Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models
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 be...
| Autores principales: | , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
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Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA)
2018
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| Acceso en línea: | 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 |
| _version_ | 1855483330648080384 |
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| author | Turino, Ludmila Noelia Cristaldi, Mariano Daniel Mariano, Rodolfo Nicolás Boimvaser, Sonia Scandolo Lucini, Daniel Edgardo |
| author_browse | Boimvaser, Sonia Cristaldi, Mariano Daniel Mariano, Rodolfo Nicolás Scandolo Lucini, Daniel Edgardo Turino, Ludmila Noelia |
| author_facet | Turino, Ludmila Noelia Cristaldi, Mariano Daniel Mariano, Rodolfo Nicolás Boimvaser, Sonia Scandolo Lucini, Daniel Edgardo |
| author_sort | Turino, Ludmila Noelia |
| collection | INTA Digital |
| 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. |
| format | Artículo |
| id | INTA3921 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) |
| publisherStr | Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) |
| record_format | dspace |
| spelling | INTA39212018-11-16T15:17:52Z Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models Turino, Ludmila Noelia Cristaldi, Mariano Daniel Mariano, Rodolfo Nicolás Boimvaser, Sonia Scandolo Lucini, Daniel Edgardo Vacas Lecheras Razas (animales) Progesterona Análisis de Probabilidad Métodos Estadísticos Dairy Cows Breeds (animals) Progesterone Probability Analysis Statistical Methods Raza Holstein 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 2018-11-16T15:16:39Z 2018-11-16T15:16:39Z 2014 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng 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) application/pdf Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) Spanish Journal of Agricultural Research 12 (2) : 396-404 (2014) |
| spellingShingle | Vacas Lecheras Razas (animales) Progesterona Análisis de Probabilidad Métodos Estadísticos Dairy Cows Breeds (animals) Progesterone Probability Analysis Statistical Methods Raza Holstein Turino, Ludmila Noelia Cristaldi, Mariano Daniel Mariano, Rodolfo Nicolás Boimvaser, Sonia Scandolo Lucini, Daniel Edgardo 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 |
| 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_short | 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 |
| topic | Vacas Lecheras Razas (animales) Progesterona Análisis de Probabilidad Métodos Estadísticos Dairy Cows Breeds (animals) Progesterone Probability Analysis Statistical Methods Raza Holstein |
| 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 |
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