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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Vet. Sci.</journal-id>
<journal-title>Frontiers in Veterinary Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Vet. Sci.</abbrev-journal-title>
<issn pub-type="epub">2297-1769</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fvets.2019.00123</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Veterinary Science</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Age-Related Serum Biochemical Reference Intervals Established for Unweaned Calves and Piglets in the Post-weaning Period</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Yu</surname> <given-names>Kuai</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x0002A;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/636744/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Canalias</surname> <given-names>Francesca</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/718776/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Sol&#x000E0;-Oriol</surname> <given-names>David</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/718661/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Arroyo</surname> <given-names>Laura</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Pato</surname> <given-names>Raquel</given-names></name>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/719254/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Saco</surname> <given-names>Yolanda</given-names></name>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/719188/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Terr&#x000E9;</surname> <given-names>Marta</given-names></name>
<xref ref-type="aff" rid="aff5"><sup>5</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/662919/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Bassols</surname> <given-names>Anna</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/149344/overview"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Departament de Bioqu&#x000ED;mica i Biologia Molecular, Facultat de Veterin&#x000E0;ria, Universitat Aut&#x000F2;noma de Barcelona</institution>, <addr-line>Barcelona</addr-line>, <country>Spain</country></aff>
<aff id="aff2"><sup>2</sup><institution>Departament de Bioqu&#x000ED;mica i Biologia Molecular, Facultat de Medicina, Universitat Aut&#x000F2;noma de Barcelona</institution>, <addr-line>Barcelona</addr-line>, <country>Spain</country></aff>
<aff id="aff3"><sup>3</sup><institution>Animal Nutrition and Welfare Service, Animal and Food Science Department, Facultat de Veterin&#x000E0;ria, Universitat Aut&#x000F2;noma de Barcelona</institution>, <addr-line>Barcelona</addr-line>, <country>Spain</country></aff>
<aff id="aff4"><sup>4</sup><institution>Servei de Bioqu&#x000ED;mica Cl&#x000ED;nica Veterin&#x000E0;ria, Facultat de Veterin&#x000E0;ria, Universitat Aut&#x000F2;noma de Barcelona</institution>, <addr-line>Barcelona</addr-line>, <country>Spain</country></aff>
<aff id="aff5"><sup>5</sup><institution>Departament de Producci&#x000F3; de Remugants, Institut de Recerca i Tecnologia Agroaliment&#x000E0;ries</institution>, <addr-line>Barcelona</addr-line>, <country>Spain</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Amy Louise Warren, University of Calgary, Canada</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Stein Istre Thoresen, Norwegian University of Life Sciences, Norway; Panagiotis Dimitrios Katsoulos, Aristotle University of Thessaloniki, Greece</p></fn>
<corresp id="c001">&#x0002A;Correspondence: Kuai Yu <email>kuaiyu.vet&#x00040;hotmail.com</email></corresp>
<fn fn-type="other" id="fn001"><p>This article was submitted to Veterinary Experimental and Diagnostic Pathology, a section of the journal Frontiers in Veterinary Science</p></fn></author-notes>
<pub-date pub-type="epub">
<day>24</day>
<month>04</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="collection">
<year>2019</year>
</pub-date>
<volume>6</volume>
<elocation-id>123</elocation-id>
<history>
<date date-type="received">
<day>14</day>
<month>12</month>
<year>2018</year>
</date>
<date date-type="accepted">
<day>02</day>
<month>04</month>
<year>2019</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2019 Yu, Canalias, Sol&#x000E0;-Oriol, Arroyo, Pato, Saco, Terr&#x000E9; and Bassols.</copyright-statement>
<copyright-year>2019</copyright-year>
<copyright-holder>Yu, Canalias, Sol&#x000E0;-Oriol, Arroyo, Pato, Saco, Terr&#x000E9; and Bassols</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract><p>The purpose of the present study is to establish the influence of age on serum biochemistry reference intervals (RIs) for unweaned calves and recently-weaned piglets using large number of animals sampled at different ages from populations under different season trials. Specifically, milk replacer (MR)-fed calves from April&#x02013;July 2017 (<italic>n</italic> &#x0003D; 60); from December 2016&#x02013;March 2017 (<italic>n</italic> &#x0003D; 76) and from April&#x02013;August 2018 (<italic>n</italic> &#x0003D; 57) and one group of healthy weaned piglets (<italic>n</italic> &#x0003D; 72) were subjected to the study. Serum enzymes and metabolites of calves at age of 24 h (24 h after colostrum intake), 2, 5, and 7 weeks from merged trials and piglets at 0, 7, and 14 days post-weaning (at 21, 28, and 35 days of age) were studied. The main variable is age whereas no major trial- or sex-biased differences were noticed. In calves, ALT, AST, GGT, GPx, SOD, NEFAs, triglycerides, glucose, creatinine, total protein, and urea were greatly elevated (<italic>p</italic> &#x0003C; 0.001) at 24 h compared with other ages; glucose, creatinine, total protein, and urea constantly decreased through the age; cholesterol&#x00027;s lowest level (<italic>p</italic> &#x0003C; 0.001) was found in 24 h compared with other ages and the levels of haptoglobin remained unchanged (<italic>p</italic> &#x0003E; 0.1) during the study. In comparison with the adult RIs, creatinine from 24 h, NEFAs from 2 w, GGT from 5 w, and urea from 7 w are fully comparable with RIs or lie within RIs determined for adult. In piglets, no changes were noticed on glucose (<italic>p</italic> &#x0003E; 0.1) and haptoglobin (<italic>p</italic> &#x0003E; 0.1) and there were no major changes on hepatic enzymes (ALT, AST, and GGT), total protein, creatinine and urea even though several statistical differences were noticed on 7 days post-weaning. Cholesterol, triglycerides, NEFAs, cortisol and PigMAP were found increased (<italic>p</italic> &#x0003C; 0.05) while TNF-alpha was found less concentrated (<italic>p</italic> &#x0003C; 0.001) at 0 days post-weaning compared with other times. Moreover, the RIs of creatinine and GGT are fully comparable with RIs or lie within RIs determined for adult. In conclusion, clinical biochemistry analytes RIs were established for unweaned calves and recently-weaned piglets and among them some can vary at different ages.</p></abstract>
<kwd-group>
<kwd>serum clinical biochemistry</kwd>
<kwd>reference interval</kwd>
<kwd>non-parametric</kwd>
<kwd>unweaned calf</kwd>
<kwd>weaning piglet</kwd>
<kwd>age</kwd>
</kwd-group>
<contract-num rid="cn001">AGL2015-68463-C2-1-P</contract-num>
<contract-num rid="cn001">AGL2015-68463-C2-2-P</contract-num>
<contract-sponsor id="cn001">Ministerio de Econom&#x000ED;a y Competitividad<named-content content-type="fundref-id">10.13039/501100003329</named-content></contract-sponsor>
<contract-sponsor id="cn002">China Scholarship Council<named-content content-type="fundref-id">10.13039/501100004543</named-content></contract-sponsor>
<counts>
<fig-count count="2"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="68"/>
<page-count count="12"/>
<word-count count="8743"/>
</counts>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<title>Introduction</title>
<p>Serum biochemical reference intervals (RIs) play an important role in assessing animal health. However, for farm animals, the majority of RIs are set for adult individuals. Similar to human medicine, where pediatric RIs are readily available, there is a necessity of robust and specific RIs for young animals in veterinary science, since their physiology is different from adults and it may be markedly affected by milk-feeding and weaning. The use of adult RIs intervals may be inadequate since young animals may metabolize in a different way. Furthermore, young animals must be closely monitored since they are more vulnerable to diseases, and their diets may have a bigger impact on the health condition in comparison with the adult. Thus, many of the RIs publications in veterinary science established for adults (<xref ref-type="bibr" rid="B1">1</xref>, <xref ref-type="bibr" rid="B2">2</xref>) may be misleading (<xref ref-type="bibr" rid="B3">3</xref>) and the establishment of RIs in young animals is needed to adequately discriminate between healthy and diseased individuals.</p>
<p>In newborn calves, their growth involves several morphological, metabolic and physiological changes (<xref ref-type="bibr" rid="B4">4</xref>) and these changes may be reflected by metabolites and enzymes&#x00027; levels in blood (<xref ref-type="bibr" rid="B5">5</xref>, <xref ref-type="bibr" rid="B6">6</xref>). Calves undergo metabolic and digestive tract physical changes during the weaning process (in the dairy industry it occurs between 6 and 9 weeks of age) (<xref ref-type="bibr" rid="B7">7</xref>). At birth, most of the nutrients (glucose, amino acids, and long-chain fatty acids) from milk are digested in the abomasum and absorbed in the small intestine. Thus, young calves are non-ruminant. As calf starts to consume solid feeds (like concentrate feeds), the rumen starts to develop. At weaning, the rumen is mostly developed and it supports part of the nutrient absorption from solid feed fermentation. This different nutrient supply at weaning also entails a shift in the hepatic function from a glycolytic to glucogenic liver altering metabolites and enzymes levels in blood.</p>
<p>In swine industry, an &#x0201C;early weaning&#x0201D; is commonly used with the objective of reducing and improving the sow productive cycle. Normally weaning occurs abruptly at 21&#x02013;28 days of age and the piglet needs to be adapted to eat a novel food (usually solid diet) after the separation from the sow. In contrast to the natural weaning which gradually process until 17 weeks of age (<xref ref-type="bibr" rid="B8">8</xref>), early weaning may become stressful for piglets. The immediate post-weaning period is critical for the piglet health since at that moment they are very susceptible to gastrointestinal problems and infections.</p>
<p>In both species, the early stage age involves several biochemical and metabolic changes that may be fundamentally different from the adult ones. Moreover, since young animals are more vulnerable to certain pathogens, basic health laboratory routine check based on serum biochemical analytes is essential.</p>
