) and G30- (N); and one set shared by clades 22F and 23A containing changes A27- (S), F486P (S) and G30- (N). Booster doses showed a slight improvement in protection against Omicron clades. Regarding 21J (Delta) two sets of sequences from Dataset_2 exhibited the combination of non-clade mutations P2046L (ORF1a), P2287S (ORF1a), L829I (ORF1b), T95I (S), Y145H (S), R158- (S) and Q9L (N), that was associated with vaccine failure. Discussion: Vaccine coverage associations appear to be influenced by the mutations harbored by marketed vaccines. An analysis of the physicochemical properties of amino acid revealed that primarily hydrophobic and polar amino acid substitutions occurred. Our results suggest that non-defining mutations across the proteome of SARS-CoV-2 variants could affect the extent of protection of the COVID-19 vaccine. In addition, alteration of the physicochemical characteristics of viral amino acids could potentially disrupt protein structure or function or both.

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Editorial design of interactive picture book with mobile application based on uxd user experience design

2020 , Borja Galeas, Carlos , Guevara Maldonado, César Byron , Amagua M.

This research presents an interactive human-computer learning system model applying adaptive editorial design. The proposal aims to generate an interactive picture book and a mobile application based on user experience design (UxD). The results will be obtained using UX metrics and will have the particularity of working with the technique of participatory design and reticular deconstruction. This book includes its presentation as an audiobook and an editorial composition with pop-ups and pages to paint. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

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Accessibility assessment in mobile applications for android

2020 , Acosta-Vargas, P. , Salvador-Ullauri, L. , Jadán Guerrero, Janio , Guevara Maldonado, César Byron , Sanchez-Gordon, S. , Calle-Jimenez, T. , Lara Álvarez, Patricio , Medina, A. , Nunes, I.L.

At present, the lack of adequate methods to test whether a mobile application is accessible has become a major challenge for accessibility experts. This study was applied to ten mobile applications, the most popular according to PCMAG. We propose to use the Web Content Accessibility Guidelines 2.1 through manual review and automatic review with the Google Play Store Accessibility Scanner validator for the Android. The evaluation results of the mobile applications indicate that the applications are not accessible because they do not comply with the minimum required level proposed by WCAG 2.1. The research proposes suggestions to improve and raise awareness among the designers of mobile applications, in such a way that more inclusive mobile applications accessible to all types of users are built. © Springer Nature Switzerland AG 2020.

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Effect of Burnout Syndrome on work performance in administrative personnel

2024 , Verónica Adriana Freire Palacios , Sridam David Arévalo Lara , María Belén Espíndola Lara , Andrea Ramírez Casco , David Miguel Larrea Luzuriaga , Guevara Maldonado, César Byron

Burnout syndrome can negatively affect workers' performance. Objective: To determine the prevalence of Burnout Syndrome and its impact on the Administrative Performance of the Human Talent at the Chimborazo Sports Federation. This study is quantitative, descriptive, and cross-sectional, involving 21 administrative workers. The Maslach Burnout Inventory Questionnaire was used to measure burnout, and a Job Performance Questionnaire was applied. Descriptive and correlational analyses were conducted. Results showed that 10 % had high levels of burnout, 14 % medium, and 76 % low. The most affected dimensions were personal accomplishment and depersonalization. Job performance was mostly regular (90 %). A significant correlation was found between burnout and job performance (r=0,689, p=0,001). Burnout explained 41,7 % of the variability in performance. Conclusions: There is an inverse relationship between burnout syndrome and job performance in this group of workers. Preventive measures are recommended.

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Health Impact of Gnathostomiasis and its Integral Approach to Parasitic Infection: A Systematic Review

2024 , Gisnella María Cedeño Cajas , José Andrés Zaporta Ramos , Andrea Stefannia Flores Villacrés , Guevara Maldonado, César Byron

The present study focuses on gnathostomiasis, a parasitic disease caused by the nematode gnathostoma that affects both humans and other animals, with a prevalence of 0,14 %. The aim of the study is to analyze the main research related to gnathostomiasis, its diagnosis and treatment. To achieve this objective, a systematic review of clinical cases, observational and retrospective studies of the disease was carried out, following the PRISMA methodology. The literature search, conducted between 2018 and 2022 in the Web of Science, Scopus, PubMed, Redalyc and Dialnet databases, resulted in the identification of five articles relevant and pertinent to the topic. The study findings indicate that gnathostomiasis, on the rise in Latin America and Asia, is transmitted mainly through the consumption of raw fish infected with Gnathostoma larvae. Although preventive measures and treatments, such as albendazole, are available, their efficacy is limited, and it is difficult to implement changes in dietary habits. Therefore, more research is needed to better understand the disease, develop more effective diagnostics and treatments, and raise awareness among physicians of its increasing global prevalence.

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The triple helix model linked to knowledge transfer and economic progress from universities [El modelo de la triple hélice vinculado a la transferencia de conocimiento y progreso económico desde las universidades]

2023 , Bonilla-Jurado, Diego , Guevara Maldonado, César Byron , Montero I.K.S. , Pazmiño S.J.I. , Zuta M.E.C.

The strategic actions that are being developed from the Ecuadorian universities, are heading towards the linking of innovative factors under an interrelated scheme known as Triple Helix, whose intention is framed in connecting entrepreneurship, using knowledge and society as a platform, generating a model sustainable development between university-state-business. The objective of this research is to show the relationship between university-company-state with entrepreneurial research through the triple helix functional model, with a view to innovative potentializing that serves as a boost to socioeconomic progress. The research approach is qualitative at a descriptive level, using a hermeneutical review focused on entrepreneurship studies, business and university alliances, government plans and the triple helix theory. The results indicate that scientific research based on the triple helix method should be strengthened, the main obstacles being lack of communication, business disinterest and distorted state policies. The Ecuadorian universities UEM, UTB and UEB must make concerted efforts so that the investigations are directed towards the true social needs of each area. The conclusions indicate that the links of the triple helix model lead to socioeconomic strengthening through the development of research and scientific projects, without neglecting technological advances. © Este es un artículo en acceso abierto.

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