Comparison of Pipeline embolization device versus Tubridge embolization device in unruptured intracranial aneurysms: a multicenter, propensity score matched study.

BACKGROUND: Flow diverter devices (FDs) are increasingly used for treating unruptured intracranial aneurysms (UIAs), but limited studies compared different FDs. OBJECTIVE: To conduct a propensity score matched analysis comparing the Pipeline embolization device (PED) and Tubridge embolization device (TED) for UIAs. METHODS: Patients with UIAs treated with either PED or TED between July 2016 and July 2022 were included. Propensity score matching was performed to adjust for age, sex, comorbidities, smoking, drinking, aneurysm size, morphology, neck, location, parent artery diameter, adjunctive coiling, and angiographic follow-up duration. Perioperative complications and clinical and angiographic outcomes were compared after matching. RESULTS: 735 patients treated by PED and 290 patients treated by TED were enrolled. Compared with the PED group, patients in the TED group had a greater number of women and patients with ischemia, a smaller proportion of vertebrobasilar and non-saccular aneurysms, a smaller size and neck, and fewer adjunctive coils and overlapping stents, but a larger parent artery diameter and lumen disparities. After adjusting for these differences, 275 pairs were matched. No differences were found in perioperative complications (4.4% vs 2.5%, P=0.350), in-stent stenosis (16.0% vs 15.6%, P>0.999), or favorable prognosis (98.9% vs 98.5%, P>0.999). However, PED showed a trend towards better complete occlusion over a median 8-month angiographic follow-up (81.8% vs 75.3%, P=0.077). CONCLUSION: Compared with PED, TED provides a comparable rate of perioperative and short-term outcomes. Nevertheless, a better occlusion status in the PED group needs to be further verified over a longer follow-up period.

NOTCH4 Single-Nucleotide Polymorphism Is Associated with Brain Arteriovenous Malformation in a Chinese Han Population.

INTRODUCTION: Brain arteriovenous malformations (BAVMs) are high-flow intracranial vascular malformations characterized by the direct connection of arteries to veins without an intervening capillary bed. They are one of the main causes of intracranial hemorrhage and epilepsy, although morbidity is low. Angiogenesis, heredity, inflammation, and arteriovenous malformation syndromes play important roles in BAVM formation. Animal experiments and previous studies have confirmed that NOTCH4 may be associated with BAVM development. Our study identifies a connection between NOTCH4 gene polymorphisms and BAVM in a Chinese Han population. METHODS: We enrolled 150 patients with BAVMs confirmed by digital subtraction angiography (DSA) in the Department of Neurosurgery, Zhujiang Hospital, Southern Medical University from June 2017 to July 2019. Simultaneously, 150 patients without cerebrovascular disease were confirmed by computed tomography angiography/magnetic resonance angiography/DSA. DNA was extracted from peripheral blood and NOTCH4 genotypes were identified by PCR-ligase detection reaction. The χ2 test or Fisher's exact test was used to evaluate the differences in allele and genotype frequencies between the BAVM group, control group, bleeding group, and other complications. RESULTS: Two single-nucleotide polymorphisms (SNPs), rs443198 and rs438475, were significantly associated with BAVM. No SNP genotypes were significantly associated with hemorrhage or epilepsy. SNPs rs443198_AA-SNP and rs438475_AA-SNP may be associated with a lower risk of BAVM (p = 0.011, odds ratio (OR) = 0.459, 95% confidence interval (CI): 0.250-0.845; p = 0.033, OR = 0.759, 95% CI: 0.479-1.204). CONCLUSION: NOTCH4 gene polymorphisms were associated with BAVM and may be a risk factor in a Chinese Han population.

Evaluation of aneurysm rupture risk based upon flowrate-independent hemodynamic parameters: a multi-center pilot study.

BACKGROUND: Specifying generic flow boundary conditions in aneurysm hemodynamic simulations yields a great degree of uncertainty for the evaluation of aneurysm rupture risk. Herein, we proposed the use of flowrate-independent parameters in discriminating unstable aneurysms and compared their prognostic performance against that of conventional absolute parameters. METHODS: This retrospective study included 186 aneurysms collected from three international centers, with the stable aneurysms having a minimum follow-up period of 24 months. The flowrate-independent aneurysmal wall shear stress (WSS) and energy loss (EL) were defined as the coefficients of the second-order polynomials characterizing the relationships between the respective parameters and the parent-artery flows. Performance of the flowrate-independent parameters in discriminating unstable aneurysms with the logistic regression, Adaboost, and support-vector machine (SVM) methods was quantified and compared against that of the conventional parameters, in terms of sensitivity, specificity, and area under the curve (AUC). RESULTS: In discriminating unstable aneurysms, the proposed flowrate-independent EL achieved the highest sensitivity (0.833, 95% CI 0.586 to 0.964) and specificity (0.833, 95% CI 0.672 to 0.936) on the SVM, with the AUC outperforming the conventional EL by 0.133 (95% CI 0.039 to 0.226, p=0.006). Likewise, the flowrate-independent WSS outperformed the conventional WSS in terms of the AUC (difference: 0.137, 95% CI 0.033 to 0.241, p=0.010). CONCLUSION: The flowrate-independent hemodynamic parameters surpassed their conventional counterparts in predicting the stability of aneurysms, which may serve as a promising set of hemodynamic metrics to be used for the prediction of aneurysm rupture risk when physiologically real vascular boundary conditions are unavailable.

A deep learning-based automatic system for intracranial aneurysms diagnosis on three-dimensional digital subtraction angiographic images.

BACKGROUND: Intracranial aneurysms (IAs) are a life-threatening disease. Their rupture can lead to hemorrhagic stroke. Most studies applying deep learning for the detection of aneurysms are based on angiographic images. However, critical diagnostic information such as morphology and aneurysm location are not captured by deep learning algorithms and still require manual assessments. PURPOSE: Digital subtraction angiography (DSA) is the gold standard for aneurysm diagnosis. To facilitate the fully automatic diagnosis of aneurysms, we proposed a comprehensive system for the detection, morphology measurement, and location classification of aneurysms on three-dimensional DSA images, allowing automatic diagnosis without further human input. METHODS: The system comprised three neural networks: a network for aneurysm detection, a network for morphology measurement, and a network for aneurysm location identification. A cross-scale dual-path transformer module was proposed to effectively fuse local and global information to capture aneurysms of varying sizes. A multitask learning approach was also proposed to allow an accurate localization of aneurysm neck for morphology measurement. RESULTS: The cross-scale dual-path transformer module was shown to outperform other state-of-the-art network architectures, improving segmentation, and classification accuracy. The detection network in our system achieved an F2 score of 0.946 (recall 93%, precision 100%), better than the winning team in the Cerebral Aneurysm Detection and Analysis challenge. The measurement network achieved a relative error of less than 10% for morphology measurement, at the same level as human operators. Perfect accuracy (100%) was achieved on aneurysm location classification. CONCLUSIONS: We have demonstrated that a comprehensive system can automatically detect, measure morphology and report the aneurysm location of aneurysms without human intervention. This can be a potential tool for the diagnosis of IAs, improving radiologists' performance and reducing their workload.

Nonlinear Association of Glycosylated Hemoglobin With Single Intracranial Aneurysm Rupture in Patients With Diabetes Mellitus: A Cross-Sectional Study.

Background: The published literature linking diabetes mellitus (DM) to intracranial aneurysm (IA) ruptured has been controversial and limited by methodology. Thus, this study was performed to examine whether hyperglycemia control status is independently associated with single IA rupture in patients with DM. Methods: We conducted a cross-sectional study on two Chinese hospitals between January 2010 and November 2017. Medical records of 223 patients with single IA and DM were reviewed and analyzed. We used glycosylated hemoglobin (GHB) as the independent variable of interest, and the outcome variable was ruptured status of IA. Covariates included data on demographics, morphological parameters, lifestyle habits, clinical features, and comorbidities. Results: Multivariable adjusted binary logistic regression and sensitivity analyses indicated that GHB was not associated with IA rupture (odds ratio OR, = 1.07, 95% CI 0.84-1.35). A nonlinear association between GHB and IA rupture was observed, whose inflection points were 5.5 and 8.9. The OR values (95% confidence intervals) were 0.38 (0.16-0.9) at the range of 1.88-5.5% of GHB, 1.6 (1.03, 2.5) at the range of 5.5-8.9%, and 0.56 (0.06-5.34) at the range of 8.9-10.1, respectively. Conclusion: The independent correlation between GHB and risk of IA rupture presented is nonlinear. The good glycemic control in single IA patients with DM can reduce the risk of IA rupture, and vice versa.

