24-h variations of blood serum metabolites in high yielding dairy cows and calves
Abstract Background Blood profile testing is commonly used to monitor herd health status, diagnose disorders, and predict the risk of diseases in cows and calves, with subsequent optimization the production of dairy herds. By understanding the physiological ranges of serum metabolites relative to ag...
Ausführliche Beschreibung
Autor*in: |
Hussein Awad Hussein [verfasserIn] Jan-Peter Thurmann [verfasserIn] Rudolf Staufenbiel [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2020 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
In: BMC Veterinary Research - BMC, 2005, 16(2020), 1, Seite 11 |
---|---|
Übergeordnetes Werk: |
volume:16 ; year:2020 ; number:1 ; pages:11 |
Links: |
---|
DOI / URN: |
10.1186/s12917-020-02551-9 |
---|
Katalog-ID: |
DOAJ006870058 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ006870058 | ||
003 | DE-627 | ||
005 | 20230309210309.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230225s2020 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1186/s12917-020-02551-9 |2 doi | |
035 | |a (DE-627)DOAJ006870058 | ||
035 | |a (DE-599)DOAJffa509706b054159bdb181beffd4231c | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a SF600-1100 | |
100 | 0 | |a Hussein Awad Hussein |e verfasserin |4 aut | |
245 | 1 | 0 | |a 24-h variations of blood serum metabolites in high yielding dairy cows and calves |
264 | 1 | |c 2020 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a Abstract Background Blood profile testing is commonly used to monitor herd health status, diagnose disorders, and predict the risk of diseases in cows and calves, with subsequent optimization the production of dairy herds. By understanding the physiological ranges of serum metabolites relative to age, lactation stage, and the sampling time in healthy cows and calves, the dairy practitioners can accurately diagnose abnormalities with a blood test. The effect of sampling time on the variation of serum metabolites within 24 h were evaluated in 83 cattle. All animals were originated from a dairy herd, where the animals, based on their ages and lactation stages, were classified into eight groups. The blood samples were collected from each animal every 4 h within a day. Results The time of sampling within the day showed significant influences on the serum concentrations of glucose, β-hydroxybutyric acid (BHBA) and urea. BHBA was the most metabolite that showed day variation among cows’ groups. Furthermore, the concentrations of total cholesterol were the most stable metabolite in all groups. The mean values of albumin, total proteins, glucose, non-esterified fatty acids (NEFA), BHBA, total cholesterol, total bilirubin, urea, and creatinine revealed significant variations among the different studied groups. Conclusions A certain suitable time of blood sample collection cannot be recommended. However, care shall be taken for the time of sampling for measurements of glucose, NEFA, BHBA and urea, otherwise the comparative values of these metabolites at different sampling time points may differ significantly from each other’s, without a disease cause. It may be recommended, for metabolic assessment of dairy herds, classification the subjects into different groups based on lactation stages and ages of animals. | ||
650 | 4 | |a Calves | |
650 | 4 | |a Diurnal | |
650 | 4 | |a Dairy cows | |
650 | 4 | |a Metabolic profile | |
650 | 4 | |a Sampling | |
650 | 4 | |a Variations | |
653 | 0 | |a Veterinary medicine | |
700 | 0 | |a Jan-Peter Thurmann |e verfasserin |4 aut | |
700 | 0 | |a Rudolf Staufenbiel |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t BMC Veterinary Research |d BMC, 2005 |g 16(2020), 1, Seite 11 |w (DE-627)489256538 |w (DE-600)2191675-5 |x 17466148 |7 nnns |
773 | 1 | 8 | |g volume:16 |g year:2020 |g number:1 |g pages:11 |
856 | 4 | 0 | |u https://doi.org/10.1186/s12917-020-02551-9 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/ffa509706b054159bdb181beffd4231c |z kostenfrei |
856 | 4 | 0 | |u http://link.springer.com/article/10.1186/s12917-020-02551-9 |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/1746-6148 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_74 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_206 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_702 | ||
912 | |a GBV_ILN_2001 | ||
912 | |a GBV_ILN_2003 | ||
912 | |a GBV_ILN_2005 | ||
912 | |a GBV_ILN_2006 | ||
912 | |a GBV_ILN_2008 | ||
912 | |a GBV_ILN_2009 | ||
912 | |a GBV_ILN_2010 | ||
