Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10201/117192

Título: The connection between stress and immune status in pigs: a first salivary analytical panel to disease differentiation.
Fecha de defensa / creación: 13-jul-2021
Materias relacionadas: CDU::6 - Ciencias aplicadas::63 - Agricultura. Silvicultura. Zootecnia. Caza. Pesca::636 - Veterinaria. Explotación y cría de animales. Cría del ganado y de animales domésticos
Palabras clave: machine learning
disease discrimination
salivary analytics
field study
Resumen: The association between stress and immune response activations in different diseases has been analyzed in this paper based on salivary analytics. Moreover, a first attempt to discriminate between diseases was performed by machine learning. The salivary analytics consisted of the measurement of physical (cortisol) and psychological stress (salivary alpha-amylase) indicators, innate (acute phase proteins: C-reactive protein and haptoglobin), and adaptive immune (adenosine deaminase, Cu and Zn) markers and oxidative stress parameters (antioxidant capacity and oxidative status). A total of 107 commercial growing pigs in the field were divided into six groups according to the signs of disease after proper veterinary clinical examination, specifically, healthy pigs, pigs with rectal prolapse, tail-biting, diarrhea, lameness or dyspnea. Associations between stress and immune markers were observed with different intensities. The higher associations (r = 0.61) were observed between oxidative stress markers and adaptive immune markers. On the other hand, moderate associations (r = 0.31-0.48) between physical and psychological stress markers with both innate and adaptive immune markers were observed. All pathological conditions showed statistically significant differences in at least 4 out of the 11 salivary markers studied, with no individual marker dysregulated in all the diseases. Moreover, each disease condition showed differences in the degree of activation of the systems analyzed which could be used to create different salivary profiles. A total of two dimensions were selected according to the machine learning analysis to explain the 48,3% of the variance of our data. Lameness and prolapse were the two pathological conditions most distant from the healthy condition followed by dyspnea. Tail biting and diarrhea were also far from the other diseases but nearer healthy animals. There is still room for improvements, but these preliminary results showed a great power for disease detection and characterization using salivary biomarkers profiling in the near future.
Autor/es principal/es: Sánchez, J
Matas Quintanilla, Marta
Ibañez-López, FJ
Hernández, I
Sotillo Mesanza, Juan
Gutiérrez, AM
Facultad/Departamentos/Servicios: Facultades, Departamentos, Servicios y Escuelas::Departamentos de la UMU::Medicina y Cirugía Animal
URI: http://hdl.handle.net/10201/117192
Tipo de documento: info:eu-repo/semantics/dataset
Número páginas / Extensión: 1
Derechos: info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Aparece en las colecciones:Datos de investigación: Medicina y cirugía animal

Ficheros en este ítem:
Fichero Descripción TamañoFormato 
Raw data open access.xlsx27,87 kBUnknownVisualizar/Abrir

Este ítem está sujeto a una licencia Creative Commons Licencia Creative Commons Creative Commons