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Principal component analysis (PCA) showing the effects of the 47 significant discriminatory peaks on piglets positive or negative to PRRSV infection. The figure shows a projection of the measured peak intensities profiles onto the plane spanned by the three principal components (PCAs) that are the axes along which the data vary the most, for the 35 PRRSV-positive (blue) and the 35 PRRSV-negative (red) piglets of the validation study. PCA1, PCA2, and PCA3 accounted for 58.2%, 17.9%, and 12.9% of the variability in the data, respectively. PCA analysis illustrates a 3-dimentional plot comparison of PCA1, PCA2 and PCA3 in the three axes (A), as well as 2-dimentional score plot comparisons between PCA1 and PCA2 (B).

Principal component analysis (PCA) showing the effects of the 47 significant discriminatory peaks on piglets positive or negative to PRRSV infection. The figure shows a projection of the measured peak intensities profiles onto the plane spanned by the three principal components (PCAs) that are the axes along which the data vary the most, for the 35 PRRSV-positive (blue) and the 35 PRRSV-negative (red) piglets of the validation study. PCA1, PCA2, and PCA3 accounted for 58.2%, 17.9%, and 12.9% of the variability in the data, respectively. PCA analysis illustrates a 3-dimentional plot comparison of PCA1, PCA2 and PCA3 in the three axes (A), as well as 2-dimentional score plot comparisons between PCA1 and PCA2 (B).

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Background Porcine reproductive and respiratory syndrome (PRRS) is one of the most significant swine diseases worldwide. Despite its relevance, serum biomarkers associated with early-onset viral infection, when clinical signs are not detectable and the disease is characterized by a weak anti-viral response and persistent infection, have not yet bee...

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... analysis showed that 58.2% (PCA1), 17.9% (PCA2), and 12.9% (PCA3) of the total variability within the data was accounted for the X, Y, and Z axes, respectively. These axes were used to plot the data (Figure 2) and they provide an overview of the variation between the individual samples and show how samples grouped. Figure 2A showed three-dimensionally that the PCA peak profiles of piglets positive to PRRSV differed from piglets negative to PRRSV and revealed a good separation among the profiles of the two different groups, especially considering the high heterogeneity of the samples included in the study, as reported in the MM section and in [Additional file 1: Table S1 and Additional file 2: Table S2]. ...
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... axes were used to plot the data (Figure 2) and they provide an overview of the variation between the individual samples and show how samples grouped. Figure 2A showed three-dimensionally that the PCA peak profiles of piglets positive to PRRSV differed from piglets negative to PRRSV and revealed a good separation among the profiles of the two different groups, especially considering the high heterogeneity of the samples included in the study, as reported in the MM section and in [Additional file 1: Table S1 and Additional file 2: Table S2]. Furthermore, with the exception of few The 785 total number of peaks detected and the 200 statistically significant (p < 0.05) discriminatory peaks associated with PRRS infection that were identified by the Ciphergen Express software are reported with the fraction, the array surface, and the acquisition focus mass (low: 1-20 kDa; high: 20-200 kDa). ...
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... with the exception of few The 785 total number of peaks detected and the 200 statistically significant (p < 0.05) discriminatory peaks associated with PRRS infection that were identified by the Ciphergen Express software are reported with the fraction, the array surface, and the acquisition focus mass (low: 1-20 kDa; high: 20-200 kDa). outliers, PCA1 combined with PCA2 also separated well the two piglet populations ( Figure 2B). ...

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... Some more reports have instead dealt with specific pathological conditions: these conditions included bacterial (Muk et al. 2019) and viral (Genini et al. 2008;Liu et al. 2011;Genini et al. 2012;Koene et al. 2012) infections; trauma as hypoxia-ischemia (Kyng et al. 2018), hemorrhagic shock, tissue injury, liver reperfusion, hypothermia, and comminuted bone fracture (Cudjoe et al. 2019)), and diet-induced metabolic syndrome (Bell et al. 2010;Pas et al. 2018). The analytical approach was usually LC-MS/MS, but it was SELDI-TOF in a few cases (Genini et al. 2008;Genini et al. 2012;Koene et al. 2012). ...
... Some more reports have instead dealt with specific pathological conditions: these conditions included bacterial (Muk et al. 2019) and viral (Genini et al. 2008;Liu et al. 2011;Genini et al. 2012;Koene et al. 2012) infections; trauma as hypoxia-ischemia (Kyng et al. 2018), hemorrhagic shock, tissue injury, liver reperfusion, hypothermia, and comminuted bone fracture (Cudjoe et al. 2019)), and diet-induced metabolic syndrome (Bell et al. 2010;Pas et al. 2018). The analytical approach was usually LC-MS/MS, but it was SELDI-TOF in a few cases (Genini et al. 2008;Genini et al. 2012;Koene et al. 2012). ...
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... Quantitative translation initiation sequencing 339 (QTI-seq), a modified form of ribosome profiling which determines the location and approximate usage efficiency of initiation codons, could be used to assess whether initiation occurs in frame with ORF1b after prominent ORF1b sgRNA TRS bodies, and quantify any contribution this makes to overall ORF1b translation. Similarly to RNASeq, several proteomics datasets of PRRSV-infected cells are publicly available from studies which quantify changes in the host proteome in response to infection [719][720][721][722][723][724][725] . The viral proteome could be analysed in these datasets to confirm expression of ORF1b-overlapping ORFs and to determine whether the abundance of (ORF1b-frame) peptides from the 3′-proximal regions of ORF1b is greater than those at the 5′ end. ...
Thesis
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... Prdx-2 plays a role in different metabolic pathways, including mediating insulin sensitivity (Fazakerley et al., 2018;Kim et al., 2018). Several oxidative stress challenges in livestock-including heat stress (Cruzen et al., 2015), LPS immune challenge (Outhouse et al., 2015), physiological stress (Marco-Ramell et al., 2016), and health challenge (Genini et al., 2012)-affect abundance and profile of Prdx-2. ...
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