Elucidating the Synergistic Effect of Multiple Chinese Herbal Prescriptions in the Treatment of Post-stroke Neurological Damage.

Background: Traditional Chinese medicine (TCM) has been widely used in the treatment of human diseases. However, the synergistic effects of multiple TCM prescriptions in the treatment of stroke have not been thoroughly studied. Objective of the study: This study aimed to reveal the mechanisms underlying the synergistic effects of these TCM prescriptions in stroke treatment and identify the active compounds. Methods: Herbs and compounds in the Di-Tan Decoction (DTD), Xue-Fu Zhu-Yu Decoction (XFZYD), and Xiao-Xu-Ming Decoction (XXMD) were acquired from the TCMSP database. SEA, HitPick, and TargetNet web servers were used for target prediction. The compound-target (C-T) networks of three prescriptions were constructed and then filtered using the collaborative filtering algorithm. We combined KEGG enrichment analysis, molecular docking, and network analysis approaches to identify active compounds, followed by verification of these compounds with an oxygen-glucose deprivation and reoxygenation (OGD/R) model. Results: The filtered DTD network contained 39 compounds and 534 targets, the filtered XFZYD network contained 40 compounds and 508 targets, and the filtered XXMD network contained 55 compounds and 599 targets. The filtered C-T networks retained approximately 80% of the biological functions of the original networks. Based on the enriched pathways, molecular docking, and network analysis results, we constructed a complex network containing 3 prescriptions, 14 botanical drugs, 26 compounds, 13 targets, and 5 pathways. By calculating the synergy score, we identified the top 5 candidate compounds. The experimental results showed that quercetin, baicalin, and ginsenoside Rg1 independently and synergistically increased cell viability. Conclusion: By integrating pharmacological and chemoinformatic approaches, our study provides a new method for identifying the effective synergistic compounds of TCM prescriptions. The filtered compounds and their synergistic effects on stroke require further research.

Morphology-aware multi-source fusion-based intracranial aneurysms rupture prediction.

OBJECTIVES: We proposed a new approach to train deep learning model for aneurysm rupture prediction which only uses a limited amount of labeled data. METHOD: Using segmented aneurysm mask as input, a backbone model was pretrained using a self-supervised method to learn deep embeddings of aneurysm morphology from 947 unlabeled cases of angiographic images. Subsequently, the backbone model was finetuned using 120 labeled cases with known rupture status. Clinical information was integrated with deep embeddings to further improve prediction performance. The proposed model was compared with radiomics and conventional morphology models in prediction performance. An assistive diagnosis system was also developed based on the model and was tested with five neurosurgeons. RESULT: Our method achieved an area under the receiver operating characteristic curve (AUC) of 0.823, outperforming deep learning model trained from scratch (0.787). By integrating with clinical information, the proposed model's performance was further improved to AUC = 0.853, making the results significantly better than model based on radiomics (AUC = 0.805, p = 0.007) or model based on conventional morphology parameters (AUC = 0.766, p = 0.001). Our model also achieved the highest sensitivity, PPV, NPV, and accuracy among the others. Neurosurgeons' prediction performance was improved from AUC=0.877 to 0.945 (p = 0.037) with the assistive diagnosis system. CONCLUSION: Our proposed method could develop competitive deep learning model for rupture prediction using only a limited amount of data. The assistive diagnosis system could be useful for neurosurgeons to predict rupture. KEY POINTS: • A self-supervised learning method was proposed to mitigate the data-hungry issue of deep learning, enabling training deep neural network with a limited amount of data. • Using the proposed method, deep embeddings were extracted to represent intracranial aneurysm morphology. Prediction model based on deep embeddings was significantly better than conventional morphology model and radiomics model. • An assistive diagnosis system was developed using deep embeddings for case-based reasoning, which was shown to significantly improve neurosurgeons' performance to predict rupture.

Automated Machine Learning Model Development for Intracranial Aneurysm Treatment Outcome Prediction: A Feasibility Study.