<p>In the past 20 years, neonatal clinical biochemistry gradually has been established and the biochemical RIs on unweaned cattle calves of different breeds and ages up to 3 months of old (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B9">9</xref>&#x02013;<xref ref-type="bibr" rid="B11">11</xref>) and for piglets younger than 1 month of age (<xref ref-type="bibr" rid="B12">12</xref>, <xref ref-type="bibr" rid="B13">13</xref>) have been published. In all these studies, several noticeable biochemical changes along the age were reported. Nevertheless, these studies have two main drawbacks. First, RIs have been calculated from small populations (&#x0003C;35), and this does not meet the requirement of non-parametrical RI. As it is recommended in Clinical and Laboratory Standards Institute (CLSI) Guideline, a non-parametric method is recommended due to its simplicity and reliability (<xref ref-type="bibr" rid="B14">14</xref>), thus a sufficient population for RI establishment could be more trustworthy. Second, many RIs are calculated from individual experiments, and since factors like diet, geography, season, sex, and breed may affect the RI, thus the obtained value may not applicable in other conditions.</p>
<p>In this study, the first objective is to determine clinical biochemistry values from a large set of healthy unweaned calf populations from 3 different nutritional programs and breeding seasons at 24 h, 2 weeks, 5 weeks, and 7 weeks of age; the second objective is to determine healthy recent-weaned piglets&#x00027; clinical biochemistry values from both sexes at 0 days, 7 days, and 14 days post-weaning and the third objective is to evaluate all the values and establish RIs. The purpose is to obtain RIs useful for the veterinary practitioner and for research.</p>
</sec>
<sec sec-type="materials and methods" id="s2">
<title>Materials and Methods</title>
<sec>
<title>Calf Housing and Diet Management</title>
<p>Male Holstein calves were managed according to the recommendations of the Animal Care Committee of Institut de Recerca i Tecnologia Agroaliment&#x000E0;ries (IRTA) under the approval research protocol FUE-2017-00587321, authorization code 9733. Holstein male calves were obtained from Granja Murucuc (Gurb, Barcelona, Spain) and housed individually at IRTA in Torre Marimon (Barcelona, Spain). Plastic ear tag identification with the animal&#x00027;s number was used. In all cases, animals were under veterinary control and no health problems were observed during the experimental period. The total number of individuals included in the study was <italic>n</italic> &#x0003D; 193. Animals belonged to three trials.</p>
<p>In Trial 1 (T1), 60 new-born calves (age &#x0003D; 0 h) were fed with colostrum within the first 6 h after birth. At 24 h of age, blood samples were taken. Then, at age of 3 &#x000B1; 1.3 days old, animals were fed with a MR and blood samples were collected at 2 and 5 weeks of age 4 h after the morning feeding. The T1 was carried out between April and July 2017.</p>
<p>In Trial 2 (T2), 76 calves (average age &#x0003D; 3 &#x000B1; 1.7 d) were fed with MR and blood samples were collected at 2, 5, and 7 weeks of age 4 h after the morning feeding. The T2 started in December 2016 and ended in March 2017.</p>
<p>In Trial 3 (T3), 57 calves (average age &#x0003D; 5 &#x000B1; 2.2 d) were fed with MR and blood samples were taken at 2, 5, and 7 weeks of age 4 h after the morning feeding. The T3 was carried out between April and August 2018.</p>
<p>All animals followed the same MR feeding program that consisted on feeding 2-L of MR (Nukamel, Weert, The Netherlands) twice a day containing at 12.5% concentration of MR powder (500 g/d) for the first 4 days, subsequently MR was increased to 5 L/d of MR at 12.5% concentration of MR powder (625 g/d) for the next 3 days, then MR was increased to 6 L/d of MR at 12.5% concentration of MR powder (750g/d) for the next 7 days and then 2 meals of 3 L at 15% concentration (900 g/d) until 49 d of age. Then, MR was limited to a single offer of 3 L also at 15% (450 g/d) until end of the study. Animals also had access to water and chopped barley straw <italic>ad libitum</italic>. Chemical composition and ingredient of the MRs and the starter intake information are shown in <xref ref-type="supplementary-material" rid="SM1">Tables S1</xref>, <xref ref-type="supplementary-material" rid="SM2">S2</xref>.</p>
</sec>
<sec>
<title>Piglet Housing and Sampling</title>
<p>A total of 336 commercial crossing piglets ([Large White x Landrace] x Pietrain), male and female 21-d old piglets of 4&#x02013;7 kg of body weight (BW) were weaned. The animals were moved from the farrowing to the nursery unit (within the same commercial farm without transport) in the morning on the day of weaning. Plastic ear tag identification with the animal&#x00027;s number was used. In the nursery room, pigs were then allocated in 24 pens (14 piglets/pen). Each pen has had a commercial non-lidded hopper and a nipple waterer to ensure <italic>ad libitum</italic> feeding and free water access. The experimental procedure has been approved by ethical committee (CEEAH) of Universitat Aut&#x000F2;noma de Barcelona (Registration n&#x000B0;:1406 of the Departament de Medi Ambient i Habitatge of the Generalitat de Catalunya).</p>
<p>At weaning, animals were offered the same basal pre-starter diets following the same specification. The pre-starter and starter diets were fed <italic>ad libitum</italic> for fourteen consecutive days (P1) and 21 consecutive days (P2), respectively. A basal diet was formulated to contain 2,470 kcal NE/kg; 20.5% CP/kg and 1.35% Dig Lys for P1 and 2,400 kcal NE/kg; 18.1% CP/kg and 1.20% Dig Lys for P2. Diets were formulated to meet the requirements for growth of newly weaned piglets (<xref ref-type="bibr" rid="B15">15</xref>). No antibiotics and ZnO were included in the basal diets.</p>
<p>Blood samples from 3 piglets per pen (a total of 72 animals) were collected avoiding cross-contamination between pens at 8 am at week 3 of age (day 0 after weaning), week 4 (day 7 after weaning), and week 5 (day 14 after weaning). Piglets were individually weighted before weaning and distributed for a balanced body weight within each pen. Piglets were sorted by descending BW within each pen and the one in the middle (median), the ones immediately above and below the median were picked out to be sampled. The same animals per pen were sampled on weeks 4 and 5, respectively.</p>
</sec>
<sec>
<title>Sample Collection and Analysis</title>
<p>Blood samples obtained from jugular venipuncture were collected in 10 ml Vacutainer tubes without anticoagulant. Sera were obtained by centrifugation at 1,500 g for 10 min and transferred to new tubes to be stored in aliquots at &#x02212;80&#x000B0;C until further analysis. At the day of analysis, aliquots were thawed. Measurement of serum clinical biochemistry analytes was performed on the Olympus AU400 analyser except cortisol and TNF-alpha. The protocols for the use of Olympus System Reagents (OSRs) and other commercial reagents were in reference of previous studies (<xref ref-type="bibr" rid="B16">16</xref>, <xref ref-type="bibr" rid="B17">17</xref>). Quality control protocols were based on the daily quantification of two control sera of low and high concentration (Control serum I and Control serum II, Beckman Coulter). For cortisol and TNF-alpha in piglets, two commercial ELISA kits were used: Cortisol ELISA Kit (DRG, Marburg, Germany) and Porcine TNF-alpha Quantikine ELISA Kit (R&#x00026;D Systems, Abingdon, UK). The analytes, methods, reagents, and coefficients of variation (CV%) are shown in <xref ref-type="supplementary-material" rid="SM3">Table S3</xref>.</p>
</sec>
<sec>
<title>RI Establishment and Statistics</title>
<p>The RI establishment was in accordance with American Society for Veterinary Clinical Pathology (ASVCP) guidelines (<xref ref-type="bibr" rid="B18">18</xref>). These guidelines mirror the CLSI recommendations. All RIs were generated by Reference Value Advisor (<xref ref-type="bibr" rid="B19">19</xref>), an add-in program in Excel (Office 365, Microsoft, Redmond, WA, USA). For population amounts over 120, non-parametric RIs were directly used since this method doesn&#x00027;t assume any specific shape for data distribution. For populations &#x0003C;120 (24 h of age for calves and piglet samples), non-parametric RIs were not able to be computed. The criteria of outlier removal are following: (a) For interquartile range (IQR) &#x0003D; Q<sub>3</sub>(third quartiles)&#x02014;Q<sub>1</sub>(first quartiles), values that exceed interquartile fence set at Q<sub>1</sub>&#x02212;3<sup>&#x0002A;</sup>IQR and Q<sub>3</sub>&#x0002B;3<sup>&#x0002A;</sup>IQR are considered outliers; (b) Without removing certain outliers, if Reference Value Advisor computes result with good distribution and/or symmetricity, then the outliers are retained; (c) If no normal distribution and/or symmetricity are observed even outliers are removed and data are power transformed, such population is calculated with Cook&#x00027;s distance and Cook&#x00027;s outliers are detected and deleted. After the removal of outliers, populations were recalculated for RIs. Once the RIs were established, one-way analysis of variance (ANOVA) and Tukey&#x00027;s HSD with Bonferroni correction test, boxplots with difference annotations and distribution histograms were made by &#x0201C;R&#x0201D; (<xref ref-type="bibr" rid="B20">20</xref>) under RStudio environment (<xref ref-type="bibr" rid="B21">21</xref>) using the combinations of &#x0201C;multcompView&#x0201D; (<xref ref-type="bibr" rid="B22">22</xref>), &#x0201C;nortest&#x0201D; (<xref ref-type="bibr" rid="B23">23</xref>), &#x0201C;ggplot2&#x0201D; (<xref ref-type="bibr" rid="B24">24</xref>), &#x0201C;gridExtra&#x0201D; (<xref ref-type="bibr" rid="B25">25</xref>), and &#x0201C;ggpubr&#x0201D; (<xref ref-type="bibr" rid="B26">26</xref>) packages and values that exceed interquartile fence set at Q<sub>1</sub>&#x02212;3<sup>&#x0002A;</sup>IQR and Q<sub>3</sub>&#x0002B;3<sup>&#x0002A;</sup>IQR were considered outliers.</p>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>Results</title>
<p>For calves and piglets, the frequency of distribution histograms of all analytes from all the individuals (both trial-wise and merged-data-wise) were visualized before the RIs were established (shown in <xref ref-type="supplementary-material" rid="SM6">Figures S1</xref>, <xref ref-type="supplementary-material" rid="SM7">S2</xref>). The distribution from the different sets of animals overlapped and differences between groups were not significant, indicating that no year season, experimental or sex-biased distributions were found and in consequence, all the individuals of the same age were included in the same reference population. There were only two exceptions identified in calves: AST and cholesterol at week 2, where T3&#x00027;s population displayed a non-overlapping distribution curve.</p>
<p>For calves, the methods of RI calculations of 24 h was parametric since population consisted in 30 individuals and from 2 w non-parametric method was used as the populations are over 120.</p>
<p>For piglets, since the population size didn&#x00027;t reach 60, parametric method was used for all ages and analytes except one non-parametric method was suggested using by the software on AST at 7 d due to unfixable normality of distribution.</p>
