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. In this paper, the objective was to explore whether injection of a novel self-assembling peptide hydrogel (SAPH) Alpha2 was feasible, safe and could stimulate brain tissue regeneration, in a collagenase-induced ICH model in rats. Alpha2 was administered intracerebrally at 7 days post ICH and functional outcome measures, histological markers of damage and repair and RNA-sequencing were investigated for up to 8 weeks. The hydrogel Alpha2 was safe, well-tolerated and was retained in the lesion for several weeks, where it allowed infiltration of host cells. The hydrogel had a largely neutral effect on functional outcomes and expression of angiogenic and neurogenic markers but led to increased numbers of proliferating cells. RNAseq and pathway analysis showed that ICH altered genes related to inflammatory and phagocytic pathways, and these changes were also observed after administration of hydrogel. Overall, the results show that the novel hydrogel was safe when injected intracerebrally and had no negative effects on functional outcomes but increased cell proliferation. To elicit a regenerative effect, future studies could use a functionalised hydrogel or combine it with an adjunct therapy.

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. To minimise erroneous associations, findings were restricted to microRNAs reported to change in more than two independent studies. Of the papers assessed, 155 papers reported a change in microRNA expression observed in more than two independent studies. In ischaemic studies, two microRNAs were consistently differentially expressed in clinical samples (miR-29b & miR-146a) and four were altered in preclinical samples (miR-137, miR-146a, miR-181b & miR-223-3p). Across clinical and preclinical haemorrhagic studies, four microRNAs were downregulated consistently (miR-26a, miR-126, miR-146a & miR-155). Across included studies, miR-126 and miR-146a were the only two microRNAs to be differentially expressed in clinical and preclinical cohorts following ischaemic or haemorrhagic stroke. Further studies, employing larger populations with consistent methodologies, are required to validate the true clinical value of circulating microRNAs as biomarkers of ischaemic and haemorrhagic stroke.

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. New biomaterials such as hydrogels have been recently investigated for their therapeutic benefit in both experimental IS and TBI, owing to their provision of architectural support for damaged brain tissue and ability to deliver cellular and molecular therapies. Conversely, research on the use of hydrogels for ICH therapy is still in its infancy, with very few published reports investigating their therapeutic potential. Here, the published use of hydrogels in experimental ICH is commented upon and how approaches reported in the IS and TBI fields may be applied to ICH research to inform the design of future therapies is described. Unique aspects of ICH that are distinct from IS and TBI that should be considered when translating biomaterial-based therapies between disease models are also highlighted.

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. METHODS: Studies were identified by a comprehensive literature search of two online sources and subsequent screening. Studies using lesion volume or neurological score as outcome measures were included. Standardised mean difference (SMD) and 95% CIs were calculated using a restricted maximum likelihood random effects model. Publication bias was assessed with Egger's regression and presented as funnel plots with trim and fill analysis. Subgroup analysis was performed to assess the effects of different study variables. Study quality and risk of bias were assessed using the CAMARADES checklist. RESULTS: A total of 20 publications were included in the systematic review, of which 19 were assessed in the meta-analysis (43 comparisons). Overall, EV interventions improved lesion volume (SMD: -1.95, 95% CI -2.72 to 1.18) and neurological scores (SMD: -1.26, 95% CI -1.64 to 0.87) compared with control groups. Funnel plots were asymmetrical suggesting publication bias, and trim and fill analysis predicted seven missing studies for lesion volume. Subgroup analysis suggested administration at 0-23 hours after stroke was the most effective timepoint for EV treatment. The median score on the CAMARADES checklist was 7 (IQR: 5-8). CONCLUSIONS: EVs may offer a promising new avenue for stroke therapies, as EV-based interventions had positive impacts on lesion volume and neurological score in preclinical stroke models. PROSPERO REGISTRATION NUMBER: CRD42019134925.

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. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory-initially detected in brain-network topological self-organization and afterwards generalized to any complex network-is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug-target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering.