Weisfeiler-Lehman goes dynamic: An analysis of the expressive power of Graph Neural Networks for attributed and dynamic graphs.

Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent theoretical studies on the expressive power of GNNs have focused on two issues. On the one hand, it has been proven that GNNs are as powerful as the Weisfeiler-Lehman test (1-WL) in their ability to distinguish graphs.

Intracerebral Administration of a Novel Self-Assembling Peptide Hydrogel Is Safe and Supports Cell Proliferation in Experimental Intracerebral Haemorrhage.

Intracerebral haemorrhage (ICH) is the deadliest form of stroke, but current treatment options are limited, meaning ICH survivors are often left with life-changing disabilities. The significant unmet clinical need and socioeconomic burden of ICH mean novel regenerative medicine approaches are gaining interest. To facilitate the regeneration of the ICH lesion, injectable biomimetic hydrogels are proposed as both scaffolds for endogenous repair and delivery platforms for pro-regenerative therapies.

Systematic review: Association between circulating microRNA expression & stroke.

This systematic review aimed to establish the range and quality of clinical and preclinical evidence supporting the association of individual microRNAs, and the use of microRNA expression in the diagnosis and prognosis of ischaemic or haemorrhagic stroke. Electronic databases were searched from 1993 to October 2021, using key words relevant to concepts of stroke and microRNA. Studies that met specific inclusion and exclusion criteria were selected for data extraction.

Regenerative Potential of Hydrogels for Intracerebral Hemorrhage: Lessons from Ischemic Stroke and Traumatic Brain Injury Research.

Intracerebral hemorrhage (ICH) is a deadly and debilitating type of stroke, caused by the rupture of cerebral blood vessels. To date, there are no restorative interventions approved for use in ICH patients, highlighting a critical unmet need. ICH shares some pathological features with other acute brain injuries such as ischemic stroke (IS) and traumatic brain injury (TBI), including the loss of brain tissue, disruption of the blood-brain barrier, and activation of a potent inflammatory response.

Therapeutic potential of extracellular vesicles in preclinical stroke models: a systematic review and meta-analysis.

Objectives: Currently there is a paucity of clinically available regenerative therapies for stroke. Extracellular vesicles (EV) have been investigated for their potential as modulators of regeneration in the poststroke brain. This systematic review and meta-analysis aims to provide a summary of the efficacy of therapeutic EVs in preclinical stroke models, to inform future research in this emerging field.

Machine learning meets complex networks via coalescent embedding in the hyperbolic space.

Physicists recently observed that realistic complex networks emerge as discrete samples from a continuous hyperbolic geometry enclosed in a circle: the radius represents the node centrality and the angular displacement between two nodes resembles their topological proximity. The hyperbolic circle aims to become a universal space of representation and analysis of many real networks. Yet, inferring the angular coordinates to map a real network back to its latent geometry remains a challenging inverse problem.

Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory.

The bipartite network representation of the drug-target interactions (DTIs) in a biosystem enhances understanding of the drugs' multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared-using standard and innovative validation frameworks-with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks.