NAD deficiency contributes to progressive kidney disease in HIV nephropathy mice.

HIV disease remains prevalent in the USA and is particularly prevalent in sub-Saharan Africa. Recent investigations revealed that mitochondrial dysfunction in kidney contributes to HIV-associated nephropathy (HIVAN) in Tg26 transgenic mice. We hypothesized that nicotinamide adenine dinucleotide (NAD) deficiency contributes to energetic dysfunction and progressive tubular injury. We investigated metabolomic mechanisms of HIVAN tubulopathy. Tg26 and wild-type (WT) mice were treated with the farnesoid-X receptor (FXR) agonist INT-747 or nicotinamide riboside (NR) from 6 to 12 weeks of age. Multi-omic approaches were used to characterize kidney tissue transcriptomes and metabolomes. Treatment with INT-747 or NR ameliorated kidney tubular injury, as shown by serum creatinine, the tubular injury marker urinary neutrophil-associated lipocalin and tubular morphometry. Integrated analysis of metabolomic and transcriptomic measurements showed that NAD levels and production were globally downregulated in Tg26 mouse kidney, especially nicotinamide phosphoribosyltransferase (NAMPT), the rate-limiting enzyme in the NAD salvage pathway. Further, NAD-dependent deacetylase sirtuin3 activity and mitochondrial oxidative phosphorylation activity were lower in ex vivo proximal tubules from Tg26 mouse kidneys compared to those of WT mice. Restoration of NAD levels in kidney improved these abnormalities. These data suggest that NAD deficiency might be a treatable target for HIVAN.

Dual Layer vs Single Layer Woven EndoBridge Device in the Treatment of Intracranial Aneurysms: A Propensity Score-Matched Analysis.

BACKGROUND: The Woven EndoBridge (WEB) devices have been used for treating wide neck bifurcation aneurysms (WNBAs) with several generational enhancements to improve clinical outcomes. The original device dual-layer (WEB DL) was replaced by a single-layer (WEB SL) device in 2013. This study aimed to compare the effectiveness and safety of these devices in managing intracranial aneurysms. METHODS: A multicenter cohort study was conducted, and data from 1,289 patients with intracranial aneurysms treated with either the WEB SL or WEB DL devices were retrospectively analyzed. Propensity score matching was utilized to balance the baseline characteristics between the two groups. Outcomes assessed included immediate occlusion rate, complete occlusion at last follow-up, retreatment rate, device compaction, and aneurysmal rupture. RESULTS: Before propensity score matching, patients treated with the WEB SL had a significantly higher rate of complete occlusion at the last follow-up and a lower rate of retreatment. After matching, there was no significant difference in immediate occlusion rate, retreatment rate, or device compaction between the WEB SL and DL groups. However, the SL group maintained a higher rate of complete occlusion at the final follow-up. Regression analysis showed that SL was associated with higher rates of complete occlusion (OR: 0.19; CI: 0.04 to 0.8, p = 0.029) and lower rates of retreatment (OR: 0.12; CI: 0 to 4.12, p = 0.23). CONCLUSION: The WEB SL and DL devices demonstrated similar performances in immediate occlusion rates and retreatment requirements for intracranial aneurysms. The SL device showed a higher rate of complete occlusion at the final follow-up.

Histologically interpretable clot radiomic features predict treatment outcomes of mechanical thrombectomy for ischemic stroke.

PURPOSE: Radiomics features (RFs) extracted from CT images may provide valuable information on the biological structure of ischemic stroke blood clots and mechanical thrombectomy outcome. Here, we aimed to identify RFs predictive of thrombectomy outcomes and use clot histomics to explore the biology and structure related to these RFs. METHODS: We extracted 293 RFs from co-registered non-contrast CT and CTA. RFs predictive of revascularization outcomes defined by first-pass effect (FPE, near to complete clot removal in one thrombectomy pass), were selected. We then trained and cross-validated a balanced logistic regression model fivefold, to assess the RFs in outcome prediction. On a subset of cases, we performed digital histopathology on the clots and computed 227 histomic features from their whole slide images as a means to interpret the biology behind significant RF. RESULTS: We identified 6 significantly-associated RFs. RFs reflective of continuity in lower intensities, scattered higher intensities, and intensities with abrupt changes in texture were associated with successful revascularization outcome. For FPE prediction, the multi-variate model had high performance, with AUC = 0.832 ± 0.031 and accuracy = 0.760 ± 0.059 in training, and AUC = 0.787 ± 0.115 and accuracy = 0.787 ± 0.127 in cross-validation testing. Each of the 6 RFs was related to clot component organization in terms of red blood cell and fibrin/platelet distribution. Clots with more diversity of components, with varying sizes of red blood cells and fibrin/platelet regions in the section, were associated with RFs predictive of FPE. CONCLUSION: Upon future validation in larger datasets, clot RFs on CT imaging are potential candidate markers for FPE prediction.

