APLICACIÓN DE UN SENSOR TÉRMICO EN EL ESTUDIO DEL COMPORTAMIENTO DE UN ROEDOR (APPLICATION OF A THERMAL SENSOR IN THE STUDY OF THE BEHAVIOR OF A RODENT)

ResumenEl siguiente trabajo muestra el estudio del comportamiento de un roedor durante una prueba de reconocimiento de objetos, utilizando una cámara térmica. Esta prueba es utilizada para valorar los efectos que tienen los fármacos sobre la memoria y el aprendizaje de dichos animales. La mayoría de pruebas en roedores son observadas y video grabadas para su estudio posterior, esto genera un factor de error humano que interfiere con los resultados de las pruebas. En el mercado existen dispositivos especializados en procesamiento de imagen usados en dichas pruebas, pero llegan a ser poco accesibles por sus altos costos. Consecuentemente, proponemos el análisis del comportamiento de un roedor mediante hardware y software con capacidad suficiente para procesar los datos obtenidos. Utilizando una Raspberry Pi Zero W es posible capturar videos de los experimentos en el rango infrarrojo. Finalmente, al aplicar un algoritmo de procesamiento digital de imágenes implementado en MATLAB, se obtiene un gráfico de la ubicación del roedor en el tiempo, a su vez, podemos conocer las zonas que el roedor frecuentó más, logrando visualizar de manera rápida los resultados, minimizando el tiempo que tarda un humano en analizar los mismos datos.Palabra(s) Clave: Cámara térmica, Comportamiento roedor, Procesamiento de imágenes, Raspberry Pi. AbstractThe present work shows behavior study of rodent during object recognition test using thermal camera. This test is capable to measure drugs effects on memory and learning in these animals. The most of rodent`s tests are video recorded to examination. This induce human error factor that it interferes with results. There are specialized image processing devices in market for such tests, but they become inaccessible due to their high cost. Therefore, we propose to do rodent behavior analysis through hardware and software with sufficient capacity to process data acquired. With Raspberry Pi Zero W, infrared videos of each experiment can be recorded. Last, applying digital image processing algorithm in MATLAB, we obtain rodent location graph in time, making it possible to visualize a quick analysis, reducing the time taken to interpret all data by a human.Keywords: Image processing, Raspberry Pi, Rodent behavior, Thermal camera

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality