Corrigendum: Exploring the bi-directional relationship and shared genes between depression and stroke via NHANES and bioinformatic analysis.

[This corrects the article DOI: 10.3389/fgene.2023.1004457.].

Genetic correlations, shared risk genes and immunity landscapes between COVID-19 and venous thromboembolism: evidence from GWAS and bulk transcriptome data.

BACKGROUND: Patients with coronavirus disease 2019 (COVID-19) were vulnerable to venous thromboembolism (VTE), which further increases the risk of unfavorable outcomes. However, neither genetic correlations nor shared genes underlying COVID-19 and VTE are well understood. OBJECTIVE: This study aimed to characterize genetic correlations and common pathogenic mechanisms between COVID-19 and VTE. METHODS: We used linkage disequilibrium score (LDSC) regression and Mendelian Randomization (MR) analysis to investigate the genetic associations and causal effects between COVID-19 and VTE, respectively. Then, the COVID-19 and VTE-related datasets were obtained from the Gene Expression Omnibus (GEO) database and analyzed by bioinformatics and systems biology approaches with R software, including weighted gene co-expression network analysis (WGCNA), enrichment analysis, and single-cell transcriptome sequencing analysis. The miRNA-genes and transcription factor (TF)-genes interaction networks were conducted by NetworkAnalyst. We performed the secondary analysis of the ATAC-seq and Chip-seq datasets to address the epigenetic-regulating relationship of the shared genes. RESULTS: This study demonstrated positive correlations between VTE and COVID-19 by LDSC and bidirectional MR analysis. A total of 26 potential shared genes were discovered from the COVID-19 dataset (GSE196822) and the VTE dataset (GSE19151), with 19 genes showing positive associations and 7 genes exhibiting negative associations with these diseases. After incorporating two additional datasets, GSE164805 (COVID-19) and GSE48000 (VTE), two hub genes TP53I3 and SLPI were identified and showed up-regulation and diagnostic capabilities in both illnesses. Furthermore, this study illustrated the landscapes of immune processes in COVID-19 and VTE, revealing the downregulation in effector memory CD8+ T cells and activated B cells. The single-cell sequencing analysis suggested that the hub genes were predominantly expressed in the monocytes of COVID-19 patients at high levels. Additionally, we identified common regulators of hub genes, including five miRNAs (miR-1-3p, miR-203a-3p, miR-210-3p, miR-603, and miR-124-3p) and one transcription factor (RELA). CONCLUSIONS: Collectively, our results highlighted the significant correlations between COVID-19 and VTE and pinpointed TP53I3 and SLPI as hub genes that potentially link the severity of both conditions. The hub genes and their common regulators might present an opportunity for the simultaneous treatment of these two diseases.

Machine Learning Models for Brain Arteriovenous Malformations Presenting with Hemorrhage Based on Clinical and Angioarchitectural Characteristics.

RATIONALE AND OBJECTIVES: This study aims to develop the best diagnostic model for brain arteriovenous malformations (bAVMs) rupture by using machine learning (ML) algorithms. MATERIALS AND METHODS: We retrospectively included 353 adult patients with ruptured and unruptured bAVMs. The clinical and radiological data on patients were collected. The significant variables were selected using univariable logistic regression. We constructed and compared the predictive models using five supervised ML algorithms, multivariable logistic regression, and R2eDAVM scoring system. A complementary systematic review and meta-analysis of studies was aggregated to explore the potential predictors for bAVMs rupture. RESULTS: We found that a small nidus size of <3 cm, deep and infratentorial location, longer filling time, and deep and single venous drainage were associated with a higher risk of bAVMs rupture. The multilayer perceptron model showed the best performance with an area under the curve value of 0.736 (95% CI 0.67-0.801) and 0.713 (95% CI 0.647-0.779) in the training and test dataset, respectively. In our pooled analysis, we also found that the male sex, a single feeding artery, hypertension, non-White race, low Spetzler-Martin grade, and coexisting aneurysms are risk factors for bAVMs rupture. CONCLUSION: This study further demonstrated the clinical and angioarchitectural characteristics in predicting bAVMs hemorrhage.

Proximal Clipping and Distal High-Flow Bypass in the Treatment of Giant/Complex Intracranial Aneurysm: An Opportunity or a Risk from a Fluid-Structural Interaction Analysis.

