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, D. E.
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.
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; 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, D. E.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Rafaela; Argentina
Materia
BAYESIAN MODELING
HILL EQUATION
METABOLISM
MILK YIELD
PROGESTERONE PHARMACOKINETIC
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/22764
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic modelsTurino, Ludmila NoeliaCristaldi, Mariano DanielMariano, Rodolfo NicolásBoimvaser, SoniaScandolo, D. E.BAYESIAN MODELINGHILL EQUATIONMETABOLISMMILK YIELDPROGESTERONE PHARMACOKINETICAdministration 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.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; ArgentinaFil: Cristaldi, Mariano Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; 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, D. E.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Rafaela; ArgentinaSpanish National Institute for Agriculture and Food Research and Technology2014-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/22764Turino, Ludmila Noelia; Cristaldi, Mariano Daniel; Mariano, Rodolfo Nicolás; Boimvaser, Sonia; Scandolo, D. E.; Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models; Spanish National Institute for Agriculture and Food Research and Technology; Spanish Journal Of Agricultural Research; 12; 2; 5-2014; 396-4041695-971XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.5424/sjar/2014122-5271info:eu-repo/semantics/altIdentifier/url/http://revistas.inia.es/index.php/sjar/article/view/5271info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:58:27Zoai:ri.conicet.gov.ar:11336/22764instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:58:27.667CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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
BAYESIAN MODELING
HILL EQUATION
METABOLISM
MILK YIELD
PROGESTERONE PHARMACOKINETIC
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, D. E.
author Turino, Ludmila Noelia
author_facet Turino, Ludmila Noelia
Cristaldi, Mariano Daniel
Mariano, Rodolfo Nicolás
Boimvaser, Sonia
Scandolo, D. E.
author_role author
author2 Cristaldi, Mariano Daniel
Mariano, Rodolfo Nicolás
Boimvaser, Sonia
Scandolo, D. E.
author2_role author
author
author
author
dc.subject.none.fl_str_mv BAYESIAN MODELING
HILL EQUATION
METABOLISM
MILK YIELD
PROGESTERONE PHARMACOKINETIC
topic BAYESIAN MODELING
HILL EQUATION
METABOLISM
MILK YIELD
PROGESTERONE PHARMACOKINETIC
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.
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; 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, D. E.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. 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 2014-05
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://hdl.handle.net/11336/22764
Turino, Ludmila Noelia; Cristaldi, Mariano Daniel; Mariano, Rodolfo Nicolás; Boimvaser, Sonia; Scandolo, D. E.; Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models; Spanish National Institute for Agriculture and Food Research and Technology; Spanish Journal Of Agricultural Research; 12; 2; 5-2014; 396-404
1695-971X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/22764
identifier_str_mv Turino, Ludmila Noelia; Cristaldi, Mariano Daniel; Mariano, Rodolfo Nicolás; Boimvaser, Sonia; Scandolo, D. E.; Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models; Spanish National Institute for Agriculture and Food Research and Technology; Spanish Journal Of Agricultural Research; 12; 2; 5-2014; 396-404
1695-971X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.5424/sjar/2014122-5271
info:eu-repo/semantics/altIdentifier/url/http://revistas.inia.es/index.php/sjar/article/view/5271
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Spanish National Institute for Agriculture and Food Research and Technology
publisher.none.fl_str_mv Spanish National Institute for Agriculture and Food Research and Technology
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842269520989782016
score 13.13397

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