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.

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.

Decoding Molecular Mechanisms Underlying Outcomes After Ischemic Stroke Thrombectomy by RNA Sequencing of Retrieved Clots.

BACKGROUND: Transcriptomic profiling has emerged as a powerful tool for exploring the molecular landscape of ischemic stroke clots and providing insights into the pathophysiological mechanisms underlying stroke progression and recovery. In this study, we aimed to investigate the relationship between stroke clot transcriptomes and stroke thrombectomy outcome, as measured by early neurological improvement (ENI) 30 (i.e., a 30% reduction in NIHSS at 24 h post-thrombectomy). HYPOTHESIS: We hypothesized that there exist distinct clot gene expression patterns between good and poor neurological outcomes. METHODS: Transcriptomic analysis of 32 stroke clots retrieved by mechanical thrombectomy was conducted. Transcriptome data of these clots were analyzed to identify differentially expressed genes (DEGs), defined as those with a log(fold-change) ≥ 1.5 and q < 0.05 between samples with good and poor early neurological outcomes. Gene ontology and bioinformatics analyses were performed on genes with p < 0.01 to identify enriched biological processes and Ingenuity Pathway Analysis canonical pathways. Moreover, AUC analysis assessed the predictive power of DEGs for 90-day function outcome (mRS ≤ 2) and cellular composition of clot was predicted using CIBERSORT. We also assessed whether differential enrichment of immune cell types could indicate patient survival. RESULTS: A total of 41 DEGs were identified. Bioinformatics showed that enriched biological processes and pathways emphasized the chronic immune response and matrix metalloproteinase inhibition. Moreover, 25 of the DEGs were found to be significant predictors of 90-day mRS. These genes were indicative of monocytes enrichment and neutrophil depletion in patients with poorer outcomes. CONCLUSION: Our study revealed a distinct gene expression pattern and dysregulated biological pathways associated with ENI. This expression pattern was also predictive of long-term outcome, suggesting a biological link between those ENIs and 90-day mRS.

Hybrid Clot Histomic-Transcriptomic Models Predict Functional Outcome After Mechanical Thrombectomy in Acute Ischemic Stroke.

BACKGROUND AND OBJECTIVES: Histologic and transcriptomic analyses of retrieved stroke clots have identified features associated with patient outcomes. Previous studies have demonstrated the predictive capacity of histology or expression features in isolation. Few studies, however, have investigated how paired histologic image features and expression patterns from the retrieved clots can improve understanding of clot pathobiology and our ability to predict long-term prognosis. We hypothesized that computational models trained using clot histomics and mRNA expression can predict early neurological improvement (ENI) and 90-day functional outcome (modified Rankin Scale Score, mRS) better than models developed using histological composition or expression data alone. METHODS: We performed paired histological and transcriptomic analysis of 32 stroke clots. ENI was defined as a delta-National Institutes of Health Stroke Score/Scale > 4, and a good long-term outcome was defined as mRS ≤2 at 90 days after procedure. Clots were H&E-stained and whole-slide imaged at 40×. An established digital pathology pipeline was used to extract 237 histomic features and to compute clot percent composition (%Comp). When dichotomized by either the ENI or mRS thresholds, differentially expressed genes were identified as those with absolute fold-change >1.5 and q < 0.05. Machine learning with recursive feature elimination (RFE) was used to select clot features and evaluate computational models for outcome prognostication. RESULTS: For ENI, RFE identified 9 optimal histologic and transcriptomic features for the hybrid model, which achieved an accuracy of 90.8% (area under the curve [AUC] = 0.98 ± 0.08) in testing and outperformed models based on histomics (AUC = 0.94 ± 0.09), transcriptomics (AUC = 0.86 ± 0.16), or %Comp (AUC = 0.70 ± 0.15) alone. For mRS, RFE identified 7 optimal histomic and transcriptomic features for the hybrid model. This model achieved an accuracy of 93.7% (AUC = 0.94 ± 0.09) in testing, also outperforming models based on histomics (AUC = 0.90 ± 0.11), transcriptomics (AUC = 0.55 ± 0.27), or %Comp (AUC = 0.58 ± 0.16) alone. CONCLUSION: Hybrid models offer improved outcome prognostication for patients with stroke. Identified digital histology and mRNA signatures warrant further investigation as biomarkers of patient functional outcome after thrombectomy.