Evaluating a 3D deep learning pipeline for cerebral vessel and intracranial aneurysm segmentation from computed tomography angiography-digital subtraction angiography image pairs.

OBJECTIVE: Computed tomography angiography (CTA) is the most widely used imaging modality for intracranial aneurysm (IA) management, yet it remains inferior to digital subtraction angiography (DSA) for IA detection, particularly of small IAs in the cavernous carotid region. The authors evaluated a deep learning pipeline for segmentation of vessels and IAs from CTA using coregistered, segmented DSA images as ground truth. METHODS: Using 50 paired CTA-DSA images, the authors trained (n = 27), validated (n = 3), and tested (n = 20) a deep learning model (3D DeepMedic) for cerebrovasculature segmentation from CTA. A landmark-based coregistration algorithm was used for registration and upsampling of CTA images to paired DSA images. Segmented vessels from the DSA were used as the ground truth. Accuracy of the model for vessel segmentation was evaluated using conventional metrics (dice similarity coefficient [DSC]) and vessel segmentation-specific metrics, like connectivity-area-length (CAL). On the test cases (20 IAs), 3 expert raters attempted to detect and segment IAs. For each rater, the authors recorded the rate of IA detection, and for detected IAs, raters segmented and calculated important IA morphology parameters to quantify the differences in IA segmentation by raters to segmentations by DeepMedic. The agreement between raters, DeepMedic, and ground truth was assessed using Krippendorf's alpha. RESULTS: In testing, the DeepMedic model yielded a CAL of 0.971 ± 0.007 and a DSC of 0.868 ± 0.008. The model prediction delineated all IAs and resulted in average error rates of < 10% for all IA morphometrics. Conversely, average IA detection accuracy by the raters was 0.653 (undetected IAs were present to a significantly greater degree on the ICA, likely due to those in the cavernous region, and were significantly smaller). Error rates for IA morphometrics in rater-segmented cases were significantly higher than in DeepMedic-segmented cases, particularly for neck (p = 0.003) and surface area (p = 0.04). For IA morphology, agreement between the raters was acceptable for most metrics, except for the undulation index (α = 0.36) and the nonsphericity index (α = 0.69). Agreement between DeepMedic and ground truth was consistently higher compared with that between expert raters and ground truth. CONCLUSIONS: This CTA segmentation network (DeepMedic trained on DSA-segmented vessels) provides a high-fidelity solution for CTA vessel segmentation, particularly for vessels and IAs in the carotid cavernous region.

Multicenter Study for the Treatment of Sidewall versus Bifurcation Intracranial Aneurysms with Use of Woven EndoBridge (WEB).

Background The Woven EndoBridge (WEB) device was explicitly designed for wide-neck intracranial bifurcation aneurysms. Small-scale reports have evaluated the off-label use of WEB devices for the treatment of sidewall aneurysms, with promising outcomes. Purpose To compare the angiographic and clinical outcomes of the WEB device for the treatment of sidewall aneurysms compared with the treatment of bifurcation aneurysms. Materials and Methods A retrospective review of the WorldWideWEB Consortium, a synthesis of retrospective databases spanning from January 2011 to June 2021 at 22 academic institutions in North America, South America, and Europe, was performed to identify patients with intracranial aneurysms treated with the WEB device. Characteristics and outcomes were compared between bifurcation and sidewall aneurysms. Propensity score matching (PSM) was used to match by age, pretreatment ordinal modified Rankin Scale score, ruptured aneurysms, location of aneurysm, multiple aneurysms, prior treatment, neck, height, dome width, daughter sac, and incorporated branch. Results A total of 683 intracranial aneurysms were treated using the WEB device in 671 patients (median age, 61 years [IQR, 53-68 years]; male-to-female ratio, 1:2.5). Of those, 572 were bifurcation aneurysms and 111 were sidewall aneurysms. PSM was performed, resulting in 91 bifurcation and sidewall aneurysms pairs. No significant difference was observed in occlusion status at last follow-up, deployment success, or complication rates between the two groups. Conclusion No significantly different outcomes were observed following the off-label use of the Woven EndoBridge, or WEB, device for treatment of sidewall aneurysms compared with bifurcation aneurysms. The correct characterization of the sidewall aneurysm location, neck angle, and size is crucial for successful treatment and lower retreatment rate. © RSNA, 2022 See also the editorial by Hetts in this issue.

