Cardiovascular disease (CVD) is a significant cause of morbidity and mortality across the world. A large proportion of CVD deaths are secondary to coronary artery disease (CAD) and myocardial infarction (MI). Even though prevention is the best strategy to reduce risk factors associated with MI, the use of cardioprotective interventions aimed at improving patient outcomes is of great interest. Opioid conditioning has been shown to be effective in reducing myocardial ischemia-reperfusion injury (IRI) and cardiomyocyte death. However, the molecular mechanisms behind these effects are under investigation and could provide the basis for the development of novel therapeutic approaches in the treatment of CVD. Non-coding RNAs (ncRNAs), which are functional RNA molecules that do not translate into proteins, are critical modulators of cardiac gene expression during heart development and disease. Moreover, ncRNAs such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are known to be induced by opioid receptor activation and regulate opioid signaling pathways. Recent advances in experimental and computational tools have accelerated the discovery and functional characterization of ncRNAs. In this study, we review the current understanding of the role of ncRNAs in opioid signaling and opioid-induced cardioprotection.

Severe acute respiratory syndrome coronavirus 2 has caused a global pandemic, leading to health, economic, and political crisis. The virus triggers the activation of inflammatory reactants including interleukin-6 (IL-6), ferritin, and C-reactive protein (CRP), causing multiorgan damage, particularly affecting the lungs. Tocilizumab, an IL-6 receptor blocker, has the potential to diminish the progression of the disease and reduce organ damage and long-term complications. The aim of this observational retrospective cohort study was to evaluate the efficacy of tocilizumab in decreasing CRP levels in hospitalized coronavirus disease 2019 (COVID-19) patients compared to standard care without the drug. The study included 141 patients during their Hospital Stay (HS), with 100 in the Tocilizumab group and 41 in the non-Tocilizumab group. Clinical information was collected from the electronic clinical record, analyzed using statistical software, and homogenized the CRP levels from the severe group to the levels of the less complicated group at 48 h of hospitalization. The results showed a statistically significant greater decrease in CRP levels in the Tocilizumab group at 48 h after the use of the treatment, with no differences in mortality or length of stay between the groups. In conclusion, tocilizumab accelerates the diminishing of CRP levels compared to standard treatment alone, and its use may have potential benefits in the management of severe COVID-19 patients when used alongside with follow-up quantification of CRP levels reduction. IMPORTANCE: Severe acute respiratory syndrome coronavirus 2 has caused a global pandemic, leading to health, economic, and political crises. International guidelines for managing coronavirus disease 2019 (COVID-19) give recommendations according to the severity of the disease and the level of oxygen therapy needed. Tocilizumab is an option for the therapeutic management of hospitalized patients with any level of oxygen therapy; IL-6 serum level is the parameter for the follow-up on the efficacy, but it is not available at many hospitals. In this study, we demonstrate that C-reactive protein determination can predict the response to tocilizumab in severe COVID-19, the target patients for treatment with this drug. The use of this affordable and extensively available biomarker supports clinical decisions for the early escalation of the therapy and for the rational use of this drug on those prone to improve with the use of it.

Objective: To describe the profile of multidrug-resistance (MDR), extensively resistance (XDR) and pandrug-resistance (PDR) to antibacterial drugs in isolates from patient samples from a third level private hospital in the North of Mexico. Method: A retrospective study was carried out during the period from February 2016 to April 2019. From 156 clinical samples of urine, wounds, blood, expectoration and other fluids, 166 bacteria were isolated; 10 samples included two microorganisms. Isolated microorganisms were classified into MDR, XDR or PDR. Results: 78% of the Gram negative and 69% of the Gram positive isolates showed MDR, XDR or PDR. Staphylococcus epidermidis was the most frequently isolated MDR Gram positive bacteria. Escherichia coli and Klebsiella sp. were among the most frequent MDR Gram negative. In two cases, the clinical isolates of Pseudomonas aeruginosa from the neonatal intensive care unit showed PDR. Conclusions: The intensive care, surgery and neonatal intensive care unit services deserve special attention due to the high proportion of MDR isolates and the presence of PDR due to P. aeruginosa.

Manual curation is a bottleneck in the processing of the vast amounts of knowledge present in the scientific literature in order to make such knowledge available in computational resources e.g., structured databases. Furthermore, the extraction of content is by necessity limited to the pre-defined concepts, features and relationships that conform to the model inherent in any knowledgebase. These pre-defined elements contrast with the rich knowledge that natural language is capable of conveying. Here we present a novel experiment of what we call “soft curation” supported by an ad-hoc tuned robust natural language processing development that quantifies semantic similarity across all sentences of a given corpus of literature. This underlying machine supports novel ways to navigate and read within individual papers as well as across papers of a corpus. As a first proof-of-principle experiment, we applied this approach to more than 100 collections of papers, selected from RegulonDB, that support knowledge of the regulation transcription initiation in E. coli K-12 , resulting in L-Regulon (L for “linguistic”) version 1.0. Furthermore, we have initiated the mapping of RegulonDB curated promoters, promoters, to their evidence sentence in the given publication. We believe this is the first step in a novel approach for users and curators, in order to increase the accessibility of knowledge in ways yet to be discovered.

Antecedentes: El bajo rendimiento académico es una problemática significativa en los programas de medicina que afecta la trayectoria formativa y el bienestar estudiantil. Este fenómeno resulta de la interacción compleja entre factores intrínsecos (cognitivos y emocionales) y extrínsecos (contextuales y sociales). Sin embargo, persiste una carencia de instrumentos validados que evalúen de forma integral esta multiplicidad de factores en el contexto mexicano. La identificación sistemática de estos factores es esencial para diseñar intervenciones preventivas efectivas. Objetivo: Diseñar y validar un instrumento para identificar y evaluar de manera sistemática los factores intrínsecos y extrínsecos que influyen en el rendimiento académico de estudiantes de medicina. Método: Estudio instrumental con diseño secuencial exploratorio. Fase I: revisión de literatura en seis bases de datos especializadas, 57 entrevistas exploratorias con análisis de discurso, y construcción preliminar. Fase II: dos pruebas piloto (n₁=33, n₂=45) y dos rondas de validación por juicio de expertos (5 y 6 expertos respectivamente). Se empleó el Coeficiente de Validez de Contenido (CVC) como medida de confiabilidad. Resultados: La primera ronda de validación de los 42 ítems preliminares obtuvo un CVC total de 0.902. Tras incorporar las recomendaciones de los expertos, la versión final de 38 ítems alcanzó un CVC total de 0.974, superando los criterios de aceptación internacionales. El instrumento final integra cuatro dimensiones que operacionalizan los factores clave: (1) características sociodemográficas y antecedentes académicos; (2) metacognición y autorregulación; (3) emociones académicas y red de apoyo; y (4) hábitos de sueño y salud mental. Conclusiones: Se generó un instrumento validado, confiable e integral que operacionaliza los factores intrínsecos (metacognición, regulación emocional, salud mental) y extrínsecos (antecedentes académicos, contexto socioeconómico, red de apoyo) determinantes del rendimiento académico en estudiantes de medicina mexicanos. Esta herramienta facilita la identificación temprana de riesgo académico y fundamenta el diseño de intervenciones educativas contextualizadas y personalizadas.