Predicting cerebrovascular age and its clinical relevance: Modeling using 3D morphological features of brain vessels.

Aging manifests as many phenotypes, among which age-related changes in brain vessels are important, but underexplored. Thus, in the present study, we constructed a model to predict age using cerebrovascular morphological features, further assessing their clinical relevance using a novel pipeline. Age prediction models were first developed using data from a normal cohort (n = 1181), after which their relevance was tested in two stroke cohorts (n = 564 and n = 455). Our novel pipeline adapted an existing framework to compute generic vessel features for brain vessels, resulting in 126 morphological features. We further built various machine learning models to predict age using only clinical factors, only brain vessel features, and a combination of both. We further assessed deviation from healthy aging using the age gap and explored its clinical relevance by correlating the predicted age and age gap with various risk factors. The models constructed using only brain vessel features and those combining clinical factors with vessel features were better predictors of age than the clinical factor-only model (r = 0.37, 0.48, and 0.26, respectively). Predicted age was associated with many known clinical factors, and the associations were stronger for the age gap in the normal cohort. The age gap was also associated with important factors in the pooled cohort atherosclerotic cardiovascular disease risk score and white matter hyperintensity measurements. Cerebrovascular age, computed using the morphological features of brain vessels, could serve as a potential individualized marker for the early detection of various cerebrovascular diseases.

Global intracranial arterial tortuosity is associated with intracranial atherosclerotic burden.

The effect of arterial tortuosity on intracranial atherosclerosis (ICAS) is not well understood. This study aimed to evaluate the effect of global intracranial arterial tortuosity on intracranial atherosclerotic burden in patients with ischemic stroke. We included patients with acute ischemic stroke who underwent magnetic resonance angiography (MRA) and classified them into three groups according to the ICAS burden. Global tortuosity index (GTI) was defined as the standardized mean curvature of the entire intracranial arteries, measured by in-house vessel analysis software. Of the 516 patients included, 274 patients had no ICAS, 140 patients had a low ICAS burden, and 102 patients had a high ICAS burden. GTI increased with higher ICAS burden. After adjustment for age, sex, vascular risk factors, and standardized mean arterial area, GTI was independently associated with ICAS burden (adjusted odds ratio [adjusted OR] 1.33; 95% confidence interval [CI] 1.09-1.62). The degree of association increased when the arterial tortuosity was analyzed limited to the basal arteries (adjusted OR 1.48; 95% CI 1.22-1.81). We demonstrated that GTI is associated with ICAS burden in patients with ischemic stroke, suggesting a role for global arterial tortuosity in ICAS.

Imaging and clinical predictors of acute constipation in patients with acute ischemic stroke.

Background: Constipation symptoms are highly prevalent in acute ischemic stroke, but the clinical and neuroimaging predictors are unknown. This study aimed to identify lesions and clinical features associated with acute constipation. Methods: Data from patients with acute ischemic stroke registered in a hospital-based stroke registry between January 2018 and December 2019 were analyzed. Clinical, laboratory, and imaging features were examined for associations with acute constipation. Using the topographic lesion on diffusion-weighted images, multivariate support vector regression-based lesion-symptom mapping (SVR-LSM) was conducted and compared between the non-constipation and acute constipation groups. Results: A total of 256 patients (mean age 67 years, men: 64%) were included. Acute constipation was noted in 81 patients (32%). Initial stroke severity, represented by initial National Institutes of Health and Stroke Scale (NIHSS) scores, was associated with acute constipation. Laboratory parameters, including fibrin degradation products (FDP), fibrinogen, D-dimer, lipoprotein (a), and free fatty acid levels, also showed statistically significant differences between the non-constipation and constipation groups. FDP, D-dimer, and free fatty acid levels were independently associated with acute constipation in the logistic regression model after adjusting for initial NIHSS scores and potassium levels. SVR-LSM revealed that bilateral lesions in the precentral gyrus, insula, opercular part of the inferior frontal gyrus, the inferior parietal lobule, and lesions in the right middle frontal gyrus were significantly associated with acute constipation. The results were consistent after controlling for the initial NIHSS scores and poststroke potassium levels. When cardioembolic stroke subjects were excluded, the right insular and prefrontal cortex lesions lost their association with acute constipation. Conclusion: Acute constipation symptoms after acute ischemic stroke are mainly related to bilateral lesions in the insula, precentral gyrus, postcentral gyrus, and inferior parietal lobule. Clinically important predictors of acute constipation include initial neurological severity and thromboembolic markers of stroke.

The effect of intensive statin therapy in non-symptomatic intracranial arteries: The STAMINA-MRI sub-study.

