Evaluation of haemoglobin in blister fluid as an indicator of paediatric burn wound depth.

The early and accurate assessment of burns is essential to inform patient treatment regimens; however, this first critical step in clinical practice remains a challenge for specialist burns clinicians worldwide. In this regard, protein biomarkers are a potential adjunct diagnostic tool to assist experienced clinical judgement. Free circulating haemoglobin has previously shown some promise as an indicator of burn depth in a murine animal model. Using blister fluid collected from paediatric burn patients, haemoglobin abundance was measured using semi-quantitative Western blot and immunoassays. Although a trend was observed in which haemoglobin abundance increased with burn wound severity, several patient samples deviated significantly from this trend. Further, it was found that haemoglobin concentration decreased significantly when whole cells, cell debris and fibrinous matrix was removed from the blister fluid by centrifugation; although the relationship to depth was still present. Statistical analyses showed that haemoglobin abundance in the fluid was more strongly related to the time between injury and sample collection and the time taken for spontaneous re-epithelialisation. We hypothesise that prolonged exposure to the blister fluid microenvironment may result in an increased haemoglobin abundance due to erythrocyte lysis, and delayed wound healing.

Urinary biomarkers of physical activity: candidates and clinical utility.

Chronic physical inactivity is a major risk factor for a number of important lifestyle diseases, while inappropriate exposure to high physical demands is a risk factor for musculoskeletal injury and fatigue. Proteomic and metabolomic investigations of the physical activity continuum - extreme sedentariness to extremes in physical performance - offer increasing insight into the biological impacts of physical activity. Moreover, biomarkers, revealed in such studies, may have utility in the monitoring of metabolic and musculoskeletal health or recovery following injury. As a diagnostic matrix, urine is non-invasive to collect and it contains many biomolecules, which reflect both positive and negative adaptations to physical activity exposure. This review examines the utility and landscape of biomarkers of physical activity with particular reference to those found in urine.

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.

A comparison of methods for classifying clinical samples based on proteomics data: a case study for statistical and machine learning approaches.

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called "omics" disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.