912 | |a GBV_ILN_2011 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2015 | ||
912 | |a GBV_ILN_2020 | ||
912 | |a GBV_ILN_2021 | ||
912 | |a GBV_ILN_2025 | ||
912 | |a GBV_ILN_2031 | ||
912 | |a GBV_ILN_2038 | ||
912 | |a GBV_ILN_2044 | ||
912 | |a GBV_ILN_2048 | ||
912 | |a GBV_ILN_2050 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_2056 | ||
912 | |a GBV_ILN_2057 | ||
912 | |a GBV_ILN_2061 | ||
912 | |a GBV_ILN_2111 | ||
912 | |a GBV_ILN_2113 | ||
912 | |a GBV_ILN_2190 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 16 |j 2020 |e 1 |h 11 |
author_variant |
h a h hah j p t jpt r s rs |
---|---|
matchkey_str |
article:17466148:2020----::4vrainobodeumtbltsnihilig |
hierarchy_sort_str |
2020 |
callnumber-subject-code |
SF |
publishDate |
2020 |
allfields |
10.1186/s12917-020-02551-9 doi (DE-627)DOAJ006870058 (DE-599)DOAJffa509706b054159bdb181beffd4231c DE-627 ger DE-627 rakwb eng SF600-1100 Hussein Awad Hussein verfasserin aut 24-h variations of blood serum metabolites in high yielding dairy cows and calves 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Blood profile testing is commonly used to monitor herd health status, diagnose disorders, and predict the risk of diseases in cows and calves, with subsequent optimization the production of dairy herds. By understanding the physiological ranges of serum metabolites relative to age, lactation stage, and the sampling time in healthy cows and calves, the dairy practitioners can accurately diagnose abnormalities with a blood test. The effect of sampling time on the variation of serum metabolites within 24 h were evaluated in 83 cattle. All animals were originated from a dairy herd, where the animals, based on their ages and lactation stages, were classified into eight groups. The blood samples were collected from each animal every 4 h within a day. Results The time of sampling within the day showed significant influences on the serum concentrations of glucose, β-hydroxybutyric acid (BHBA) and urea. BHBA was the most metabolite that showed day variation among cows’ groups. Furthermore, the concentrations of total cholesterol were the most stable metabolite in all groups. The mean values of albumin, total proteins, glucose, non-esterified fatty acids (NEFA), BHBA, total cholesterol, total bilirubin, urea, and creatinine revealed significant variations among the different studied groups. Conclusions A certain suitable time of blood sample collection cannot be recommended. However, care shall be taken for the time of sampling for measurements of glucose, NEFA, BHBA and urea, otherwise the comparative values of these metabolites at different sampling time points may differ significantly from each other’s, without a disease cause. It may be recommended, for metabolic assessment of dairy herds, classification the subjects into different groups based on lactation stages and ages of animals. Calves Diurnal Dairy cows Metabolic profile Sampling Variations Veterinary medicine Jan-Peter Thurmann verfasserin aut Rudolf Staufenbiel verfasserin aut In BMC Veterinary Research BMC, 2005 16(2020), 1, Seite 11 (DE-627)489256538 (DE-600)2191675-5 17466148 nnns volume:16 year:2020 number:1 pages:11 https://doi.org/10.1186/s12917-020-02551-9 kostenfrei https://doaj.org/article/ffa509706b054159bdb181beffd4231c kostenfrei http://link.springer.com/article/10.1186/s12917-020-02551-9 kostenfrei https://doaj.org/toc/1746-6148 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2020 1 11 |
spelling |
10.1186/s12917-020-02551-9 doi (DE-627)DOAJ006870058 (DE-599)DOAJffa509706b054159bdb181beffd4231c DE-627 ger DE-627 rakwb eng SF600-1100 Hussein Awad Hussein verfasserin aut 24-h variations of blood serum metabolites in high yielding dairy cows and calves 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Blood profile testing is commonly used to monitor herd health status, diagnose disorders, and predict the risk of diseases in cows and calves, with subsequent optimization the production of dairy herds. By understanding the physiological ranges of serum metabolites relative to age, lactation stage, and the sampling time in healthy cows and calves, the dairy practitioners can accurately diagnose abnormalities with a blood test. The effect of sampling time on the variation of serum metabolites within 24 h were evaluated in 83 cattle. All animals were originated from a dairy herd, where the animals, based on their ages and lactation stages, were classified into eight groups. The blood samples were collected from each animal every 