Gene expression profiles of ischemic stroke clots retrieved by mechanical thrombectomy are associated with disease etiology.

BACKGROUND: Determining stroke etiology is crucial for secondary prevention, but intensive workups fail to classify ~30% of strokes that are cryptogenic. OBJECTIVE: To examine the hypothesis that the transcriptomic profiles of clots retrieved during mechanical thrombectomy are unique to strokes of different subtypes. METHODS: We isolated RNA from the clots of 73 patients undergoing mechanical thrombectomy. Samples of sufficient quality were subjected to 100-cycle, paired-end RNAseq, and transcriptomes with less than 10 million unique reads were excluded from analysis. Significant differentially expressed genes (DEGs) between subtypes (defined by the Trial of Org 10 172 in Acute Stroke Treatment) were identified by expression analysis in edgeR. Gene ontology enrichment analysis was used to study the biologic differences between stroke etiologies. RESULTS: In all, 38 clot transcriptomes were analyzed; 6 from large artery atherosclerosis (LAA), 21 from cardioembolism (CE), 5 from strokes of other determined origin, and 6 from cryptogenic strokes. Among all comparisons, there were 816 unique DEGs, 174 of which were shared by at least two comparisons, and 20 of which were shared by all three. Gene ontology analysis showed that CE clots reflected high levels of inflammation, LAA clots had greater oxidoreduction and T-cell processes, and clots of other determined origin were enriched for aberrant platelet and hemoglobin-related processes. Principal component analysis indicated separation between these subtypes and showed cryptogenic samples clustered among several different groups. CONCLUSIONS: Expression profiles of stroke clots were identified between stroke etiologies and reflected different biologic responses. Cryptogenic thrombi may be related to multiple etiologies.

Stroke thrombectomy volume, rather than stroke center accreditation status of hospitals, is associated with mortality and discharge disposition.

BACKGROUND: Few studies have explored the association between stroke thrombectomy (ST) volume and hospital accreditation with clinical outcomes. OBJECTIVE: To assess the association of ST case volume and accreditation status with in-hospital mortality and home discharge disposition using the national Medicare Provider Analysis and Review (MEDPAR) database. METHODS: Rates of hospital mortality, home discharge disposition, and hospital stay were compared between accredited and non-accredited hospitals using 2017-2018 MEDPAR data. The association of annual ST case volume with mortality and home disposition was determined using Pearson's correlation. Median rate of mortality and number of ST cases at hospitals within the central quartiles were estimated. RESULTS: A total of 29 355 cases were performed over 2 years at 847 US centers. Of these, 354 were accredited. There were no significant differences between accredited and non-accredited centers for hospital mortality (14.8% vs 14.5%, p=0.34) and home discharge (12.1% vs 12.0%, p=0.78). A significant positive correlation was observed between thrombectomy volume and home discharge (r=0.88; 95% CI 0.58 to 0.97, p=0.001). A significant negative relationship was found between thrombectomy volume and mortality (r=-0.86; 95% CI -0.97 to -0.49, p=0.002). Within the central quartiles, the median number of ST cases at hospitals with mortality was 24/year, and the median number of ST cases at hospitals with home discharge rate was 23/year. CONCLUSION: A higher volume of ST cases was associated with lower mortality and higher home discharge rate. No significant differences in mortality and discharge disposition were found between accredited and non-accredited hospitals.

Artificial Intelligence for Large-Vessel Occlusion Stroke: A Systematic Review.