<p>A summary of the results of both species at different ages are shown in <xref ref-type="table" rid="T1">Tables 1</xref>, <xref ref-type="table" rid="T2">2</xref>, respectively. <xref ref-type="fig" rid="F1">Figures 1</xref>, <xref ref-type="fig" rid="F2">2</xref> show the pattern of age-related differences for all parameters. The <italic>post-hoc</italic> multi-comparison results are shown in <xref ref-type="supplementary-material" rid="SM4">Tables S4</xref>, <xref ref-type="supplementary-material" rid="SM5">S5</xref>. Missing and undetectable samples were discarded in this study.</p>
<table-wrap position="float" id="T1">
<label>Table 1</label>
<caption><p>Summary of statistical values for clinical biochemistry analytes from calves of 24 h (24 h after colostrum taking), 2, 5, and 7 weeks of age.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th/>
<th valign="top" align="left" style="border-bottom: thin solid #000000;"><bold>Analyte</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>ALT</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>AST</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>Cholesterol</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>Creatinine</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>GGT</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>Glucose</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>GPx</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>Haptoglobin</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>NEFAs</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>TGs</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>TP</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>SOD</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>Urea</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="left"><bold>Unit</bold></th>
<th valign="top" align="center"><bold>U/L</bold></th>
<th valign="top" align="center"><bold>U/L</bold></th>
<th valign="top" align="center"><bold>mg/dL</bold></th>
<th valign="top" align="center"><bold>mg/dL</bold></th>
<th valign="top" align="center"><bold>U/L</bold></th>
<th valign="top" align="center"><bold>mg/dL</bold></th>
<th valign="top" align="center"><bold>U/L</bold></th>
<th valign="top" align="center"><bold>mg/mL</bold></th>
<th valign="top" align="center"><bold>mmol/L</bold></th>
<th valign="top" align="center"><bold>mg/dL</bold></th>
<th valign="top" align="center"><bold>g/dL</bold></th>
<th valign="top" align="center"><bold>U/mL</bold></th>
<th valign="top" align="center"><bold>mg/dL</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">24 h</td>
<td valign="top" align="left">Mean</td>
<td valign="top" align="center">12.1</td>
<td valign="top" align="center">87.5</td>
<td valign="top" align="center">36.1</td>
<td valign="top" align="center">1.22</td>
<td valign="top" align="center">1459.8</td>
<td valign="top" align="center">125.8</td>
<td valign="top" align="center">737.1</td>
<td valign="top" align="center">0.16</td>
<td valign="top" align="center">0.41</td>
<td valign="top" align="center">39.1</td>
<td valign="top" align="center">6.02</td>
<td valign="top" align="center">0.90</td>
<td valign="top" align="center">19.4</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Median</td>
<td valign="top" align="center">10.8</td>
<td valign="top" align="center">85.0</td>
<td valign="top" align="center">33.6</td>
<td valign="top" align="center">1.20</td>
<td valign="top" align="center">1303.0</td>
<td valign="top" align="center">119.4</td>
<td valign="top" align="center">632.0</td>
<td valign="top" align="center">0.14</td>
<td valign="top" align="center">0.40</td>
<td valign="top" align="center">38.2</td>
<td valign="top" align="center">6.15</td>
<td valign="top" align="center">0.53</td>
<td valign="top" align="center">19.3</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">SD</td>
<td valign="top" align="center">4.4</td>
<td valign="top" align="center">20.4</td>
<td valign="top" align="center">11.1</td>
<td valign="top" align="center">0.17</td>
<td valign="top" align="center">831.3</td>
<td valign="top" align="center">28.5</td>
<td valign="top" align="center">347.4</td>
<td valign="top" align="center">0.05</td>
<td valign="top" align="center">0.15</td>
<td valign="top" align="center">19.7</td>
<td valign="top" align="center">0.71</td>
<td valign="top" align="center">0.89</td>
<td valign="top" align="center">5.6</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Minimum</td>
<td valign="top" align="center">6.8</td>
<td valign="top" align="center">53.0</td>
<td valign="top" align="center">20.6</td>
<td valign="top" align="center">0.96</td>
<td valign="top" align="center">288.0</td>
<td valign="top" align="center">68.5</td>
<td valign="top" align="center">343.1</td>
<td valign="top" align="center">0.09</td>
<td valign="top" align="center">0.19</td>
<td valign="top" align="center">13.5</td>
<td valign="top" align="center">4.42</td>
<td valign="top" align="center">0.05</td>
<td valign="top" align="center">8.7</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Maximum</td>
<td valign="top" align="center">22.3</td>
<td valign="top" align="center">134.0</td>
<td valign="top" align="center">67.4</td>
<td valign="top" align="center">1.64</td>
<td valign="top" align="center">3170.0</td>
<td valign="top" align="center">211.1</td>
<td valign="top" align="center">1650.4</td>
<td valign="top" align="center">0.32</td>
<td valign="top" align="center">0.77</td>
<td valign="top" align="center">87.0</td>
<td valign="top" align="center">7.43</td>
<td valign="top" align="center">2.79</td>
<td valign="top" align="center">34.9</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">N</td>
<td valign="top" align="center">29</td>
<td valign="top" align="center">30</td>
<td valign="top" align="center">30</td>
<td valign="top" align="center">30</td>
<td valign="top" align="center">28</td>
<td valign="top" align="center">30</td>
<td valign="top" align="center">28</td>
<td valign="top" align="center">28</td>
<td valign="top" align="center">30</td>
<td valign="top" align="center">30</td>
<td valign="top" align="center">30</td>
<td valign="top" align="center">30</td>
<td valign="top" align="center">30</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Method</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Untrans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Untrans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
</tr>
<tr>
<td valign="top" align="left">2 w</td>
<td valign="top" align="left">Mean</td>
<td valign="top" align="center">6.8</td>
<td valign="top" align="center">36.6</td>
<td valign="top" align="center">71.5</td>
<td valign="top" align="center">1.00</td>
<td valign="top" align="center">100.8</td>
<td valign="top" align="center">110.6</td>
<td valign="top" align="center">505.0</td>
<td valign="top" align="center">0.14</td>
<td valign="top" align="center">0.10</td>
<td valign="top" align="center">19.6</td>
<td valign="top" align="center">5.11</td>
<td valign="top" align="center">0.31</td>
<td valign="top" align="center">15.2</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Median</td>
<td valign="top" align="center">6.6</td>
<td valign="top" align="center">38.0</td>
<td valign="top" align="center">78.3</td>
<td valign="top" align="center">0.99</td>
<td valign="top" align="center">76.0</td>
<td valign="top" align="center">110.6</td>
<td valign="top" align="center">452.4</td>
<td valign="top" align="center">0.13</td>
<td valign="top" align="center">0.09</td>
<td valign="top" align="center">16.0</td>
<td valign="top" align="center">5.10</td>
<td valign="top" align="center">0.25</td>
<td valign="top" align="center">13.9</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">SD</td>
<td valign="top" align="center">1.7</td>
<td valign="top" align="center">9.3</td>
<td valign="top" align="center">29.3</td>
<td valign="top" align="center">0.20</td>
<td valign="top" align="center">70.6</td>
<td valign="top" align="center">22.1</td>
<td valign="top" align="center">194.0</td>
<td valign="top" align="center">0.12</td>
<td valign="top" align="center">0.06</td>
<td valign="top" align="center">12.1</td>
<td valign="top" align="center">0.63</td>
<td valign="top" align="center">0.21</td>
<td valign="top" align="center">7.9</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Minimum</td>
<td valign="top" align="center">3.3</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">11.5</td>
<td valign="top" align="center">0.54</td>
<td valign="top" align="center">12.0</td>
<td valign="top" align="center">65.4</td>
<td valign="top" align="center">231.6</td>
<td valign="top" align="center">0.06</td>
<td valign="top" align="center">0.03</td>
<td valign="top" align="center">5.5</td>
<td valign="top" align="center">3.75</td>
<td valign="top" align="center">0.02</td>
<td valign="top" align="center">6.3</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Maximum</td>
<td valign="top" align="center">12.1</td>
<td valign="top" align="center">60.0</td>
<td valign="top" align="center">145.0</td>
<td valign="top" align="center">1.71</td>
<td valign="top" align="center">348.0</td>
<td valign="top" align="center">222.5</td>
<td valign="top" align="center">1610.7</td>
<td valign="top" align="center">1.48</td>
<td valign="top" align="center">0.31</td>
<td valign="top" align="center">81.8</td>
<td valign="top" align="center">7.14</td>
<td valign="top" align="center">1.33</td>
<td valign="top" align="center">92.7</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">N</td>
<td valign="top" align="center">188</td>
<td valign="top" align="center">188</td>
<td valign="top" align="center">188</td>
<td valign="top" align="center">188</td>
<td valign="top" align="center">188</td>
<td valign="top" align="center">188</td>
<td valign="top" align="center">186</td>
<td valign="top" align="center">190</td>
<td valign="top" align="center">188</td>
<td valign="top" align="center">188</td>
<td valign="top" align="center">188</td>
<td valign="top" align="center">185</td>
<td valign="top" align="center">188</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Method</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
</tr>
<tr>
<td valign="top" align="left">5 w</td>
<td valign="top" align="left">Mean</td>
<td valign="top" align="center">8.3</td>
<td valign="top" align="center">42.2</td>
<td valign="top" align="center">82.1</td>
<td valign="top" align="center">0.83</td>
<td valign="top" align="center">24.5</td>
<td valign="top" align="center">101.8</td>
<td valign="top" align="center">421.1</td>
<td valign="top" align="center">0.14</td>
<td valign="top" align="center">0.15</td>
<td valign="top" align="center">21.9</td>
<td valign="top" align="center">4.99</td>
<td valign="top" align="center">0.23</td>
<td valign="top" align="center">13.9</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Median</td>
<td valign="top" align="center">8.1</td>
<td valign="top" align="center">42.0</td>
<td valign="top" align="center">77.0</td>
<td valign="top" align="center">0.82</td>
<td valign="top" align="center">23.0</td>
<td valign="top" align="center">101.4</td>