The association between hemodynamics and wall characteristics in human intracranial aneurysms: a review.

Hemodynamics plays a key role in the natural history of intracranial aneurysms (IAs). However, studies exploring the association between aneurysmal hemodynamics and the biological and mechanical characteristics of the IA wall in humans are sparse. In this review, we survey the current body of literature, summarize the studies' methodologies and findings, and assess the degree of consensus among them. We used PubMed to perform a systematic review of studies that explored the association between hemodynamics and human IA wall features using different sources. We identified 28 publications characterizing aneurysmal flow and the IA wall: 4 using resected tissues, 17 using intraoperative images, and 7 using vessel wall magnetic resonance imaging (MRI). Based on correlation to IA tissue, higher flow conditions, such as high wall shear stress (WSS) with complex pattern and elevated pressure, were associated with degenerated walls and collagens with unphysiological orientation and faster synthesis. MRI studies strongly supported that low flow, characterized by low WSS and high blood residence time, was associated with thicker walls and post-contrast enhancement. While significant discrepancies were found among those utilized intraoperative images, they generally supported that thicker walls coexist at regions with prolonged residence time and that thinner regions are mainly exposed to higher pressure with complex WSS patterns. The current body of literature supports a theory of two general hemodynamic-biologic mechanisms for IA development. One, where low flow conditions are associated with thickening and atherosclerotic-like remodeling, and the other where high and impinging flow conditions are related to wall degeneration, thinning, and collagen remodeling.

Endogenous animal models of intracranial aneurysm development: a review.

The pathogenesis and natural history of intracranial aneurysm (IA) remains poorly understood. To this end, animal models with induced cerebral vessel lesions mimicking human aneurysms have provided the ability to greatly expand our understanding. In this review, we comprehensively searched the published literature to identify studies that endogenously induced IA formation in animals. Studies that constructed aneurysms (i.e., by surgically creating a sac) were excluded. From the eligible studies, we reported information including the animal species, method for aneurysm induction, aneurysm definitions, evaluation methods, aneurysm characteristics, formation rate, rupture rate, and time course. Between 1960 and 2019, 174 articles reported endogenous animal models of IA. The majority used flow modification, hypertension, and vessel wall weakening (i.e., elastase treatment) to induce IAs, primarily in rats and mice. Most studies utilized subjective or qualitative descriptions to define experimental aneurysms and histology to study them. In general, experimental IAs resembled the pathobiology of the human disease in terms of internal elastic lamina loss, medial layer degradation, and inflammatory cell infiltration. After the early 2000s, many endogenous animal models of IA began to incorporate state-of-the-art technology, such as gene expression profiling and 9.4-T magnetic resonance imaging (MRI) in vivo imaging, to quantitatively analyze the biological mechanisms of IA. Future studies aimed at longitudinally assessing IA pathobiology in models that incorporate aneurysm growth will likely have the largest impact on our understanding of the disease. We believe this will be aided by high-resolution, small animal, survival imaging, in situ live-cell imaging, and next-generation omics technology.

Is Endovascular Therapy for Stroke Cost-Effective Globally? A Systematic Review of the Literature.