BACKGROUND: Optimal outcomes after large-vessel occlusion (LVO) stroke are highly dependent on prompt diagnosis, effective communication, and treatment, making LVO an attractive avenue for the application of artificial intelligence (AI), specifically machine learning (ML). Our objective is to conduct a systematic review to describe existing AI applications for LVO strokes, delineate its effectiveness, and identify areas for future AI applications in stroke treatment and prognostication. METHODS: A systematic review was conducted by searching the PubMed, Embase, and Scopus databases. After deduplication, studies were screened by title and abstract. Full-text studies were screened for final inclusion based on prespecified inclusion and exclusion criteria. Relevant data were extracted from each study. RESULTS: Of 11,512 resultant articles, 40 were included. Of 30 studies with reported ML algorithms, the most commonly used ML algorithms were convolutional neural networks in 10 (33.3%), support vector machines in 10 (33.0%), and random forests in 9 (30.0%). Studies examining triage favored direct transport to a stroke center and predicted improved outcomes. ML techniques proved vastly accurate in identifying LVO on computed tomography. Applications of AI to patient selection for thrombectomy are lacking, although some studies determine individual patient eligibility for endovascular treatment with high accuracy. ML algorithms have reasonable accuracy in predicting clinical and angiographic outcomes and associated factors. CONCLUSIONS: AI has shown promise in the diagnosis and triage of patients with acute stroke. However, the role of AI in the management and prognostication remains limited and warrants further research to help in decision support.

Hemodynamic Analysis Shows High Wall Shear Stress Is Associated with Intraoperatively Observed Thin Wall Regions of Intracranial Aneurysms.

BACKGROUND: Studying the relationship between hemodynamics and local intracranial aneurysm (IA) pathobiology can help us understand the natural history of IA. We characterized the relationship between the IA wall appearance, using intraoperative imaging, and the hemodynamics from CFD simulations. METHODS: Three-dimensional geometries of 15 IAs were constructed and used for CFD. Two-dimensional intraoperative images were subjected to wall classification using a machine learning approach, after which the wall type was mapped onto the 3D surface. IA wall regions included thick (white), normal (purple-crimson), and thin/translucent (red) regions. IA-wide and local statistical analyses were performed to assess the relationship between hemodynamics and wall type. RESULTS: Thin regions of the IA sac had significantly higher WSS, Normalized WSS, WSS Divergence and Transverse WSS, compared to both normal and thick regions. Thicker regions tended to co-locate with significantly higher RRT than thin regions. These trends were observed on a local scale as well. Regression analysis showed a significant positive correlation between WSS and thin regions and a significant negative correlation between WSSD and thick regions. CONCLUSION: Hemodynamic simulation results were associated with the intraoperatively observed IA wall type. We consistently found that elevated WSS and WSSNorm were associated with thin regions of the IA wall rather than thick and normal regions.

Clot imaging characteristics predict first pass effect of aspiration-first approach to thrombectomy.

BACKGROUND: The value of clot imaging in patients with emergent large vessel occlusion (ELVO) treated with thrombectomy is unknown. METHODS: We performed retrospective analysis of clot imaging (clot density, perviousness, length, diameter, distance to the internal carotid artery (ICA) terminus and angle of interaction (AOI) between clot and the aspiration catheter) of consecutive cases of middle cerebral artery (MCA) occlusion and its association with first pass effect (FPE, TICI 2c-3 after a first attempt). RESULTS: Patients (n = 90 total) with FPE had shorter clot length (9.9 ± 4.5 mm vs. 11.7 ± 4.6 mm, P = 0.07), shorter distance from ICA terminus (11.0 ± 7.1 mm vs. 14.7 ± 9.8 mm, P = 0.048), higher perviousness (39.39 ± 29.5 vs 25.43 ± 17.6, P = 0.006) and larger AOI (153.6 ± 17.6 vs 140.3 ± 23.5, P = 0.004) compared to no-FPE patients. In multivariate analysis, distance from ICA terminus to clot ≤13.5 mm (odds ratio (OR) 11.05, 95% confidence interval (CI) 2.65-46.15, P = 0.001), clot length ≤9.9 mm (OR 7.34; 95% CI 1.8-29.96, P = 0.005), perviousness ≥ 19.9 (OR 2.54, 95% CI 0.84-7.6, P = 0.09) and AOI ≥ 137°^ (OR 6.8, 95% CI 1.55-29.8, P = 0.011) were independent predictors of FPE. The optimal cut off derived using Youden's index was 6.5. The area under the curve of a score predictive of FPE success was 0.816 (0.728-0.904, P < 0.001). In a validation cohort (n = 30), sensitivity, specificity, positive and negative predictive value of a score of 6-10 were 72.7%, 73.6%, 61.5% and 82.3%. CONCLUSIONS: Clot imaging predicts the likelihood of achieving FPE in patients with MCA ELVO treated with the aspiration-first approach.