<td valign="top" align="center">383.0</td>
<td valign="top" align="center">0.12</td>
<td valign="top" align="center">0.14</td>
<td valign="top" align="center">18.1</td>
<td valign="top" align="center">4.99</td>
<td valign="top" align="center">0.20</td>
<td valign="top" align="center">13.7</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">SD</td>
<td valign="top" align="center">2.1</td>
<td valign="top" align="center">15.0</td>
<td valign="top" align="center">29.4</td>
<td valign="top" align="center">0.16</td>
<td valign="top" align="center">9.5</td>
<td valign="top" align="center">18.4</td>
<td valign="top" align="center">157.6</td>
<td valign="top" align="center">0.18</td>
<td valign="top" align="center">0.06</td>
<td valign="top" align="center">12.0</td>
<td valign="top" align="center">0.50</td>
<td valign="top" align="center">0.14</td>
<td valign="top" align="center">3.9</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Minimum</td>
<td valign="top" align="center">4.3</td>
<td valign="top" align="center">19.0</td>
<td valign="top" align="center">30.6</td>
<td valign="top" align="center">0.50</td>
<td valign="top" align="center">7.0</td>
<td valign="top" align="center">43.8</td>
<td valign="top" align="center">242.2</td>
<td valign="top" align="center">0.06</td>
<td valign="top" align="center">0.05</td>
<td valign="top" align="center">7.4</td>
<td valign="top" align="center">3.77</td>
<td valign="top" align="center">0.02</td>
<td valign="top" align="center">6.3</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Maximum</td>
<td valign="top" align="center">19.5</td>
<td valign="top" align="center">178.0</td>
<td valign="top" align="center">187.7</td>
<td valign="top" align="center">1.40</td>
<td valign="top" align="center">54.0</td>
<td valign="top" align="center">157.9</td>
<td valign="top" align="center">1525.3</td>
<td valign="top" align="center">2.21</td>
<td valign="top" align="center">0.30</td>
<td valign="top" align="center">71.7</td>
<td valign="top" align="center">6.49</td>
<td valign="top" align="center">0.94</td>
<td valign="top" align="center">27.3</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">N</td>
<td valign="top" align="center">193</td>
<td valign="top" align="center">193</td>
<td valign="top" align="center">193</td>
<td valign="top" align="center">193</td>
<td valign="top" align="center">193</td>
<td valign="top" align="center">193</td>
<td valign="top" align="center">192</td>
<td valign="top" align="center">193</td>
<td valign="top" align="center">193</td>
<td valign="top" align="center">193</td>
<td valign="top" align="center">193</td>
<td valign="top" align="center">191</td>
<td valign="top" align="center">193</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Method</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
</tr>
<tr>
<td valign="top" align="left">7 w</td>
<td valign="top" align="left">Mean</td>
<td valign="top" align="center">9.5</td>
<td valign="top" align="center">37.5</td>
<td valign="top" align="center">65.5</td>
<td valign="top" align="center">0.67</td>
<td valign="top" align="center">16.8</td>
<td valign="top" align="center">93.0</td>
<td valign="top" align="center">437.1</td>
<td valign="top" align="center">0.13</td>
<td valign="top" align="center">0.18</td>
<td valign="top" align="center">24.1</td>
<td valign="top" align="center">4.80</td>
<td valign="top" align="center">0.23</td>
<td valign="top" align="center">11.2</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Median</td>
<td valign="top" align="center">9.4</td>
<td valign="top" align="center">36.5</td>
<td valign="top" align="center">64.9</td>
<td valign="top" align="center">0.66</td>
<td valign="top" align="center">16.0</td>
<td valign="top" align="center">93.3</td>
<td valign="top" align="center">413.2</td>
<td valign="top" align="center">0.12</td>
<td valign="top" align="center">0.17</td>
<td valign="top" align="center">21.5</td>
<td valign="top" align="center">4.70</td>
<td valign="top" align="center">0.20</td>
<td valign="top" align="center">10.9</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">SD</td>
<td valign="top" align="center">2.4</td>
<td valign="top" align="center">10.8</td>
<td valign="top" align="center">23.0</td>
<td valign="top" align="center">0.12</td>
<td valign="top" align="center">4.4</td>
<td valign="top" align="center">15.3</td>
<td valign="top" align="center">128.0</td>
<td valign="top" align="center">0.06</td>
<td valign="top" align="center">0.07</td>
<td valign="top" align="center">14.3</td>
<td valign="top" align="center">0.54</td>
<td valign="top" align="center">0.13</td>
<td valign="top" align="center">2.9</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Minimum</td>
<td valign="top" align="center">4.7</td>
<td valign="top" align="center">16.0</td>
<td valign="top" align="center">18.8</td>
<td valign="top" align="center">0.43</td>
<td valign="top" align="center">8.0</td>
<td valign="top" align="center">59.2</td>
<td valign="top" align="center">262.4</td>
<td valign="top" align="center">0.07</td>
<td valign="top" align="center">0.05</td>
<td valign="top" align="center">6.1</td>
<td valign="top" align="center">3.66</td>
<td valign="top" align="center">0.01</td>
<td valign="top" align="center">5.8</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Maximum</td>
<td valign="top" align="center">19.0</td>
<td valign="top" align="center">77.0</td>
<td valign="top" align="center">133.4</td>
<td valign="top" align="center">1.03</td>
<td valign="top" align="center">31.0</td>
<td valign="top" align="center">147.8</td>
<td valign="top" align="center">1330.8</td>
<td valign="top" align="center">0.56</td>
<td valign="top" align="center">0.40</td>
<td valign="top" align="center">101.9</td>
<td valign="top" align="center">6.47</td>
<td valign="top" align="center">0.66</td>
<td valign="top" align="center">18.8</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">N</td>
<td valign="top" align="center">134</td>
<td valign="top" align="center">134</td>
<td valign="top" align="center">134</td>
<td valign="top" align="center">134</td>
<td valign="top" align="center">134</td>
<td valign="top" align="center">134</td>
<td valign="top" align="center">133</td>
<td valign="top" align="center">134</td>
<td valign="top" align="center">134</td>
<td valign="top" align="center">134</td>
<td valign="top" align="center">134</td>
<td valign="top" align="center">130</td>
<td valign="top" align="center">134</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Method</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
<td valign="top" align="center">NP</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>SD, Standard Deviation; N, Number of animals; ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase; GGT, Gamma-Glutamyl Transferase; GPx, Glutathione Peroxidase; NEFAs, Non-Esterified Fatty Acids; TGs, Triglycerides; TP, Total Proteins; SOD, Superoxide Dismutase</italic>.</p>
<p><italic>Methods: NP, Non-parametric; Trans. Std., Transformed data using standard method; Untrans. Std., Untransformed data using standard method</italic>.</p>
</table-wrap-foot>
</table-wrap>
<table-wrap position="float" id="T2">
<label>Table 2</label>
<caption><p>Summary of statistical values for clinical biochemistry analytes from piglets at 0, 7, and 14 days post-weaning (at 21, 28, and 35 days of age).</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th/>
<th valign="top" align="left" style="border-bottom: thin solid #000000;"><bold>Analyte</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>ALT</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>AST</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>Cholesterol</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>Cortisol</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>Creatinine</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>GGT</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>Glucose</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>Haptoglobin</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>NEFAs</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>PigMAP</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>TGs</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>TNF-alpha</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>TP</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>Urea</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="left"><bold>Unit</bold></th>
<th valign="top" align="left"><bold>U/L</bold></th>
<th valign="top" align="center"><bold>U/L</bold></th>
<th valign="top" align="center"><bold>mg/dL</bold></th>
<th valign="top" align="center"><bold>ng/L</bold></th>
<th valign="top" align="center"><bold>mg/dL</bold></th>
<th valign="top" align="center"><bold>U/L</bold></th>
<th valign="top" align="center"><bold>mg/dL</bold></th>
<th valign="top" align="center"><bold>mg/mL</bold></th>
<th valign="top" align="center"><bold>mmol/L</bold></th>
<th valign="top" align="center"><bold>g/L</bold></th>
<th valign="top" align="center"><bold>mg/dL</bold></th>
<th valign="top" align="center"><bold>pg/mL</bold></th>
<th valign="top" align="center"><bold>g/dL</bold></th>
<th valign="top" align="center"><bold>mg/dL</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">0 d</td>
<td valign="top" align="left">Mean</td>
<td valign="top" align="center">32.8</td>
<td valign="top" align="center">78.4</td>
<td valign="top" align="center">125.5</td>
<td valign="top" align="center">74.9</td>
<td valign="top" align="center">0.80</td>
<td valign="top" align="center">46.0</td>
<td valign="top" align="center">84.9</td>
<td valign="top" align="center">0.50</td>
<td valign="top" align="center">0.40</td>
<td valign="top" align="center">3.26</td>
<td valign="top" align="center">43.2</td>
<td valign="top" align="center">54.3</td>
<td valign="top" align="center">3.78</td>
<td valign="top" align="center">12.1</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Median</td>
<td valign="top" align="center">31.3</td>
<td valign="top" align="center">63.0</td>
<td valign="top" align="center">122.0</td>
<td valign="top" align="center">60.2</td>
<td valign="top" align="center">0.79</td>
<td valign="top" align="center">41.0</td>
<td valign="top" align="center">80.8</td>
<td valign="top" align="center">0.41</td>
<td valign="top" align="center">0.40</td>
<td valign="top" align="center">2.88</td>
<td valign="top" align="center">40.9</td>
<td valign="top" align="center">48.8</td>
<td valign="top" align="center">3.68</td>
<td valign="top" align="center">11.7</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">SD</td>
<td valign="top" align="center">9.4</td>
<td valign="top" align="center">47.5</td>
<td valign="top" align="center">41.8</td>
<td valign="top" align="center">50.1</td>
<td valign="top" align="center">0.15</td>
<td valign="top" align="center">16.5</td>
<td valign="top" align="center">17.8</td>
<td valign="top" align="center">0.39</td>
<td valign="top" align="center">0.14</td>
<td valign="top" align="center">1.22</td>
<td valign="top" align="center">13.1</td>
<td valign="top" align="center">22.3</td>
<td valign="top" align="center">0.64</td>
<td valign="top" align="center">3.6</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Minimum</td>
