Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models

Autores
Turino, Ludmila Noelia; Cristaldi, Mariano Daniel; Mariano, Rodolfo Nicolás; Boimvaser, Sonia; Scandolo Lucini, Daniel Edgardo
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Administration of exogenous progesterone is widely used in hormonal protocols for estrous (re)synchronization of dairy cattle without regarding pharmacological issues for dose calculation. This happens because it is difficult to estimate the metabolic level of progesterone for each individual cow before administration. In the present contribution, progesterone pharmacokinetics has been determined in lactating Holstein cows with different milk production yields. A Bayesian approach has been implemented to build two probabilistic progesterone pharmacokinetic models for high and low yield dairy cows. Such models are based on a one-compartment Hill structure. Posterior probabilistic models have been structurally set up and parametric probability density functions have been empirically estimated. Moreover, a global sensitivity analysis has been done to know sensitivity profile of each model. Finally, posterior probabilistic models have adequately recognized cow’s progesterone metabolic level in a validation set when Kullback-Leibler based indices were used. These results suggest that milk yield may be a good index for estimating pharmacokinetic level of progesterone.
EEA Rafaela
Fil: Turino, Ludmila Noelia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Cristaldi, Mariano Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Instituto de Desarrollo y Diseño; Argentina
Fil: Mariano, Rodolfo Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Boimvaser, Sonia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Scandolo Lucini, Daniel Edgardo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; Argentina
Fuente
Spanish Journal of Agricultural Research 12 (2) : 396-404 (2014)
Materia
Vacas Lecheras
Razas (animales)
Progesterona
Análisis de Probabilidad
Métodos Estadísticos
Dairy Cows
Breeds (animals)
Progesterone
Probability Analysis
Statistical Methods
Raza Holstein
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/3921
Acceder
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oai_identifier_str oai:localhost:20.500.12123/3921
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network_name_str INTA Digital (INTA)
spelling Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic modelsTurino, Ludmila NoeliaCristaldi, Mariano DanielMariano, Rodolfo NicolásBoimvaser, SoniaScandolo Lucini, Daniel EdgardoVacas LecherasRazas (animales)ProgesteronaAnálisis de ProbabilidadMétodos EstadísticosDairy CowsBreeds (animals)ProgesteroneProbability AnalysisStatistical MethodsRaza HolsteinAdministration of exogenous progesterone is widely used in hormonal protocols for estrous (re)synchronization of dairy cattle without regarding pharmacological issues for dose calculation. This happens because it is difficult to estimate the metabolic level of progesterone for each individual cow before administration. In the present contribution, progesterone pharmacokinetics has been determined in lactating Holstein cows with different milk production yields. A Bayesian approach has been implemented to build two probabilistic progesterone pharmacokinetic models for high and low yield dairy cows. Such models are based on a one-compartment Hill structure. Posterior probabilistic models have been structurally set up and parametric probability density functions have been empirically estimated. Moreover, a global sensitivity analysis has been done to know sensitivity profile of each model. Finally, posterior probabilistic models have adequately recognized cow’s progesterone metabolic level in a validation set when Kullback-Leibler based indices were used. These results suggest that milk yield may be a good index for estimating pharmacokinetic level of progesterone.EEA RafaelaFil: Turino, Ludmila Noelia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Cristaldi, Mariano Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Instituto de Desarrollo y Diseño; ArgentinaFil: Mariano, Rodolfo Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Boimvaser, Sonia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Scandolo Lucini, Daniel Edgardo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaInstituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA)2018-11-16T15:16:39Z2018-11-16T15:16:39Z2014info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://revistas.inia.es/index.php/sjar/article/view/5271/2069http://hdl.handle.net/20.500.12123/39211695-971X2171-9292http://dx.doi.org/10.5424/sjar/2014122-5271Spanish Journal of Agricultural Research 12 (2) : 396-404 (2014)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-04T09:47:42Zoai:localhost:20.500.12123/3921instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-04 09:47:42.53INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models
title Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models
spellingShingle Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models
Turino, Ludmila Noelia
Vacas Lecheras
