Conteo de bacterias y levaduras en imágenes digitales

RESUMEN Fundamento: en los laboratorios de microbiología, la identificación y conteo de microorganismos es un procedimiento habitual. Aunque existen en el mercado equipos que posibilitan su realización de manera automática o semiautomática, son muy costosos, por lo cual esta tarea, difícil e irritante para los ojos, la siguen realizando los expertos de manera tradicional mediante la observación de las muestras en los microscopios, con la consiguiente variabilidad entre ellos. Objetivo: proponer un nuevo método para el conteo de bacterias y levaduras en imágenes digitales, bajo diferentes magnificaciones, tomadas a bioproductos de origen microbiano obtenidos por fermentación. Métodos: el sensor empleado para la toma de imágenes de las muestras fue una cámara digital modelo HDCE-X, con un sensor CMOS de ½", con una resolución de 2592 píxeles por 1944 píxeles (5 Mp). Se emplearon dos tipos de magnificaciones: magnificación 40x (PL40, 0.65 apertura numérica and 0.17 de distancia de trabajo) y magnificación 100x (HI plan 100/1.25 con inmersión de aceite). El método propuesto se basa en técnicas de procesamiento digital de imágenes, utilizando herramientas como la detección de contornos, operaciones morfológicas y análisis estadístico, y fue desarrollado en lenguaje Python con empleo de la biblioteca OpenCV. Resultados: la detección y conteo de bacterias se logró con una exactitud y precisión aceptable, en ambos casos por encima de 0,95; no en el caso de las levaduras cuya exactitud y precisión fueron menores, alrededor de 0,78 y 0,86 respectivamente. Se proponen flujos de trabajo basados en técnicas de procesamiento digital de imágenes, fundamentalmente en detección de contornos, operaciones morfológicas y análisis estadístico. Conclusiones: el método posee una efectividad aceptable para el contexto y depende de las características que presenten las imágenes.

ABSTRACT Background: In microbiology laboratories, the identification and counting of microorganisms is a common procedure; and although there is a variety of equipment on the market that possibility to carry out these processes automatically or semi-automatically, it is usually expensive to many laboratories. These are some of the reasons why this arduous and difficult task is still performed in many laboratories by experts in the traditional way, through the observation of samples in microscope, consuming a great time and having variations in the results between experts. Objective: The present work aims to propose a new method for counting bacteria and yeasts in digital images, taken under different magnifications, of microbial bioproducts obtained by fermentation. Methods: The sensor used to take images of the samples was a digital camera model HDCE-X, with a ½" CMOS sensor, with a resolution of 2592 pixels by 1944 pixels (5 Mp). Two types of magnifications were used: 40x magnification (PL40, 0.65 numerical aperture and 0.17 working distance) and 100x magnification (HI plan 100/1.25 with oil immersion). The proposed method is based on digital image processing technics, using tools as contour detection, morphological operations and statistical analysis, and was developed in Python language using the OpenCV library. The work also presents a comparison with the results obtained using ImageJ software for the same purpose. Results: the detection and count of bacteria was achieved with an acceptable accuracy and precision, in both cases above 0.95; not in the case of yeasts whose accuracy and precision was lower, around 0.78 for accuracy and 0.86 for precision. Workflows based on digital image processing techniques are proposed, using tools as contour detection, morphological operations and statistical analysis. Conclusions: the method has an acceptable effectiveness for the context and depends on the characteristics presented by the images.

Protein compounds of Bacillus subtilis with in vitro antifungal activity against Pseudocercospora fijiensis (Morelet).

The metabolites of Bacillus subtilis CCIBP-M27 were evaluated as an antagonist of Pseudocercospora fijiensis. The culture filtrate did not inhibit ascospore germination but significantly reduced conidial germination and mycelial growth. Through microscopic analysis, deformations were observed as vacuolization and swelling in P. fijiensis mycelia when exposed to culture filtrate during 48 h. A similar response was induced by peptide-type compounds found on Bacillus subtilis CCIBP-M27 culture filtrate. The results obtained suggest that the in vitro antifungal effect of the strain CCIBP-M27 against P. fijiensis is related to the action of diffused metabolites such as proteins or peptide substances.

Effect of Bacillus pumilus CCIBP-C5 on Musa-Pseudocercospora fijiensis interaction.

The effect of antifungal activity of culture filtrate (CF) of Bacillus pumilus strain CCIBP-C5, an isolate from a phyllosphere of banana (Musa) leaves, was determined on Pseudocercospora fijiensis challenged banana plants. The CF was shown to decrease the fungal biomass and induce changes in banana plant. In this sense, at 70 days post inoculation (dpi), a lower infection index as well as a decrease in fungal biomass after 6 dpi was obtained in treated plants with respect to control ones. At the same time, changes in the activities of several enzymes related to plant defense responses, such as phenylalanine ammonia lyase, chitinases, ß-1,3-glucanases and peroxidases were observed. These results indicate that B. pumilus CCIBP-C5 has a potential role for biological control of P. fijiensis possibly due to the production of antifungal metabolites.