Background: The prediction of aneurysm treatment outcomes can help to optimize the treatment strategies. Machine learning (ML) has shown positive results in many clinical areas. However, the development of such models requires expertise in ML, which is not an easy task for surgeons. Objectives: The recently emerged automated machine learning (AutoML) has shown promise in making ML more accessible to non-computer experts. We aimed to evaluate the feasibility of applying AutoML to develop the ML models for treatment outcome prediction. Methods: The patients with aneurysms treated by endovascular treatment were prospectively recruited from 2016 to 2020. Treatment was considered successful if angiographic complete occlusion was achieved at follow-up. A statistical prediction model was developed using multivariate logistic regression. In addition, two ML models were developed. One was developed manually and the other was developed by AutoML. Three models were compared based on their area under the precision-recall curve (AUPRC) and area under the receiver operating characteristic curve (AUROC). Results: The aneurysm size, stent-assisted coiling (SAC), and posterior circulation were the three significant and independent variables associated with treatment outcome. The statistical model showed an AUPRC of 0.432 and AUROC of 0.745. The conventional manually trained ML model showed an improved AUPRC of 0.545 and AUROC of 0.781. The AutoML derived ML model showed the best performance with AUPRC of 0.632 and AUROC of 0.832, significantly better than the other two models. Conclusions: This study demonstrated the feasibility of using AutoML to develop a high-quality ML model, which may outperform the statistical model and manually derived ML models. AutoML could be a useful tool that makes ML more accessible to the clinical researchers.

U-Shaped Association of Aspect Ratio and Single Intracranial Aneurysm Rupture in Chinese Patients: A Cross-Sectional Study.

Background: Previous studies have analyzed the association of aspect ratio (AR) on the ruptured intracranial aneurysm (IA), but the findings are inconclusive and controversial. Therefore, the study aimed to derive a more detailed estimation of this association between AR and ruptured IA in Chinese IA patients. Methods: The present work was a cross-sectional study. We retrospectively collected 1,588 Chinese patients with a single IA from January 2010 to November 2017. The relationship was examined between AR at diagnosis and ruptured IA. Covariates included data of demographics, morphological parameters, lifestyle habits, clinical features, and comorbidities. Binary logistic regression and two-piecewise linear models were used to analyze independent associations of AR with ruptured IA. Results: The results suggest that the association between AR and IA rupture was U-shaped. In the AR range of 1.08-1.99, the prevalence of IA rupture was 13% lower for each 0.1-unit increment in AR [odds ratio 0.87, 95% confidence interval (CI) 0.80-0.98]. Conversely, for every 0.1-unit increase in AR, the prevalence of IA rupture increased by ~3% (odds ratio 1.03, 95% CI 1.01-1.06) in the AR range of 3.42-4.08. Conclusion: The relationship between AR and ruptured IA was U-shaped, with the negative association at AR of 1.08-1.99 and positive association at AR of 3.42-4.08.

Flow diverter modeled as heterogeneous and anisotropic porous medium: Simulation, experimental validation and case analysis.

Simulation of flow diverter (FD) treated aneurysm can evaluate treatment efficacy and aid treatment planning. However, explicit modeling of thin wires of FD impose extremely high demand of computational resources and time, which limit its use in time-sensitive presurgical planning. One alternative approach is to model FD as homogenous porous medium, which saves time but with compromise in accuracy. We proposed a new method to model FD as heterogeneous and anisotropic porous medium whose properties were determined from local porosity. The new method was validated by comparing with PIV measurement from an in-vitro phantom. Simulation result was in good agreement with experimental measurement. Four patient cases were further analyzed to compare the new method with the homogenous porous media method. Results showed that in patient cases with curved artery, new method was preferred over the homogenous method, as the assumption of homogenous porosity led to overpredicted flow reduction effect by as much as 87.9%, which may lead to overoptimistic decision making and poor prognosis. Our new method can provide timely and accurate simulation to aid in the treatment planning of aneurysms.

A preliminary investigation of radiomics differences between ruptured and unruptured intracranial aneurysms.

