Cost-effective use of silver dressings for the treatment of hard-to-heal chronic venous leg ulcers.

AIM: To estimate the cost-effectiveness of silver dressings using a health economic model based on time-to-wound-healing in hard-to-heal chronic venous leg ulcers (VLUs). BACKGROUND: Chronic venous ulceration affects 1-3% of the adult population and typically has a protracted course of healing, resulting in considerable costs to the healthcare system. The pathogenesis of VLUs includes excessive and prolonged inflammation which is often related to critical colonisation and early infection. The use of silver dressings to control this bioburden and improve wound healing rates remains controversial. METHODS: A decision tree was constructed to evaluate the cost-effectiveness of treatment with silver compared with non-silver dressings for four weeks in a primary care setting. The outcomes: 'Healed ulcer', 'Healing ulcer' or 'No improvement' were developed, reflecting the relative reduction in ulcer area from baseline to four weeks of treatment. A data set from a recent meta-analysis, based on four RCTs, was applied to the model. RESULTS: Treatment with silver dressings for an initial four weeks was found to give a total cost saving (£141.57) compared with treatment with non-silver dressings. In addition, patients treated with silver dressings had a faster wound closure compared with those who had been treated with non-silver dressings. CONCLUSION: The use of silver dressings improves healing time and can lead to overall cost savings. These results can be used to guide healthcare decision makers in evaluating the economic aspects of treatment with silver dressings in hard-to-heal chronic VLUs.

Selecting informative data for developing peptide-MHC binding predictors using a query by committee approach.

Strategies for selecting informative data points for training prediction algorithms are important, particularly when data points are difficult and costly to obtain. A Query by Committee (QBC) training strategy for selecting new data points uses the disagreement between a committee of different algorithms to suggest new data points, which most rationally complement existing data, that is, they are the most informative data points. In order to evaluate this QBC approach on a real-world problem, we compared strategies for selecting new data points. We trained neural network algorithms to obtain methods to predict the binding affinity of peptides binding to the MHC class I molecule, HLA-A2. We show that the QBC strategy leads to a higher performance than a baseline strategy where new data points are selected at random from a pool of available data. Most peptides bind HLA-A2 with a low affinity, and as expected using a strategy of selecting peptides that are predicted to have high binding affinities also lead to more accurate predictors than the base line strategy. The QBC value is shown to correlate with the measured binding affinity. This demonstrates that the different predictors can easily learn if a peptide will fail to bind, but often conflict in predicting if a peptide binds. Using a carefully constructed computational setup, we demonstrate that selecting peptides with a high QBC performs better than low QBC peptides independently from binding affinity. When predictors are trained on a very limited set of data they cannot be expected to disagree in a meaningful way and we find a data limit below which the QBC strategy fails. Finally, it should be noted that data selection strategies similar to those used here might be of use in other settings in which generation of more data is a costly process.

Optimization and immune recognition of multiple novel conserved HLA-A2, human immunodeficiency virus type 1-specific CTL epitopes.

MHC-I-restricted cytotoxic responses are considered a critical component of protective immunity against viruses, including human immunodeficiency virus type 1 (HIV-1). CTLs directed against accessory and early regulatory HIV-1 proteins might be particularly effective; however, CTL epitopes in these proteins are rarely found. Novel artificial neural networks (ANNs) were used to quantitatively predict HLA-A2-binding CTL epitope peptides from publicly available full-length HIV-1 protein sequences. Epitopes were selected based on their novelty, predicted HLA-A2-binding affinity and conservation among HIV-1 strains. HLA-A2 binding was validated experimentally and binders were tested for their ability to induce CTL and IFN-gamma responses. About 69 % were immunogenic in HLA-A2 transgenic mice and 61 % were recognized by CD8(+) T-cells from 17 HLA-A2 HIV-1-positive patients. Thus, 31 novel conserved CTL epitopes were identified in eight HIV-1 proteins, including the first HLA-A2 minimal epitopes ever reported in the accessory and regulatory proteins Vif, Vpu and Rev. Interestingly, intermediate-binding peptides of low or no immunogenicity (i.e. subdominant epitopes) were found to be antigenic and more conserved. Such epitope peptides were anchor-optimized to improve immunogenicity and further increase the number of potential vaccine epitopes. About 67 % of anchor-optimized vaccine epitopes induced immune responses against the corresponding non-immunogenic naturally occurring epitopes. This study demonstrates the potency of ANNs for identifying putative virus CTL epitopes, and the new HIV-1 CTL epitopes identified should have significant implications for HIV-1 vaccine development. As a novel vaccine approach, it is proposed to increase the coverage of HIV variants by including multiple anchor-optimized variants of the more conserved subdominant epitopes.

Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.

In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.