Development of a rabbit human glioblastoma model for testing of endovascular selective intra-arterial infusion (ESIA) of novel stem cell-based therapeutics.

BACKGROUND: Endovascular selective intra-arterial (ESIA) infusion of cellular oncotherapeutics is a rapidly evolving strategy for treating glioblastoma. Evaluation of ESIA infusion requires a unique animal model. Our goal was to create a rabbit human GBM model to test IA infusions of cellular therapies and to test its usefulness by employing clinical-grade microcatheters and infusion methods to deliver mesenchymal stem cells loaded with an oncolytic adenovirus, Delta-24-RGD (MSC-D24). METHODS: Rabbits were immunosuppressed with mycophenolate mofetil, dexamethasone, and tacrolimus. They underwent stereotactic xenoimplantation of human GBM cell lines (U87, MDA-GSC-17, and MDA-GSC-8-11) into the right frontal lobe. Tumor formation was confirmed on magnetic resonance imaging, histologic, and immunohistochemistry analysis. Selective microcatheter infusion of MSC-D24 was performed via the ipsilateral internal carotid artery to assess model utility and the efficacy and safety of this approach. RESULTS: Twenty-five rabbits were implanted (18 with U87, 2 MDA-GSC-17, and 5 MDA-GSC-8-11). Tumors formed in 68% of rabbits (77.8% for U87, 50.0% for MDA-GSC-17, and 40.0% for MDA-GSC-8-11). On MRI, the tumors were hyperintense on T2-weighted image with variable enhancement (evidence of blood brain barrier breakdown). Histologically, tumors showed phenotypic traits of human GBM including varying levels of vascularity. ESIA infusion into the distal internal carotid artery of 2 ml of MSCs-D24 (107 cells) was safe in the model. Examination of post infusion specimens documented that MSCs-D24 homed to the implanted tumor at 24 hours. CONCLUSIONS: The intracranial immunosuppressed rabbit human GBM model allows testing of ESIA infusion of novel therapeutics (eg, MSC-D24) in a clinically relevant fashion.

Validation of an automated machine learning algorithm for the detection and analysis of cerebral aneurysms.

OBJECTIVE: Machine learning algorithms have shown groundbreaking results in neuroimaging. The authors herein evaluated the performance of a newly developed convolutional neural network (CNN) to detect and analyze intracranial aneurysms (IAs) on CTA. METHODS: Consecutive patients with CTA studies between January 2015 and July 2021 at a single center were identified. The ground truth determination of cerebral aneurysm presence or absence was made from the neuroradiology report. The primary outcome was the performance of the CNN in detecting IAs in an external validation set, measured using area under the receiver operating characteristic curve statistics. Secondary outcomes included accuracy for location and size measurement. RESULTS: The independent validation imaging data set consisted of 400 patients with CTA studies, median age 40 years (IQR 34 years) and 141 (35.3%) of whom were male; 193 patients (48.3%) had a diagnosis of IA on neuroradiologist evaluation. The median maximum IA diameter was 3.7 mm (IQR 2.5 mm). In the independent validation imaging data set, the CNN performed well with 93.8% sensitivity (95% CI 0.87-0.98), 94.2% specificity (95% CI 0.90-0.97), and a positive predictive value of 88.2% (95% CI 0.80-0.94) in the subgroup with an IA diameter ≥ 4 mm. CONCLUSIONS: The described Viz.ai Aneurysm CNN performed well in identifying the presence or absence of IAs in an independent validation imaging set. Further studies are necessary to investigate the impact of the software on detection rates in a real-world setting.

Automated detection and analysis of subdural hematomas using a machine learning algorithm.

