/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

https://www.kidney-international.org/article/S2157-1716(15)32188-2/pdf

Kidney donation after circulatory death (DCD): state of the art

https://onlinelibrary.wiley.com/doi/pdf/10.1111/ajt.14752

Sensitization in Transplantation: Assessment of Risk (STAR) 2017 Working Group Meeting Report.

Despite more than two decades of use, the optimal maintenance dose of tacrolimus for kidney transplant recipients is unknown. We hypothesized that HLA class II de novo donor-specific antibody (dnDSA) development correlates with tacrolimus trough levels and the recipient's individualized alloimmune risk determined by HLA-DR/DQ epitope mismatch. A cohort of 596 renal transplant recipients with 50,011 serial tacrolimus trough levels had HLA-DR/DQ eplet mismatch determined using HLAMatchmaker software. We analyzed the frequency of tacrolimus trough levels below a series of thresholds 6 months before dnDSA development in the same patients. Recipients with a high-risk HLA eplet mismatch score were less likely to tolerate low tacrolimus levels without developing dnDSA. We conclude that HLA-DR/DQ eplet mismatch and tacrolimus trough levels are independent predictors of dnDSA development. Recipients with high HLA alloimmune risk should not target tacrolimus levels <5 ng/ml unless essential, and monitoring for dnDSA may be advisable in this setting.

Class II Eplet Mismatch Modulates Tacrolimus Trough Levels Required to Prevent Donor-Specific Antibody Development

BackgroundA program of normothermic ex situ liver perfusion (NESLiP) was developed to facilitate better assessment and use of marginal livers, while minimizing cold ischemia.MethodsDeclined marginal livers and those offered for research were evaluated. Normothermic ex situ liver perfusion was perfor

/pdf/normothermic-perfusion-in-the-assessment-and-preservation-of-1n6jh4sg6f.pdf

Normothermic Perfusion in the Assessment and Preservation of Declined Livers Before Transplantation: Hyperoxia and Vasoplegia-Important Lessons From the First 12 Cases.

Analysis of ischemia/reperfusion injury in time‐zero biopsies predicts liver allograft outcomes

https://onlinelibrary.wiley.com/doi/pdf/10.1111/ajt.13162

Early graft loss after kidney transplantation: risk factors and consequences

https://onlinelibrary.wiley.com/doi/pdf/10.1111/ajt.13707

Alloantibody Responses After Renal Transplant Failure Can Be Better Predicted by Donor-Recipient HLA Amino Acid Sequence and Physicochemical Disparities Than Conventional HLA Matching.

We propose a new, generic and flexible methodology for non-parametric function estimation, in which we first estimate the number and locations of any features that may be present in the function and then estimate the function parametrically between each pair of neighbouring detected features. Examples of features handled by our methodology include change points in the piecewise constant signal model, kinks in the piecewise linear signal model and other similar irregularities, which we also refer to as generalized change points. Our methodology works with only minor modifications across a range of generalized change point scenarios, and we achieve such a high degree of generality by proposing and using a new multiple generalized change point detection device, termed narrowest-over-threshold (NOT) detection. The key ingredient of the NOT method is its focus on the smallest local sections of the data on which the existence of a feature is suspected. For selected scenarios, we show the consistency and near optimality of the NOT algorithm in detecting the number and locations of generalized change points. The NOT estimators are easy to implement and rapid to compute. Importantly, the NOT approach is easy to extend by the user to tailor to their own needs. Our methodology is implemented in the R package not.

Narrowest‐over‐threshold detection of multiple change points and change‐point‐like features

Summary
We study generalized additive models, with shape restrictions (e.g. monotonicity, convexity and concavity) imposed on each component of the additive prediction function. We show that this framework facilitates a non-parametric estimator of each additive component, obtained by maximizing the likelihood. The procedure is free of tuning parameters and under mild conditions is proved to be uniformly consistent on compact intervals. More generally, our methodology can be applied to generalized additive index models. Here again, the procedure can be justified on theoretical grounds and, like the original algorithm, has highly competitive finite sample performance. Practical utility is illustrated through the use of these methods in the analysis of two real data sets. Our algorithms are publicly available in the R package scar, short for shape-constrained additive regression.

/pdf/generalized-additive-and-index-models-with-shape-constraints-4j5t5a6klr.pdf

Yining Chen

Papers

Analysis of ischemia/reperfusion injury in time‐zero biopsies predicts liver allograft outcomes

Early graft loss after kidney transplantation: risk factors and consequences

Alloantibody Responses After Renal Transplant Failure Can Be Better Predicted by Donor-Recipient HLA Amino Acid Sequence and Physicochemical Disparities Than Conventional HLA Matching.

Narrowest‐over‐threshold detection of multiple change points and change‐point‐like features

Generalized additive and index models with shape constraints