Cerebral aneurysm flow diverter modeled as a thin inhomogeneous porous medium in hemodynamic simulations.

Rapid and accurate simulation of cerebral aneurysm flow modifications by flow diverters (FDs) can help improving patient-specific intervention and predicting treatment outcome. However, when FD devices are explicitly represented in computational fluid dynamics (CFD) simulations, flow around the stent wires must be resolved, leading to high computational cost. Classic porous medium (PM) methods can reduce computational expense but cannot capture the inhomogeneous FD wire distribution once implanted on a cerebral artery and thus cannot accurately model the post-stenting aneurysmal flow. We report a novel approach that models the FD flow modification as a thin inhomogeneous porous medium (iPM). It improves over the classic PM approaches in two ways. First, the FD is more appropriately treated as a thin screen, which is more accurate than the classic 3D-PM-based Darcy-Forchheimer relation. Second, pressure drop is calculated cell-by-cell using the local FD geometric parameters across an inhomogeneous PM. We applied the iPM technique to simulating the post-stenting hemodynamics of three patient-specific aneurysms. To test its accuracy and speed, we compared the results from the iPM technique against CFD simulations with explicit FD devices. The iPM CFD ran 500% faster than the explicit CFD while achieving 94%-99% accuracy; thus, iPM is a promising clinical bedside modeling tool to assist endovascular interventions with FD and stents.

Automated Cerebral Vessel Segmentation of Magnetic Resonance Imaging in Patients with Intracranial Atherosclerotic Diseases.

Time-of-flight (TOF) magnetic resonance angiography is a non-invasive imaging modality for the diagnosis of intracranial atherosclerotic diseases (ICAD). Evaluation of the degree of the stenosis and status of posterior and anterior communicating arteries to supply enough blood flow to the distal arteries is very critical, which requires accurate evaluation of arteries. Recently, deep-learning methods have been firmly established as a robust tool in medical image segmentation, which has been resulted in developing multiple customized algorithms. For instance, BRAVE-NET, a context-based successor of U-Net-has shown promising results in MRA cerebrovascular segmentation. Another widely used context-based 3D CNN-DeepMedic-has been shown to outperform U-Net in cerebrovascular segmentation of 3D digital subtraction angiography. In this study, we aim to train and compare the two state-of-the-art deep-learning networks, BRAVE-NET and DeepMedic, for automated and reliable brain vessel segmentation from TOF-MRA images in ICAD patients. Using specially labeled data-labeled on TOF MRA and corrected on high-resolution black-blood MRI, of 51 patients with ICAD due to severe stenosis, we trained and tested both models. On an independent test dataset of 11 cases, DeepMedic slightly outperformed BRAVE-NET in terms of DSC (0.905±0.012 vs 0.893±0.015, p: 0.539) and 95HD (0.754±0.223 vs 1.768±0.609, p: 0.134), and significantly outperformed BRAVE-NET in terms of Recall (0.940±0.023 vs 0.855±0.030, p: 0.036). Qualitative assessment confirmed the superiority of DeepMedic in capturing the small and distal arteries. While BRAVE-NET consistently reported higher precision, DeepMedic generally overpredicted and could better visualize the smaller and distal arteries. In future studies, ensemble models that can leverage best of both should be developed and tested on larger datasets.Clinical Relevance- This study helps elevate the state-of-the-art for brain vessel segmentation from non-invasive MRA, which could accelerate the translation of vessel status-based biomarkers into the clinical setting.