4 h within a day. Results The time of sampling within the day showed significant influences on the serum concentrations of glucose, β-hydroxybutyric acid (BHBA) and urea. BHBA was the most metabolite that showed day variation among cows’ groups. Furthermore, the concentrations of total cholesterol were the most stable metabolite in all groups. The mean values of albumin, total proteins, glucose, non-esterified fatty acids (NEFA), BHBA, total cholesterol, total bilirubin, urea, and creatinine revealed significant variations among the different studied groups. Conclusions A certain suitable time of blood sample collection cannot be recommended. However, care shall be taken for the time of sampling for measurements of glucose, NEFA, BHBA and urea, otherwise the comparative values of these metabolites at different sampling time points may differ significantly from each other’s, without a disease cause. It may be recommended, for metabolic assessment of dairy herds, classification the subjects into different groups based on lactation stages and ages of animals. Calves Diurnal Dairy cows Metabolic profile Sampling Variations Veterinary medicine Jan-Peter Thurmann verfasserin aut Rudolf Staufenbiel verfasserin aut In BMC Veterinary Research BMC, 2005 16(2020), 1, Seite 11 (DE-627)489256538 (DE-600)2191675-5 17466148 nnns volume:16 year:2020 number:1 pages:11 https://doi.org/10.1186/s12917-020-02551-9 kostenfrei https://doaj.org/article/ffa509706b054159bdb181beffd4231c kostenfrei http://link.springer.com/article/10.1186/s12917-020-02551-9 kostenfrei https://doaj.org/toc/1746-6148 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2020 1 11 |
allfields_unstemmed |
10.1186/s12917-020-02551-9 doi (DE-627)DOAJ006870058 (DE-599)DOAJffa509706b054159bdb181beffd4231c DE-627 ger DE-627 rakwb eng SF600-1100 Hussein Awad Hussein verfasserin aut 24-h variations of blood serum metabolites in high yielding dairy cows and calves 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Blood profile testing is commonly used to monitor herd health status, diagnose disorders, and predict the risk of diseases in cows and calves, with subsequent optimization the production of dairy herds. By understanding the physiological ranges of serum metabolites relative to age, lactation stage, and the sampling time in healthy cows and calves, the dairy practitioners can accurately diagnose abnormalities with a blood test. The effect of sampling time on the variation of serum metabolites within 24 h were evaluated in 83 cattle. All animals were originated from a dairy herd, where the animals, based on their ages and lactation stages, were classified into eight groups. The blood samples were collected from each animal every 4 h within a day. Results The time of sampling within the day showed significant influences on the serum concentrations of glucose, β-hydroxybutyric acid (BHBA) and urea. BHBA was the most metabolite that showed day variation among cows’ groups. Furthermore, the concentrations of total cholesterol were the most stable metabolite in all groups. The mean values of albumin, total proteins, glucose, non-esterified fatty acids (NEFA), BHBA, total cholesterol, total bilirubin, urea, and creatinine revealed significant variations among the different studied groups. Conclusions A certain suitable time of blood sample collection cannot be recommended. However, care shall be taken for the time of sampling for measurements of glucose, NEFA, BHBA and urea, otherwise the comparative values of these metabolites at different sampling time points may differ significantly from each other’s, without a disease cause. It may be recommended, for metabolic assessment of dairy herds, classification the subjects into different groups based on lactation stages and ages of animals. Calves Diurnal Dairy cows Metabolic profile Sampling Variations Veterinary medicine Jan-Peter Thurmann verfasserin aut Rudolf Staufenbiel verfasserin aut In BMC Veterinary Research BMC, 2005 16(2020), 1, Seite 11 (DE-627)489256538 (DE-600)2191675-5 17466148 nnns volume:16 year:2020 number:1 pages:11 https://doi.org/10.1186/s12917-020-02551-9 kostenfrei https://doaj.org/article/ffa509706b054159bdb181beffd4231c kostenfrei http://link.springer.com/article/10.1186/s12917-020-02551-9 kostenfrei https://doaj.org/toc/1746-6148 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2020 1 11 |
allfieldsGer |
