Martha Palacios Macias

En este estudio se identificaron y analizaron las enfermedades más comunes en caninos que acudieron a la Clínica Veterinaria Burgos en Guayaquil durante octubre y noviembre de 2024. El objetivo principal fue determinar la prevalencia de las patologías más frecuentes que afectan a la población canina. Se realizó una investigación observacional, descriptiva y no experimental de tipo transversal, con un enfoque cuantitativo en 35 caninos de diferentes edades, sexos y razas que presentaron signos y síntomas de enfermedad. Se utilizaron herramientas diagnósticas como exámenes serológicos, hemogramas, pruebas bioquímicas, ecografía y radiografías. Los resultados mostraron que el 54,28% (19/35) de los caninos presentaron enfermedades infecciosas con diagnóstico definitivo, mientras que el 5,71% (2/35) presentaron enfermedades metabólicas. La identificación y análisis de estas patologías permiten llevar un registro actualizado y evaluar su prevalencia. Los hallazgos de este estudio son de utilidad práctica tanto para veterinarios, al facilitar la toma de decisiones clínicas y la implementación de protocolos de prevención, como para los propietarios, quienes podrán reforzar el cuidado y la detección temprana de signos de enfermedad en sus mascotas. Referencias 1. Bonilla-Aldana, D. K., Gutiérrez-Grajales, E. J., Martínez-Arboleda, J. P., Reina-Mora, M. A., Trejos-Mendoza, A. E., Pérez-Vargas, S., Valencia-Mejía, L., Marín-Arboleda, L. F., Osorio-Navia, D., Chacón-Peña, M., González-Colonia, L. V., Cardona-Ospina, J. A., Jiménez-Posada, E. V., Díaz, A., Salazar, J. C., Sierra, M., Muñoz-Lara, F., Zambrano, L. I., Ramírez-Vallejo, E., & Rodríguez-Morales, A. J. (2022). Seroprevalence canine survey for selected vector-borne pathogens and its relationship with poverty in metropolitan Pereira, Colombia, 2020. Parasite Epidemiology and Control, 17, e00249. https://doi.org/10.1016/j.parepi.2022.e00249 2. Cuesta,K.(2024). Obesidad en caninos y felinos de Guayaquil como consecuencia del tipo de alimento. [Trabajo de titulación, Universidad de Guayaquil]. Repositorio Institucional Universidad de Guayaquil.https://repositorio.ug.edu.ec/handle/XXXX 3. Duque-García, Y., Echeverri-Zuluaga, M., Trejos-Suarez, J., & Ruiz-Sáenz, J. (2019). Prevalece and molecular epidemiology of Canine parvovirus 2 in diarrheic dogs in Colombia, South America: A possible new CPV-2a is emerging? Veterinary Microbiology, 2001, 56–61. https://doi.org/10.1016/j.vetmic.2016.12.039 4. Escobar, K., & Martínez, J. (diciembre del 2022). Títulos de anticuerpos IgG protectores obtenidos a través de vacunación o infección no protegen contra parvovirus canino tipo 2C en perros”, Revista de investigación veterinaria de Perú, 33(6). doi: http://dx.doi.org/10.15381/rivep. v33i6.22327 5. Gallego, M. M., Triana-Chávez, O., Mejía-Jaramillo, A. M., & Jaimes-Dueñez, J. (2023). Molecular characterization of Ehrlichia canis and Babesia vogeli reveals multiple genogroups associated with clinical traits in dogs from urban areas of Colombia. Ticks and Tick-Borne Diseases, 14(2). https://doi.org/10.1016/j.ttbdis.2022.102111 6. Gómez-Flores, A. I., Chávez-López, J. J., & Villatoro-Chacón, D. M. (2021). Characterization of neurological diseases in canines: University of San Carlos of Guatemala, year 2017. Revista MVZ Córdoba, 26(3), 1–6. https://doi.org/10.21897/rmvz.2047 7. Kelman, M., Barrs, V. R., Norris, J. M., & Ward, M. P. (2020). Socioeconomic, geographic and climatic risk factors for canine parvovirus infection and euthanasia in Australia. Preventive Veterinary Medicine, 174. https://doi.org/10.1016/j.prevetmed.2019.104816 8. Mia, Md. M., & Hasan, M. (2021). Update on Canine Parvovirus Infection: A Review from the Literature. Veterinary Sciences: Research and Reviews, 7(1). https://doi.org/10.17582/journal.vsrr/2021.7.2.92.100 9. Pawlak, A., Brunetto, M. A., Br, M., Marchi, P. H., Vendramini, T. H. A., Perini, M. P., Zafalon, R. V. A., Amaral, A. R., Ochamotto, V. A., Da Silveira, J. C., & Dagli, M. L. Z. (2022). Obesity, inflammation, and cáncer in dogs: Review and perspectives. 10. Qi, S., Zhao, J., Guo, D., & Sun, D. (2020). A Mini-Review on the Epidemiology of Canine Parvovirus in China. In Frontiers in Veterinary Science (Vol. 7). Frontiers Media S.A. https://doi.org/10.3389/fvets.2020.00005 11. Sato, M., Veir, J. K., Shropshire, S. B., & Lappin, M. R. (2020). Ehrlichia canis in dogs experimentally infected, treated, and then immune suppressed during the acute or subclinical phases. Journal of Veterinary Internal Medicine, 34(3), 1214–1221. https://doi.org/10.1111/jvim.15750 12. Sukara, R., Andrić, N., Andrić, J. F., Mihaljica, D., Veinović, G., Ranković, V., & Tomanović, S. (2023). Autochthonous infection with Ehrlichia Canis and Hepatozoon Canis in dogs from Serbia. Veterinary Medicine and Science, 9(1), 111–118. https://doi.org/10.1002/vms3.1061 13. Taques, I. I. G. G., Campos, A. N. S., Kavasaki, M. L., de Almeida, S. L. H., & de Aguiar, D. M. (2020). Geographic distribution of Ehrlichia canis trp genotypes in brazil. Veterinary Sciences, 7(4), 1–8. https://doi.org/10.3390/vetsci7040165 14. Tion, M. T., Zon, G. A., Fotina, H. A., Ogbu, K. I., Nguetyo, S. A., Amine, A. A., Oochi, J. O., Ojeamiren, M. T., & Saganuwan, A. A. (2019). Epizootiology of Infectious Diseases of Dog in Some States in Nigeria (2015–2018). Scientific Messenger of LNU of Veterinary Medicine and Biotechnologies, 21(96), 33–40. https://doi.org/10.32718/nvlvet9606 15. Uddin, M. M., Talukder, H., Islam, O., Asaduzzaman, M., Das, M., Ahsan, M. I., & Islam, S. (2021). Magnitudes of diseases in dogs vary among different...