OBJECTIVES: Prediction of intracranial aneurysm rupture is important in the management of unruptured aneurysms. The application of radiomics in predicting aneurysm rupture remained largely unexplored. This study aims to evaluate the radiomics differences between ruptured and unruptured aneurysms and explore its potential use in predicting aneurysm rupture. METHODS: One hundred twenty-two aneurysms were included in the study (93 unruptured). Morphological and radiomics features were extracted for each case. Statistical analysis was performed to identify significant features which were incorporated into prediction models constructed with a machine learning algorithm. To investigate the usefulness of radiomics features, three models were constructed and compared. The baseline model A was constructed with morphological features, while model B was constructed with addition of radiomics shape features and model C with more radiomics features. Multivariate analysis was performed for the ten most important variables in model C to identify independent risk factors. A simplified model based on independent risk factors was constructed for clinical use. RESULTS: Five morphological features and 89 radiomics features were significantly associated with rupture. Model A, model B, and model C achieved the area under the receiver operating characteristic curve of 0.767, 0.807, and 0.879, respectively. Model C was significantly better than model A and model B (p < 0.001). Multivariate analysis identified two radiomics features which were used to construct the simplified model showing an AUROC of 0.876. CONCLUSIONS: Radiomics signatures were different between ruptured and unruptured aneurysms. The use of radiomics features, especially texture features, may significantly improve rupture prediction performance. KEY POINTS: • Significant radiomics differences exist between ruptured and unruptured intracranial aneurysms. • Radiomics shape features can significantly improve rupture prediction performance over conventional morphology-based prediction model. The inclusion of histogram and texture radiomics features can further improve the performance. • A simplified model with two variables achieved a similar level of performance as the more complex ones. Our prediction model can serve as a promising tool for the risk management of intracranial aneurysms.

Elevated Lipid Infiltration Is Associated With Cerebral Aneurysm Rupture.

Background: Intracranial aneurysm wall degradation can be associated with lipid infiltration. However, the relationship between lipid infiltration and aneurysm rupture has not been explored quantitatively. To investigate the correlation between lipid infiltration and aneurysm rupture, we utilized patient-specific simulation of low-density lipoprotein (LDL) transport to analyze lipid infiltration in the cerebral aneurysm wall. Methods: Sixty-two aneurysms were analyzed. Patient blood pressure, plasma LDL concentration, and three-dimensional angiographic images were obtained to simulate LDL transport in aneurysms. Morphological, hemodynamic, and lipid accumulation parameters were compared between ruptures and unruptured groups. Multivariate logistic regression was also performed to determine parameters that are independently associated with rupture. Results: Size ratio, wall shear stress, low shear area, relative residence time, area-averaged LDL infiltration rate, and maximum LDL infiltration rate were significant parameters in univariate analysis (P < 0.05). Multivariate analysis revealed that only average LDL infiltration remained as a significant variable (P < 0.05). The prediction model derived showed good performance for rupture prediction (AUC, 0.885; 95% CI, 0.794-0.976). Conclusions: Ruptured aneurysms showed significantly higher LDL infiltration compared to unruptured ones. Our results suggested that lipid infiltration may promote aneurysm rupture. Lipid infiltration characteristics should be considered when assessing aneurysm rupture risk.

lncRNA Mtss1 promotes inflammatory responses and secondary brain injury after intracerebral hemorrhage by targeting miR-709 in mice.

Secondary brain injuries following intracerebral hemorrhage (ICH) are mediated by inflammatory pathway activation. The present study aimed to characterize long noncoding RNAs (lncRNAs) that are differentially expressed in cerebral tissues during ICH pathogenesis and to investigate their pathogenic functions. An ICH mouse model established by collagenase injection was used to obtain differentially expressed lncRNAs for deep sequencing. A cellular inflammation model was established by treating mouse microglia with lipopolysaccharide. Expression of lncRNA and miRNA was assessed by quantitative RT-PCR, and protein abundance was measured by western blot. Cytokine levels in mouse serum and cell culture supernatants were analyzed using enzyme-linked immunosorbent assay. Cerebral injury was evaluated by hematoxylin-eosin and Nissl staining, the ratio of brain dry weight/brain wet weight, and neurobehavior scoring. Ionized calcium-binding adaptor molecule 1 (IBA1) expression in the brain sections was assessed using immunohistochemistry. A total of 3681 lncRNAs were differentially expressed in the brain tissue of the ICH mice group compared with the Sham group. Of these, lncRNA metastasis suppressor-1 (Mtss1) expression was increased. Mtss1 knockdown by siRNA in the cellular model strongly suppressed TIR-domain-containing adapter-inducing interferon-ß (TRIF) expression, P65 phosphorylation, and tumor necrosis factor (TNF)-α and interleukin (IL)-1ß secretion. Mtss1 knockdown in ICH mice inhibited secondary brain injury and decreased IBA1, TNF-α, and IL-1ß. Mtss1 was predicted to bind miR-709, and Mtss1 knockdown elevated miR-709 expression in the cellular inflammation model and ICH mice. High expression of Mtss1 promoted inflammatory brain injuries after ICH by enhancing inflammatory cytokine secretion and targeting miR-709 expression.