10.1186/s12917-020-02551-9 doi (DE-627)DOAJ006870058 (DE-599)DOAJffa509706b054159bdb181beffd4231c DE-627 ger DE-627 rakwb eng SF600-1100 Hussein Awad Hussein verfasserin aut 24-h variations of blood serum metabolites in high yielding dairy cows and calves 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Blood profile testing is commonly used to monitor herd health status, diagnose disorders, and predict the risk of diseases in cows and calves, with subsequent optimization the production of dairy herds. By understanding the physiological ranges of serum metabolites relative to age, lactation stage, and the sampling time in healthy cows and calves, the dairy practitioners can accurately diagnose abnormalities with a blood test. The effect of sampling time on the variation of serum metabolites within 24 h were evaluated in 83 cattle. All animals were originated from a dairy herd, where the animals, based on their ages and lactation stages, were classified into eight groups. The blood samples were collected from each animal every 4 h within a day. Results The time of sampling within the day showed significant influences on the serum concentrations of glucose, β-hydroxybutyric acid (BHBA) and urea. BHBA was the most metabolite that showed day variation among cows’ groups. Furthermore, the concentrations of total cholesterol were the most stable metabolite in all groups. The mean values of albumin, total proteins, glucose, non-esterified fatty acids (NEFA), BHBA, total cholesterol, total bilirubin, urea, and creatinine revealed significant variations among the different studied groups. Conclusions A certain suitable time of blood sample collection cannot be recommended. However, care shall be taken for the time of sampling for measurements of glucose, NEFA, BHBA and urea, otherwise the comparative values of these metabolites at different sampling time points may differ significantly from each other’s, without a disease cause. It may be recommended, for metabolic assessment of dairy herds, classification the subjects into different groups based on lactation stages and ages of animals. Calves Diurnal Dairy cows Metabolic profile Sampling Variations Veterinary medicine Jan-Peter Thurmann verfasserin aut Rudolf Staufenbiel verfasserin aut In BMC Veterinary Research BMC, 2005 16(2020), 1, Seite 11 (DE-627)489256538 (DE-600)2191675-5 17466148 nnns volume:16 year:2020 number:1 pages:11 https://doi.org/10.1186/s12917-020-02551-9 kostenfrei https://doaj.org/article/ffa509706b054159bdb181beffd4231c kostenfrei http://link.springer.com/article/10.1186/s12917-020-02551-9 kostenfrei https://doaj.org/toc/1746-6148 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2020 1 11 |
allfieldsSound |
10.1186/s12917-020-02551-9 doi (DE-627)DOAJ006870058 (DE-599)DOAJffa509706b054159bdb181beffd4231c DE-627 ger DE-627 rakwb eng SF600-1100 Hussein Awad Hussein verfasserin aut 24-h variations of blood serum metabolites in high yielding dairy cows and calves 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Blood profile testing is commonly used to monitor herd health status, diagnose disorders, and predict the risk of diseases in cows and calves, with subsequent optimization the production of dairy herds. By understanding the physiological ranges of serum metabolites relative to age, lactation stage, and the sampling time in healthy cows and calves, the dairy practitioners can accurately diagnose abnormalities with a blood test. The effect of sampling time on the variation of serum metabolites within 24 h were evaluated in 83 cattle. All animals were originated from a dairy herd, where the animals, based on their ages and lactation stages, were classified into eight groups. The blood samples were collected from each animal every 4 h within a day. Results The time of sampling within the day showed significant influences on the serum concentrations of glucose, β-hydroxybutyric acid (BHBA) and urea. BHBA was the most metabolite that showed day variation among cows’ groups. Furthermore, the concentrations of total cholesterol were the most stable metabolite in all groups. The mean values of albumin, total proteins, glucose, non-esterified fatty acids (NEFA), BHBA, total cholesterol, total bilirubin, urea, and creatinine revealed significant variations among the different studied groups. Conclusions A certain suitable time of blood sample collection cannot be recommended. However, care shall be taken for the time of sampling for measurements of glucose, NEFA, BHBA and urea, otherwise the comparative values of these metabolites at different sampling time points may differ significantly from each other’s, without a disease cause. It may be