<td valign="top" align="center">10.2</td>
<td valign="top" align="center">20.0</td>
<td valign="top" align="center">51.4</td>
<td valign="top" align="center">2.0</td>
<td valign="top" align="center">0.57</td>
<td valign="top" align="center">11</td>
<td valign="top" align="center">49.0</td>
<td valign="top" align="center">0.05</td>
<td valign="top" align="center">0.15</td>
<td valign="top" align="center">1.22</td>
<td valign="top" align="center">22.6</td>
<td valign="top" align="center">23.1</td>
<td valign="top" align="center">2.34</td>
<td valign="top" align="center">6.7</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Maximum</td>
<td valign="top" align="center">63.7</td>
<td valign="top" align="center">242.0</td>
<td valign="top" align="center">272.2</td>
<td valign="top" align="center">202.2</td>
<td valign="top" align="center">1.16</td>
<td valign="top" align="center">89</td>
<td valign="top" align="center">141.4</td>
<td valign="top" align="center">1.68</td>
<td valign="top" align="center">0.76</td>
<td valign="top" align="center">5.88</td>
<td valign="top" align="center">85.0</td>
<td valign="top" align="center">110.6</td>
<td valign="top" align="center">5.42</td>
<td valign="top" align="center">25.2</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">N</td>
<td valign="top" align="center">66</td>
<td valign="top" align="center">68</td>
<td valign="top" align="center">61</td>
<td valign="top" align="center">71</td>
<td valign="top" align="center">54</td>
<td valign="top" align="center">65</td>
<td valign="top" align="center">57</td>
<td valign="top" align="center">61</td>
<td valign="top" align="center">61</td>
<td valign="top" align="center">71</td>
<td valign="top" align="center">59</td>
<td valign="top" align="center">67</td>
<td valign="top" align="center">58</td>
<td valign="top" align="center">57</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Method</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Untrans. Rob.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Untrans. Rob.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
</tr>
<tr>
<td valign="top" align="left">7 d</td>
<td valign="top" align="left">Mean</td>
<td valign="top" align="center">24.8</td>
<td valign="top" align="center">45.8</td>
<td valign="top" align="center">53.1</td>
<td valign="top" align="center">19.0</td>
<td valign="top" align="center">0.94</td>
<td valign="top" align="center">40.9</td>
<td valign="top" align="center">79.1</td>
<td valign="top" align="center">0.62</td>
<td valign="top" align="center">0.18</td>
<td valign="top" align="center">0.99</td>
<td valign="top" align="center">35.5</td>
<td valign="top" align="center">103.2</td>
<td valign="top" align="center">3.49</td>
<td valign="top" align="center">18.2</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Median</td>
<td valign="top" align="center">22.8</td>
<td valign="top" align="center">37.0</td>
<td valign="top" align="center">52.0</td>
<td valign="top" align="center">15.6</td>
<td valign="top" align="center">0.93</td>
<td valign="top" align="center">40.0</td>
<td valign="top" align="center">78.9</td>
<td valign="top" align="center">0.49</td>
<td valign="top" align="center">0.15</td>
<td valign="top" align="center">0.94</td>
<td valign="top" align="center">31.7</td>
<td valign="top" align="center">101.7</td>
<td valign="top" align="center">3.46</td>
<td valign="top" align="center">17.2</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">SD</td>
<td valign="top" align="center">8.8</td>
<td valign="top" align="center">22.3</td>
<td valign="top" align="center">14.3</td>
<td valign="top" align="center">12.4</td>
<td valign="top" align="center">0.16</td>
<td valign="top" align="center">13.9</td>
<td valign="top" align="center">12.4</td>
<td valign="top" align="center">0.52</td>
<td valign="top" align="center">0.11</td>
<td valign="top" align="center">0.31</td>
<td valign="top" align="center">11.2</td>
<td valign="top" align="center">27.7</td>
<td valign="top" align="center">0.48</td>
<td valign="top" align="center">6.9</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Minimum</td>
<td valign="top" align="center">8.2</td>
<td valign="top" align="center">15.0</td>
<td valign="top" align="center">27.0</td>
<td valign="top" align="center">1.1</td>
<td valign="top" align="center">0.66</td>
<td valign="top" align="center">13</td>
<td valign="top" align="center">55.3</td>
<td valign="top" align="center">0.07</td>
<td valign="top" align="center">0.04</td>
<td valign="top" align="center">0.43</td>
<td valign="top" align="center">18.8</td>
<td valign="top" align="center">41.4</td>
<td valign="top" align="center">2.39</td>
<td valign="top" align="center">4.3</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Maximum</td>
<td valign="top" align="center">53.3</td>
<td valign="top" align="center">125.0</td>
<td valign="top" align="center">95.5</td>
<td valign="top" align="center">52.5</td>
<td valign="top" align="center">1.51</td>
<td valign="top" align="center">74</td>
<td valign="top" align="center">107.6</td>
<td valign="top" align="center">2.39</td>
<td valign="top" align="center">0.58</td>
<td valign="top" align="center">2.24</td>
<td valign="top" align="center">73.3</td>
<td valign="top" align="center">165.7</td>
<td valign="top" align="center">4.57</td>
<td valign="top" align="center">41.3</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">N</td>
<td valign="top" align="center">67</td>
<td valign="top" align="center">67</td>
<td valign="top" align="center">65</td>
<td valign="top" align="center">68</td>
<td valign="top" align="center">65</td>
<td valign="top" align="center">67</td>
<td valign="top" align="center">64</td>
<td valign="top" align="center">66</td>
<td valign="top" align="center">62</td>
<td valign="top" align="center">69</td>
<td valign="top" align="center">65</td>
<td valign="top" align="center">72</td>
<td valign="top" align="center">65</td>
<td valign="top" align="center">64</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Method</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">NP<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
</tr>
<tr>
<td valign="top" align="left">14 d</td>
<td valign="top" align="left">Mean</td>
<td valign="top" align="center">35.5</td>
<td valign="top" align="center">65.6</td>
<td valign="top" align="center">69.7</td>
<td valign="top" align="center">21.2</td>
<td valign="top" align="center">0.85</td>
<td valign="top" align="center">50.9</td>
<td valign="top" align="center">83.0</td>
<td valign="top" align="center">0.57</td>
<td valign="top" align="center">0.11</td>
<td valign="top" align="center">0.74</td>
<td valign="top" align="center">38.8</td>
<td valign="top" align="center">98.1</td>
<td valign="top" align="center">3.84</td>
<td valign="top" align="center">14.7</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Median</td>
<td valign="top" align="center">28.7</td>
<td valign="top" align="center">48.0</td>
<td valign="top" align="center">62.4</td>
<td valign="top" align="center">18.9</td>
<td valign="top" align="center">0.82</td>
<td valign="top" align="center">49.0</td>
<td valign="top" align="center">85.8</td>
<td valign="top" align="center">0.30</td>
<td valign="top" align="center">0.10</td>
<td valign="top" align="center">0.70</td>
<td valign="top" align="center">38.0</td>
<td valign="top" align="center">92.5</td>
<td valign="top" align="center">3.74</td>
<td valign="top" align="center">12.9</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">SD</td>
<td valign="top" align="center">23.6</td>
<td valign="top" align="center">45.7</td>
<td valign="top" align="center">36.9</td>
<td valign="top" align="center">16.7</td>
<td valign="top" align="center">0.17</td>
<td valign="top" align="center">17.4</td>
<td valign="top" align="center">16.5</td>
<td valign="top" align="center">0.66</td>
<td valign="top" align="center">0.03</td>
<td valign="top" align="center">0.28</td>
<td valign="top" align="center">10.0</td>
<td valign="top" align="center">42.8</td>
<td valign="top" align="center">0.69</td>
<td valign="top" align="center">7.5</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Minimum</td>
<td valign="top" align="center">11.4</td>
<td valign="top" align="center">23.0</td>
<td valign="top" align="center">37.5</td>
<td valign="top" align="center">1.6</td>
<td valign="top" align="center">0.53</td>
<td valign="top" align="center">17</td>
<td valign="top" align="center">34.5</td>
<td valign="top" align="center">0.05</td>
<td valign="top" align="center">0.05</td>
<td valign="top" align="center">0.32</td>
<td valign="top" align="center">20.2</td>
<td valign="top" align="center">43.6</td>
<td valign="top" align="center">2.76</td>
<td valign="top" align="center">4.6</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Maximum</td>
<td valign="top" align="center">177.9</td>
<td valign="top" align="center">260.0</td>
<td valign="top" align="center">303.9</td>
<td valign="top" align="center">83.8</td>
<td valign="top" align="center">1.26</td>
<td valign="top" align="center">112</td>
<td valign="top" align="center">126.9</td>
<td valign="top" align="center">3.05</td>
<td valign="top" align="center">0.21</td>
<td valign="top" align="center">2.13</td>
<td valign="top" align="center">68.3</td>
<td valign="top" align="center">292.0</td>
<td valign="top" align="center">6.36</td>
<td valign="top" align="center">38.5</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">N</td>
<td valign="top" align="center">64</td>
<td valign="top" align="center">62</td>
<td valign="top" align="center">62</td>
<td valign="top" align="center">65</td>
<td valign="top" align="center">57</td>
<td valign="top" align="center">66</td>
<td valign="top" align="center">60</td>
<td valign="top" align="center">61</td>
<td valign="top" align="center">55</td>
<td valign="top" align="center">63</td>
<td valign="top" align="center">60</td>
<td valign="top" align="center">71</td>
<td valign="top" align="center">59</td>
<td valign="top" align="center">57</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Method</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
<td valign="top" align="center">Trans. Std.</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>SD, Standard Deviation; N, Number of animals; ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase; GGT, Gamma-Glutamyl Transferase; NEFAs, Non-Esterified Fatty Acids; PigMAP, Pig Major Acute Phase Protein; TGs, Triglycerides; TNF-alpha, Tumor Necrosis Factor alpha; TP, Total Proteins</italic>.</p>
<p><italic>Methods: NP, Non-parametric method; Trans. Std., Transformed data using standard method; Untrans. Rob., Untransformed data using robust method</italic>.</p>
<fn id="TN1">
<label>&#x0002A;</label>
<p><italic>Non-parametric method was used due to unfixable distribution [p-value (Anderson-darling) &#x0003C; 0.05 and p-value (symmetry test) &#x0003C; 0.01] both before and after transformation. This method was recommended by the software</italic>.</p></fn>
</table-wrap-foot>
</table-wrap>
<fig id="F1" position="float">
<label>Figure 1</label>
