The adaptive immune system in the retina of diabetics.

As the prevalence of diabetes mellitus increases each year, its most common microvascular complication, diabetic retinopathy (DR), is also on the rise. DR is now regarded as an inflammatory disease in which innate immunity plays a crucial role, and a large number of innate immune cells with associated cytokines are involved in the pathologic process of DR. The role of adaptive immunity in DR is seldom mentioned, probably due to the general perception of the immune privileged environment of the retina; however, in recent years there has been a gradual increase in research on the role of adaptive immunity in DR, and with the discovery of the retinal lymphatic system, it seems that the role of adaptive immunity can no longer be ignored.

Aberrant Lipid Metabolism and Complement Activation in Age-Related Macular Degeneration.

Age-related macular degeneration (AMD) stands as a leading cause of severe visual impairment and blindness among the elderly globally. As a multifactorial disease, AMD's pathogenesis is influenced by genetic, environmental, and age-related factors, with lipid metabolism abnormalities and complement system dysregulation playing critical roles. This review delves into recent advancements in understanding the intricate interaction between these two crucial pathways, highlighting their contribution to the disease's progression through chronic inflammation, drusen formation, and retinal pigment epithelium dysfunction.

Disentangled Representation Learning.

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying and disentangling the underlying factors hidden in the observable data in representation form. The process of separating underlying factors of variation into variables with semantic meaning benefits in learning explainable representations of data, which imitates the meaningful understanding process of humans when observing an object or relation. As a general learning strategy, DRL has demonstrated its power in improving the model explainability, controlability, robustness, as well as generalization capacity in a wide range of scenarios such as computer vision, natural language processing, and data mining.