Unlocking the Potential of Stroke Blood Biomarkers: Early Diagnosis, Ischemic vs. Haemorrhagic Differentiation and Haemorrhagic Transformation Risk: A Comprehensive Review

Revolutionizing Stroke Care: Nanotechnology-Based Brain Delivery as a Novel Paradigm for Treatment and Diagnosis

Stroke, a severe medical condition arising from abnormalities in the coagulation-fibrinolysis cycle and metabolic processes, results in brain cell impairment and injury due to blood flow obstruction within the brain. Prompt and efficient therapeutic approaches are imperative to control and preserve brain functions. Conventional stroke medications, including fibrinolytic agents, play a crucial role in facilitating reperfusion to the ischemic brain. However, their clinical efficacy is hampered by short plasma half-lives, limited brain tissue distribution attributed to the blood-brain barrier (BBB), and lack of targeted drug delivery to the ischemic region. To address these challenges, diverse nanomedicine strategies, such as vesicular systems, polymeric nanoparticles, dendrimers, exosomes, inorganic nanoparticles, and biomimetic nanoparticles, have emerged. These platforms enhance drug pharmacokinetics by facilitating targeted drug accumulation at the ischemic site. By leveraging nanocarriers, engineered drug delivery systems hold the potential to overcome challenges associated with conventional stroke medications. This comprehensive review explores the pathophysiological mechanism underlying stroke and BBB disruption in stroke. Additionally, this review investigates the utilization of nanocarriers for current therapeutic and diagnostic interventions in stroke management. By addressing these aspects, the review aims to provide insight into potential strategies for improving stroke treatment and diagnosis through a nanomedicine approach.

Identification of m6A-Related Biomarkers in Systemic Lupus Erythematosus: A Bioinformation-Based Analysis

Background

Systemic Lupus Erythematosus (SLE), a prototypical autoimmune disorder, presents a challenge due to the absence of reliable biomarkers for discerning organ-specific damage within SLE. A growing body of evidence underscores the pivotal involvement of N6-methyladenosine (m6A) in the etiology of autoimmune conditions.

Methods

The datasets, which primarily encompassed the expression profiles of m6A regulatory genes, were retrieved from the Gene Expression Omnibus (GEO) repository. The optimal model, selected from either Random Forest (RF) or Support Vector Machine (SVM), was employed for the development of a predictive nomogram model. To identify pivotal genes associated with SLE, a comprehensive screening process was conducted utilizing LASSO, SVM-RFE, and RF techniques. Within the realm of SLE susceptibility, Weighted Gene Co-expression Network Analysis (WGCNA) was harnessed to delineate relevant modules and hub genes. Additionally, MeRIP-qPCR assays were performed to elucidate key genes correlated with m6A targets. Furthermore, a Mendelian randomization study was conducted based on genome-wide association studies to assess the causative influence of MMP9 on ischemic stroke (IS), which is not only a severe cerebrovascular event but also a common complication of SLE.

Results

Twelve m6A regulatory genes was identified, demonstrating statistical significance (p < 0.05) and utilized for constructing a nomogram model using the RF algorithm. EPSTI1, USP18, HP, and MMP9, as the hub genes, were identified. MMP9 uniquely correlates with m6A modification and was causally linked to an increased risk of IS, as indicated by our inverse variance weighting analysis showing an odds ratio of 1.0134 (95% CI=1.0004-1.0266, p = 0.0440).

Conclusion

Our study identified twelve m6A regulators, shedding light on the molecular mechanisms underlying SLE risk genes. Importantly, our analysis established a causal relationship between MMP9, a key m6A-related gene, and ischemic stroke, a common complication of SLE, thereby providing critical insights for presymptomatic diagnostic approaches.

The causal effects between gut microbiota and hemorrhagic stroke: a bidirectional two-sample Mendelian randomization study

Background

Recent studies have suggested that the composition of gut microbiota (GM) may change after intracerebral hemorrhage. However, the causal inference of GM and hemorrhagic stroke is unknown. Mendelian Randomization (MR) is an effective research method that removes confounding factors and investigates the causal relationship between exposure and outcome. This study intends to explore the causal relationship between GM and hemorrhagic stroke with the help of MR.

Methods

Univariable and multivariable MR analyses were performed using summary statistics of the GM (n = 18,340) in the MiBioGen consortium vs. the FinnGen consortium R9 summary statistics (intracerebral hemorrhage, subarachnoid hemorrhage, and nontraumatic intracranial hemorrhage). Causal associations between gut microbiota and hemorrhagic stroke were analyzed using inverse variance weighted, MR-Egger regression, weighted median, weighted mode, simple mode, and MR-PRESSO. Cochran's Q statistic, MR-Egger regression, and leave-one-out analysis were used to test for multiplicity and heterogeneity of instrumental variables. Separate reverse MR analyses were performed for microbiota found to be causally associated with hemorrhagic stroke in the forward MR analysis. Also, multivariate MR analyses were conducted after incorporating common confounders.

Results

Based on the results of univariable and multivariate MR analyses, Actinobacteria (phylum) (OR, 0.80; 95%CI, 0.66-0.97; p = 0.025) had a protective effect against hemorrhagic stroke, while Rikenellaceae RC9 gut group (genus) (OR, 0.81; 95%CI, 0.67-0.99; p = 0.039) had a potential protective effect. Furthermore, Dorea (genus) (OR, 1.77; 95%CI, 1.27-2.46; p = 0.001), Eisenbergiella (genus) (OR, 1.24; 95%CI, 1.05-1.48; p = 0.013) and Lachnospiraceae UCG008 (genus) (OR, 1.28; 95%CI, 1.01-1.62; p = 0.041) acted as potential risk factors for hemorrhagic stroke. The abundance of Dorea (genus) (β, 0.05; 95%CI, 0.002 ~ 0.101; p = 0.041) may increase, and that of Eisenbergiella (genus) (β, -0.072; 95%CI, -0.137 ~ -0.007; p = 0.030) decreased after hemorrhagic stroke according to the results of reverse MR analysis. No significant pleiotropy or heterogeneity was detected in any of the MR analyses.

Conclusion

There is a significant causal relationship between GM and hemorrhagic stroke. The prevention, monitoring, and treatment of hemorrhagic stroke through GM represent a promising avenue and contribute to a deeper understanding of the mechanisms underlying hemorrhagic stroke.