Background: Immune dysfunction contributes to a high rate of infection in patients with acute decompensation of cirrhosis. CD52 is a glycoprotein prominently expressed in lymphocytes. Immune regulation by CD52 may be involved in adaptive immune dysfunction in cirrhosis. This study aimed to investigate the function of CD52 on CD4(+) T cells on the blood of patients with acute decompensation of cirrhosis. Methods: The expression of CD52 in the peripheral blood lymphocytes of 49 patients with cirrhosis was investigated using flow cytometry and transcriptomics. Potential cis-membrane ligands of CD52 were discovered via proximity labelling followed by proteomics. The function of CD52 on antigen-specific activation of CD4(+) T cells was examined using flow cytometry in CD52 CRISPR-Cas9 knockout primary T cells. Findings: CD52 expression was elevated in CD4(+) T cells in acute decompensation of cirrhosis, and this elevation was correlated with increased disease severity and mortality. Components of the T cell receptor complex including TCR beta, CD3 gamma and CD3 epsilon were identified and validated as cis-membrane ligands of CD52. Knockout of CD52 promoted antigen-specific activation, proliferation, and pro-inflammatory cytokine secretion. Interpretation: Membrane bound CD52 demonstrated cis-interaction with the T cell receptor and served as a dynamic regulator of antigen-specific activation of CD4(+) T cells. The upregulation of CD52 in the periphery of acute decompensation of cirrhosis hinders the recognition of the T cell receptor by MHC, contributing to impaired T cell function. The development of an alternative anti-CD52 antibody is required to restore T cell function and prevent infections in cirrhosis.

End-stage liver disease (ESLD) from acute liver failure to compensated advanced chronic liver disease and decompensated cirrhosis at different stages (chronic decompensation, acute decompensation with or without acute-on-chronic liver failure) has high disease severity and poor patient outcome. Infection is a common complication in patients with ESLD and it is associated with a high mortality rate. Multiple mechanisms are involved in this marked susceptibility to infections, noticeably the inadequate immune response known as immune paresis, as part of cirrhosis-associated immune dysfunction (CAID). Specifically in the adaptive immune arm, lymphocyte impairments—including inadequate activation, reduced ability to secrete effector molecules and enhanced immune suppressive phenotypes—result in compromised systemic immune responses and increased risk of infections. This review summarises current knowledge of alterations in adaptive immune responsiveness and their underlying mechanisms in ESLD. Understanding these mechanisms is of crucial importance in the identification of potential therapeutic targets and applications of targeted treatments beyond antimicrobials, such as immunotherapy.

Alzheimer’s disease (AD), the most prevalent dementia, gradually reduces the cognitive abilities of patients while also posing a significant financial burden on the healthcare system. A variety of multi-task learning methods have recently been proposed to identify potential MRI-related biomarkers and accurately predict the progression of AD. These methods, however, all use a predefined task relation structure that is rigid and insufficient to adequately capture the intricate temporal relations among tasks. Instead, we propose a novel mechanism for directly and automatically learning the temporal relation and constructing it as an Automatic Temporal relation Graph (AutoTG). We use the sparse group Lasso to select a universal MRI feature set for all tasks and particular sets for various tasks in order to find biomarkers that are useful for predicting the progression of AD. To solve the biconvex and nonsmooth objective function, we adopt the alternating optimization and show that the two related suboptimization problems are amenable to closed-form solution of the proximal operator. To solve the two problems efficiently, the accelerated proximal gradient method is used, which has the fastest convergence rate of first-order method. We have preprocessed two latest AD datasets, and the experimental results verify our proposed novel multi-task approach outperforms several baseline methods. To demonstrate the high interpretability of our approach, we visualize the automatically learned temporal relation graph and investigate the temporal patterns of the important MRI features. The implementation source is at https://github.com/menghui-zhou/MAGPP.