Tong Liu

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.

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.