Dean Billheimer

Abstract:

Aim: Rapidly evolving pathogens may exert diversifying selection on genes involved in host immune defence including those encoding antimicrobial peptides (AMPs). Amphibian skin peptides are one important defence against chytridiomycosis, an emerging infectious disease caused by the chytrid fungus Batrachochytrium dendrobatidis (Bd). We examined the population-level variation in this innate immune defence to understand its relationship with disease dynamics. Location: Queensland, Australia. Methods: We examined skin peptide defences in five geographically distinct populations of Australian green-eyed treefrogs, Litoria genimaculata. Skin peptide samples were collected from 52 frogs from three upland populations that previously declined as chytridiomycosis emerged, but subsequently recovered, and from 34 frogs in two lowland populations that did not decline. Historical samples of skin peptides preceding Bd emergence were not available from any population. Results: In general, lowland populations had more effective peptide defences than upland populations. Peptide profiles were similar among populations, although relative amounts of peptides expressed differed significantly among populations and were more variable in the uplands. Infected frogs in upland populations carried a significantly higher infection burden compared to lowland populations. The presence of effective AMPs in the skin of L. genimaculata does not eliminate infection; however, more effective peptide defences may limit infection intensity and the progression of disease. Main conclusions: The population bottleneck in upland populations caused by chytridiomycosis emergence did not appear to produce responses to selection for more effective peptide defences against chytridiomycosis compared to lowland populations of L. genimaculata. This does not exclude the possibility that current peptide defences have adapted in response to disease emergence. A suggestive (P 0.10) interaction between infection status and population indicates that in lowland populations, infected individuals tend to be those with lower relative intensities of AMPs, whereas in the upland populations, infected and uninfected individuals are similar. Thus, both the AMPs and the environment may act to mediate resistance to Bd infection. © 2010 Blackwell Publishing Ltd.

PMID: 17910796;Abstract:

Raman spectroscopy has been shown to have the potential for providing differential diagnosis in the cervix with high sensitivity and specificity in previous studies. The research presented here further evaluates the potential of near-infrared Raman spectroscopy to detect cervical dysplasia in a clinical setting. Using a portable system, Raman spectra were collected from the cervix of 79 patients using clinically feasible integration times (5 seconds on most patients). Multiple Raman measurements were taken from colposcopically normal and abnormal areas prior to the excision of tissue. Data were processed to extract Raman spectra from measured signal, which includes fluorescence and noise. The resulting spectra were correlated with the corresponding histopathologic diagnosis to determine empirical differences between different diagnostic categories. Using histology as the gold standard, logistic regression discrimination algorithms were developed to distinguish between normal ectocervix, squamous metaplasia, and high-grade dysplasia using independent training and validation sets of data. An unbiased estimate of the accuracy of the model indicates that Raman spectroscopy can distinguish between high-grade dysplasia and benign tissue with sensitivity of 89% and specificity of 81%, while colposcopy in expert hands was able to discriminate with a sensitivity and specificity of 87% and 72%. © 2007 Society for Applied Spectroscopy.

Clinical treatment decisions rely on prognostic evaluation of a patient's future health outcomes. Thus, predictive models under different treatment options are key factors for making good decisions. While many criteria exist for judging the statistical quality of a prediction model, few are available to measure its clinical utility. As a consequence, we may find that the addition of a clinical covariate or biomarker improves the statistical quality of the model, but has little effect on its clinical usefulness. We focus on the setting where a treatment decision may reduce a patient's risk of a poor outcome, but also comes at a cost; this may be monetary, inconvenience, or the potential side effects. This setting is exemplified by cancer chemoprevention, or the use of statins to reduce the risk of cardiovascular disease. We propose a novel approach to assessing a prediction model using a formal decision analytic framework. We combine the predictive model's ability to discriminate good from poor outcome with the net benefit afforded by treatment. In this framework, reduced risk is balanced against the cost of treatment. The relative cost-benefit of treatment provides a useful index to assist patient decisions. This index also identifies the relevant clinical risk regions where predictive improvement is needed. Our approach is illustrated using data from a colorectal adenoma chemoprevention trial.