A Comprehensive Analysis of the Effect of A>I(G) RNA-Editing Sites on Genotoxic Drug Response and Progression in Breast Cancer.

Dysregulated A>I(G) RNA editing, which is mainly catalyzed by ADAR1 and is a type of post-transcriptional modification, has been linked to cancer. A low response to therapy in breast cancer (BC) is a significant contributor to mortality. However, it remains unclear if there is an association between A>I(G) RNA-edited sites and sensitivity to genotoxic drugs. To address this issue, we employed a stringent bioinformatics approach to identify differentially RNA-edited sites (DESs) associated with low or high sensitivity (FDR 0.1, log2 fold change 2.5) according to the IC50 of PARP inhibitors, anthracyclines, and alkylating agents using WGS/RNA-seq data in BC cell lines. We then validated these findings in patients with basal subtype BC. These DESs are mainly located in non-coding regions, but a lesser proportion in coding regions showed predicted deleterious consequences. Notably, some of these DESs are previously reported as oncogenic variants, and in genes related to DNA damage repair, drug metabolism, gene regulation, the cell cycle, and immune response. In patients with BC, we uncovered DESs predominantly in immune response genes, and a subset with a significant association (log-rank test p < 0.05) between RNA editing level in LSR, SMPDL3B, HTRA4, and LL22NC03-80A10.6 genes, and progression-free survival. Our findings provide a landscape of RNA-edited sites that may be involved in drug response mechanisms, highlighting the value of A>I(G) RNA editing in clinical outcomes for BC.

Chronic Stress and Ovulatory Dysfunction: Implications in Times of COVID-19.

Stress is known to be associated with adverse health outcomes. The COVID-19 pandemic and its associated lockdowns are examples of chronic stressors. Lockdown measures inadvertently caused significant psychological distress and became a powerful source of anxiety/stress, sleep disturbances, nutritional changes and weight gain. Stress is known to impact women's health specifically, through hypothalamic-pituitary-gonadal (HPG) axis dysfunction and resultant ovulatory dysfunction. Such dysfunction may manifest in menstrual irregularities and/or infertility due to hypothalamic hypogonadism. Here, we review the key physiological mediators of stress and associated ovulatory dysfunction. The kisspeptinergic system is comprised of sets of neurons located in the hypothalamus, the rostral periventricular region of the third ventricle (RP3V) and the arcuate nucleus (ARC). This system links nutrition, reproductive signals and stress. It plays a key role in the function of the HPG axis. During chronic stress, the kisspeptinergic system affects the HPG axis, GnRH pulsatility, and, therefore, ovulation. Leptin, insulin and corticotrophin-releasing hormone (CRH) are thought to be additional key modulators in the behavioral responses to chronic stress and may contribute to stress-related ovulatory dysfunction. This mini-review also summarizes and appraises the available evidence on the negative impact of chronic stress as a result of the COVID-19 pandemic lockdowns. It proposes physiological mechanisms to explain the observed effects on women's reproductive health and well-being. The review suggests areas for future research.

Usando la curva de tolerancia a la glucosa para calcular el porcentaje relativo de sensibilidad insulínica y el porcentaje relativo de función beta insular / A program to estimate insulin resistance based on data from the oral glucose tolerance test

Background An instrument to help clinicians to evaluate the oral glucose tolerance test (OGTT) at-a-glance is lacking. Aim To generate a program written in HTML squeezing relevant information from the OGTT with glucose and insulin measurements. Material and Methods We reanalyzed a database comprising 90 subjects. All of them had both an OGTT and a pancreatic suppression test (PST) measuring insulin resistance directly. Thirty-seven of the 90 studied participants were insulin resistant (IR). Receiver operating characteristic (ROC) curves and Bayesian analyses delineated the diagnostic performances of four predictors of insulin resistance: HOMA, QUICKI, ISI-OL (Matsuda-DeFronzo) and I0*G60. We validated a new biochemical predictor, the Percentual Relative Insulin Sensitivity (%RIS), and calculated the Percentual Relative Beta Cell Function (%RBCF). Results The best diagnostic performance of the five predictors were those of the I0*G60 and the %RIS. The poorest diagnostic performances were those of the HOMA and QUICKI. The ISI-OL's performance was in between. The %RIS of participants with and without IR was 44.4 ± 7.3 and 101.1 ± 8.8, respectively (p < 0.05). The figures for % RBCF were 55.8 ± 11.8 and 90.8 ± 11.6, respectively (p < 0.05). Mathematical modeling of the relationship between these predictors and the Steady State Plasma Glucose Value from the PST was performed. We developed a program with 10 inputs (glucose and insulin values) and several outputs: I0*G60, HOMA, QUICKI, ISI-OL, Insulinogenic Index, Disposition Index, %RBCF, %RIS, and metabolic categorization of the OGTT (ADA 2003). Conclusions The OGTT data permitted us to write successfully an HTML program allowing the user to fully evaluate at-a-glance its metabolic information.