OBJECTIVE: Machine learning algorithms have shown groundbreaking results in neuroimaging. Herein, the authors evaluate the performance of a newly developed convolutional neural network (CNN) to detect and quantify the thickness, volume, and midline shift (MLS) of subdural hematoma (SDH) from noncontrast head CT (NCHCT). METHODS: NCHCT studies performed for the evaluation of head trauma in consecutive patients between July 2018 and April 2021 at a single institution were retrospectively identified. Ground truth determination of SDH, thickness, and MLS was established by the neuroradiology report. The primary outcome was performance of the CNN in detecting SDH in an external validation set, as measured using area under the receiver operating characteristic curve analysis. Secondary outcomes included accuracy for thickness, volume, and MLS. RESULTS: Among 263 cases with valid NCHCT according to the study criteria, 135 patients (51%) were male, the mean (± standard deviation) age was 61 ± 23 years, and 70 patients were diagnosed with SDH on neuroradiologist evaluation. The median SDH thickness was 11 mm (IQR 6 mm), and 16 patients had a median MLS of 5 mm (IQR 2.25 mm). In the independent data set, the CNN performed well, with sensitivity of 91.4% (95% CI 82.3%-96.8%), specificity of 96.4% (95% CI 92.7%-98.5%), and accuracy of 95.1% (95% CI 91.7%-97.3%); sensitivity for the subgroup with an SDH thickness above 10 mm was 100%. The maximum thickness mean absolute error was 2.75 mm (95% CI 2.14-3.37 mm), whereas the MLS mean absolute error was 0.93 mm (95% CI 0.55-1.31 mm). The Pearson correlation coefficient computed to determine agreement between automated and manual segmentation measurements was 0.97 (95% CI 0.96-0.98). CONCLUSIONS: The described Viz.ai SDH CNN performed exceptionally well at identifying and quantifying key features of SDHs in an independent validation imaging data set.

A steroid receptor coactivator small molecule "stimulator" attenuates post-stroke ischemic brain injury.

Introduction: Pathologic remodeling of the brain following ischemic stroke results in neuronal loss, increased inflammation, oxidative stress, astrogliosis, and a progressive decrease in brain function. We recently demonstrated that stimulation of steroid receptor coactivator 3 with the small-molecule stimulator MCB-613 improves cardiac function in a mouse model of myocardial ischemia. Since steroid receptor coactivators are ubiquitously expressed in the brain, we reasoned that an MCB-613 derivative (MCB-10-1), could protect the brain following ischemic injury. To test this, we administered MCB-10-1 to rats following middle cerebral artery occlusion and reperfusion. Methods: Neurologic impairment and tissue damage responses were evaluated on day 1 and day 4 following injury in rats treated with control or 10-1. Results: We show that 10-1 attenuates injury post-stroke. 10-1 decreases infarct size and mitigates neurologic impairment. When given within 30 min post middle cerebral artery occlusion and reperfusion, 10-1 induces lasting protection from tissue damage in the ischemic penumbra concomitant with: (1) promotion of reparative microglia; (2) an increase in astrocyte NRF2 and GLT-1 expression; (3) early microglia activation; and (4) attenuation of astrogliosis. Discussion: Steroid receptor coactivator stimulation with MCB-10-1 is a potential therapeutic strategy for reducing inflammation and oxidative damage that cause neurologic impairment following an acute ischemic stroke.

Flow diversion of a dissecting PICA aneurysm.

A 47-year-old female with a history of a ruptured left posterior inferior cerebellar artery (PICA) aneurysm, status post coil embolization and retreatment for recurrence, presented with evidence of a recurrent dissecting PICA aneurysm. Given that these aneurysms are considered high risk and have a greater propensity for rupture than anterior circulation aneurysms, retreatment was recommended. With the patient's strong preference for endovascular therapy, flow diversion with a Silk Vista Baby was performed. Given the low-profile design of the device, a radial artery approach and coaxial technique were used to deploy the flow diverter. The device was successfully placed, with complete obliteration of the aneurysm after 1 year. The video can be found here: https://stream.cadmore.media/r10.3171/2022.7.FOCVID2247.

A review of resilience variables in the context of disasters.

The increasing impacts of disasters, caused by more frequent extreme events coupled with the growth of adverse anthropogenic activities, has raised the importance of fostering more resilient communities. Measuring resilience is a vital step in the process of building and strengthening a community's resilience as it helps with identifying the priorities and monitoring the progress. The objective of the current research is to catalog variables proposed in the literature as measures of households' resilience to disasters. Searching the literature through content analysis and applying three selection criteria resulted in a list of 149 variables. These criteria required the variables to be influential on disaster resilience of households, to be quantitatively measurable, and to be obtainable from publicly available data sources. Additionally, a selection of resilience and vulnerability assessment models suggested in the literature were reviewed to highlight the importance of resilience variables in addressing their planned objectives. The variables were classified into five categories titled demographic, socioeconomic, infrastructural, environmental, and institutional. Further analysis of the variables led to identification of the most prevalent variables and commonalities among the categories, aimed to provide a more integrated approach toward resilience planning. This research can serve as an initial yet relatively extensive inventory for selecting variables that are deemed to be influential on households' resilience to extreme events. Further, quantifying a community's resilience using resilience variables can help with identifying and prioritizing the resilience needs, monitoring the progress, and justifying the costs of resilience programs.