<caption><p>Boxplots of analytes with Tukey&#x00027;s HSD <italic>posthoc</italic> with Bonferroni correction from calves&#x00027; sera at 24 h, 2 w, 5 w, and 7 w of age. Annotations over the boxes with different superscripts are significantly different (<italic>p</italic> &#x0003C; 0.05). Interquartile fence was set between Q1-3&#x0002A;IQR and Q3&#x0002B;3&#x0002A;IQR. 24 h, calves at 24 hours of age; 2 w, calves at 2 weeks of age; 5 w, calves at 5 weeks of age; 7 w, calves at 7 weeks of age. Analytes were alphabetically plotted according to the orders of discussion.</p></caption>
<graphic xlink:href="fvets-06-00123-g0001.tif"/>
</fig>
<fig id="F2" position="float">
<label>Figure 2</label>
<caption><p>Boxplots of analytes with Tukey&#x00027;s HSD <italic>posthoc</italic> with Bonferroni correction from piglets&#x00027; sera at 0, 7, and 14 days post-weaning (age: 21, 28, and 35 days, respectively). Annotations over the boxes with different superscripts are significantly different (<italic>p</italic> &#x0003C; 0.05). Interquartile fence was set between Q1-3&#x0002A;IQR and Q3&#x0002B;3&#x0002A;IQR. 0d, piglets at the 0 day of weaning (right after weaning); 7d, piglets at the 7th day of weaning; 14d, piglets at the 14th day of weaning. Analytes were alphabetically plotted according to the orders of discussion.</p></caption>
<graphic xlink:href="fvets-06-00123-g0002.tif"/>
</fig>
</sec>
<sec sec-type="discussion" id="s4">
<title>Discussion</title>
<p>A table with RI for calves and piglets has been designed for veterinary use with comparison of results in adults obtained from the literature (<xref ref-type="table" rid="T3">Tables 3</xref>, <xref ref-type="table" rid="T4">4</xref>).</p>
<table-wrap position="float" id="T3">
<label>Table 3</label>
<caption><p>Reference intervals (RIs) of clinical biochemistry analytes from calves of 24 h of age (24 h after colostrum taking) 2, 5, and 7 weeks of age and RIs of adult cattle from literature.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th valign="top" align="left"><bold>Analyte</bold></th>
<th valign="top" align="left"><bold>Unit</bold></th>
<th valign="top" align="center"><bold>24 h</bold></th>
<th valign="top" align="center"><bold>2 w</bold></th>
<th valign="top" align="center"><bold>5 w</bold></th>
<th valign="top" align="center"><bold>7 w</bold></th>
<th valign="top" align="center" colspan="2"><bold>Adult</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">ALT</td>
<td valign="top" align="left">U/L</td>
<td valign="top" align="center">6.3&#x02013;24.7</td>
<td valign="top" align="center">4.0&#x02013;11.1</td>
<td valign="top" align="center">4.8&#x02013;13.3</td>
<td valign="top" align="center">5.2&#x02013;15.3</td>
<td valign="top" align="center">11&#x02013;40</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">AST</td>
<td valign="top" align="left">U/L</td>
<td valign="top" align="center">49.6&#x02013;133.4</td>
<td valign="top" align="center">19.5&#x02013;55.0</td>
<td valign="top" align="center">22.0&#x02013;63.3</td>
<td valign="top" align="center">19.0&#x02013;63.3</td>
<td valign="top" align="center">78&#x02013;132</td>
<td valign="top" align="left">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">Cholesterol</td>
<td valign="top" align="left">mg/dL</td>
<td valign="top" align="center">20.8&#x02013;67.5</td>
<td valign="top" align="center">20.8&#x02013;128.9</td>
<td valign="top" align="center">33.9&#x02013;136.4</td>
<td valign="top" align="center">25.4&#x02013;118.3</td>
<td valign="top" align="center">80&#x02013;120</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">Creatinine</td>
<td valign="top" align="left">mg/dL</td>
<td valign="top" align="center">0.93&#x02013;1.65</td>
<td valign="top" align="center">0.64&#x02013;1.51</td>
<td valign="top" align="center">0.56&#x02013;1.20</td>
<td valign="top" align="center">0.47&#x02013;0.99</td>
<td valign="top" align="center">1.0&#x02013;2.0</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">GGT</td>
<td valign="top" align="left">U/L</td>
<td valign="top" align="center">0.0&#x02013;3201.8</td>
<td valign="top" align="center">16.5&#x02013;262.5</td>
<td valign="top" align="center">12.0&#x02013;48.6</td>
<td valign="top" align="center">11.0&#x02013;27.6</td>
<td valign="top" align="center">6.1&#x02013;17.4</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">Glucose</td>
<td valign="top" align="left">mg/dL</td>
<td valign="top" align="center">75.3&#x02013;193.3</td>
<td valign="top" align="center">70.3&#x02013;152.9</td>
<td valign="top" align="center">63.0&#x02013;144.1</td>
<td valign="top" align="center">63.4&#x02013;123.0</td>
<td valign="top" align="center">45&#x02013;75</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">GPx</td>
<td valign="top" align="left">U/L</td>
<td valign="top" align="center">363.7&#x02013;2116.5</td>
<td valign="top" align="center">270.5&#x02013;1023.7</td>
<td valign="top" align="center">255.5&#x02013;842.3</td>
<td valign="top" align="center">288.7&#x02013;711.0</td>
<td valign="top" align="center">-</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">Haptoglobin</td>
<td valign="top" align="left">mg/dL</td>
<td valign="top" align="center">0.09&#x02013;0.31</td>
<td valign="top" align="center">0.07&#x02013;0.36</td>
<td valign="top" align="center">0.07&#x02013;0.46</td>
<td valign="top" align="center">0.08&#x02013;0.38</td>
<td valign="top" align="center">&#x0003C; 1.0 <xref ref-type="table-fn" rid="TN2"><sup>&#x000A7;</sup></xref></td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B27">27</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">NEFAs</td>
<td valign="top" align="left">mmol/L</td>
<td valign="top" align="center">0.09&#x02013;0.72</td>
<td valign="top" align="center">0.04&#x02013;0.27</td>
<td valign="top" align="center">0.07&#x02013;0.29</td>
<td valign="top" align="center">0.06&#x02013;0.35</td>
<td valign="top" align="center">0.07&#x02013;0.46</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B28">28</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">SOD</td>
<td valign="top" align="left">U/mL</td>
<td valign="top" align="center">0.05&#x02013;4.57</td>
<td valign="top" align="center">0.04&#x02013;0.84</td>
<td valign="top" align="center">0.05&#x02013;0.62</td>
<td valign="top" align="center">0.03&#x02013;0.57</td>
<td valign="top" align="center">(12.71 &#x000B1; 0.34) <xref ref-type="table-fn" rid="TN3"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B29">29</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">TGs</td>
<td valign="top" align="left">mg/dL</td>
<td valign="top" align="center">11.3&#x02013;93.7</td>
<td valign="top" align="center">6.6&#x02013;57.5</td>
<td valign="top" align="center">8.2&#x02013;58.8</td>
<td valign="top" align="center">6.7&#x02013;58.5</td>
<td valign="top" align="center">0&#x02013;14</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">TP</td>
<td valign="top" align="left">g/dL</td>
<td valign="top" align="center">4.58&#x02013;7.52</td>
<td valign="top" align="center">3.95&#x02013;6.54</td>
<td valign="top" align="center">3.94&#x02013;5.93</td>
<td valign="top" align="center">3.87&#x02013;6.13</td>
<td valign="top" align="center">6.74&#x02013;7.46</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">Urea</td>
<td valign="top" align="left">mg/dL</td>
<td valign="top" align="center">9.2&#x02013;32.5</td>
<td valign="top" align="center">7.5&#x02013;30.4</td>
<td valign="top" align="center">7.7&#x02013;23.8</td>
<td valign="top" align="center">6.8&#x02013;18.3</td>
<td valign="top" align="center">20&#x02013;30</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase; GGT, Gamma-Glutamyl Transferase; GPx, Glutathione Peroxidase; NEFAs, Non-Esterified Fatty Acids; TGs, Triglycerides; TP, Total Proteins; SOD, Superoxide Dismutase</italic>.</p>
<fn id="TN2">
<label>&#x000A7;</label>
<p><italic>Mean concentration obtained from 10 healthy cows</italic>.</p></fn>
<fn id="TN3">
<label>&#x0002A;</label>
<p><italic>Cow in transition period (60 d before until 100 d after milk)</italic>.</p></fn>
</table-wrap-foot>
</table-wrap>
<table-wrap position="float" id="T4">
<label>Table 4</label>
<caption><p>Reference intervals (RIs) of clinical biochemistry analytes from piglets at 0, 7, and 14 days post-weaning (PW) (at 21, 28, and 35 days of age) and RIs of adult pig from literature.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th valign="top" align="left"><bold>Analyte</bold></th>
<th valign="top" align="left"><bold>Unit</bold></th>
<th valign="top" align="center"><bold>0 d PW</bold></th>
<th valign="top" align="center"><bold>7 d PW</bold></th>
<th valign="top" align="center"><bold>14 d PW</bold></th>
<th valign="top" align="center" colspan="2"><bold>Adult</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">ALT</td>
<td valign="top" align="left">U/L</td>
<td valign="top" align="center">16.5&#x02013;54.2</td>
<td valign="top" align="center">11.4&#x02013;47.1</td>
<td valign="top" align="center">13.6&#x02013;92.7</td>
<td valign="top" align="center">31&#x02013;58</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">AST</td>
<td valign="top" align="left">U/L</td>
<td valign="top" align="center">27.5&#x02013;230.4</td>
<td valign="top" align="center">15.7&#x02013;122.9</td>
<td valign="top" align="center">26.6&#x02013;223.9</td>
<td valign="top" align="center">32&#x02013;84</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">Cholesterol</td>
<td valign="top" align="left">mg/dL</td>
<td valign="top" align="center">58.9&#x02013;224.3</td>
<td valign="top" align="center">28.7&#x02013;85.5</td>
<td valign="top" align="center">39.8&#x02013;141.2</td>
<td valign="top" align="center">36&#x02013;54</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">Cortisol</td>
<td valign="top" align="left">ng/mL</td>
<td valign="top" align="center">6.9&#x02013;206.5</td>
<td valign="top" align="center">1.4&#x02013;50.8</td>
<td valign="top" align="center">1.0&#x02013;66.7</td>
<td valign="top" align="center">(29.7 &#x000B1; 1.0)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">Creatinine</td>
<td valign="top" align="left">mg/dL</td>
<td valign="top" align="center">0.50&#x02013;1.09</td>
<td valign="top" align="center">0.69&#x02013;1.34</td>
<td valign="top" align="center">0.55&#x02013;1.22</td>
<td valign="top" align="center">1.0&#x02013;2.7</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">GGT</td>
<td valign="top" align="left">U/L</td>
<td valign="top" align="center">18.7&#x02013;85.1</td>
<td valign="top" align="center">15.3&#x02013;70.8</td>
<td valign="top" align="center">23.1&#x02013;92.7</td>
<td valign="top" align="center">10&#x02013;60</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">Glucose</td>
<td valign="top" align="left">mg/dL</td>
<td valign="top" align="center">44.0&#x02013;117.8</td>
<td valign="top" align="center">56.4&#x02013;106.3</td>
<td valign="top" align="center">46.6&#x02013;113.6</td>
<td valign="top" align="center">85&#x02013;150</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">Haptoglobin</td>
<td valign="top" align="left">mg/dL</td>
<td valign="top" align="center">0.03&#x02013;1.55</td>
<td valign="top" align="center">0.08&#x02013;2.34</td>
<td valign="top" align="center">0.05&#x02013;0.18</td>