Revascularization Outcome Prediction for A Direct Aspiration-First Pass Technique (ADAPT) from Pre-Treatment Imaging and Machine Learning.

A direct aspiration-first pass technique (ADAPT) has recently gained popularity for the treatment of large vessel ischemic stroke. Here, we sought to create a machine learning-based model that uses pre-treatment imaging metrics to predict successful outcomes for ADAPT in middle cerebral artery (MCA) stroke cases. In 119 MCA strokes treated by ADAPT, we calculated four imaging parameters-clot length, perviousness, distance from the internal carotid artery (ICA) and angle of interaction (AOI) between clot/catheter. We determined treatment success by first pass effect (FPE), and performed univariate analyses. We further built and validated multivariate machine learning models in a random train-test split (75%:25%) of our data. To test model stability, we repeated the machine learning procedure over 100 randomizations, and reported the average performances. Our results show that perviousness (p = 0.002) and AOI (p = 0.031) were significantly higher and clot length (p = 0.007) was significantly lower in ADAPT cases with FPE. A logistic regression model achieved the highest accuracy (74.2%) in the testing cohort, with an AUC = 0.769. The models had similar performance over the 100 train-test randomizations (average testing AUC = 0.768 ± 0.026). This study provides feasibility of multivariate imaging-based predictors for stroke treatment outcome. Such models may help operators select the most adequate thrombectomy approach.

Isolation of RNA from Acute Ischemic Stroke Clots Retrieved by Mechanical Thrombectomy.

Mechanical thrombectomy (MT) for large vessel acute ischemic stroke (AIS) has enabled biologic analyses of resected clots. While clot histology has been well-studied, little is known about gene expression within the tissue, which could shed light on stroke pathophysiology. In this methodological study, we develop a pipeline for obtaining useful RNA from AIS clots. A total of 73 clot samples retrieved by MT were collected and stored in RNALater and in 10% phosphate-buffered formalin. RNA was extracted from all samples using a modified Chemagen magnetic bead extraction protocol on the PerkinElmer Chemagic 360. RNA was interrogated by UV-Vis absorption and electrophoretic quality control analysis. All samples with sufficient volume underwent traditional qPCR analysis and samples with sufficient RNA quality were subjected to next-generation RNA sequencing on the Illumina NovaSeq platform. Whole blood RNA samples from three patients were used as controls, and H&E-stained histological sections of the clots were used to assess clot cellular makeup. Isolated mRNA was eluted into a volume of 140 µL and had a concentration ranging from 0.01 ng/µL to 46 ng/µL. Most mRNA samples were partially degraded, with RNA integrity numbers ranging from 0 to 9.5. The majority of samples (71/73) underwent qPCR analysis, which showed linear relationships between the expression of three housekeeping genes (GAPDH, GPI, and HPRT1) across all samples. Of these, 48 samples were used for RNA sequencing, which had moderate quality based on MultiQC evaluation (on average, ~35 M reads were sequenced). Analysis of clot histology showed that more acellular samples yielded RNA of lower quantity and quality. We obtained useful mRNA from AIS clot samples stored in RNALater. qPCR analysis could be performed in almost all cases, while sequencing data could only be performed in approximately two-thirds of the samples. Acellular clots tended to have lower RNA quantity and quality.

Realistic computer modelling of stent retriever thrombectomy: a hybrid finite-element analysis-smoothed particle hydrodynamics model.

Stent retriever thrombectomy is a pre-eminent treatment modality for large vessel ischaemic stroke. Simulation of thrombectomy could help understand stent and clot mechanics in failed cases and provide a digital testbed for the development of new, safer devices. Here, we present a novel, in silico thrombectomy method using a hybrid finite-element analysis (FEA) and smoothed particle hydrodynamics (SPH). Inspired by its biological structure and components, the blood clot was modelled with the hybrid FEA-SPH method. The Solitaire self-expanding stent was parametrically reconstructed from micro-CT imaging and was modelled as three-dimensional finite beam elements. Our simulation encompassed all steps of mechanical thrombectomy, including stent packaging, delivery and self-expansion into the clot, and clot extraction. To test the feasibility of our method, we simulated clot extraction in simple straight vessels. This was compared against in vitro thrombectomies using the same stent, vessel geometry, and clot size and composition. Comparisons with benchtop tests indicated that our model was able to accurately simulate clot deflection and penetration of stent wires into the clot, the relative movement of the clot and stent during extraction, and clot fragmentation/embolus formation. In this study, we demonstrated that coupling FEA and SPH techniques could realistically model stent retriever thrombectomy.