OBJECTIVES: Cost-effectiveness of endovascular therapy (EVT) is a key consideration for broad use of this approach for emergent large vessel occlusion stroke. We evaluated the evidence on cost-effectiveness of EVT in comparison with best medical management from a global perspective. MATERIALS AND METHODS: This systematic review of studies published between January 2010 and May 2020 evaluated the cost effectiveness of EVT for patients with large vessel occlusion acute ischemic stroke. The gain in quality adjusted life year (QALY) and incremental cost-effectiveness ratio (ICER), expressed as cost per QALY resulting from EVT, were recorded. The study setting (country, economic perspective), decision model, and data sources used in economic models of EVT cost-effectiveness were recorded. RESULTS: Twenty-five original studies from 12 different countries were included in our review. Five of these studies were reported from a societal perspective; 18 were reported from a healthcare system perspective. Two studies used real-world data. The time horizon varied from 1 year to a lifetime; however, 18 studies reported a time horizon of >10 years. Twenty studies reported using outcome data from randomized, controlled clinical trials for their models. Nineteen studies reported using a Markov model. Incremental QALYs ranged from 0.09-3.5. All studies but 1 reported that EVT was cost-effective. CONCLUSIONS: Evidence from different countries and economic perspectives suggests that EVT for stroke treatment is cost-effective. Most cost-effectiveness studies are based on outcome data from randomized clinical trials. However, there is a need to study the cost-effectiveness of EVT based solely on real-world outcome data.

Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm.

BACKGROUND: Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods. METHODS: Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n = 94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n = 40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction. RESULTS: Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC) = 0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance. CONCLUSIONS: We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.

Shared and Distinct Rupture Discriminants of Small and Large Intracranial Aneurysms.

BACKGROUND AND PURPOSE: Many ruptured intracranial aneurysms (IAs) are small. Clinical presentations suggest that small and large IAs could have different phenotypes. It is unknown if small and large IAs have different characteristics that discriminate rupture. METHODS: We analyzed morphological, hemodynamic, and clinical parameters of 413 retrospectively collected IAs (training cohort; 102 ruptured IAs). Hierarchal cluster analysis was performed to determine a size cutoff to dichotomize the IA population into small and large IAs. We applied multivariate logistic regression to build rupture discrimination models for small IAs, large IAs, and an aggregation of all IAs. We validated the ability of these 3 models to predict rupture status in a second, independently collected cohort of 129 IAs (testing cohort; 14 ruptured IAs). RESULTS: Hierarchal cluster analysis in the training cohort confirmed that small and large IAs are best separated at 5 mm based on morphological and hemodynamic features (area under the curve=0.81). For small IAs (<5 mm), the resulting rupture discrimination model included undulation index, oscillatory shear index, previous subarachnoid hemorrhage, and absence of multiple IAs (area under the curve=0.84; 95% confidence interval, 0.78-0.88), whereas for large IAs (≥5 mm), the model included undulation index, low wall shear stress, previous subarachnoid hemorrhage, and IA location (area under the curve=0.87; 95% confidence interval, 0.82-0.93). The model for the aggregated training cohort retained all the parameters in the size-dichotomized models. Results in the testing cohort showed that the size-dichotomized rupture discrimination model had higher sensitivity (64% versus 29%) and accuracy (77% versus 74%), marginally higher area under the curve (0.75; 95% confidence interval, 0.61-0.88 versus 0.67; 95% confidence interval, 0.52-0.82), and similar specificity (78% versus 80%) compared with the aggregate-based model. CONCLUSIONS: Small (<5 mm) and large (≥5 mm) IAs have different hemodynamic and clinical, but not morphological, rupture discriminants. Size-dichotomized rupture discrimination models performed better than the aggregate model.

Biomarkers from circulating neutrophil transcriptomes have potential to detect unruptured intracranial aneurysms.

