A systematic review of treatments for atopic dermatitis

Atopic dermatitis is a very common inflammatory skin disease, particularly in children. A systematic review of randomised controlled trials of treatments for atopic dermatitis (AD) was carried out to assess how many trials exist, what they cover, what they do not cover, the research gaps, provide a 'blue print' for future Cochrane Reviews and assist those making treatment recommendations by summarising the available RCT evidence, using descriptive statistics. The Cochrane Collaboration systematic review process formed the basis of the methodology, from which over 4000 studies were located via electronic database searches and hand searching of journals. A total of 292 trials were finally included covering 9 treatment groups and over 48 individual treatments. There are lots of trials covering lots of interventions but gaps are evident. However, there is evidence of a benefit in the treatment of atopic dermatitis with topical corticosteroids, psychological approaches, UV light, ascomycin derivatives, topical tacrolimus and oral cyclosporin. Treatments that show limited evidence of a benefit include non-sedatory antihistamines, topical doxepin, the oral antibiotic Cefadroxil on clinically infected AD, the topical antibacterial Mupirocin on clinically uninfected AD, Chinese herbs, hypnotherapy and biofeedback, massage therapy, dietary manipulation, house dust mite reduction, patient education, emollients, allergen antibody complexes of house dust mite and thymic extracts. Treatments that show no evidence of benefit include sedatory antihistamines, oral sodium cromoglycate, oral antibiotics on clinically uninfected AD, topical antibacterials, topical antifungals, aromatherapy essential oils, borage oil, fish oil, evening primrose oil, enzyme-free clothes detergent, cotton clothing, house dust mite hyposensitisation, salt baths, topical coal tar, topical cyclosporin and platelet-activating-factor antagonist. When interpreting the conclusions of this thesis it is important to understand that lack of evidence does not equal lack of efficacy, particularly considering the interventions that are commonly in use today to treat atopic dermatitis that have not been subjected to RCTs, such as occlusive dressings, water softening devices and stress management among many others.

AllerTOP - a server for in silico prediction of allergens

Background: Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences.Results: A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity.Conclusions: AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin. © 2013 Dimitrov et al.; licensee BioMed Central Ltd.