Search

In this cross-sectional and longitudinal analysis of mapping the T-cell repertoire in kidney transplant recipients, we have investigated and validated T-cell clonality, immune repertoire chronology at rejection, and contemporaneous allograft biopsy quantitative tissue injury, to better understand the pathobiology of acute T-cell fraction, T-cell repertoire and antibody-mediated kidney transplant rejection. To follow the dynamic evolution of T-cell repertoire changes before and after engraftment and during biopsy-confirmed acute rejection, we sequenced 323 peripheral blood samples from 200 unique kidney transplant recipients, with (n=100) and without (n=100) biopsy-confirmed acute rejection. We report that patients who develop acute allograft rejection, have lower (p=0.01) T-cell fraction even before transplantation, followed by its rise after transplantation and at the time of acute rejection accompanied by high TCR repertoire turnover (p=0.004). Acute rejection episodes occurring after the first 6 months post-transplantation, and those with a component of antibody-mediated rejection, had the highest turnover; p=0.0016) of their T-cell repertoire. In conclusion, we validated that detecting repertoire changes in kidney transplantation correlates with post-transplant rejection episodes suggesting that T-cell receptor sequencing may provide recipient pre-transplant and post-transplant predictors of rejection risk.

ABSTRACT: Variants of spatial interpolation and data‐driven methods to fill gaps in daily precipitation records are developed and evaluated in this study. The evaluated methods include variations of inverse distance and correlation weighting procedures, linear weight optimization and artificial neural networks. An already existing method, support vector logistic regression‐based copula, is also assessed. Optimal weights are estimated using inverse distance and correlation‐based weighting methods, post‐corrections of spatially interpolated estimates for rain or no rain classifications using support vector machine (SVM), and variations of a single best classifier (SBC) are used. The optimal number of gauges for use in spatial interpolation methods and for artificial neural network‐based method are selected. Three benchmark methods provide a basis against which all the methods are compared: single best estimator (SBE), and spatial and climatological mean estimators (SME and CME). All of the methods are tested for estimating varying amounts of missing precipitation data at 53 rain gauges located in South Florida, USA. Results show that the linear weight optimization method with an SBE provides the best estimates of daily precipitation values based on several performance metrics. Results from evaluation of different methods and their variants indicate use of optimized exponents in distance and correlation‐based weighting methods, classifiers for rain or no rain conditions, and an optimal number of neighbours in spatial interpolation improve estimates of missing data. Corrections to missing data estimates using nearest neighbours can help in improving the accuracy of rain and no rain state determinations with a possibility of introducing bias in estimates. [ABSTRACT FROM AUTHOR]