Background and aims: Pleiotropic effects of statins result in the stabilization of symptomatic intracranial arterial plaque. However, little is known about the effect of statins in non-symptomatic cerebral arteries. We hypothesized that intensive statin therapy could produce a change in the non-symptomatic cerebral arteries. Methods: This is a sub-study of a prospective observational study under the title of "Intensive Statin Treatment in Acute Ischemic Stroke Patients with Intracranial Atherosclerosis: a High-Resolution Magnetic Resonance Imaging (HR-MRI) study." Patients with statin-naive acute ischemic stroke who had symptomatic intracranial artery stenosis (above 50%) were recruited for this study. HR-MRI was performed to assess the patients' cerebral arterial status before and 6 months after the statin therapy. To demonstrate the effect of statins in the non-symptomatic segment of intracranial cerebral arteries, we excluded symptomatic segments from the data to be analyzed. We compared the morphological changes using cerebrovascular morphometry. Results: A total of 54 patients (mean age: 62.9 ± 14.4 years, 59.3% women) were included in this study. Intensive statin therapy produced significant morphological changes of overall cerebral arteries. Among the morphological features, the arterial luminal area showed the highest number of significant changes with a range from 5.7 and 6.7%. Systolic blood pressure (SBP) was an independent factor associated with relative changes in posterior circulation bed maximal diameter percentage change (beta -0.21, 95% confidence interval -0.36 to -0.07, p = 0.005). Conclusion: Intensive statin therapy produced a favorable morphological change in cerebral arteries of not only the target arterial segment but also non-symptomatic arterial segments. The change in cerebral arterial luminal diameter was influenced by the baseline SBP and was dependent on the topographic distribution of the cerebral arteries.Clinical Trial Registration: ClinicalTrials.gov, identifier NCT02458755.

Automated in-depth cerebral arterial labelling using cerebrovascular vasculature reframing and deep neural networks.

Identifying the cerebral arterial branches is essential for undertaking a computational approach to cerebrovascular imaging. However, the complexity and inter-individual differences involved in this process have not been thoroughly studied. We used machine learning to examine the anatomical profile of the cerebral arterial tree. The method is less sensitive to inter-subject and cohort-wise anatomical variations and exhibits robust performance with an unprecedented in-depth vessel range. We applied machine learning algorithms to disease-free healthy control subjects (n = 42), patients with stroke with intracranial atherosclerosis (ICAS) (n = 46), and patients with stroke mixed with the existing controls (n = 69). We trained and tested 70% and 30% of each study cohort, respectively, incorporating spatial coordinates and geometric vessel feature vectors. Cerebral arterial images were analyzed based on the 'segmentation-stacking' method using magnetic resonance angiography. We precisely classified the cerebral arteries across the exhaustive scope of vessel components using advanced geometric characterization, redefinition of vessel unit conception, and post-processing algorithms. We verified that the neural network ensemble, with multiple joint models as the combined predictor, classified all vessel component types independent of inter-subject variations in cerebral arterial anatomy. The validity of the categorization performance of the model was tested, considering the control, ICAS, and control-blended stroke cohorts, using the area under the receiver operating characteristic (ROC) curve and precision-recall curve. The classification accuracy rarely fell outside each image's 90-99% scope, independent of cohort-dependent cerebrovascular structural variations. The classification ensemble was calibrated with high overall area rates under the ROC curve of 0.99-1.00 [0.97-1.00] in the test set across various study cohorts. Identifying an all-inclusive range of vessel components across controls, ICAS, and stroke patients, the accuracy rates of the prediction were: internal carotid arteries, 91-100%; middle cerebral arteries, 82-98%; anterior cerebral arteries, 88-100%; posterior cerebral arteries, 87-100%; and collections of superior, anterior inferior, and posterior inferior cerebellar arteries, 90-99% in the chunk-level classification. Using a voting algorithm on the queued classified vessel factors and anatomically post-processing the automatically classified results intensified quantitative prediction performance. We employed stochastic clustering and deep neural network ensembles. Ma-chine intelligence-assisted prediction of vessel structure allowed us to personalize quantitative predictions of various types of cerebral arterial structures, contributing to precise and efficient decisions regarding the cerebrovascular disease.

How Cerebral Vessel Tortuosity Affects Development and Recurrence of Aneurysm: Outer Curvature versus Bifurcation Type.

BACKGROUND AND PURPOSE: Previous studies have assessed the relationship between cerebral vessel tortuosity and intracranial aneurysm (IA) based on two-dimensional brain image analysis. We evaluated the relationship between cerebral vessel tortuosity and IA according to the hemodynamic location using three-dimensional (3D) analysis and studied the effect of tortuosity on the recurrence of treated IA. METHODS: We collected clinical and imaging data from patients with IA and disease-free controls. IAs were categorized into outer curvature and bifurcation types. Computerized analysis of the images provided information on the length of the arterial segment and tortuosity of the cerebral arteries in 3D space. RESULTS: Data from 95 patients with IA and 95 controls were analyzed. Regarding parent vessel tortuosity index (TI; P<0.01), average TI (P<0.01), basilar artery (BA; P=0.02), left posterior cerebral artery (P=0.03), both vertebral arteries (VAs; P<0.01), and right internal carotid artery (P<0.01), there was a significant difference only in the outer curvature type compared with the control group. The outer curvature type was analyzed, and the occurrence of an IA was associated with increased TI of the parent vessel, average, BA, right middle cerebral artery, and both VAs in the logistic regression analysis. However, in all aneurysm cases, recanalization of the treated aneurysm was inversely associated with increased TI of the parent vessels. CONCLUSIONS: TIs of intracranial arteries are associated with the occurrence of IA, especially in the outer curvature type. IAs with a high TI in the parent vessel showed good outcomes with endovascular treatment.