<td valign="top" align="center">0.02&#x02013;3.00 <xref ref-type="table-fn" rid="TN4"><sup>&#x000A7;</sup></xref></td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B30">30</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">NEFAs</td>
<td valign="top" align="left">mmol/L</td>
<td valign="top" align="center">0.15&#x02013;0.71</td>
<td valign="top" align="center">0.04&#x02013;0.48</td>
<td valign="top" align="center">0.05&#x02013;0.18</td>
<td valign="top" align="center">-</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">PigMAP</td>
<td valign="top" align="left">g/L</td>
<td valign="top" align="center">1.33&#x02013;6.14</td>
<td valign="top" align="center">0.51&#x02013;1.73</td>
<td valign="top" align="center">0.36&#x02013;1.39</td>
<td valign="top" align="center">0.46&#x02013;2.36 <xref ref-type="table-fn" rid="TN4"><sup>&#x000A7;</sup></xref></td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B30">30</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">TGs</td>
<td valign="top" align="left">mg/dL</td>
<td valign="top" align="center">23.9&#x02013;76.5</td>
<td valign="top" align="center">21.0&#x02013;65.9</td>
<td valign="top" align="center">22.2&#x02013;62.0</td>
<td valign="top" align="center">-</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">TNF-alpha</td>
<td valign="top" align="left">pg/mL</td>
<td valign="top" align="center">22.0&#x02013;108.4</td>
<td valign="top" align="center">51.9&#x02013;162.4</td>
<td valign="top" align="center">46.3&#x02013;202.6</td>
<td valign="top" align="center">&#x0003C; 10 <xref ref-type="table-fn" rid="TN5"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B31">31</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">TP</td>
<td valign="top" align="left">g/dL</td>
<td valign="top" align="center">2.67&#x02013;5.27</td>
<td valign="top" align="center">2.59&#x02013;4.52</td>
<td valign="top" align="center">2.79&#x02013;5.57</td>
<td valign="top" align="center">7.90&#x02013;8.90</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">Urea</td>
<td valign="top" align="left">mg/dL</td>
<td valign="top" align="center">7.1&#x02013;21.6</td>
<td valign="top" align="center">6.1&#x02013;33.7</td>
<td valign="top" align="center">5.1&#x02013;36.0</td>
<td valign="top" align="center">10&#x02013;30</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B2">2</xref>)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase; GGT, Gamma-Glutamyl Transferase; NEFAs, Non-Esterified Fatty Acids; PigMAP, Pig Major Acute Phase Protein; TGs, Triglycerides; TNF-alpha, Tumor Necrosis Factor alpha; TP, Total Proteins</italic>.</p>
<fn id="TN4">
<label>&#x000A7;</label>
<p><italic>RIs made for mixed-age pigs at 5 w old obtained from 10 commercial farms</italic>.</p></fn>
<fn id="TN5">
<label>&#x0002A;</label>
<p><italic>Undetectable TNF-alpha level for 6 healthy pigs. The lower limit for detection is &#x0003E; 10 pg according to manufacturer</italic>.</p></fn>
</table-wrap-foot>
</table-wrap>
<sec>
<title>RIs for Unweaned Calves</title>
<p>It was noticed that the all enzymes (ALT, AST, GGT, GPx, and SOD) not only showed the highest concentrations at 24 h of age, but also significantly higher than any other times of age. The high concentration in newborn animals is an indication that these enzymes are absorbed from colostrum, at least in the case of GGT and AST (<xref ref-type="bibr" rid="B9">9</xref>, <xref ref-type="bibr" rid="B32">32</xref>). The most extreme elevation occurred with GGT, which was 20 folds higher at 24 h than at 2 w and further decreases at 5 w and 7 w. Even though the GGT level is relatively low in adult cow&#x00027;s serum, its level in colostrum can peak-up to &#x0007E;800-fold greater than in the serum of the same cow (<xref ref-type="bibr" rid="B33">33</xref>). GGT can increase its activity in calves&#x00027; serum as early as 6 h after colostrum intake (intake at birth) (<xref ref-type="bibr" rid="B34">34</xref>) and similar to our results, it has been described that it takes approximately up to 5 weeks for GGT to decline to low level (adult&#x00027;s level) after the intake of colostrum (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B11">11</xref>, <xref ref-type="bibr" rid="B33">33</xref>). Other studies found that lower concentrations of GGT in new-born calves are linked with much lower levels of globulins/total protein and higher risk of death (<xref ref-type="bibr" rid="B33">33</xref>, <xref ref-type="bibr" rid="B35">35</xref>); moreover, one study showed 0% sensitivity of GGT when it comes to the detection of hepatic diseases before 6 w old (<xref ref-type="bibr" rid="B36">36</xref>) since the highly concentrated colostrum-derived GGT presented in the serum can mask the level of GGT that is derived from liver damage. Indeed, a better characterization of GGT levels for new-born calves may help to monitor calves&#x00027; health to prevent early-age death but not used to detect hepatic diseases. It is also relevant to notice the high individual variability of GGT values in 24 h old calves, probably indicating differences in colostrum intake, absorption and/or metabolization.</p>
<p>After 24 h of age, ALT&#x00027;s level reached its lowest at 2 w and gradually went uphill afterwards while AST stayed relatively steady and low levels from 2 to 7 w. However, at 7 w of age, both levels of ALT and AST were still lower compared with adult&#x00027;s RI. Other studies with longer terms showed that AST reaches adult&#x00027;s level around 8&#x02013;10 w of life (<xref ref-type="bibr" rid="B9">9</xref>, <xref ref-type="bibr" rid="B10">10</xref>). According to previous study along with GGT and other enzymes (<xref ref-type="bibr" rid="B36">36</xref>), AST&#x00027;s sensitivity to liver damage is 80% for calves &#x0003C;6 w old thus AST alone should not be considered clinical useful in diagnosing hepatic diseases. In addition, despite the statistical differences in AST throughout 2&#x02013;7 w, none of their pair-wise comparison fold changes were larger than 1.15 or &#x0003C;0.85, thus biologically these were not considered major differences. For ALT, its characterization in cow is seldom studied because is not so commonly used for hepatic disease diagnosis since its activity is low and isn&#x00027;t massively liberated into serum during hepatic diseases in large animal (<xref ref-type="bibr" rid="B2">2</xref>).</p>
<p>SOD and GPx also showed rather stable levels from 2 to 7 w. Unfortunately, we didn&#x00027;t find any RIs of GPx for adult; and for SOD only RI of cow during transition period (60 d before until 100 d after milk) was found. Both mineral dependent enzymes [Mn, Cu and Zn for SOD (<xref ref-type="bibr" rid="B37">37</xref>) and Se for (<xref ref-type="bibr" rid="B2">2</xref>, <xref ref-type="bibr" rid="B38">38</xref>)] are important enzymes whereas SOD can dismutate two O<inline-formula><mml:math id="M3"><mml:msubsup><mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mo>-</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula> molecules to H<sub>2</sub>O<sub>2</sub> and O<sub>2</sub> while GPx can catalyze H<sub>2</sub>O<sub>2</sub> to H<sub>2</sub>O (<xref ref-type="bibr" rid="B2">2</xref>). Study of SOD in RBC suggested that there is no such variation in the activity between cattle and human (<xref ref-type="bibr" rid="B39">39</xref>). Even though there are very few information about SOD and GPx, a better characterization may contribute to the monitor of minerals and oxidation in animal&#x00027;s body.</p>
<p>NEFAs and TGs were also elevated at 24 h because colostrum is rich in fat (<xref ref-type="bibr" rid="B40">40</xref>); their concentrations dropped at 2 w compared to 24 h and started increasing moderately with age until 7 w. While the level of NEFAs is almost comparable with adult&#x00027;s RI roughly from 5 w, TG&#x00027;s level is still beyond the adult&#x00027;s RI upper limit at 7 w. TGs can richly appear in chylomicrons, which are secreted by the intestine after fat-containing meal in preruminant calves. The TGs from chylomicrons can be hydrolyzed to NEFAs and then be transported to tissue for fat storage or for oxidation to produce energy (<xref ref-type="bibr" rid="B41">41</xref>); on the other hand, serum NEFAs can increase in case of fasting or negative energy balance. Indeed, the growth of calf requires a large amount of energy from fat and evidences have already shown that extra fat can increase BW gains (<xref ref-type="bibr" rid="B42">42</xref>, <xref ref-type="bibr" rid="B43">43</xref>). An adult&#x00027;s-like RI in NEFAs from 5 w and a rather higher levels of TGs throughout the study indicate that the calves demanded high amount of energy for growth and no negative energy balance was noticed. Another form of TGs is very-low-density lipoprotein (VLDL) but it is generally low in bovine compared with in human (<xref ref-type="bibr" rid="B41">41</xref>).</p>
<p>Cholesterol had a very similar trend with one study (<xref ref-type="bibr" rid="B9">9</xref>) where the lowest concentration was found at 24 h and the concentrations started to increase smoothly. Even though it was previously described that colostrum contains higher cholesterol concentration compared with milk (<xref ref-type="bibr" rid="B44">44</xref>) and the serum cholesterol concentration is proportional to the colostral cholesterol concentration (<xref ref-type="bibr" rid="B45">45</xref>), the increase in total cholesterol along with increases in NEFAs and TGs probably indicate a major lipid mobilization within tissues after 24 h.</p>
<p>Glucose showed significant drop throughout age. The glucose supply from mother in neonate stops abruptly immediately after birth and the suckling neonate is entirely dependent on glucose supply via colostrum/milk, meanwhile the neonate needs to rapidly turn on pathways of endogenous glucose production as well as for fatty acid oxidation to maintain its organs and cells&#x00027; functionality (<xref ref-type="bibr" rid="B46">46</xref>). The decrease of glucose and increase of NEFAs and TGs during the first 7 w may be the consequence of the high fat and relatively low carbohydrate content of colostrum/milk (<xref ref-type="bibr" rid="B47">47</xref>, <xref ref-type="bibr" rid="B48">48</xref>). Despite the constant fall of serum glucose throughout the study, the RIs obtained from last measurement was still higher than the adult&#x00027;s one.</p>