BACKGROUND: Intracranial aneurysms (IAs) are dangerous because of their potential to rupture and cause deadly subarachnoid hemorrhages. Previously, we found significant RNA expression differences in circulating neutrophils between patients with unruptured IAs and aneurysm-free controls. Searching for circulating biomarkers for unruptured IAs, we tested the feasibility of developing classification algorithms that use neutrophil RNA expression levels from blood samples to predict the presence of an IA. METHODS: Neutrophil RNA extracted from blood samples from 40 patients (20 with angiography-confirmed unruptured IA, 20 angiography-confirmed IA-free controls) was subjected to next-generation RNA sequencing to obtain neutrophil transcriptomes. In a randomly-selected training cohort of 30 of the 40 samples (15 with IA, 15 controls), we performed differential expression analysis. Significantly differentially expressed transcripts (false discovery rate < 0.05, fold change ≥ 1.5) were used to construct prediction models for IA using four well-known supervised machine-learning approaches (diagonal linear discriminant analysis, cosine nearest neighbors, nearest shrunken centroids, and support vector machines). These models were tested in a testing cohort of the remaining 10 neutrophil samples from the 40 patients (5 with IA, 5 controls), and model performance was assessed by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (PCR) was used to corroborate expression differences of a subset of model transcripts in neutrophil samples from a new, separate validation cohort of 10 patients (5 with IA, 5 controls). RESULTS: The training cohort yielded 26 highly significantly differentially expressed neutrophil transcripts. Models using these transcripts identified IA patients in the testing cohort with accuracy ranging from 0.60 to 0.90. The best performing model was the diagonal linear discriminant analysis classifier (area under the ROC curve = 0.80 and accuracy = 0.90). Six of seven differentially expressed genes we tested were confirmed by quantitative PCR using isolated neutrophils from the separate validation cohort. CONCLUSIONS: Our findings demonstrate the potential of machine-learning methods to classify IA cases and create predictive models for unruptured IAs using circulating neutrophil transcriptome data. Future studies are needed to replicate these findings in larger cohorts.

Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning.

OBJECTIVEFlow diverters (FDs) are designed to occlude intracranial aneurysms (IAs) while preserving flow to essential arteries. Incomplete occlusion exposes patients to risks of thromboembolic complications and rupture. A priori assessment of FD treatment outcome could enable treatment optimization leading to better outcomes. To that end, the authors applied image-based computational analysis to clinically FD-treated aneurysms to extract information regarding morphology, pre- and post-treatment hemodynamics, and FD-device characteristics and then used these parameters to train machine learning algorithms to predict 6-month clinical outcomes after FD treatment.METHODSData were retrospectively collected for 84 FD-treated sidewall aneurysms in 80 patients. Based on 6-month angiographic outcomes, IAs were classified as occluded (n = 63) or residual (incomplete occlusion, n = 21). For each case, the authors modeled FD deployment using a fast virtual stenting algorithm and hemodynamics using image-based computational fluid dynamics. Sixteen morphological, hemodynamic, and FD-based parameters were calculated for each aneurysm. Aneurysms were randomly assigned to a training or testing cohort in approximately a 3:1 ratio. The Student t-test and Mann-Whitney U-test were performed on data from the training cohort to identify significant parameters distinguishing the occluded from residual groups. Predictive models were trained using 4 types of supervised machine learning algorithms: logistic regression (LR), support vector machine (SVM; linear and Gaussian kernels), K-nearest neighbor, and neural network (NN). In the testing cohort, the authors compared outcome prediction by each model trained using all parameters versus only the significant parameters.RESULTSThe training cohort (n = 64) consisted of 48 occluded and 16 residual aneurysms and the testing cohort (n = 20) consisted of 15 occluded and 5 residual aneurysms. Significance tests yielded 2 morphological (ostium ratio and neck ratio) and 3 hemodynamic (pre-treatment inflow rate, post-treatment inflow rate, and post-treatment aneurysm averaged velocity) discriminants between the occluded (good-outcome) and the residual (bad-outcome) group. In both training and testing, all the models trained using all 16 parameters performed better than all the models trained using only the 5 significant parameters. Among the all-parameter models, NN (AUC = 0.967) performed the best during training, followed by LR and linear SVM (AUC = 0.941 and 0.914, respectively). During testing, NN and Gaussian-SVM models had the highest accuracy (90%) in predicting occlusion outcome.CONCLUSIONSNN and Gaussian-SVM models incorporating all 16 morphological, hemodynamic, and FD-related parameters predicted 6-month occlusion outcome of FD treatment with 90% accuracy. More robust models using the computational workflow and machine learning could be trained on larger patient databases toward clinical use in patient-specific treatment planning and optimization.

Methodology for Computational Fluid Dynamic Validation for Medical Use: Application to Intracranial Aneurysm.