OBJECTIVES: Conventional clipping and endovascular treatment are difficult to apply for some giant intracranial aneurysms (GIAs), and sometimes extracranial-to-intracranial (EC-IC) bypass becomes the optional choice. However, not all GIA patients can benefit from it. This study aims to recognize the underlying problems. METHODS: We included eligible patients in our care. Then, we researched from three levels: a retrospective review of clinical data, fluid-structural analysis from two representative patient-specific models, and fluid-structural interaction analysis for idealized models to investigate the hemodynamic and biomechanical mechanisms. RESULTS: In this article, we report nine patients with GIA who underwent EC-IC surgery. Of them, three experienced dangerous postoperative hemorrhage, and one patient died. Among these three patients, two lacked the A1 segment of the anterior cerebral artery (ACA). The numerical simulation showed that after surgery, for the patient with an unruptured aneurysm and existence of ACA, the wall deformation, wall stress, pressure, and area of the oscillatory shear index (OSI) > 0.2 were decreased by 43%, 39%, 33%, and 13%, while the patient without A1 segment having postoperative hemorrhage showed 36%, 45%, 13%, and 55% increased, respectively. Thus, we postulated a dangerous "stump phenomenon" in such conditions and further demonstrated it from idealized models with different sizes of ACA. Finally, we found a larger anastomosis angle and smaller diameter of the graft can alleviate this effect. CONCLUSIONS: Neurosurgeon should cautiously evaluate the opportunity and risk for such patients who have aplasia of the A1 segment of ACA when making clinical decisions.

Integrative analysis of multi-omics data to identify three immune-related genes in the formation and progression of intracranial aneurysms.

OBJECTIVE AND DESIGN: The prevalence of intracranial aneurysms (IAs) has increased globally. We performed bioinformatics analysis to identify key biomarkers associated with IA formation. METHODS AND RESULTS: We conducted a comprehensive analysis combined with multi-omics data and methods to identify immune-related genes (IRGs) and immunocytes involved in IAs. Functional enrichment analyses showed enhanced immune responses and suppressed organizations of extracellular matrix (ECM) during aneurysm progression. xCell analyses showed that the abundance of B cells, macrophages, mast cells, and monocytes significantly increased from levels in control to unruptured aneurysms and to ruptured aneurysms. Of 21 IRGs identified by overlapping, a three-gene (CXCR4, S100B, and OSM) model was constructed through LASSO logistic regression. The diagnostic ability of the three biomarkers in discriminating aneurysms from the control samples demonstrated a favorable diagnostic value. Among the three genes, OSM and CXCR4 were up-regulated and hypomethylated in IAs, while S100B was down-regulated and hypermethylated. The expression of the three IRGs was further validated by qRT-PCR and immunohistochemistry and mouse IA model using scRNA-seq analysis. CONCLUSION: The present study demonstrated heightened immune response and suppressed ECM organization in aneurysm formation and rupture. The three-gene immune-related signature (CCR4, S100B, and OSM) model may facilitate IA diagnosis and prevention.

Exploring the bi-directional relationship and shared genes between depression and stroke via NHANES and bioinformatic analysis.

Background: Stroke and depression are the two most common causes of disability worldwide. Growing evidence suggests a bi-directional relationship between stroke and depression, whereas the molecular mechanisms underlying stroke and depression are not well understood. The objectives of this study were to identify hub genes and biological pathways related to the pathogenesis of ischemic stroke (IS) and major depressive disorder (MDD) and to evaluate the infiltration of immune cells in both disorders. Methods: Participants from the United States National Health and Nutritional Examination Survey (NHANES) 2005-2018 were included to evaluate the association between stroke and MDD. Two differentially expressed genes (DEGs) sets extracted from GSE98793 and GSE16561 datasets were intersected to generate common DEGs, which were further screened out in cytoHubba to identify hub genes. GO, KEGG, Metascape, GeneMANIA, NetworkAnalyst, and DGIdb were used for functional enrichment, pathway analysis, regulatory network analysis, and candidate drugs analysis. ssGSEA algorithm was used to analyze the immune infiltration. Results: Among the 29706 participants from NHANES 2005-2018, stroke was significantly associated with MDD (OR = 2.79,95% CI:2.26-3.43, p < 0.0001). A total of 41 common upregulated genes and eight common downregulated genes were finally identified between IS and MDD. Enrichment analysis revealed that the shared genes were mainly involved in immune response and immune-related pathways. A protein-protein interaction (PPI) was constructed, from which ten (CD163, AEG1, IRAK3, S100A12, HP, PGLYRP1, CEACAM8, MPO, LCN2, and DEFA4) were screened. In addition, gene-miRNAs, transcription factor-gene interactions, and protein-drug interactions coregulatory networks with hub genes were also identified. Finally, we observed that the innate immunity was activated while acquired immunity was suppressed in both disorders. Conclusion: We successfully identified the ten hub shared genes linking the IS and MDD and constructed the regulatory networks for them that could serve as novel targeted therapy for the comorbidities.

Development and External Validation of Nomogram for Cerebral Infarction in Moyamoya Diseases.

More than 60% of moyamoya disease (MMD) patients suffers cerebral ischemia and preoperative cerebral infarction (CI) increases the risk of postoperative stroke and unfavorable outcome. We established a nomogram system for risk stratification of CI to help tailoring individualized management. We enrolled 380 patients including 680 hemispheres for the training cohort from our hospital and 183 patients including 348 hemispheres for the validation cohort from multicenter. A nomogram for CI was formulated based on the multivariable logistic regression analysis. The predictive accuracy and discriminative ability of nomogram were determined with concordance index (C-index) and calibration curve. For the training cohort, 246 hemispheres (36.2%) were found with CI. In multivariable logistic regression used generalized estimating equations approach, anterior choroidal artery (AchA) grade (grade 1, OR 0.214, 95%CI 0.124-0.372, P < 0.001; grade 2, OR 0.132, 95%CI 0.066-0.265, P < 0.001), cerebral perfusion (OR 4.796, 95%CI 2.922-7.872; P < 0.001), white matter hyperintensity (OR 3.652, 95%CI 1.933-6.902; P < 0.001), brush sign (OR 3.555, 95%CI 2.282-5.538; P < 0.001), and ivy sign (equivocal, OR 4.752, 95%CI 2.788-8.099, P < 0.001; present, OR 8.940, 95%CI 4.942-16.173, P < 0.001) were significant factors for CI. The C-index of the nomogram for predicting cerebral infarction was 0.890 (95%CI 0.866-0.915) in the training cohort and 0.847 (95%CI 0.805-0.889) in the validation cohort. The nomogram composed of AchA grade, cerebral perfusion, white matter hyperintensity, brush sign, and ivy sign could provide risk stratification of CI before surgery in patients with MMD. Active treatment might be recommended before CI, which could reduce the risk of stroke after surgery.

Antinociceptive C19-diterpenoid alkaloids from the roots of Aconitum nagarum.

Three new C19-diterpenoid alkaloids, nagarumines A-C (1-3), together two known alkaloids, deoxyaconitine (4) and N-deethyldeoxyaconitine (5), were isolated from the roots of Aconitum nagarum. The structures of the new compounds were elucidated by spectral methods such as 1D and 2D (1H-1H COSY, HMQC, and HMBC) NMR spectroscopy, as well as high resolution mass spectrometry. The in vivo pharmacological studies revealed that nagarumine C (3) possessed comparable antinociceptive activity (ED50 = 76.0 µmol/kg) with the positive control drugs aspirin and acetaminophen.

Genetically predicted tobacco consumption and risk of intracranial aneurysm: a Mendelian randomization study.