A hybrid echocardiography-CFD framework for ventricular flow simulations.

Image-based CFD is a powerful tool to study cardiovascular flows while 2D echocardiography (echo) is the most widely used noninvasive imaging modality for the diagnosis of heart disease. Here, echo is combined with CFD, that is, an echo-CFD framework, to study ventricular flows. To achieve this, the previous 3D reconstruction from multiple 2D echo at standard cross sections is extended by: (a) reconstructing aortic and mitral valves from 2D echo and closing the left-ventricle (LV) geometry by approximating a superior wall; (b) incorporating the physiological assumption of the fixed apex as a reference (fixed) point in the 3D reconstruction; and (c) incorporating several smoothing algorithms to remove the nonphysical oscillations (ringing) near the basal section. The method is applied to echo from a baseline LV and one after inducing acute myocardial ischemia (AMI). The 3D reconstruction is validated by comparing it against a reference reconstruction from many echo sections while flow simulations are validated against the Doppler ultrasound velocity measurements. The sensitivity study shows that the choice of the smoothing algorithm does not change the flow pattern inside the LV. However, the presence of the mitral valve can significantly change the flow pattern during the diastole phase. In addition, the abnormal shape of a LV with AMI can drastically change the flow during diastole. Furthermore, the hemodynamic energy loss, as an indicator of the LV pumping performance, for different test cases is calculated, which shows a larger energy loss for a LV with AMI compared to the baseline one.

Characterization of Long Non-coding RNA Signatures of Intracranial Aneurysm in Circulating Whole Blood.

BACKGROUND AND OBJECTIVE: Long non-coding RNAs (lncRNAs) may serve as biomarkers for complex disease states, such as intracranial aneurysms. In this study, we investigated lncRNA expression differences in the whole blood of patients with unruptured aneurysms. METHODS: Whole blood RNA from 67 subjects (34 with aneurysm, 33 without) was used for next-generation RNA sequencing. Differential expression analysis was used to define a signature of intracranial aneurysm-associated lncRNAs. To estimate the signature's ability to classify aneurysms and to identify the most predictive lncRNAs, we implemented a nested cross-validation pipeline to train classifiers using linear discriminant analysis. Ingenuity pathway analysis was used to study potential biological roles of differentially expressed lncRNAs, and lncRNA ontology was used to investigate ontologies enriched in signature lncRNAs. Co-expression correlation analysis was performed to investigate associated differential protein-coding messenger RNA expression. RESULTS: Of 4639 detected lncRNAs, 263 were significantly different (p < 0.05) between the two groups, and 84 of those had an absolute fold-change ≥ 1.5. An eight-lncRNA signature (q < 0.35, fold-change ≥ 1.5) was able to separate patients with and without aneurysms on principal component analysis, and had an estimated accuracy of 70.9% in nested cross-validation. Bioinformatics analyses showed that networks of differentially expressed lncRNAs (p < 0.05) were enriched for cell death and survival, connective tissue disorders, carbohydrate metabolism, and cardiovascular disease. Signature lncRNAs shared ontologies that reflected regulation of gene expression, signaling, ubiquitin processing, and p53 signaling. Co-expression analysis showed correlations with messenger RNAs related to inflammatory responses. CONCLUSIONS: Differential expression in whole blood lncRNAs is detectable in patients harboring aneurysms, and reflects expression/signaling regulation, and ubiquitin and p53 pathways. Following validation in larger cohorts, these lncRNAs may be potential diagnostic targets for aneurysm detection by blood testing.

