1,721,061 research outputs found
Hope-Simpson progressive immunity hypothesis explains herpes-zoster incidence data
No full textEpidemics 4 - Fourth International Conference on Infectious Disease Dynamics , Amsterdam, The Netherlands, November 19-22, 2013 [Poster
Ageing, immune memory and the outcome of tuberculosis infection
No full textStochastic, statistical and computational approaches to immunology, Edinburgh, July 22-26, 2013 [oral presentation
The role of immunosenescence and generation of immune memory in dynamics of human Mycobacterium tuberculosis infection
No full textA machine learning pipeline for quantitative phenotype prediction from genotype data
No full textAbstract Background Quantitative phenotypes emerge everywhere in systems biology and biomedicine due to a direct interest for quantitative traits, or to high individual variability that makes hard or impossible to classify samples into distinct categories, often the case with complex common diseases. Machine learning approaches to genotype-phenotype mapping may significantly improve Genome-Wide Association Studies (GWAS) results by explicitly focusing on predictivity and optimal feature selection in a multivariate setting. It is however essential that stringent and well documented Data Analysis Protocols (DAP) are used to control sources of variability and ensure reproducibility of results. We present a genome-to-phenotype pipeline of machine learning modules for quantitative phenotype prediction. The pipeline can be applied for the direct use of whole-genome information in functional studies. As a realistic example, the problem of fitting complex phenotypic traits in heterogeneous stock mice from single nucleotide polymorphims (SNPs) is here considered. Methods The core element in the pipeline is the L1L2 regularization method based on the naïve elastic net. The method gives at the same time a regression model and a dimensionality reduction procedure suitable for correlated features. Model and SNP markers are selected through a DAP originally developed in the MAQC-II collaborative initiative of the U.S. FDA for the identification of clinical biomarkers from microarray data. The L1L2 approach is compared with standard Support Vector Regression (SVR) and with Recursive Jump Monte Carlo Markov Chain (MCMC). Algebraic indicators of stability of partial lists are used for model selection; the final panel of markers is obtained by a procedure at the chromosome scale, termed ’saturation’, to recover SNPs in Linkage Disequilibrium with those selected. Results With respect to both MCMC and SVR, comparable accuracies are obtained by the L1L2 pipeline. Good agreement is also found between SNPs selected by the L1L2 algorithms and candidate loci previously identified by a standard GWAS. The combination of L1L2-based feature selection with a saturation procedure tackles the issue of neglecting highly correlated features that affects many feature selection algorithms. Conclusions The L1L2 pipeline has proven effective in terms of marker selection and prediction accuracy. This study indicates that machine learning techniques may support quantitative phenotype prediction, provided that adequate DAPs are employed to control bias in model selection.</p
Impatto della vaccinazione per il papillomavirus umano: prospettive da un modello matematico
No full text46°CONGRESSO NAZIONALE S.It.I, Taormina, Italy, October 17-20, 2013 [poster
The roles of immune memory and aging in protective immunity and endogenous reactivation of tuberculosis.
No full textFinding more effective vaccines against tuberculosis (TB) and improved preventive treatments against endogenous reactivation of latent TB is strategic to block transmission and reach the WHO goal of eliminating TB by 2050. Key related open questions in TB research include: i) what are the determinants of a strong memory response upon primary infection? ii) what is the role of cytokines towards protective memory response against a secondary infection? iii) what are the mechanisms responsible for the increased risk of reactivation in elderly individuals? To address these questions, we explored a computational model of the immune response to Mycobacterium tuberculosis including a mathematical description of immunosenescence and the generation and maintenance of immune memory. Sensitivity analysis techniques, together with extensive model characterization and in silico experiments, were applied to identify key mechanisms controlling TB reactivation and immunological memory. Key findings of this study are summarized by the following model predictions: i) increased strength and duration of memory protection is associated with higher levels of Tumor Necrosis Factor-[Formula: see text] (TNF) during primary infection; ii) production of TNF, but not of interferon-[Formula: see text], by memory T cells during secondary infection is a major determinant of effective protection; iii) impaired recruitment of CD4+ T cells may promote reactivation of latent TB infections in aging hosts. This study is a first attempt to consider the immune dynamics of a persistent infection throughout the lifetime of the host, taking into account immunosenescence and memory. While the model is TB specific, the results are applicable to other persistent bacterial infections and can aid in the development, evaluation and refinement of TB treatment and/or vaccine protocols
Protective immunity against HPV infection as a consequence of lesion clearance. Model-based exploration of a hypothesis
No full textEpidemics4 - Fourth International Conference on Infectious Disease Dynamics , Amsterdam, The Netherlands, November 19-22, 2013
[Poster
Epidemiologic Quantities for Monkeypox Virus Clade I from Historical Data with Implications for Current Outbreaks, Democratic Republic of the Congo
No full textWe used published data from outbreak investigations of monkeypox virus clade I in the Democratic Republic of the Congo to estimate the distributions of critical epidemiological parameters. We estimated a mean incubation period of 9.9 days (95% credible interval [CrI] 8.5-11.5 days) and a mean generation time of 17.2 days (95% CrI 14.1-20.9 days) or 11.3 days (95% CrI 9.4-14.0 days), depending on the considered dataset. Presymptomatic transmission was limited. Those estimates suggest generally slower transmission dynamics in clade I than in clade IIb. The time-varying reproduction number for clade I in the Democratic Republic of the Congo was estimated to be below the epidemic threshold in the first half of 2024. However, in the South Kivu Province, where the newly identified subclade Ib has been associated with sustained human-to-human transmission, we estimated an effective reproduction number above the epidemic threshold (95% CrI 0.96-1.27)
Strong impact of demographic changes on Varicella and Herpes Zoster
No full textEpidemics 4 - Fourth International Conference on Infectious Disease Dynamics , Amsterdam, The Netherlands, November 19-22, 2013 [Poster
Estimates of Serial Interval and Reproduction Number of Sudan Virus, Uganda, August–November 2022
No full textWe estimated the mean serial interval for Sudan virus in Uganda to be 11.7 days (95 CI% 8.2–15.8 days). Estimates for the 2022 outbreak indicate a mean basic reproduction number of 2.4–2.7 (95% CI 1.7–3.5). Estimated net reproduction numbers across districts suggest a marked spatial heterogeneity
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