recommended, for metabolic assessment of dairy herds, classification the subjects into different groups based on lactation stages and ages of animals. Calves Diurnal Dairy cows Metabolic profile Sampling Variations Veterinary medicine Jan-Peter Thurmann verfasserin aut Rudolf Staufenbiel verfasserin aut In BMC Veterinary Research BMC, 2005 16(2020), 1, Seite 11 (DE-627)489256538 (DE-600)2191675-5 17466148 nnns volume:16 year:2020 number:1 pages:11 https://doi.org/10.1186/s12917-020-02551-9 kostenfrei https://doaj.org/article/ffa509706b054159bdb181beffd4231c kostenfrei http://link.springer.com/article/10.1186/s12917-020-02551-9 kostenfrei https://doaj.org/toc/1746-6148 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2020 1 11 |
language |
English |
source |
In BMC Veterinary Research 16(2020), 1, Seite 11 volume:16 year:2020 number:1 pages:11 |
sourceStr |
In BMC Veterinary Research 16(2020), 1, Seite 11 volume:16 year:2020 number:1 pages:11 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Calves Diurnal Dairy cows Metabolic profile Sampling Variations Veterinary medicine |
isfreeaccess_bool |
true |
container_title |
BMC Veterinary Research |
authorswithroles_txt_mv |
Hussein Awad Hussein @@aut@@ Jan-Peter Thurmann @@aut@@ Rudolf Staufenbiel @@aut@@ |
publishDateDaySort_date |
2020-01-01T00:00:00Z |
hierarchy_top_id |
489256538 |
id |
DOAJ006870058 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ006870058</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309210309.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s12917-020-02551-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ006870058</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJffa509706b054159bdb181beffd4231c</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">SF600-1100</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Hussein Awad Hussein</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">24-h variations of blood serum metabolites in high yielding dairy cows and calves</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Background Blood profile testing is commonly used to monitor herd health status, diagnose disorders, and predict the risk of diseases in cows and calves, with subsequent optimization the production of dairy herds. By understanding the physiological ranges of serum metabolites relative to age, lactation stage, and the sampling time in healthy cows and calves, the dairy practitioners can accurately diagnose abnormalities with a blood test. The effect of sampling time on the variation of serum metabolites within 24 h were evaluated in 83 cattle. All animals were originated from a dairy herd, where the animals, based on their ages and lactation stages, were classified into eight groups. The blood samples were collected from each animal every 4 h within a day. Results The time of sampling within the day showed significant influences on the serum concentrations of glucose, β-hydroxybutyric acid (BHBA) and urea. BHBA was the most metabolite that showed day variation among cows’ groups. Furthermore, the concentrations of total cholesterol were the most stable metabolite in all groups. The mean values of albumin, total proteins, glucose, non-esterified fatty acids (NEFA), BHBA, total cholesterol, total bilirubin, urea, and creatinine revealed significant variations among the different studied groups. Conclusions A certain suitable time of blood sample collection cannot be recommended. However, care shall be taken for the time of sampling for measurements of glucose, NEFA, BHBA and urea, otherwise the comparative values of these metabolites at different sampling time points may differ significantly from each other’s, without a disease cause. It may be recommended, for metabolic assessment of dairy herds, classification the subjects into different groups based on lactation stages and ages of animals.