Computational fluid dynamics (CFD) is a promising tool to aid in clinical diagnoses of cardiovascular diseases. However, it uses assumptions that simplify the complexities of the real cardiovascular flow. Due to high-stakes in the clinical setting, it is critical to calculate the effect of these assumptions in the CFD simulation results. However, existing CFD validation approaches do not quantify error in the simulation results due to the CFD solver's modeling assumptions. Instead, they directly compare CFD simulation results against validation data. Thus, to quantify the accuracy of a CFD solver, we developed a validation methodology that calculates the CFD model error (arising from modeling assumptions). Our methodology identifies independent error sources in CFD and validation experiments, and calculates the model error by parsing out other sources of error inherent in simulation and experiments. To demonstrate the method, we simulated the flow field of a patient-specific intracranial aneurysm (IA) in the commercial CFD software star-ccm+. Particle image velocimetry (PIV) provided validation datasets for the flow field on two orthogonal planes. The average model error in the star-ccm+ solver was 5.63 ± 5.49% along the intersecting validation line of the orthogonal planes. Furthermore, we demonstrated that our validation method is superior to existing validation approaches by applying three representative existing validation techniques to our CFD and experimental dataset, and comparing the validation results. Our validation methodology offers a streamlined workflow to extract the "true" accuracy of a CFD solver.

C5a induces caspase-dependent apoptosis in brain vascular endothelial cells in experimental lupus.

Blood-brain barrier (BBB) dysfunction complicates central nervous system lupus, an important aspect of systemic lupus erythematosus. To gain insight into the underlying mechanism, vascular corrosion casts of brain were generated from the lupus mouse model, MRL/lpr mice and the MRL/MpJ congenic controls. Scanning electron microscopy of the casts showed loss of vascular endothelial cells in lupus mice compared with controls. Immunostaining revealed a significant increase in caspase 3 expression in the brain vascular endothelial cells, which suggests that apoptosis could be an important mechanism causing cell loss, and thereby loss of BBB integrity. Complement activation occurs in lupus resulting in increased generation of circulating C5a, which caused the endothelial layer to become 'leaky'. In this study, we show that C5a and lupus serum induced apoptosis in cultured human brain microvascular endothelial cells (HBMVECs), whereas selective C5a receptor 1 (C5aR1) antagonist reduced apoptosis in these cells, demonstrating C5a/C5aR1-dependence. Gene expression of initiator caspases, caspase 1 and caspase 8, and pro-apoptotic proteins death-associated protein kinase 1, Fas-associated protein (FADD), cell death-inducing DNA fragmentation factor 45 000 MW subunit A-like effector B (CIDEB) and BCL2-associated X protein were increased in HBMVECs treated with lupus serum or C5a, indicating that both the intrinsic and extrinsic apoptotic pathways could be critical mediators of brain endothelial cell apoptosis in this setting. Overall, our findings suggest that C5a/C5aR1 signalling induces apoptosis through activation of FADD, caspase 8/3 and CIDEB in brain endothelial cells in lupus. Further elucidation of the underlying apoptotic mechanisms mediating the reduced endothelial cell number is important in establishing the potential therapeutic effectiveness of C5aR1 inhibition that could prevent and/or reduce BBB alterations and preserve the physiological function of BBB in central nervous system lupus.

FAK and p130Cas modulate stiffness-mediated early transcription and cellular metabolism.

Cellular metabolism is influenced by the stiffness of the extracellular matrix. Focal adhesion kinase (FAK) and its binding partner, p130Cas, transmit biomechanical signals about substrate stiffness to the cell to regulate a variety of cellular responses, but their roles in early transcriptional and metabolic responses remain largely unexplored. We cultured mouse embryonic fibroblasts with or without siRNA-mediated FAK or p130Cas knockdown and assessed the early transcriptional responses of these cells to placement on soft and stiff substrates by RNA sequencing and bioinformatics analyses. Exposure to the stiff ECM altered the expression of genes important for metabolic and biosynthetic processes, and these responses were influenced by knockdown of FAK and p130Cas. Our findings reveal that FAK-p130Cas signaling mechanotransduces ECM stiffness to early transcriptional changes that alter cellular metabolism and biosynthesis.

Imaging of Intracranial Aneurysms: A Review of Standard and Advanced Imaging Techniques.

The treatment of intracranial aneurysms is dictated by its risk of rupture in the future. Several clinical and radiological risk factors for aneurysm rupture have been described and incorporated into prediction models. Despite the recent technological advancements in aneurysm imaging, linear length and visible irregularity with a bleb are the only radiological measure used in clinical prediction models. The purpose of this article is to summarize both the standard imaging techniques, including their limitations, and the advanced techniques being used experimentally to image aneurysms. It is expected that as our understanding of advanced techniques improves, and their ability to predict clinical events is demonstrated, they become an increasingly routine part of aneurysm assessment. It is important that neurovascular specialists understand the spectrum of imaging techniques available.