Rupture Risk Assessment for Cerebral Aneurysm Using Interpretable Machine Learning on Multidimensional Data.

Background: Assessment of cerebral aneurysm rupture risk is an important task, but it remains challenging. Recent works applying machine learning to rupture risk evaluation presented positive results. Yet they were based on limited aspects of data, and lack of interpretability may limit their use in clinical setting. We aimed to develop interpretable machine learning models on multidimensional data for aneurysm rupture risk assessment. Methods: Three hundred seventy-four aneurysms were included in the study. Demographic, medical history, lifestyle behaviors, lipid profile, and morphologies were collected for each patient. Prediction models were derived using machine learning methods (support vector machine, artificial neural network, and XGBoost) and conventional logistic regression. The derived models were compared with the PHASES score method. The Shapley Additive Explanations (SHAP) analysis was applied to improve the interpretability of the best machine learning model and reveal the reasoning behind the predictions made by the model. Results: The best machine learning model (XGBoost) achieved an area under the receiver operating characteristic curve of 0.882 [95% confidence interval (CI) = 0.838-0.927], significantly better than the logistic regression model (0.779; 95% CI = 0.729-0.829; P = 0.002) and the PHASES score method (0.758; 95% CI = 0.713-0.800; P = 0.001). Location, size ratio, and triglyceride level were the three most important features in predicting rupture. Two typical cases were analyzed to demonstrate the interpretability of the model. Conclusions: This study demonstrated the potential of using machine learning for aneurysm rupture risk assessment. Machine learning models performed better than conventional statistical model and the PHASES score method. The SHAP analysis can improve the interpretability of machine learning models and facilitate their use in a clinical setting.

Poor-Grade Aneurysmal Subarachnoid Hemorrhage: Risk Factors Affecting Clinical Outcomes in Intracranial Aneurysm Patients in a Multi-Center Study.

Objective: Patients with poor-grade aneurysm subarachnoid hemorrhage (SAH) have commonly been considered to have a poor prognosis. The objective of this study was to investigate the independent risk factors affecting clinical outcomes in intracranial aneurysm patients with poor-grade aneurysm subarachnoid hemorrhage (aSAH) underwent different intervention therapies. Methods: A multicenter observational registry of 324 poor-grade aSAH patients treated at tertiary referral centers from October 2010 to March 2012 were enrolled in this study. The clinical data including patient characteristics on admission and during treatment course, treatment modality, aneurysm size and location, radiologic features, signs of cerebral herniation (dilated pupils), and functional neurologic outcome were collected. Clinical outcomes were assessed via a modified Rankin Scale at 12 months. Multivariate logistic regression models were used to develop prognostic models. The area under the receiver operator characteristic curves (AUC) and Hosmer-Lemeshow tests were used to assess discrimination and calibration. WAP score was developed to predict risk of poor outcome. Results: Older age, female gender, ventilated breathing status, non-reactive pupil response, pupil dilation, lower GCS score, a WFNS grade of V, intraventricular hemorrhage, a higher Fisher grade, a higher modified Fisher grade, and conservative treatment were calculated to be associated with a relatively poor outcome. Multivariate analyses revealed that older age, lower Glasgow coma scale score (GCS), the absence of pupillary reactivity, higher modified Fisher grade, and conservative treatment were independent predictors of poor outcome, showed good discrimination and calibration. Patients with WFNS grade V, older age and non-reactive pupillary reactivity were predicted to have a poor outcome by WAP risk score. Conclusions: A simple WAP risk score had good discrimination and calibration in the prediction of outcome. The risk score can be easily measured and may complement treatment decision-making.

The role of wall shear stress in the parent artery as an independent variable in the formation status of anterior communicating artery aneurysms.

