| 1. |
- Cossarizza, A., et al.
(författare)
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Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)
- 2019
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Ingår i: European Journal of Immunology. - : Wiley. - 0014-2980 .- 1521-4141. ; 49:10, s. 1457-1973
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Tidskriftsartikel (refereegranskat)abstract
- These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.
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| 2. |
- Pantazis, N, et al.
(författare)
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Determining the likely place of HIV acquisition for migrants in Europe combining subject-specific information and biomarkers data
- 2019
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 28:7, s. 1979-1997
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Tidskriftsartikel (refereegranskat)abstract
- In most HIV-positive individuals, infection time is only known to lie between the time an individual started being at risk for HIV and diagnosis time. However, a more accurate estimate of infection time is very important in certain cases. For example, one of the objectives of the Advancing Migrant Access to Health Services in Europe (aMASE) study was to determine if HIV-positive migrants, diagnosed in Europe, were infected pre- or post-migration. We propose a method to derive subject-specific estimates of unknown infection times using information from HIV biomarkers’ measurements, demographic, clinical, and behavioral data. We assume that CD4 cell count (CD4) and HIV-RNA viral load trends after HIV infection follow a bivariate linear mixed model. Using post-diagnosis CD4 and viral load measurements and applying the Bayes’ rule, we derived the posterior distribution of the HIV infection time, whereas the prior distribution was informed by AIDS status at diagnosis and behavioral data. Parameters of the CD4–viral load and time-to-AIDS models were estimated using data from a large study of individuals with known HIV infection times (CASCADE). Simulations showed substantial predictive ability (e.g. 84% of the infections were correctly classified as pre- or post-migration). Application to the aMASE study ( n = 2009) showed that 47% of African migrants and 67% to 72% of migrants from other regions were most likely infected post-migration. Applying a Bayesian method based on bivariate modeling of CD4 and viral load, and subject-specific information, we found that the majority of HIV-positive migrants in aMASE were most likely infected after their migration to Europe.
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