Several observational studies have suggested that tobacco consumption is a risk factor for intracranial aneurysms (IAs). We here genetically predict the causal association between specific smoking features and biomarkers for smokers and IA risk. The Mendelian randomization (MR) analysis considered summary statistics from the largest current genome-wide association studies of smoking and IA. The inverse-variance weighted (IVW) method, weighted median method, MR-RAPS, and multiple variants Mendelian randomization (MVMR) were performed to estimate the effect of different smoking features and drinking in IA. We observed significant causal effects of smoking on the risk of both aneurysmal subarachnoid hemorrhage (aSAH) and unruptured IA (uIA). The ORs of IAs based on the IVW method were 1.890 (95% CI 1.486-2.405) of ever smoking regularly. MVMR analysis afforded odds ratios of 1.685 (95% CI 1.136-2.501). In the further subgroup analysis, a similar causal relationship was observed in aSAH. Moreover, our analyses suggested that higher blood cotinine level and cadmium increases aSAH risk, and ORs were 1.235 (95%CI 1.009-1.186) and 1.235 (95%CI 1.046-1.458), respectively. Our study suggests that ever smoking regularly is associated with the IA risk, which includes both uIA and aSAH. Besides, higher blood cadmium and cotinine level may increases IA and aSAH risk. Thus, tobacco control should be promoted as primordial prevention for IAs, and screening for patients with a smoking history is emphasized.

Global trends and Frontier topics about vascular smooth muscle cells phenotype switch: A bibliometric analysis from 1999 to 2021.

Objective: Vascular smooth muscle cell phenotype switch (VSMCPS) plays a significant role in vascular remodeling. This study aimed to conduct a bibliometric analysis and visualize the knowledge map of research on VSMCPS. Methods: We retrieved publications focusing on VSMCPS from the Web of Science Core Collection database (SCI-EXPANDED) from 1999 to 2021. Using bibliometric tools, VOSviewer and CiteSpace, we identified the most productive researchers, journals, institutions, and countries. At the same time, the trends, hot topics, and knowledge networks were analyzed and visualized. Results: A total of 2213 publications were included in this analysis. The number of annual publications in the VSMCPS field exhibited an upward trend and could be roughly divided into three phases. Until 2006, the most prolific authors were from the United States. As of 2008, the number of articles published in China increased dramatically to reach 126 papers in 2020. As of 2014, China was the most productive country in this field. The United States ranked first in the number of highly-influential authors, institutions, and literature from 1999 to 2022. Owens GK, Hata, Akiko, and Wen, jin-kun were the most prolific authors. Arteriosclerosis Thrombosis and Vascular Biology, Circulation Research, and Cardiovascular Research were the top-ranked journals in this field. "Vascular remodeling," "atherosclerosis," "neointima," "hypertension", and "inflammation" were the main researched topics. New diseases, new mechanisms, and new phenotype (e.g., micro RNA, macrophage-like-cell, hypoxia, autophagy, long noncoding RNA, oxidative stress, endoplasmic reticulum stress, senescence, aging, abdominal aortic aneurysm, and aortic dissection) represent the trending topics in recent years. Conclusion: This study systematically analyzed and visualized the knowledge map of VSMCPS over the past 2 decades. Our findings provide a comprehensive overview for scholars who want to understand current trends and new research frontiers in this area.

Comprehensive analysis of autoimmune-related genes in amyotrophic lateral sclerosis from the perspective of 3P medicine.

Background: Although growing evidence suggests close correlations between autoimmunity and amyotrophic lateral sclerosis (ALS), no studies have reported on autoimmune-related genes (ARGs) from the perspective of the prognostic assessment of ALS. The purpose of this study was to investigate whether the circulating ARD signature could be identified as a reliable biomarker for ALS survival for predictive, preventive, and personalized medicine. Methods: The whole blood transcriptional profiles and clinical characteristics of 454 ALS patients were downloaded from the Gene Expression Omnibus (GEO) database. A total of 4371 ARGs were obtained from GAAD and DisGeNET databases. Wilcoxon test and multivariate Cox regression were applied to identify the differentially expressed and prognostic ARGs. Then, unsupervised clustering was performed to classify patients into two distinct autoimmune-related clusters. PCA method was used to calculate the autoimmune index. LASSO and multivariate Cox regression was performed to establish risk model to predict overall survival for ALS patients. A ceRNA regulatory network was then constructed for regulating the model genes. Finally, we performed single-cell analysis to explore the expression of model genes in mutant SOD1 mice and methylation analysis in ALS patients. Results: Based on the expressions of 85 prognostic ARGs, two autoimmune-related clusters with various biological features, immune characteristics, and survival outcome were determined. Cluster 1 with a worsen prognosis was more active in immune-related biological pathways and immune infiltration than Cluster 2. A higher autoimmune index was associated with a better prognosis than a lower autoimmune index, and there were significant adverse correlations between the autoimmune index and immune infiltrating cells and immune responses. Nine model genes (KIF17, CD248, ENG, BTNL2, CLEC5A, ADORA3, PRDX5, AIM2, and XKR8) were selected to construct prognostic risk signature, indicating potent potential for survival prediction in ALS. Nomogram integrating risk model and clinical characteristics could predict the prognosis more accurately than other clinicopathological features. We constructed a ceRNA regulatory network for the model genes, including five lncRNAs, four miRNAs, and five mRNAs. Conclusion: Expression of ARGs is correlated with immune characteristics of ALS, and seven ARG signatures may have practical application as an independent prognostic factor in patients with ALS, which may serve as target for the future prognostic assessment, targeted prevention, patient stratification, and personalization of medical services in ALS. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-022-00299-w.