OBJECTIVES: The study aimed to determine which hemodynamic parameters independently characterize anterior communicating artery (AcomA) aneurysm formation and explore the threshold of wall shear stress (WSS) of the parent artery to better illustrate the correlation between the magnitude of WSS and AcomA aneurysm formation. METHODS: Eighty-one patients with AcomA aneurysms and 118 patients without intracranial aneurysms (control population), as confirmed by digital subtraction angiography (DSA) from January 2014 to May 2017, were included in this cross-sectional study. Three-dimensional-DSA was performed to evaluate the morphologic characteristics of AcomA aneurysms. Local hemodynamic parameters were obtained using transcranial color-coded duplex (TCCD). Multivariate logistic regression and a two-piecewise linear regression model were used to determine which hemodynamic parameters are independent predictors of AcomA aneurysm formation and identify the threshold effect of WSS of the parent artery with respect to AcomA aneurysm formation. RESULTS: Univariate analyses showed that the WSS (p < 0.0001), angle between the A1 and A2 segments of the anterior cerebral artery (ACA) (p < 0.001), hypertension (grade II) (p = 0.007), fasting blood glucose (FBG; > 6.0 mmol/L) (p = 0.005), and dominant A1 (p < 0.001) were the significant parameters. Multivariate analyses showed a significant association between WSS of the parent artery and AcomA aneurysm formation (p = 0.0001). WSS of the parent artery (7.8-12.3 dyne/cm2) had a significant association between WSS and aneurysm formation (HR 2.0, 95% CI 1.3-2.8, p < 0.001). CONCLUSIONS: WSS ranging between 7.8 and 12.3 dyne/cm2 independently characterizes AcomA aneurysm formation. With each additional unit of WSS, there was a one-fold increase in the risk of AcomA aneurysm formation. KEY POINTS: • Multivariate analyses and a two-piecewise linear regression model were used to evaluate the risk factors for AcomA aneurysm formation and the threshold effect of WSS on AcomA aneurysm formation. • WSS ranging between 7.8 and 12.3 dyne/cm 2 was shown to be a reliable hemodynamic parameter in the formation of AcomA aneurysms. The probability of AcomA aneurysm formation increased one-fold for each additional unit of WSS. • An ultrasound-based TCCD technique is a simple and accessible noninvasive method for detecting WSS in vivo; thus, it can be applied as a screening tool for evaluating the probability of aneurysm formation in primary care facilities and community hospitals because of the relatively low resource intensity.

High wall shear stress beyond a certain range in the parent artery could predict the risk of anterior communicating artery aneurysm rupture at follow-up.

OBJECTIVE: Among clinical and morphological criteria, hemodynamics is the main predictor of aneurysm growth and rupture. This study aimed to identify which hemodynamic parameter in the parent artery could independently predict the rupture of anterior communicating artery (ACoA) aneurysms by using multivariate logistic regression and two-piecewise linear regression models. An additional objective was to look for a more simplified and convenient alternative to the widely used computational fluid dynamics (CFD) techniques to detect wall shear stress (WSS) as a screening tool for predicting the risk of aneurysm rupture during the follow-up of patients who did not undergo embolization or surgery. METHODS: One hundred sixty-two patients harboring ACoA aneurysms (130 ruptured and 32 unruptured) confirmed by 3D digital subtraction angiography at three centers were selected for this study. Morphological and hemodynamic parameters were evaluated for significance with respect to aneurysm rupture. Local hemodynamic parameters were obtained by MR angiography and transcranial color-coded duplex sonography to calculate WSS magnitude. Multivariate logistic regression and a two-piecewise linear regression analysis were performed to identify which hemodynamic parameter independently characterizes the rupture status of ACoA aneurysms. RESULTS: Univariate analysis showed that WSS (p < 0.001), circumferential wall tension (p = 0.005), age (p < 0.001), the angle between the A1 and A2 segments of the anterior cerebral artery (p < 0.001), size ratio (p = 0.023), aneurysm angle (p < 0.001), irregular shape (p = 0.005), and hypertension (grade II) (p = 0.006) were significant parameters. Multivariate analyses showed significant association between WSS in the parent artery and ACoA aneurysm rupture (p = 0.0001). WSS magnitude, evaluated by a two-piecewise linear regression model, was significantly correlated with the rupture of the ACoA aneurysm when the magnitude was higher than 12.3 dyne/cm2 (HR 7.2, 95% CI 1.5-33.6, p = 0.013). CONCLUSIONS: WSS in the parent artery may be one of the reliable hemodynamic parameters characterizing the rupture status of ACoA aneurysms when the WSS magnitude is higher than 12.3 dyne/cm2. Analysis showed that with each additional unit of WSS (even with a 1-unit increase of WSS), there was a 6.2-fold increase in the risk of rupture for ACoA aneurysms.

