3 results for Allardyce, R.

  • Classifying algorithms for SIFT-MS technology and medical diagnosis

    Moorhead, K.T.; Lee, D.S.; Chase, J.G.; Moot, A.R.; Ledingham, K.; Scotter, J.; Allardyce, R.; Sentilomohan, S.T.; Endre, Z. (2008)

    Journal Articles
    University of Canterbury Library

    Selected Ion Flow Tube-Mass spectrometry (SIFT-MS) is an analytical technique for realtime quantification of trace gases in air or breath samples. SIFT-MS system thus offers unique potential for early, rapid detection of disease states. Identification of volatile organic compound (VOC) masses that contribute strongly towards a successful classification clearly highlights potential new biomarkers. A method utilising kernel density estimates is thus presented for classifying unknown samples. It is validated in a simple known case and a clinical setting before–after dialysis. The simple case with nitrogen in Tedlar bags returned a 100% success rate, as expected. The clinical proof-of-concept with seven tests on one patient had an ROC curve area of 0.89. These results validate the method presented and illustrate the emerging clinical potential of this technology.

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  • Classification Algorithms for SIFT-MS Medical Diagnosis

    Moorhead, K.; Lee, D.; Chase, J.G.; Moot, A.; Ledingham, K.; Scotter, J.; Allardyce, R.; Senthilmohan, S.; Endre, Z. (2007)

    Conference Contributions - Published
    University of Canterbury Library

    Selected Ion Flow Tube - Mass Spectrometry (SIFTMS) is an analytical technique for the real-time quantification of trace gases in air or breath samples. The SIFT-MS system can potentially offer unique capability in the early and rapid detection of a wide variety of diseases, infectious bacteria and patient conditions, by using a classifier to differentiate between control and test groups. By identifying which masses and Volatile Organic Compounds (VOCs) contribute most strongly towards a successful classification, biomarkers for a particular disease state may be discovered. A classification method is presented and validated in a simple study in which saturated nitrogen in tedlar bags was differentiated from dry nitrogen in tedlar bags. Several biomarkers were identified, with the most reliable being N2H+.H2O, and isotopes and water clusters of H3O+, as expected. The classifier was then applied in a clinical setting to differentiate between patient breath samples after one and four hours of dialysis treatment. Biomarkers for classification were ammonia, acetaldehyde, ethanol, isoprene and acetone. The model classifies significantly better than random, with an ROC area of 0.89.

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  • Breath Ammonia Reduction Ratio (ARR) Measures Dialysis Efficacy

    Endre, Z.; Moorhead, K.; Storer, M.; Hu, W-P.; Dean, J.; Logan, K.; Allardyce, R.; Ledingham, K.; McGregor, D.; Senthilmohan, S.; Lee, D.; Scotter, J.; Chase, J.G. (2007)

    Conference Contributions - Other
    University of Canterbury Library

    Contemporary evidence supports the centuries old notion that expired breath and the headspaces above body fluids and products can serve as biomarkers of organ function. Clinical responsiveness to alterations in clinical status or therapy is dependent upon timely, accurate, relevant physiological data. Current measures of urea and creatinine to assess renal urea reduction are invasive and cannot be repeated frequently or reported quickly enough to define individual response to treatment in real time. In contrast, breath analysis is minimally invasive and can provide real time information about low molecular weight volatile organic compounds (VOCs) such as ammonia1,2.

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