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Calves</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Diurnal</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dairy cows</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Metabolic profile</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sampling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Variations</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Veterinary medicine</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jan-Peter Thurmann</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Rudolf Staufenbiel</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">BMC Veterinary Research</subfield><subfield code="d">BMC, 2005</subfield><subfield code="g">16(2020), 1, Seite 11</subfield><subfield code="w">(DE-627)489256538</subfield><subfield code="w">(DE-600)2191675-5</subfield><subfield code="x">17466148</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:16</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:11</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s12917-020-02551-9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/ffa509706b054159bdb181beffd4231c</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://link.springer.com/article/10.1186/s12917-020-02551-9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1746-6148</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">16</subfield><subfield code="j">2020</subfield><subfield code="e">1</subfield><subfield code="h">11</subfield></datafield></record></collection>
|
callnumber-first |
S - Agriculture |
author |
Hussein Awad Hussein |
spellingShingle |
Hussein Awad Hussein misc SF600-1100 misc Calves misc Diurnal misc Dairy cows misc Metabolic profile misc Sampling misc Variations misc Veterinary medicine 24-h variations of blood serum metabolites in high yielding dairy cows and calves |
authorStr |
Hussein Awad Hussein |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)489256538 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
SF600-1100 |
illustrated |
Not Illustrated |
issn |
17466148 |
topic_title |
SF600-1100 24-h variations of blood serum metabolites in high yielding dairy cows and calves Calves Diurnal Dairy cows Metabolic profile Sampling Variations |
topic |
misc SF600-1100 misc Calves misc Diurnal misc Dairy cows misc Metabolic profile misc Sampling misc Variations misc Veterinary medicine |
topic_unstemmed |
misc SF600-1100 misc Calves misc Diurnal misc Dairy cows misc Metabolic profile misc Sampling misc Variations misc Veterinary medicine |
topic_browse |
misc SF600-1100 misc Calves misc Diurnal misc Dairy cows misc Metabolic profile misc Sampling misc Variations misc Veterinary medicine |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
BMC Veterinary Research |
hierarchy_parent_id |
489256538 |
hierarchy_top_title |
BMC Veterinary Research |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)489256538 (DE-600)2191675-5 |
title |
24-h variations of blood serum metabolites in high yielding dairy cows and calves |
ctrlnum |
(DE-627)DOAJ006870058 (DE-599)DOAJffa509706b054159bdb181beffd4231c |
title_full |
24-h variations of blood serum metabolites in high yielding dairy cows and calves |
author_sort |
Hussein Awad Hussein |
journal |
BMC Veterinary Research |
journalStr |
BMC Veterinary Research |
callnumber-first-code |
S |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2020 |
contenttype_str_mv |
txt |
container_start_page |
11 |
author_browse |
Hussein Awad Hussein Jan-Peter Thurmann Rudolf Staufenbiel |
container_volume |
16 |
class |
SF600-1100 |
format_se |
Elektronische Aufsätze |
author-letter |
Hussein Awad Hussein |
doi_str_mv |
10.1186/s12917-020-02551-9 |
author2-role |
verfasserin |
title_sort |
24-h variations of blood serum metabolites in high yielding dairy cows and calves |
callnumber |
SF600-1100 |
title_auth |
24-h variations of blood serum metabolites in high yielding dairy cows and calves |
abstract |
Abstract Background Blood profile testing is commonly used to monitor herd health status, diagnose disorders, and predict the risk of diseases in cows and calves, with subsequent optimization the production of dairy herds. By understanding the physiological ranges of serum metabolites relative to age, lactation stage, and the sampling time in healthy cows and calves, the dairy practitioners can accurately diagnose abnormalities with a blood test. The effect of sampling time on the variation of serum metabolites within 24 h were evaluated in 83 cattle. All animals were originated from a dairy herd, where the animals, based on their ages and lactation stages, were classified into eight groups. The blood samples were collected from each animal every 4 h within a day. Results The time of sampling within the day showed significant influences on the serum concentrations of glucose, β-hydroxybutyric acid (BHBA) and urea. BHBA was the most metabolite that showed day variation among cows’ groups. Furthermore, the concentrations of total cholesterol were the most stable metabolite in all groups. The mean values of albumin, total proteins, glucose, non-esterified fatty acids (NEFA), BHBA, total cholesterol, total bilirubin, urea, and creatinine revealed significant variations among the different studied groups. Conclusions A certain suitable time of blood sample collection cannot be recommended. However, care shall be taken for the time of sampling for measurements of glucose, NEFA, BHBA and urea, otherwise the comparative values of these metabolites at different sampling time points may differ significantly from each other’s, without a disease cause. It may be recommended, for metabolic assessment of dairy herds, classification the subjects into different groups based on lactation stages and ages of animals. |
