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dc.contributor.author Arteaga-Troncoso, Gabriel
dc.contributor.author Luna-Alvarez, Miguel
dc.contributor.author Hernández-Andrade, Laura
dc.contributor.author Jiménez-Estrada, Juan Manuel
dc.contributor.author Sánchez-Cordero, Victor
dc.contributor.author Montes de Oca-Jiménez, Roberto
dc.contributor.author Guerra-Infante, Fernando M
dc.contributor.author López-Hurtado, Marcela
dc.date.accessioned 2023-10-12T23:55:18Z
dc.date.available 2023-10-12T23:55:18Z
dc.date.issued 2023-09-18
dc.identifier.issn 2076-2615
dc.identifier.uri http://hdl.handle.net/20.500.11799/138986
dc.description Using data generated from an epidemiological survey and from the lab, the abortion burden of multiple microorganisms in sheep was predicted according to the artificial neural network approach and Gen eralized Linear Model (GLM) in a geographic area of the Mexican highlands. The results showed that the best GLM is integrated by the serological detection of Leptospira interrogans serovar Hardjo and Brucella ovis in animals on the slopes with elevation between 2600 and 2800 masl in the municipality of Xalatlaco. The sheep pen built with materials of metal grids and untreated wood, dirt and concrete floors, bed of straw, and the well water supply were also remained independently associated with infectious abortion. We suggest that sensitizing stakeholders on good agricultural practices could improve public health surveillance. es
dc.description.abstract Unidentified abortion, of which leptospirosis, brucellosis, and ovine enzootic abortion are important factors, is the main cause of disease spread between animals and humans in all agricultural systems in most developing countries. Although there are well-defined risk factors for these diseases, these characteristics do not represent the prevalence of the disease in different regions. This study predicts the unidentified abortion burden from multi-microorganisms in ewes based on an artificial neural networks approach and the GLM. Methods: A two-stage cluster survey design was conducted to estimate the seroprevalence of abortifacient microorganisms and to identify putative factors of infectious abortion. Results: The overall seroprevalence of Brucella was 70.7%, while Leptospira spp. was 55.2%, C. abortus was 21.9%, and B. ovis was 7.4%. Serological detection with four abortion causing microorganisms was determined only in 0.87% of sheep sampled. The best GLM is integrated via serological detection of serovar Hardjo and Brucella ovis in animals of the slopes with elevation between 2600 and 2800 meters above sea level from the municipality of Xalatlaco. Other covariates included in the GLM, such as the sheep pen built with materials of metal grids and untreated wood, dirt and concrete floors, bed of straw, and the well water supply were also remained independently associated with infectious abortion. Approximately 80% of those respondents did not wear gloves or masks to prevent the transmission of the abortifacient zoonotic microorganisms. Conclusions: Sensitizing stakeholders on good agricultural practices could improve public health surveillance. Further studies on the effect of animal–human transmission in such a setting is worthwhile to further support the One Health initiative. es
dc.description.sponsorship Department of Cellular Biology and Development, Instituto Nacional de Perinatología, Military School of Health Officers, University of the Mexican Army and Air Force, SEDENA, Laboratory of Leptospirosis, National Centre for Disciplinary Research in Animal Health, and Food Safety (CENID-SAI, INIFAP), Laboratory of Bacteriology, National Centre for Disciplinary Research in Animal Health, and Food Safety (CENID-SAI, INIFAP), Ciudad de Mexico 05110, Mexico; Laboratory of Molecular Biology, Public Health Laboratory of State of Mexico, ISEM, Department of Zoology and National Pavilion of Biodiversity, Institute of Biology, National Autonomous University of Mexico, Faculty of Veterinary Medicine, Universidad Autónoma del Estado de Mexico, UAEM, Department of Infectology and Immunology, Instituto Nacional de Perinatología, Department of Veterinary Microbiology, Escuela Superior de Ciencias Biológicas, IPN. es
dc.language.iso eng es
dc.publisher animals es
dc.rights openAccess es
dc.rights.uri http://creativecommons.org/licenses/by/4.0 es
dc.subject machine learning; Leptospira spp.; smooth Brucella spp.; Brucella ovis; Chlamydia abortus; zoonoses es
dc.subject.classification CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA es
dc.title Modelling the Unidentified Abortion Burden from Four Infectious Pathogenic Microorganisms (Leptospira interrogans, Brucella abortus, Brucella ovis, and Chlamydia abortus) in Ewes Based on Artificial Neural Networks Approach: The Epidemiological Basis for a Control Policy es
dc.type Artículo es
dc.provenance Científica es
dc.road Verde es
dc.organismo Medicina Veterinaria y Zootecnia es
dc.ambito Internacional es
dc.cve.CenCos 21401 es
dc.relation.vol 13
dc.relation.doi https://doi.org/10.3390/ani13182955


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  • Título
  • Modelling the Unidentified Abortion Burden from Four Infectious Pathogenic Microorganisms (Leptospira interrogans, Brucella abortus, Brucella ovis, and Chlamydia abortus) in Ewes Based on Artificial Neural Networks Approach: The Epidemiological Basis for a Control Policy
  • Autor
  • Arteaga-Troncoso, Gabriel
  • Luna-Alvarez, Miguel
  • Hernández-Andrade, Laura
  • Jiménez-Estrada, Juan Manuel
  • Sánchez-Cordero, Victor
  • Montes de Oca-Jiménez, Roberto
  • Guerra-Infante, Fernando M
  • López-Hurtado, Marcela
  • Fecha de publicación
  • 2023-09-18
  • Editor
  • animals
  • Tipo de documento
  • Artículo
  • Palabras clave
  • machine learning; Leptospira spp.; smooth Brucella spp.; Brucella ovis; Chlamydia abortus; zoonoses
  • Los documentos depositados en el Repositorio Institucional de la Universidad Autónoma del Estado de México se encuentran a disposición en Acceso Abierto bajo la licencia Creative Commons: Atribución-NoComercial-SinDerivar 4.0 Internacional (CC BY-NC-ND 4.0)

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