Animal model contributes to the development of intracranial aneurysm: A bibliometric analysis.

Introduction: Studies on intracranial aneurysms (IAs) using animal models have evolved for decades. This study aimed to analyze major contributors and trends in IA-related animal research using bibliometric analysis. Methods: IA-related animal studies were retrieved from the Web of Science database. Microsoft Excel 2010, GraphPad Prism 6, VOSviewer, and CiteSpace were used to collect and analyze the characteristics of this field. Results: A total of 273 publications were retrieved. All publications were published between 1976 and 2021, and the peak publication year is 2019. Rat model were used in most of the publications, followed by mice and rabbits. Japan (35.5%), the United States (30.0%), and China (20.1%) were the top three most prolific countries. Although China ranks third in the number of publications, it still lacks high-quality articles and influential institutions. Stroke was the most prolific journal that accepted publications related to IA research using animal models. Circulation has the highest impact factor with IA-related animal studies. Hashimoto N contributed the largest number of articles. Meng hui journal published the first and second highest cited publications. The keywords "subarachnoid hemorrhage," "macrophage," "rupture," "mice," "elastase," "gene," "protein," "proliferation," and "risk factors" might be a new trend for studying IA-related animal research. Conclusions: Japan and the Unites States contributed the most to IA-related animal studies, in terms of both researchers and institutions. Although China ranks third in terms of the number of publications, it should strengthen the quality of its publications. Researchers should pay attention to the latest progress of Stroke, Journal of Neurosurgery, Neurosurgery, and Circulation for their high-quality IA-related animal studies. Using animal IA models, especially mice, to investigate the molecular mechanisms of IA may be the frontier topic now and in future.

Construction of an immune-related signature for predicting the ischemic events in patients undergoing carotid endarterectomy.

Background: Inflammatory responses have drawn more attention to atherosclerosis; however, the immune-related genes (IRGs) as a prognostic factor in atherosclerotic plaque remain to be fully elucidated. Here, the purpose of this study was to investigate whether the IRGs could be identified as a reliable biomarker for predicting ischemic events in patients undergoing carotid endarterectomy (CEA). Methods: Two datasets GSE97210 and GSE21545 were downloaded from the Gene Expression Omnibus (GEO) database. The dataset GSE97210 was used to explore the significant pathways and differentially expressed IRGs (DEIRGs) between plaques and controls, which were further screened to identify the prognostic DEIRGs in the GSE21545 dataset. The identification of molecular subgroups with the prognostic gene expression patterns was achieved through nonnegative matrix factorization (NMF) clustering. Functional analyses including GO, KEGG, GSVA, and GSEA analyses, and immune analyses including xCell and ssGSEA algorithms were conducted to elucidate the underlying mechanisms. The prognostic risk model was constructed using the LASSO algorithm and multivariate Cox regression analysis. Results: A total of 796 DEIRGs (including 588 upregulated and 208 downregulated) were identified. Nine prognostic DEIRGs were further screened with univariate Cox regression analysis. Two clusters with different prognosis were grouped based on the prognostic DEIRGs. Immune infiltration analysis shows that cluster 2 with a better prognosis presented with a higher immune response than cluster 1. A prognostic model based on seven IRGs (IL2RA, NR4A2, DES, ERAP2, SLPI, RASGRP1, and AGTR2) was developed and verified. Consistent with the immune analysis of the cluster, the immune infiltration in the low-risk group with a better prognosis was also more active than that in the high-risk group. Finally, a nomogram based on the seven genes was constructed, which might have future implications in clinical care. Conclusion: The expression of immune-related genes is correlated with the immune microenvironment of atherosclerotic patients and could be applied to predict the ischemic events in patients undergoing CEA accurately.

