An online study combining the constructs from the theory of planned behaviour and protection motivation theory in predicting intention to test for chlamydia in two testing contexts

Chlamydia is a common sexually transmitted infection that has potentially serious consequences unless detected and treated early. The health service in the UK offers clinic-based testing for chlamydia but uptake is low. Identifying the predictors of testing behaviours may inform interventions to increase uptake. Self-tests for chlamydia may facilitate testing and treatment in people who avoid clinic-based testing. Self-testing and being tested by a health care professional (HCP) involve two contrasting contexts that may influence testing behaviour. However, little is known about how predictors of behaviour differ as a function of context. In this study, theoretical models of behaviour were used to assess factors that may predict intention to test in two different contexts: self-testing and being tested by a HCP. Individuals searching for or reading about chlamydia testing online were recruited using Google Adwords. Participants completed an online questionnaire that addressed previous testing behaviour and measured constructs of the Theory of Planned Behaviour and Protection Motivation Theory, which propose a total of eight possible predictors of intention. The questionnaire was completed by 310 participants. Sufficient data for multiple regression were provided by 102 and 118 respondents for self-testing and testing by a HCP respectively. Intention to self-test was predicted by vulnerability and self-efficacy, with a trend-level effect for response efficacy. Intention to be tested by a HCP was predicted by vulnerability, attitude and subjective norm. Thus, intentions to carry out two testing behaviours with very similar goals can have different predictors depending on test context. We conclude that interventions to increase self-testing should be based on evidence specifically related to test context.

Association between body composition, circulating irisin and telomere length in healthy individuals and individuals with Type 2 diabetes

Aims: Obesity and Type 2 diabetes are associated with accelerated ageing. The underlying mechanisms behind this, however, are poorly understood. In this study, we investigated the association between circulating irisin - a novel my okine involved in energy regulation - and telomere length (TL) (a marker of aging) in healthy individuals and individuals with Type 2 diabetes. Methods: Eighty-two healthy people and 67 subjects with Type 2 diabetes were recruited to this cross-sectional study. Anthropometric measurements including body composition measured by biompedance were recorded. Plasma irisin was measured by ELISA on a fasted blood sample. Relative TL was determined using real-time PCR. Associations between anthropometric measures and irisin and TL were explored using Pearson’s bivariate correlations. Multiple regression was used to explore all the significant predictors of TL using backward elimination. Results: In healthy individuals chronological age was a strong negative predictor of TL (=0.552, p < 0.001). Multiple regression analysis using backward elimination (excluding age) revealed the greater relative TL could be predicted by greater total muscle mass(b = 0.046, p = 0.001), less visceral fat (b = =0.183, p < 0.001)and higher plasma irisin levels (b = 0.01, p = 0.027). There were no significant associations between chronological age, plasmairisin, anthropometric measures and TL in patients with Type 2diabetes (p > 0.1). Conclusion: These data support the view that body composition and plasma irisin may have a role in modulation of energy balance and the aging process in healthy individuals. This relationship is altered in individuals with Type 2 diabetes.

Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics:comparison of discriminant analysis and multiple linear regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex (MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder. Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study, we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.