<p>Moreover, creatinine, TP and urea also decreased throughout age until 7 w. Creatinine is a breakdown product of creatine (Cr) and creatine phosphate (PCr) in muscle and is cleared by the kidney. In human neonatology, it was well explained that the kidneys in fetus don&#x00027;t play a major role in maintaining fetal homeostasis (<xref ref-type="bibr" rid="B49">49</xref>, <xref ref-type="bibr" rid="B50">50</xref>); also, the high blood concentration of creatinine comes from maternal transfer and endogenous Cr and PCr degradation and it does not indicate fetal renal failure. It was noticed that at 2 and 5 w the median creatinine levels were around the bottom line of the adult&#x00027;s RI while at 7 w it was below [median and SD at 7 w was 0.66 &#x000B1; 0.12 mg/dL and adult&#x00027;s RIs are 1.0&#x02013;2.0 mg/dL according to Kaneko et al. (<xref ref-type="bibr" rid="B2">2</xref>) and 0.9&#x02013;1.4 mg/dL according to P&#x000E9;rez-Santos et al. (<xref ref-type="bibr" rid="B11">11</xref>)], comparing to another similar study (<xref ref-type="bibr" rid="B11">11</xref>), where mean creatinine laid within normal adult&#x00027;s RI from 6 d until 90 d of age. Yet we didn&#x00027;t find any other issues related to the muscle or health in these calves and a possible reason of this is due to diet/season/genetic variabilities between the farms. TP was also elevated at 24 h due to passive transfer of protein which mainly contains immunoglobulins from colostrum (<xref ref-type="bibr" rid="B32">32</xref>, <xref ref-type="bibr" rid="B51">51</xref>, <xref ref-type="bibr" rid="B52">52</xref>). This result is similar to those were reported in previous studies (<xref ref-type="bibr" rid="B11">11</xref>, <xref ref-type="bibr" rid="B53">53</xref>). Urea had a similar change pattern to the TP along the time. It is the product of protein breakdown (<xref ref-type="bibr" rid="B52">52</xref>) and is synthesized by the liver and finally excreted by the kidney, which is directly influenced by the total protein level.</p>
<p>A special attention was paid to markers of stress since assessing a good welfare status is one of the main problems in calf production. The levels of haptoglobin didn&#x00027;t change along the time, similar to one previous study (<xref ref-type="bibr" rid="B54">54</xref>) and are lying within the health adult&#x00027;s RI range (10 mg/dL) (<xref ref-type="bibr" rid="B27">27</xref>). It is important to have specific RIs for haptoglobin in young calves since it is an acute-phase protein used as biomarker of inflammatory and infectious diseases (<xref ref-type="bibr" rid="B55">55</xref>), very common in young calves. The individuals with high haptoglobin values identified as outliers may be individuals with subclinical diseases, which were recognized as healthy in a clinical inspection. The antioxidant enzymes GPx and SOD, markers of oxidative stress (<xref ref-type="bibr" rid="B56">56</xref>) had higher values at 24 h, but the RIs are similar afterwards.</p>
<p>As it was previously mentioned, on week 2 we found trial-biased distributions (<xref ref-type="supplementary-material" rid="SM6">Figure S1</xref>) on AST and cholesterol, where T3 showed lower concentration range compared with others. This reason may be because the average age in T3 is &#x0007E;2 d older than other two. This difference indicates that only 2-days age difference can cause mean concentration difference in some parameters, thus special care should be taken when comparing local clinical values with RIs in literature.</p>
</sec>
<sec>
<title>RIs for Post-weaning Piglets</title>
<p>No major changes were noticed on hepatic enzymes (ALT, AST, and GGT), glucose and TP. Cholesterol, NEFAs and TGs showed their highest values just after weaning (0 d). This may be due to the sudden dietary change from high fat content of milk to less-digestible, more-complex solid feed that piglets eat after weaning (<xref ref-type="bibr" rid="B57">57</xref>).</p>
<p>Throughout the ages, all medians of urea were within adult&#x00027;s RI while all medians of creatinine were &#x0003C;0.8 folds to the adult&#x00027;s RI lower fence. Both analytes showed highest concentrations at 7 d post-weaning. The increase in urea and creatinine only at 7 d post-weaning may be due to transient dehydration after the dietary change. These pattern changes are very similar to one study between 5-day-old unweaned piglets and 30-day-old post-weaning piglets (weaned at 28 d of life) supplemented with iron in solid diet (<xref ref-type="bibr" rid="B12">12</xref>). However, since this report only measured 5 d and 30 d, different from our opinion, the author&#x00027;s explanation of the increases was due to the increase of muscle mass and helped by the gradual supplementation of enriched solid diet.</p>
<p>Special interest has been devoted to markers of stress and inflammation. Thus, cortisol was measured as the paradigmatic stress hormone and found higher just after weaning, similar to other studies (<xref ref-type="bibr" rid="B58">58</xref>&#x02013;<xref ref-type="bibr" rid="B60">60</xref>), this probably is due to the stress associated to separation of the mother and change of diet; it&#x00027;s also interesting to notice that from 7 d post-weaning, the mean concentrations of piglets are comparable to healthy adults&#x00027; one (<xref ref-type="bibr" rid="B31">31</xref>), which suggests that in this study the diet change only caused a transient stress to the animals. The acute phase proteins haptoglobin and Pig-MAP were also measured; whereas haptoglobin RIs were not influenced by age and weaning, Pig-MAP was higher at 3 weeks-old just after weaning. Absolute values for haptoglobin were similar to those reported at 3-week-old (<xref ref-type="bibr" rid="B13">13</xref>). In that study, reference values for both acute phase proteins were established for pigs older than 4 weeks. Pig-MAP is also considered a stress marker, which increases in stressful conditions like transport (<xref ref-type="bibr" rid="B61">61</xref>&#x02013;<xref ref-type="bibr" rid="B63">63</xref>). Finally, the cytokine TNF-alpha is frequently used as a marker of infection, especially in gastrointestinal disorders which often affect piglets at weaning (<xref ref-type="bibr" rid="B31">31</xref>, <xref ref-type="bibr" rid="B64">64</xref>, <xref ref-type="bibr" rid="B65">65</xref>). For this reason, it is interesting to have RIs available in healthy animals. A moderate increase in TNF-alpha was shown at 7 and 14 days after weaning, probably due to the change of diet. This increase in serum may be secondary to the transient response in gene expression of inflammatory cytokines in the gut after weaning (<xref ref-type="bibr" rid="B66">66</xref>).</p>
<p>Similar to the calves, creatinine from 0 d post-weaning (3 w of age) was also comparable to the adult&#x00027;s RIs. GGT&#x00027;s RI is also comparable to adult&#x00027;s one from 7 d of age while for calves it takes 5 weeks to drop into adult&#x00027;s RI (<xref ref-type="bibr" rid="B2">2</xref>).</p>
</sec>
</sec>
<sec sec-type="conclusions" id="s5">
<title>Conclusions</title>
<p>Specific information about reference intervals for young animals is a requirement for the adequate clinical and nutritional management of the individuals. Even in farm animals, where collective evaluation of health is frequent instead of an individual assessment, the availability of specific RIs for young ages is essential to for an accurate diagnosis of health problems. In this sense, most important life periods in these two farm animal species: in cattle, colostrum uptake and suckling is a critical period during which the calf starts growing and obtains the immune defenses necessary for a sustained growth. In piglets, the post-weaning period is the most critical due to the high incidence of gastrointestinal and respiratory disorders. In this work, we have used a large number of individuals to obtain RIs specific for calves and piglets at these critical stages, and the most relevant parameters related to these stages evaluated.</p>
<p>The first set of analytical parameters are related to nutrition and metabolism, i.e., indicators of carbohydrate, lipid, and protein metabolism. Many health problems in young animals arise from nutritional unbalances and management procedures.</p>
<p>Secondly, enzymatic markers of oxidative stress were also evaluated. Oxidative stress is a relevant issue in cattle (<xref ref-type="bibr" rid="B67">67</xref>) and, specifically, neonates are particularly susceptible because of the challenge due to the transition from the hypoxic intrauterine environment to extrauterine life, susceptibility of neonates to infection, and limited antioxidant protection (<xref ref-type="bibr" rid="B68">68</xref>). In piglets, several diseases with severe consequences on health occur concomitantly with oxidative stress (<xref ref-type="bibr" rid="B56">56</xref>).</p>
<p>Third, markers of stress (cortisol) and inflammation/infection have been also evaluated. In this sense, the TNF-alpha cytokine and the acute phase proteins Pig-MAP and haptoglobin have been analyzed for piglets and calves, respectively.</p>
<p>This information will help veterinarians and animal science researchers to better practice their professions by providing them with more adequate diagnostic tools.</p>
</sec>
<sec id="s6">
<title>Ethics Statement</title>
<p>Male Holstein calves were managed according to the recommendations of the Animal Care Committee of Institut de Recerca i Tecnologia Agroaliment&#x000E0;ries (IRTA) under the approval research protocol FUE-2017-00587321, authorization code 9733. Piglets&#x00027; procedure has been approved by ethical committee (CEEAH) of Universitat Aut&#x000F2;noma de Barcelona (Registration no: 1406 of the Departament de Medi Ambient i Habitatge of the Generalitat de Catalunya).</p>
</sec>
<sec id="s7">
<title>Author Contributions</title>
<p>KY performed the manuscript writing, RI establishment and statistics analyses. FC provided statistical advices. Handling of animals and sample collection was performed by DS-O for piglets and MT for calves. LA, RP, and YS performed sample analyses and AB conducted in the conceptualization, supervised the analytical work, and revised the manuscript. All authors read and approved the final manuscript.</p>
<sec>
<title>Conflict of Interest Statement</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
</sec>
</body>
<back>
<ack><p>The authors gratefully thank all the staff of the IRTA (Torre Marimon, Caldes de Montbui, Spain) and pig farm (Els Campassos, Monistrol de Calders, Spain) for their assistance in feeding and care of the animals.</p>
</ack>
<sec sec-type="supplementary-material" id="s8">
<title>Supplementary Material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fvets.2019.00123/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fvets.2019.00123/full#supplementary-material</ext-link></p>
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<supplementary-material xlink:href="Table_2.XLSX" id="SM2" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Table_3.xlsx" id="SM3" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Table_4.xlsx" id="SM4" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Table_5.xlsx" id="SM5" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Image_1.PNG" id="SM6" mimetype="image/png" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Image_2.PNG" id="SM7" mimetype="image/png" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
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<fn fn-type="financial-disclosure"><p><bold>Funding.</bold> This research was supported by the Spanish Ministry of Economy and Competitivity (AGL2015-68463-C2-1-P to MT and AGL2015-68463-C2-2-P to AB). The author KY acknowledges China Scholarship Council (CSC) for the graduate scholarship program&#x00027;s funding.</p>
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