Novel insight into ferroptosis-related genes, molecular subtypes, and immune characteristics in intracranial aneurysms.

OBJECTIVES: This study aimed to identify the role of ferroptosis in intracranial aneurysm (IA). METHODS: GSE122897, GSE75436, GSE15629, and GSE75434 datasets were downloaded from the Gene Expression Omnibus database. The differentially expressed ferroptosis-related genes (DEFRGs) were selected to construct a diagnostic model integrating with machine learning. Then, a consensus clustering algorithm was performed to classify IA patients into distinct ferroptosis-related clusters. Functional analyses, including GO, KEGG, GSVA, and GSEA analyses, were conducted to elucidate the underlying mechanisms. ssGSEA and xCell algorithms were performed to uncover the immune characteristics. RESULTS: We identified 28 DEFRGs between IAs and controls from the GSE122897 dataset. GO and KEGG results showed that these genes were enriched in cytokine activity, ferroptosis, and the IL-17 signaling pathway. Immune analysis showed that the IAs had higher levels of immune infiltration. A four FRGs model (MT3, CDKN1A, ZEP69B, and ABCC1) was established and validated with great IA diagnostic ability. We divided the IA samples into two clusters and found that cluster 2 had a higher proportion of rupture and immune infiltration. We identified 10 ferroptosis phenotypes-related markers in IAs. CONCLUSION: Ferroptosis and the immune microenvironment are closely associated with IAs, providing a basis for understanding the IA development.

Novel insight into m6A regulator-mediated methylation modification patterns and immune characteristics in intracranial aneurysm.

Background: Growing evidence demonstrated that m6A modification in cardiovascular diseases. However, how it is involved in the intracranial aneurysm (IA) is still unclear. This study aimed to identify the role of m6A modification in IA. Methods: Three datasets downloaded from the Gene Expression Omnibus (GEO) database were used, including GSE122897, GSE15629, and GSE3679. The landscapes of 24 m6A regulators were depicted using the STRING database, Pearson's correlation analysis, and Wilcoxon test. The targets of differentially expressed m6A (DEm6A) were predicted in the m6A2Target database and the modification m6A sites of hub targets were identified in SRAMP online tool. A diagnostic model based on DEm6A was constructed and verified in training and test databases. A consensus clustering algorithm was performed to classify IA patients into distinct m6A-related clusters. Functional analyses including gene ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set variation analysis, and gene set enrichment analysis analyses were conducted to elucidate the underlying mechanisms. ssGSEA algorithm was performed to uncover the immune characteristics. A PCA method was adopted to quantify the m6A score. Results: Nine DEm6A (IGF2BP1, IGF2BP3, YTHDF2, ZNF217, RBM15, YTHDF3, YTHDC1, FTO, and LRPPRC) significantly differed between IA and controls. Biological annotations showed that immune-related pathways (such as complement activation, inflammatory response, and interleukin signaling) and apoptosis were more enriched in IAs than in controls. Immune analyses indicate that the abundance of immune cells, immune responses, and HLA gene expression were elevated in IA samples than in controls. PCA results showed that IA has a lower m6A score than controls. An immune/apoptosis-related network modified by DEm6A was constructed. The m6A sites of six hub targets (CDK1, ASPM, AURKB, BUB1B, MKI67, and TPX2) were predicted with very high confidence. A diagnostic model with four genes (LRPPRC, YTHDF3, IGF2BP1, and ZNF217) was constructed and verified. Two m6A modification subtypes were identified with unsupervised cluster analysis. Immune infiltration analysis revealed that cluster 1 had higher immune activation than cluster 2. Further study showed that cluster 1 had a larger proportion of ruptured IAs. Conclusion: The m6A modification may shape the IAs microenvironment and participates in the formation and rupture of IAs by regulating immune infiltration.

Angioarchitectural features of brain arteriovenous malformation presented with seizures.