Early warning systems rely on statistical prediction models, with environmental risks and remote sensing data serving as essential sources of information for their development. The present work is focused on the use of remote sensing for the estimation of transmission risk and the prediction of malaria cases in northwest Argentina. This study was conducted in the city of San Ramón de la Nueva Orán, where cases of the disease have been reported from 1986 to 2005. The relationship between reported malaria cases and climatic/environmental variables—including the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and land surface temperature (LST)—obtained from Landsat 5 and 7 satellite images was analyzed using multilevel Poisson regression analyses. An increased abundance of reported malaria cases was observed in summer. An ARIMA (autoregressive integrated moving average) temporal series model incorporating environmental variables was developed to forecast malaria cases in the year 2000. The analysis of the relationship between malaria cases and environmental and climatic factors showed that malaria cases were associated with increases in LST and mean temperature and a decrease in the NDVI. Early warning systems that provide information about spatial and temporal predictions of epidemics could help to control and prevent malaria outbreaks. Based on these findings, this study is expected to support the development of future prevention and control measures by health officials.

The early warning systems based on statistical prediction models; indicators of environmental risks and remote sensing constitute an essential source of environmental information for the development of these warning systems. The present work is focused on the use of remote sensing for the estimation of the risk of transmission and the prediction of malaria cases in the northwest of Argentina. The study was carried out in the city of San Ramón de la Nueva Orán, where cases of the disease have been reported from 1986 to 2005. The existent relationship between reported malaria cases and climatic/environmental variables (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Land Surface Temperature (LST)), obtained from Landsat 5 and 7 satellite images, was analyzed through multilevel Poisson regression analyses. A higher abundance of reported cases of malaria in summer was observed. A model of ARIMA (Autoregressive Integrated Moving Average) temporal series, which included the environmental variables, was generated to forecast malaria cases in the year 2000. In turn, the relationship between malaria cases and environmental/climatic factors showed that malaria cases were associated with an increase in LST and mean temperature and a decrease in NDVI. Early warning systems provide information about spatial and temporal predictions of epidemics might help control and prevent malaria outbreaks. Based on the results, this study is expected to be used for the development of future prevention and control actions by the health officials.