Learning histology through game-based learning supported by mobile technology / Enseñanza de la histología a través del aprendizaje basado en juegos apoyado por tecnología móvil

Digital game-based learning and the testing effect have been shown to be effective in improving learning. The use of screens offers the opportunity to test innovative learning strategies in the classroom. Here, we report the impact of implementing a game and testing effect-based learning tool in a histology course. Seventy nine students participated in the study (mean age 19.5 years, 65 % female). The students observed a slide-based class and then participated in a game, answering questions about key concepts, using their smartphones. Two surveys, asking about aspects related to perceptions/motivations and use of mobile technologies, were applied. The game allowed for immediate feedback, revealing student performance in every evaluated concept, and allowed teachers to give corrections after detecting conceptual mistakes. Students perceived the methodology as fun, interesting, interactive and attractive. Moreover, 96 % of students participated and enjoyed the game and, among them, all agreed to use the methodology again. In parallel, about 87 % of students use mobile technology to study and 97 % to find academic information, frequently. The results indicate that the vast majority of students use mobile technology to study and positively perceive the game-based strategy. Strategies allowing fast feedback and dynamic relationships in the classroom could potentially improve significant learning on concept acquisition.

El aprendizaje basado en juegos digitales y pruebas han demostrado ser efectivos en el mejoramiento del aprendizaje. El uso de pantallas ofrece la oportunidad de probar estrategias de aprendizaje innovadoras en el aula. En este estudio se presenta el impacto de la implementación de una herramienta de aprendizaje basada en juegos y pruebas aplicadas en un curso de histología. Setenta y nueve estudiantes participaron en el estudio (edad promedio 19,5 años, 65 % mujeres). Los estudiantes observaron una clase basada en diapositivas y luego participaron en un juego, respondiendo preguntas sobre conceptos clave, utilizando sus teléfonos inteligentes. Se aplicaron dos encuestas, preguntando sobre aspectos relacionados con las percepciones/motivaciones y el uso de tecnologías móviles. El juego permitió una retroalimentación inmediata, revelando el desempeño de los estudiantes en cada concepto evaluado, y permitió a los profesores dar correcciones cuando se detectaron errores conceptuales. Los estudiantes percibieron la metodología como divertida, interesante, interactiva y atractiva. Además, el 96 % de los estudiantes participaron y disfrutaron del juego y, de ellos, todos relataron la intención de utilizar la metodología nuevamente. En paralelo, 87 % de los estudiantes utilizan la tecnología móvil para estudiar y el 97 % para encontrar información académica, frecuentemente. Los resultados indican que la gran mayoría de los estudiantes usa tecnología móvil para estudiar y perciben positivamente la estrategia basada en juego. Las estrategias que permiten retroalimentación rápida y relaciones dinámicas en el aula podrían potencialmente mejorar el aprendizaje significativo en la adquisición de conceptos.

A Simple and Improved Predictor of Insulin Resistance Extracted From the Oral Glucose Tolerance Test: The I0*G60.

OBJECTIVE: To evaluate the diagnostic performance of several biochemical predictors of insulin resistance (IR). DESIGN: A total of 90 nondiabetic subjects were tested with both the pancreatic suppression test (PST) and the oral glucose tolerance test (OGTT). Of them, 53 were non-insulin-resistant (NIR) subjects and the remaining 37 were insulin resistant subjects. RESULTS: All glucose and insulin values from the OGTT were positively correlated with the steady-state plasma glucose (SSPG) value of the PST. Among the OGTT values, basal insulin (I0) displayed a stronger correlation with SSPG (r = 0.604). Receiver operating characteristic analysis of the OGTT data demonstrated that I0 exhibited the highest area under the receiver operating characteristic curve (AUROC), compared with the rest of the OGTT data. However, the reduced sensitivity of this predictor precluded its clinical use.We then tested six potential predictors of IR derived from the OGTT values. Of them, the I0*G60 had a correlation coefficient of 0.697 with the SSPG and an AUROC of 0.867, surpassing the respective values of the traditional biochemical predictors of IR. Its cutoff predicting IR was >1110 mg/dL*µΙU/mL (>428 nM*pM), its sensitivity was 0.865, and its global accuracy was 0.822. We then selected the six best biochemical predictors of IR according to their posttest probability ratio. The order was as follows: I0*G60, ISI composite, AUC-Gl*In/', quantitative insulin sensitivity check index, homeostatic model assessment 1 (HOMA1), and HOMA2. CONCLUSION: We conclude that the I0*G60 is a promising, inexpensive, and easily calculable predictor of IR that outperforms the predictive power of the traditional predictors of IR, including the insulin sensitivity index composite.