abstractGer |
Abstract Background Blood profile testing is commonly used to monitor herd health status, diagnose disorders, and predict the risk of diseases in cows and calves, with subsequent optimization the production of dairy herds. By understanding the physiological ranges of serum metabolites relative to age, lactation stage, and the sampling time in healthy cows and calves, the dairy practitioners can accurately diagnose abnormalities with a blood test. The effect of sampling time on the variation of serum metabolites within 24 h were evaluated in 83 cattle. All animals were originated from a dairy herd, where the animals, based on their ages and lactation stages, were classified into eight groups. The blood samples were collected from each animal every 4 h within a day. Results The time of sampling within the day showed significant influences on the serum concentrations of glucose, β-hydroxybutyric acid (BHBA) and urea. BHBA was the most metabolite that showed day variation among cows’ groups. Furthermore, the concentrations of total cholesterol were the most stable metabolite in all groups. The mean values of albumin, total proteins, glucose, non-esterified fatty acids (NEFA), BHBA, total cholesterol, total bilirubin, urea, and creatinine revealed significant variations among the different studied groups. Conclusions A certain suitable time of blood sample collection cannot be recommended. However, care shall be taken for the time of sampling for measurements of glucose, NEFA, BHBA and urea, otherwise the comparative values of these metabolites at different sampling time points may differ significantly from each other’s, without a disease cause. It may be recommended, for metabolic assessment of dairy herds, classification the subjects into different groups based on lactation stages and ages of animals. |
abstract_unstemmed |
Abstract Background Blood profile testing is commonly used to monitor herd health status, diagnose disorders, and predict the risk of diseases in cows and calves, with subsequent optimization the production of dairy herds. By understanding the physiological ranges of serum metabolites relative to age, lactation stage, and the sampling time in healthy cows and calves, the dairy practitioners can accurately diagnose abnormalities with a blood test. The effect of sampling time on the variation of serum metabolites within 24 h were evaluated in 83 cattle. All animals were originated from a dairy herd, where the animals, based on their ages and lactation stages, were classified into eight groups. The blood samples were collected from each animal every 4 h within a day. Results The time of sampling within the day showed significant influences on the serum concentrations of glucose, β-hydroxybutyric acid (BHBA) and urea. BHBA was the most metabolite that showed day variation among cows’ groups. Furthermore, the concentrations of total cholesterol were the most stable metabolite in all groups. The mean values of albumin, total proteins, glucose, non-esterified fatty acids (NEFA), BHBA, total cholesterol, total bilirubin, urea, and creatinine revealed significant variations among the different studied groups. Conclusions A certain suitable time of blood sample collection cannot be recommended. However, care shall be taken for the time of sampling for measurements of glucose, NEFA, BHBA and urea, otherwise the comparative values of these metabolites at different sampling time points may differ significantly from each other’s, without a disease cause. It may be recommended, for metabolic assessment of dairy herds, classification the subjects into different groups based on lactation stages and ages of animals. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 |
container_issue |
1 |
title_short |
24-h variations of blood serum metabolites in high yielding dairy cows and calves |
url |
