The Potential Role of the Leucocyte Immunoglobulin-Like Receptors in Kidney Transplant Rejection: A Mini Review.

Antibody-mediated rejection (ABMR) remains one of the main causes of long-term graft failure after kidney transplantation, despite the development of powerful immunosuppressive therapy. A detailed understanding of the complex interaction between recipient-derived immune cells and the allograft is therefore essential. Until recently, ABMR mechanisms were thought to be solely caused by adaptive immunity, namely, by anti-human leucocyte antigen (HLA) donor-specific antibody. However recent reports support other and/or additive mechanisms, designating monocytes/macrophages as innate immune contributors of ABMR histological lesions. In particular, in mouse models of experimental allograft rejection, monocytes/macrophages are readily able to discriminate non-self via paired immunoglobulin receptors (PIRs) and thus accelerate rejection. The human orthologs of PIRs are leukocyte immunoglobulin-like receptors (LILRs). Among those, LILRB3 has recently been reported as a potential binder of HLA class I molecules, shedding new light on LILRB3 potential as a myeloid mediator of allograft rejection. In this issue, we review the current data on the role of LILRB3 and discuss the potential mechanisms of its biological functions.

Deep learning automation of MEST-C classification in IgA nephropathy.

BACKGROUND: Although the MEST-C classification is among the best prognostic tools in immunoglobulin A nephropathy (IgAN), it has a wide interobserver variability between specialized pathologists and others. Therefore we trained and evaluated a tool using a neural network to automate the MEST-C grading. METHODS: Biopsies of patients with IgAN were divided into three independent groups: the Training cohort (n = 42) to train the network, the Test cohort (n = 66) to compare its pixel segmentation to that made by pathologists and the Application cohort (n = 88) to compare the MEST-C scores computed by the network or by pathologists. RESULTS: In the Test cohort, >73% of pixels were correctly identified by the network as M, E, S or C. In the Application cohort, the neural network area under the receiver operating characteristics curves were 0.88, 0.91, 0.88, 0.94, 0.96, 0.96 and 0.92 to predict M1, E1, S1, T1, T2, C1 and C2, respectively. The kappa coefficients between pathologists and the network assessments were substantial for E, S, T and C scores (kappa scores of 0.68, 0.79, 0.73 and 0.70, respectively) and moderate for M score (kappa score of 0.52). Network S and T scores were associated with the occurrence of the composite survival endpoint (death, dialysis, transplantation or doubling of serum creatinine) [hazard ratios 9.67 (P = .006) and 7.67 (P < .001), respectively]. CONCLUSIONS: This work highlights the possibility of automated recognition and quantification of each element of the MEST-C classification using deep learning methods.