A novel small-molecule activator of Sirtuin-1 induces autophagic cell death/mitophagy as a potential therapeutic strategy in glioblastoma.

Sirtuin-1 (SIRT1), the mammalian ortholog of yeast Sir2p, is well known to be a highly conserved NAD+-dependent protein deacetylase that has been emerging as a key cancer target. Autophagy, an evolutionarily conserved, multi-step lysosomal degradation process, has been implicated in cancer. Accumulating evidence has recently revealed that SIRT1 may act as a tumor suppressor in several types of cancer, and thus activating SIRT1 would represent a possible therapeutic strategy. Thus, in our study, we identified that SIRT1 was a key prognostic factor in brain cancer based upon The Cancer Genome Atlas and tissue microarray analyses. Subsequently, we screened a series of potential small-molecule activators of SIRT1 from Drugbank, and found the best candidate compound F0911-7667 (hereafter, named Comp 5), which showed a good deacetylase activity for SIRT1 rather than other Sirtuins. In addition, we demonstrated that Comp 5-induced autophagic cell death via the AMPK-mTOR-ULK complex in U87MG and T98G cells. Interestingly, Comp 5-induced mitophagy by the SIRT1-PINK1-Parkin pathway. Further iTRAQ-based proteomics analyses revealed that Comp 5 could induce autophagy/mitophagy by downregulating 14-3-3γ, catalase, profilin-1, and HSP90α. Moreover, we showed that Comp 5 had a therapeutic potential on glioblastoma (GBM) and induced autophagy/mitophagy by activating SIRT1 in vivo. Together, these results demonstrate a novel small-molecule activator of SIRT1 that induces autophagic cell death/mitophagy in GBM cells, which would be utilized to exploit this compound as a leading drug for future cancer therapy.

Kissing aneurysms of the distal anterior cerebral artery: A case report and literature review.

Intracranial 'kissing' aneurysms are rare types of multiple aneurysms referring to two adjacent aneurysms arising from identical or different arteries with separate origins and partially adherent walls. The present study reported a 54-year-old female patient, who was identified with a 'kissing' aneurysm in the A3 segment of the bilateral anterior cerebral arteries, as demonstrated by head computed tomography and emergency cerebral digital subtraction angiography analysis. In total, 12 days following the clipping of the aneurysms, the patient was discharged with a Modified Rankin Scale=0 and recovered well with no neurological deficits. Based on previous literature, it was indicated that the majority of patients with 'kissing' aneurysm have a good prognosis and the cure rate is as high as 96.8%. However, the recovery rate may not be that high as the sample size is not large enough to thoroughly demonstrate the complete prognosis of 'kissing' aneurysms.

Cerebral Microbleeds Could Be Independently Associated with Intracranial Aneurysm Rupture: A Cross-Sectional Population-Based Study.

OBJECTIVE: To determine whether the presence of cerebral microbleeds (CMBs) is independently associated with intracranial aneurysm rupture and to identify the time interval of CMB-related intracranial aneurysm rupture. METHODS: This cross-sectional study included 1847 patients with unruptured and ruptured intracranial aneurysms from January 2010 to November 2017. Clinical records and imaging, including T2-weighted gradient-recalled echo sequence magnetic resonance imaging that identified the presence of CMBs preoperatively, were evaluated. Univariate analysis and multivariate logistic regression were done to determine which parameters were independent factors for aneurysm rupture. The time interval of CMB-related intracranial aneurysm rupture was also evaluated. RESULTS: CMBs confirmed by magnetic resonance imaging were present in 142 patients (142/1847; 7.7%). Of 142 patients with CMBs, 56 patients (including 17 ruptured aneurysms) who received endovascular treatment and another 86 consecutive patients who did not receive embolization or surgery for various reasons were followed for 3-49 months. The incidence of CMB-related intracranial aneurysm rupture was 27.9% (24/86) during the follow-up period. The time interval of CMB-related intracranial aneurysm rupture was 3-27 months (median 9.5 months). Multivariate analyses showed CMBs were significantly correlated with intracranial aneurysm rupture (odds ratio = 1.6; 95% confidence interval, 1.1-2.4; P = 0.010). CONCLUSIONS: CMBs were independently associated with intracranial aneurysm rupture. Patients with CMBs have a 60% increased risk of aneurysm rupture compared with patients without CMBs.