https://doi.org/10.1186/s12917-020-02551-9 https://doaj.org/article/ffa509706b054159bdb181beffd4231c http://link.springer.com/article/10.1186/s12917-020-02551-9 https://doaj.org/toc/1746-6148 |
remote_bool |
true |
author2 |
Jan-Peter Thurmann Rudolf Staufenbiel |
author2Str |
Jan-Peter Thurmann Rudolf Staufenbiel |
ppnlink |
489256538 |
callnumber-subject |
SF - Animal Culture |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.1186/s12917-020-02551-9 |
callnumber-a |
SF600-1100 |
up_date |
2024-07-03T23:18:25.099Z |
_version_ |
1803601790998937600 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ006870058</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309210309.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s12917-020-02551-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ006870058</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJffa509706b054159bdb181beffd4231c</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">SF600-1100</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Hussein Awad Hussein</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">24-h variations of blood serum metabolites in high yielding dairy cows and calves</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Background Blood profile testing is commonly used to monitor herd health status, diagnose disorders, and predict the risk of diseases in cows and calves, with subsequent optimization the production of dairy herds. By understanding the physiological ranges of serum metabolites relative to age, lactation stage, and the sampling time in healthy cows and calves, the dairy practitioners can accurately diagnose abnormalities with a blood test. The effect of sampling time on the variation of serum metabolites within 24 h were evaluated in 83 cattle. All animals were originated from a dairy herd, where the animals, based on their ages and lactation stages, were classified into eight groups. The blood samples were collected from each animal every 4 h within a day. Results The time of sampling within the day showed significant influences on the serum concentrations of glucose, β-hydroxybutyric acid (BHBA) and urea. BHBA was the most metabolite that showed day variation among cows’ groups. Furthermore, the concentrations of total cholesterol were the most stable metabolite in all groups. The mean values of albumin, total proteins, glucose, non-esterified fatty acids (NEFA), BHBA, total cholesterol, total bilirubin, urea, and creatinine revealed significant variations among the different studied groups. Conclusions A certain suitable time of blood sample collection cannot be recommended. However, care shall be taken for the time of sampling for measurements of glucose, NEFA, BHBA and urea, otherwise the comparative values of these metabolites at different sampling time points may differ significantly from each other’s, without a disease cause. It may be recommended, for metabolic assessment of dairy herds, classification the subjects into different groups based on lactation stages and ages of animals.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Calves</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Diurnal</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dairy cows</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Metabolic profile</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sampling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Variations</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Veterinary medicine</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jan-Peter Thurmann</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Rudolf Staufenbiel</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">BMC Veterinary Research</subfield><subfield code="d">BMC, 2005</subfield><subfield code="g">16(2020), 1, Seite 11</subfield><subfield code="w">(DE-627)489256538</subfield><subfield code="w">(DE-600)2191675-5</subfield><subfield code="x">17466148</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:16</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:11</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s12917-020-02551-9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/ffa509706b054159bdb181beffd4231c</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://link.springer.com/article/10.1186/s12917-020-02551-9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1746-6148</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">16</subfield><subfield code="j">2020</subfield><subfield code="e">1</subfield><subfield code="h">11</subfield></datafield></record></collection>
|
score |
7.3996 |