Razas (animales)
Progesterona
Análisis de Probabilidad
Métodos Estadísticos
Dairy Cows
Breeds (animals)
Progesterone
Probability Analysis
Statistical Methods
Raza Holstein
title_short Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models
title_full Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models
title_fullStr Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models
title_full_unstemmed Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models
title_sort Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models
dc.creator.none.fl_str_mv Turino, Ludmila Noelia
Cristaldi, Mariano Daniel
Mariano, Rodolfo Nicolás
Boimvaser, Sonia
Scandolo Lucini, Daniel Edgardo
author Turino, Ludmila Noelia
author_facet Turino, Ludmila Noelia
Cristaldi, Mariano Daniel
Mariano, Rodolfo Nicolás
Boimvaser, Sonia
Scandolo Lucini, Daniel Edgardo
author_role author
author2 Cristaldi, Mariano Daniel
Mariano, Rodolfo Nicolás
Boimvaser, Sonia
Scandolo Lucini, Daniel Edgardo
author2_role author
author
author
author
dc.subject.none.fl_str_mv Vacas Lecheras
Razas (animales)
Progesterona
Análisis de Probabilidad
Métodos Estadísticos
Dairy Cows
Breeds (animals)
Progesterone
Probability Analysis
Statistical Methods
Raza Holstein
topic Vacas Lecheras
Razas (animales)
Progesterona
Análisis de Probabilidad
Métodos Estadísticos
Dairy Cows
Breeds (animals)
Progesterone
Probability Analysis
Statistical Methods
Raza Holstein
dc.description.none.fl_txt_mv Administration of exogenous progesterone is widely used in hormonal protocols for estrous (re)synchronization of dairy cattle without regarding pharmacological issues for dose calculation. This happens because it is difficult to estimate the metabolic level of progesterone for each individual cow before administration. In the present contribution, progesterone pharmacokinetics has been determined in lactating Holstein cows with different milk production yields. A Bayesian approach has been implemented to build two probabilistic progesterone pharmacokinetic models for high and low yield dairy cows. Such models are based on a one-compartment Hill structure. Posterior probabilistic models have been structurally set up and parametric probability density functions have been empirically estimated. Moreover, a global sensitivity analysis has been done to know sensitivity profile of each model. Finally, posterior probabilistic models have adequately recognized cow’s progesterone metabolic level in a validation set when Kullback-Leibler based indices were used. These results suggest that milk yield may be a good index for estimating pharmacokinetic level of progesterone.
EEA Rafaela
Fil: Turino, Ludmila Noelia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Cristaldi, Mariano Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Instituto de Desarrollo y Diseño; Argentina
Fil: Mariano, Rodolfo Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Boimvaser, Sonia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Scandolo Lucini, Daniel Edgardo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; Argentina
description Administration of exogenous progesterone is widely used in hormonal protocols for estrous (re)synchronization of dairy cattle without regarding pharmacological issues for dose calculation. This happens because it is difficult to estimate the metabolic level of progesterone for each individual cow before administration. In the present contribution, progesterone pharmacokinetics has been determined in lactating Holstein cows with different milk production yields. A Bayesian approach has been implemented to build two probabilistic progesterone pharmacokinetic models for high and low yield dairy cows. Such models are based on a one-compartment Hill structure. Posterior probabilistic models have been structurally set up and parametric probability density functions have been empirically estimated. Moreover, a global sensitivity analysis has been done to know sensitivity profile of each model. Finally, posterior probabilistic models have adequately recognized cow’s progesterone metabolic level in a validation set when Kullback-Leibler based indices were used. These results suggest that milk yield may be a good index for estimating pharmacokinetic level of progesterone.
publishDate 2014
dc.date.none.fl_str_mv 2014
2018-11-16T15:16:39Z
2018-11-16T15:16:39Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://revistas.inia.es/index.php/sjar/article/view/5271/2069
http://hdl.handle.net/20.500.12123/3921
1695-971X
2171-9292
http://dx.doi.org/10.5424/sjar/2014122-5271
url http://revistas.inia.es/index.php/sjar/article/view/5271/2069
http://hdl.handle.net/20.500.12123/3921
http://dx.doi.org/10.5424/sjar/2014122-5271
identifier_str_mv 1695-971X
2171-9292
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA)
publisher.none.fl_str_mv Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA)
dc.source.none.fl_str_mv Spanish Journal of Agricultural Research 12 (2) : 396-404 (2014)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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score 12.623145

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