In this work, an exhaustive revision is given of the literature associated with advanced information and communication technologies in agriculture within a window of 25 years using bibliometric tools enabled to detect of the main actors, structure, and dynamics in the scientific papers. The main findings are a trend of growth in the dynamics of publications associated with advanced information and communication technologies in agriculture productivity. Another assertion is that countries, like the USA, China, and Brazil, stand out in many publications due to allocating more resources to research, development, and agricultural productivity. In addition, the collaboration networks between countries are frequently in regions with closer cultural and idiomatic ties; additionally, terms’ occurrence are obtained with Louvain algorithm predominating four clusters: precision agriculture, smart agriculture, remote sensing, and climate smart agriculture. Finally, the thematic-map characterization with Callon’s density and centrality is applied in three periods. The first period of thematic analysis shows a transition in detecting the variability of a nutrient, such as nitrogen, through the help of immature georeferenced techniques, towards greater remote sensing involvement. In the transition from the second to the third stage, the maturation of technologies, such as unmanned aerial vehicles, wireless sensor networks, and the machine learning area, is observed.

In this work, we present a diagnostic analysis of strengths, weaknesses, opportunities and threats (SWOT) of the current state of Bioinformatics in Mexico. We conducted semi-structured interviews among researchers and academics with key expertise in this field, identified by bibliometric analyses and qualitative sampling techniques. Additionally, an online survey was conducted reaching a higher number of respondents. Among the relevant findings of our study, the lack of specialized human resources and technological infrastructure stood out, along with deficiencies in the number and quality of academic programs, scarce public investment and a weak relationship between public and private institutions. However, there are great opportunities for developing a national Bioinformatics to support different economic sectors. In our opinion, this work could be useful to favor a comprehensive network among Mexican researchers, in order to lay the foundations of a national strategy towards a well designed public policy.

Bioinformatics is a very important informatics tool for health and biological sciences, focusing on biological data management. The objective of this work was to perform a bibliometric analysis regarding the development of Mexican bioinformatics. An exhaustive revision of the literature associated with Mexican bioinformatics in a period of 25-years was performed. Bibliometric tools, such as performance analysis and science mapping were included in the analysis. We identified the main actors as well as the structure and dynamics of Mexican bioinformatics. Some of the main findings were as follows: the thematic structure in the field is defined by the research lines of outstanding authors; the outstanding collaborations of Mexican institutions with foreign countries and institutions are influenced by the geographic proximity and binational agreements, as well as philanthropic and academic programs that promote collaborations, and there is an inclination for health issues promoted by public health financing and philanthropic organizations. It is identified that publications had an explosion since 2012, we consider that this growth may be influenced by the democratization of data, derived from the mass sequencing of biological molecules stored in public databases.

Introduction: Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic disease in childhood, affecting one to four of every 1000 children worldwide. It is characterized by joint inflammation lasting more than six weeks in children under 16 years. The aim of this study was to estimate the frequency of JIA subtypes in the Mexican patient population; compare clinical, immunological and inflammation markers by JIA subtype; and examine the correlation between these variables. Methods: We conducted a cross-sectional study of 50 patients with JIA (2–15 years). We estimated the frequency of each JIA subtype, assessed and compared the immunological characteristics (RF, ANA and anti-CCP) by JIA subtype at the time of diagnosis using Kruskal–Wallis or chi-square tests, and calculated Spearman correlation coefficients between the assessments. Results: Our analysis included 50 patients, 29 (58%) girls and 21 (42%) boys, aged at the time of diagnosis 10.56 ± 3.99 years. The frequencies of JIA subtypes were RF-seropositive polyarthritis (34%), RF-seronegative polyarthritis (28%), systemic arthritis (16%), oligoarthritis (14%) and arthritis-related enthesitis (8%). We found a significant association between sex and JIA subtype (p = 0.014). There was a significant difference in anti-CCP levels by JIA subtype (p < 0.001). We also detected positive correlations between RF and anti-CCP (r = 0.63, p < 0.001) and between age and anti-CCP (r = 0.29, p = 0.041). Conclusions: Our study suggests that the frequency of the polyarticular subtypes of JIA is higher in Mexican children compared to other populations. Our findings highlight the importance of considering the presence of anti-CCP and RF as important criteria when deciding on treatment for JIA patients as elevated levels of these antibodies may indicate early forms of adult rheumatoid arthritis.

In this study, a web application was developed that comprises scientific literature associated with the Coronaviridae family, specifically for those viruses that are members of the Genus Betacoronavirus, responsible for emerging diseases with a great impact on human health: Middle East Respiratory Syndrome-Related Coronavirus (MERS-CoV) and Severe Acute Respiratory Syndrome-Related Coronavirus (SARS-CoV, SARS-CoV-2). The information compiled on this webserver aims to understand the basics of these viruses’ infection, and the nature of their pathogenesis, enabling the identification of molecular and cellular components that may function as potential targets on the design and development of successful treatments for the diseases associated with the Coronaviridae family. Some of the web application’s primary functions are searching for keywords within the scientific literature, natural language processing for the extraction of genes and words, the generation and visualization of gene networks associated with viral diseases derived from the analysis of latent semantic space, and cosine similarity measures. Interestingly, our gene association analysis reveals drug targets in understudies, and new targets suggested in the scientific literature to treat coronavirus.

Purpose: To describe the pre- and post-admission characteristics of hospitalized patients in a tertiary care hospital and to adjust machine learning models capable of predicting and identifying the factors that are associated with and have a greater prognostic value for in-hospital death.Materials and methods: This was a retrospective study based on data from patients who were discharged from a Mexican tertiary care hospital during its first 10 years of operation (2007–2016). Preadmission characteristics were analyzed using descriptive statistics. Comparison tests (Mann–Whitney U) and association tests (chi-square) were applied according to the absence or presence of in-hospital death. Multivariate models (logistic regression, random forest and XGBoost) were fitted. Their ROC curves were compared using the DeLong test, and performance metrics were evaluated.Results: In total, 55,253 hospital discharges were considered, only 45,011 (0–101 years) had complete data, and the rate of in-hospital death was 4.17%. In total, 70% of the data were used for training and 30% for testing. Two-to-two comparisons between areas under the curve (AUCs) revealed that XGBoost (AUC = 0.9162) outperformed logistic regression (AUC = 0.9036) and random forest (AUC = 0.8978) (p-value < 0.001 in both cases). XGBoost had a sensitivity of 87%, specificity of 81.3% and balanced efficiency of 84.2%. The most relevant predictive factors were medical service that performed the admission, number of conditions, origin of the outpatient consultation of the hospital, and the main condition diagnosed at admission according to the ICD-10, age, month of admission, and day of the week of admission.Conclusions: Owing to its ability to capture complex patterns, the XGBoost model makes it possible to identify patients with a relatively high risk of in-hospital death using the data available at hospital admission. This constitutes a support tool for decision-making, helping to determine which patients require closer monitoring and follow-up during their hospital stay to improve the quality of medical care.