16 research outputs found
A Fragment of the LG3 Peptide of Endorepellin Is Present in the Urine of Physically Active Mining Workers: A Potential Marker of Physical Activity
Biomarker analysis has been implemented in sports research in an attempt to monitor the effects of exertion and fatigue in athletes. This study proposed that while such biomarkers may be useful for monitoring injury risk in workers, proteomic approaches might also be utilised to identify novel exertion or injury markers. We found that urinary urea and cortisol levels were significantly elevated in mining workers following a 12 hour overnight shift. These levels failed to return to baseline over 24 h in the more active maintenance crew compared to truck drivers (operators) suggesting a lack of recovery between shifts. Use of a SELDI-TOF MS approach to detect novel exertion or injury markers revealed a spectral feature which was associated with workers in both work categories who were engaged in higher levels of physical activity. This feature was identified as the LG3 peptide, a C-terminal fragment of the anti-angiogenic/anti-tumourigenic protein endorepellin. This finding suggests that urinary LG3 peptide may be a biomarker of physical activity. It is also possible that the activity mediated release of LG3/endorepellin into the circulation may represent a biological mechanism for the known inverse association between physical activity and cancer risk/survival
Misclassification rates of dimension reduction classifiers using the trimmed datasets.
<p>Mean misclassification rates for each of the dimension reduction-based methods using the trimmed dataset to build the classification model. <b>A</b>) Is from the OC dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Lee1" target="_blank">[16]</a>, <b>B</b>) is from the Gaucher disease dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Hendriks1" target="_blank">[46]</a>, <b>C</b>) is from the LC datasets and <b>D</b>) is from the CRC dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Schleif1" target="_blank">[14]</a>. Blue circles illustrate PLS-LDA classification results, red triangles are from a PLS-RF classifier and purple crosses show results obtained from a PCA-LDA classifier.</p
Dimension Reduction Classifier Performance Summary.
<p>The performance summary (MCRâ=âMisclassification rate, AUCâ=âArea under the curve, Sensâ=âSensitivity, Specâ=âSpecificity, No. Componentsâ=âthe number of components used in the model) of each classifier for both the full dataset (âfullâ) and the trimmed dataset (âtrimmedâ) that underwent variable selection using a univariate moderated t-statistic. These are mean values based on 1000 bootstrap samples for each dataset except the OC data which used 200 bootstrap samples.</p
Comparison of PLS and PCA for dimension reduction.
<p>These plots demonstrate the capacity PLS has to separate classes based on the top 30 variables (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone-0024973-g004" target="_blank"><i>Figure 4A</i></a>) in the Gaucher dataset when compared to PCA (Note that this class separation is being heavily influenced by the loadings highlighted in Blue. Additionally, the vectors highlighted in red explain the within class variation in the control group. This is a key advantage PLS has over other methods.</p
SVM tuning results.
<p>The performance summary (<b>MCR</b>â=âMisclassification rate) of a SVM-based classifier for both the full dataset (âfullâ) and the trimmed dataset (âtrimmedâ) that underwent variable selection using a univariate moderated t-statistic. These are mean values based on 1000 bootstrap samples for each dataset except for the OC data which used 200 bootstrap samples.</p
Misclassification rates of dimension reduction classifiers using the untrimmed datasets.
<p>Mean misclassification rates for each of the dimension reduction-based methods using the full dataset (all variables) in the dataset to build the classification model. <b>A</b>) Is from the OC dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Lee1" target="_blank">[16]</a>, <b>B</b>) is from the Gaucher disease dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Hendriks1" target="_blank">[46]</a>, <b>C</b>) is from the LC datasets and <b>D</b>) is from the CRC dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Schleif1" target="_blank">[14]</a>. Blue circles illustrate PLS-LDA classification results, red triangles are from a PLS-RF classifier and purple crosses show results obtained from a PCA-LDA classifier.</p
The highly abundant urinary metabolite urobilin interferes with the bicinchoninic acid assay
Estimation of total protein concentration is an essential step in any protein- or peptide-centric analysis\ud
pipeline. This study demonstrates that urobilin, a breakdown product of heme and a major constituent of\ud
urine, interferes considerably with the bicinchoninic acid (BCA) assay. This interference is probably due\ud
to the propensity of urobilin to reduce cupric ions (Cu2+) to cuprous ions (Cu1+), thus mimicking the\ud
reduction of copper by proteins, which the assay was designed to do. In addition, it is demonstrated that\ud
the Bradford assay is more resistant to the influence of urobilin and other small molecules. As such, urobilin\ud
has a strong confounding effect on the estimate of total protein concentrations obtained by BCA\ud
assay and thus this assay should not be used for urinary protein quantification. It is recommended that\ud
the Bradford assay be used instead
The spectral feature at m/z 16881 is a broad tri-phasic peak, visible by SDS-PAGE.
<p><b>a</b>) The hypothesised pattern of intensity of m/z 16881 in stacked replicate spectra, expected to be observed in an SDS-PAGE gel. <b>b</b>) A band which matched the expected pattern of intensity for the feature at m/z 16881 was detected at âŒ20 kDa by SDS-PAGE (<b>arrow)</b> suggesting that the bands at âŒ20 kDa in the gel were the proteins which constituted m/z 16881 in the spectra. <b>c</b>) The protein at âŒ20 kDa was extracted from excised bands from a non-stained replicate SDS-PAGE gel. Examination of the extracted protein by SELDI-TOF MS confirmed that the âŒ20 kDa band was the feature originally detected at m/z 16881.</p
Urinary urea and cortisol levels trend toward recovery in operators but not in maintenance crew.
<p><b>a</b>) Urinary, urea levels were determined by an automated kinetic assay (analytic coefficient of variation being <4%). <b>b</b>) Urinary cortisol levels were determined by competitive immunoassay (analytic coefficient of variation being<4%). Both urea and cortisol measurements were standardised for dieresis against urinary creatinine levels which were determined by the Jaffe method (analytic coefficient of variation being<3%).</p