Seizures are the second most common manifestations of brain arteriovenous malformations (bAVMs). This study was conducted to investigate the clinical and angioarchitectural features of bAVMs with seizures and provide guidelines for the clinical management of these patients. We collected clinical and radiological data on patients with bAVMs diagnosed by digital subtraction angiography between January 2013 and December 2020 and dichotomized the patients into the seizures and non-seizures groups. We identified differences in demographic and angiographic features. Logistic regression and random forest (RF) models were developed and compared. The diagnostic capacity was assessed using receiver operating characteristic (ROC) curves. A nomogram was constructed, and the clinical impact was determined by decision curve analysis. A total of 414 patients with bAVMs were included in the analysis, of which 78 (18.8%) had bAVM-related seizures. In the multivariable logistic regression model, the location and side of bAVMs were independently associated with seizures. In RF models, the maximal diameter of veins and the cross-sectional area of feeding arteries and draining veins were the most important features. ROC curves showed that the RF model was not better than MLR in predicting seizures. Decision curve analysis revealed that the use of a constructed nomogram to stratify the seizure patients was beneficial at all threshold probabilities in our study. The side and location of bAVMs are specific angioarchitectural features independently associated with the occurrences of seizures with bAVMs.

The Transcriptional Landscapes and Key Genes in Brain Arteriovenous Malformation Progression in a Venous Hypertension Rat Model Revealed by RNA Sequencing.

BACKGROUND: Brain arteriovenous malformations (bAVM) are abnormal vascular lesions characterized by direct connections between arteries and veins without an intervening capillary bed. The primary goal for brain AVM treatment is to prevent rupture and hemorrhage; however, the underlying molecular mechanisms are still unknown. METHODS: We constructed venous hypertension (VH) rat model with end-to-end anastomosis of the proximal left common carotid artery and the left distal external jugular vein. Thirty-eight adult rats were randomly assigned to four groups: the 0-week (n=5), the 1-week VH group (n=12), the 3-week VH group (n=9), and the 6-week VH group (n=12). We measured the hemodynamics and diameter of the arterialized veins. An RNA sequencing of arterialized veins was conducted, followed by comprehensive bioinformatics analysis to identify key genes and biological pathways involved in VH progression. The candidate genes from RNA-Seq were validated by RT-qPCR and immunostaining in human tissues. RESULTS: We observed high-flow and low resistance characteristics in VH models. A total of 317 upregulated and 258 downregulated common genes were consistently differentially expressed during VH progression. Thirteen co-expression modules were obtained by WGCNA analysis, and 4 key modules were identified. Thirteen genes: Adamts8, Adamtsl3, Spon2, Adamtsl2, Chad, Itga7, Comp, Itga8, Bmp6, Fst, Smad6, Smad7, Grem1, and Nog with differential expressions were identified using the density of maximum neighborhood component (DMNC) algorithm in Cytohubba. The expression of five potential genes (Adamts8, Adamtsl3, Spon2, Adamtsl2, Itga8) were increased in RT-qPCR, while in human bAVM tissue, the protein levels of Adamtsl2 and Itga8 were significant elevated and Spon2 and Adamtsl3 were decreased. CONCLUSION: The identified gene networks of Adamtsl3, Spon2, Adamtsl2, and Itga8 provided key genes for further intervention.

Medial Gap: A Structural Factor at the Arterial Bifurcation Aggravating Hemodynamic Insult.

Previous studies have reported that intracranial aneurysms frequently occur adjacent to the medial gap. However, the role of the medial gap in aneurysm formation is controversial. We designed this study to explore the potential role of the medial gap in aneurysm formation. Widened artery bifurcations with or without medial gaps were microsurgically created and pathologically stained in the carotid arteries of 30 rats. Numerical artery bifurcation models were constructed, and bidirectional fluid-solid interaction analyses were performed. Animal experiments showed that the apexes of widened bifurcations with a medial gap were prone to being insulted by blood flow compared to those without a medial gap. The bidirectional fluid-solid interaction analyses indicated that artery bifurcations with the medial gap exhibited higher wall shear stress (WSS) and von Mises stress (VMS) at the apex of the bifurcation. The disparity of stress between the gap and no-gap model was larger for widened bifurcations, peaking at 180° with a maximum of 1.9 folds. The maximum VMS and relatively high WSS were located at the junction between the medial gap and the adjacent arterial wall. Our results suggest that the medial gap at the widened arterial bifurcation may promote aneurysm formation.