Landmark-Based Shape Analysis on Middle Cerebral Intracranial Aneurysms: A Geometric Morphometrics Approach to Infer Natural History.

BACKGROUND: Due to the scarcity of longitudinal data, the morphologic development of intracranial aneurysms (IAs) during their natural history remains poorly understood. However, longitudinal information can often be inferred from cross-sectional datasets as demonstrated by anatomists' use of geometric morphometrics to build evolutionary trees, reconstructing species inter-relationships based on morphologic landmarks. OBJECTIVE: We adopted these tools to analyze cross-sectional image data and infer relationships between IA morphologies. METHODS: On 3D reconstructions of 52 middle cerebral arteries (MCA) IAs (9 ruptured) and 10 IAfree MCAs (baseline geometries), 7 semi-automated landmarks were placed at the proximal parent artery and maximum height. From these, 64 additional landmarks were computationally generated to create a 71-landmark point cloud of 213 xyz coordinates. This data was normalized by Procrustes transformation and used in the principal component analysis, hierarchical clustering, and phylogenetic analyses. RESULTS: Principal component analysis showed separation of IA-free MCA geometries and grouping of ruptured IAs from unruptured IAs. Hierarchical clustering delineated a cluster of only unruptured IAs that were significantly smaller and more spherical than clusters that had ruptured IAs. Phylogenetic classification placed ruptured IAs more distally in the tree than unruptured IAs, indicating greater shape derivation. Groups of unruptured IAs were observed, but ruptured IAs were invariably found in mixed lineages with unruptured IAs, suggesting that some pathways of shape change may be benign while others are more associated with rupture. CONCLUSION: Geometric morphometric analyses of larger datasets may indicate particular pathways of shape change leading toward aneurysm rupture versus stabilization.

Aneurysm characteristics, coil packing, and post-coiling hemodynamics affect long-term treatment outcome.

BACKGROUND: Recurrence of intracranial aneurysms after endovascular coiling is a serious clinical concern. OBJECTIVE: We hypothesized that recurrence is associated with aneurysm morphology and flow, as well as the coil intervention and the induced flow modifications. METHODS: We collected 52 primary-coiling aneurysm cases that were either occluded (n=34) or recurrent (n=18) at >1 year follow-up. We created aneurysm models from pre-coiling digital subtraction angiographic images, calculated aneurysm morphology, simulated pre-coiling hemodynamics, modeled coil deployment, and obtained post-coiling hemodynamics for each case. We performed univariable analysis on 26 morphologic, treatment-specific, and hemodynamic parameters to distinguish between recurrent and occluded groups, and multivariable analysis to identify independently significant parameters associated with recurrence. Univariable analysis was also performed on ruptured and unruptured aneurysm subcohorts separately to investigate if they shared specific significant parameters. RESULTS: Recurrence was associated with pre-coiling aneurysm morphologic and flow parameters including larger size (maximum dimension and volume), larger neck (diameter, area, and neck-to-parent-artery ratio), and higher flow momentum and kinetic energy. Recurrence was also associated with lower coil packing (packing density and uncoiled volume), higher post-treatment flow (velocity, momentum, and kinetic energy), lower post-treatment washout time, and higher post-treatment impingement force at the neck. Multivariable analysis identified two aneurysmal characteristics (neck diameter and pre-coiling flow kinetic energy), one coil packing parameter (uncoiled volume), and one post-treatment hemodynamic parameter (flow momentum) that were independently associated with recurrence. In ruptured aneurysms, recurrence was associated with larger neck (diameter and area), whereas in unruptured aneurysms, recurrence was associated with larger size (maximum dimension and volume). In both subcohorts, recurrence was associated with higher post-coiling flow momentum and kinetic energy. CONCLUSION: Recurrence at >1 year after coil treatment is associated with intrinsic aneurysm characteristics, coiling itself, and flow changes induced by coiling. Larger aneurysm size and neck, less coil packing, and higher intra-aneurysmal flow before and after coiling predict recurrence.