MicroCT and Histological Analysis of Clot Composition in Acute Ischemic Stroke : A Comparative Study of MT-Retrieved Clots and Clot Analogs.

PURPOSE: Assessing clot composition on prethrombectomy computed tomography (CT) imaging may help in stroke treatment planning. In this study we seek to use microCT imaging of fabricated blood clots to understand the relationship between CT radiographic signals and the biological makeup. METHODS: Clots (n = 10) retrieved by mechanical thrombectomy (MT) were collected, and 6 clot analogs of varying RBC composition were made. We performed paired microCT and histological image analysis of all 16 clots using a ScanCo microCT 100 (4.9 µm resolution) and standard H&E staining (imaged at 40×). From these data types, first order statistic (FOS) radiomics were computed from microCT, and percent composition of RBCs (%RBC) was computed from histology. Polynomial and linear regression (LR) were used to build statistical models based on retrieved thrombus microCT and %RBC that were evaluated for their ability to predict the %RBC of clot analogs from mean HU. Correlation analyses of microCT FOS with composition were completed for both retrieved clots and analogs. RESULTS: The LR model fits relating MT-retrieved clot %RBC with mean (R2 = 0.625, p = 0.006) and standard deviation (R2 = 0.564, p < 0.05) in HUs on microCT were significant. Similarly, LR models relating analog histological %RBC to analog protocol %RBC (R2 = 0.915, p = 0.003) and mean HUs on microCT (R2 = 0.872, p = 0.007) were also significant. When the LR model built using MT-retrieved clots was used to predict analog %RBC from mean HUs, significant correlation was observed between predictions and actual histological %RBC (R2 = 0.852, p = 0.009). For retrieved clots, significant correlations were observed for energy and total energy with %RBC and %FP (|R| > 0.7, q < 0.01). Analogs further demonstrated significant correlation between FOS energy, total energy, variance and %WBC (|R| > 0.9, q < 0.01). CONCLUSION: MicroCT can be used to build models that predict AIS clot composition from routine CT parameters and help us to better understand radiomic signatures associated with clot composition and first pass outcomes. In future work, such observations can be used to better infer clot composition and inform thrombectomy prognostics from pretreatment CTs.

Preradiosurgical Embolization of Arteriovenous Malformations Reduces Target Volume - The Main Determinant for Complete Obliteration.

BACKGROUND: Embolization and stereotactic radiosurgery (SRS) have increasingly been used to treat complex arteriovenous malformations (AVMs). We studied outcomes of AVM patients treated through a multidisciplinary approach, examined the effect of embolization on SRS success, and analyzed predictors of treatment failure. METHODS: We retrospectively reviewed a prospectively maintained database of patients with AVMs treated with Gamma Knife (Leksell) SRS over an 11-year period. Patients with incomplete medical records and follow-up <2 years were excluded. Demographics, clinical presentation, previous rupture history, angiographic nidus size, Spetzler-Martin (S-M) grade, adjunctive endovascular embolization and microsurgical resection, radiologic evidence of obliteration and hemorrhage, and clinical outcomes (modified Rankin Scale [mRS] scores) were recorded. Radiosurgery-related details including nidus volume and number of sessions and radiosurgery-, embolization-, and resection-associated complications were also recorded. RESULTS: Eighty-three patients (mean age, 41.0 ± 21.3 years) were included. Mean reduction in AVM nidus target volume with endovascular embolization was 66.0 ± 19.7%. S-M grade reduction was achieved in 51.6% cases. Total obliteration after SRS was achieved in 56 AVMs (67.5%) after 2 years, and in 38 (86.4%) after 4 years. Two (2.4%) patients had rehemorrhage after SRS. Overall complication rate was 3.6%. Median angiographic follow-up was 55.5 months. Favorable outcomes (mRS = 0-2) were seen in 77.1%. SRS target volume was an independent predictor of treatment failure regardless of pre-SRS embolization. CONCLUSIONS: High AVM obliteration rates were achieved with judicious use of radiosurgery alone or with embolization. Embolization reduced target nidus volume by an average of 66%. SRS target volume was an independent predictor of treatment failure.