Comprehensive analysis of immunocyte infiltration and the key genes associated with intraplaque hemorrhage in carotid atherosclerotic plaques.

PURPOSE: To identify key biomarkers associated with intraplaque hemorrhage (IPH). METHODS: We conducted a comprehensive analysis combined with DEGs, xCell, WGCNA, GSEA, and GSVA methods to identify immune infiltration cells and key genes involved in IPH by using GSE163154 from the gene expression omnibus (GEO). E-MTAB-1470 and E-MTAB-2055 from the ArrayExpress database were utilized as the verification datasets. Finally, the candidate hub genes were further validated by RT-qPCR in clinical samples. RESULTS: A total of 280 genes were upregulated and 234 genes were downregulated in GSE163154. Among the upregulated pathways, the lysosome and chemokine signaling pathway were enriched, while the vascular smooth muscle (VSMC) contraction and focal adhesion were downregulated. In addition, ten coexpression modules were obtained by using the WGCNA method and two IPH and immunity-related modules were identified. In total, 454 genes overlapped by DEGs and WGCNA results were imported into Cytoscape to construct a protein-protein network. Eight genes (FCER1G, ITGB2, VAV1, CSF1R, ITGAM, TYROBP, PTK2, and PTPN11) were identified as the IPH-related gene set with area under curves (AUC) of 0.961, 0.905, and 0.857 in GSE163154, E-MTAB-2055, and E-MTAB-1470, respectively. The expression of four genes (ITGB2, VAV1, ITGAM, TYROBP) from our analysis were consistent with RT-qPCR results. CONCLUSION: Eight genes were found to be involved in IPH, and four genes (ITGB2, VAV1, ITGAM, TYROBP) may be an important biomarkers for IPH.

Comprehensive molecular analyses of an autoimmune-related gene predictive model and immune infiltrations using machine learning methods in moyamoya disease.

Background: Growing evidence suggests the links between moyamoya disease (MMD) and autoimmune diseases. However, the molecular mechanism from genetic perspective remains unclear. This study aims to clarify the potential roles of autoimmune-related genes (ARGs) in the pathogenesis of MMD. Methods: Two transcription profiles (GSE157628 and GSE141025) of MMD were downloaded from GEO databases. ARGs were obtained from the Gene and Autoimmune Disease Association Database (GAAD) and DisGeNET databases. Differentially expressed ARGs (DEARGs) were identified using "limma" R packages. GO, KEGG, GSVA, and GSEA analyses were conducted to elucidate the underlying molecular function. There machine learning methods (LASSO logistic regression, random forest (RF), support vector machine-recursive feature elimination (SVM-RFE)) were used to screen out important genes. An artificial neural network was applied to construct an autoimmune-related signature predictive model of MMD. The immune characteristics, including immune cell infiltration, immune responses, and HLA gene expression in MMD, were explored using ssGSEA. The miRNA-gene regulatory network and the potential therapeutic drugs for hub genes were predicted. Results: A total of 260 DEARGs were identified in GSE157628 dataset. These genes were involved in immune-related pathways, infectious diseases, and autoimmune diseases. We identified six diagnostic genes by overlapping the three machine learning algorithms: CD38, PTPN11, NOTCH1, TLR7, KAT2B, and ISG15. A predictive neural network model was constructed based on the six genes and presented with great diagnostic ability with area under the curve (AUC) = 1 in the GSE157628 dataset and further validated by GSE141025 dataset. Immune infiltration analysis showed that the abundance of eosinophils, natural killer T (NKT) cells, Th2 cells were significant different between MMD and controls. The expression levels of HLA-A, HLA-B, HLA-C, HLA-DMA, HLA-DRB6, HLA-F, and HLA-G were significantly upregulated in MMD. Four miRNAs (mir-26a-5p, mir-1343-3p, mir-129-2-3p, and mir-124-3p) were identified because of their interaction at least with four hub DEARGs. Conclusion: Machine learning was used to develop a reliable predictive model for the diagnosis of MMD based on ARGs. The uncovered immune infiltration and gene-miRNA and gene-drugs regulatory network may provide new insight into the pathogenesis and treatment of MMD.