TiO2 particles induce ER stress and apoptosis in human hepatoma cells, HepG2, in a particle size-dependent manner.

The cytotoxicity of TiO2 nanoparticles are well-known, but the particle size-dependent induction of ER stress and apoptosis by TiO2 in hepatocytes has not been elucidated clearly. In the present study, we investigated whether a fine TiO2 particle and two types of TiO2 nanoparticles induce ER stress and apoptosis differently in HepG2 cells. A particle size-dependent decrease in cell viability was observed after exposure to the TiO2 particles. The levels of ER stress-related proteins (BiP, CHOP, ATF6α, and p-PERK) were increased with decreasing particle size. TiO2 particles induced ER stress-mediated apoptosis in a particle size-dependent manner as seen by a decrease in the expression of Bcl-2, and increases in the expression of Bax, caspase-12, and cleaved caspase-3. These results indicated that the cytotoxicity produced by TiO2 particles was related to particle size, with smaller TiO2 nanoparticles producing greater toxic effects involving ER stress and apoptosis in the HepG2 cells.

Evaluation of Diffusion Lesion Volume Measurements in Acute Ischemic Stroke Using Encoder-Decoder Convolutional Network.

Background and Purpose- Automatic segmentation of cerebral infarction on diffusion-weighted imaging (DWI) is typically performed based on a fixed apparent diffusion coefficient (ADC) threshold. Fixed ADC threshold methods may not be accurate because ADC values vary over time after stroke onset. Deep learning has the potential to improve the accuracy, provided that a large set of correctly annotated lesion data is used for training. The purpose of this study was to evaluate deep learning-based methods and compare them with commercial software in terms of lesion volume measurements. Methods- U-net, an encoder-decoder convolutional neural network, was adopted to train segmentation models. Two U-net models were developed: a U-net (DWI+ADC) model, trained on DWI and ADC data, and a U-net (DWI) model, trained on DWI data only. A total of 296 subjects were used for training and 134 for external validation. An expert neurologist manually delineated the stroke lesions on DWI images, which were used as the ground-truth reference. Lesion volume measurements from the U-net methods were compared against the expert's manual segmentation and Rapid Processing of Perfusion and Diffusion (RAPID; iSchemaView Inc) analysis. Results- In external validation, U-net (DWI+ADC) showed the highest intraclass correlation coefficient with manual segmentation (intraclass correlation coefficient, 1.0; 95% CI, 0.99-1.00) and sufficiently high correlation with the RAPID results (intraclass correlation coefficient, 0.99; 95% CI, 0.98-0.99). U-net (DWI+ADC) and manual segmentation resulted in the smallest 95% Bland-Altman limits of agreement (-5.31 to 4.93 mL) with a mean difference of -0.19 mL. Conclusions- The presented deep learning-based method is fully automatic and shows a high correlation of diffusion lesion volume measurements with manual segmentation and commercial software. The method has the potential to be used in patient selection for endovascular reperfusion therapy in the late time window of acute stroke.

Endoplasmic reticulum stress induced by an ethanol extract of Coicis semen in Chang liver cells.

BACKGROUND: It is well known that endoplasmic reticulum (ER) stress plays a huge role in development of metabolic diseases. Specially, ER stress-induced cellular dysfunction has a significant involvement in the pathogenesis of human chronic disorders. This study was designed to study to assess whether an ethanol extract of Coicis Semen (CSE) and coixol induces the ER stress in Chang liver cells. METHODS: Coicis Semen was mixed with 95% ethanol at a ratio of 1:10 (w/v) and freeze dried. Chang liver cells were seeded to 96-well plates and treated with or without CSE (100, 200, 300, 500, or 1000 µg/mL) or coixol (100, 200, 300, 500, 750, or 1000 µg/mL). cell viability was analyzed with MTT assay. Effects of CSE and coixol on expression of the genes for ER stress markers were determined with qRT-PCR and the expression of the protein levels of ER stress markers were determined with western blotting. RESULTS: The concentration causing 50% inhibition (IC50) for CSE and coixol was 250 and 350 µg/mL, respectively. The CSE and coixol increased the gene expression of BiP and CHOP in a dose-dependent manner. Furthermore, CSE and coixol dose-dependently increased the the expression of XBP1. CONCLUSIONS: CSE or coixol may have cytotoxic effect to Chang liver cells and, may induce ER stress and stimulate the UPR via activation of the PERK and IRE1 pathways in normal liver cells.

Mechanical stability and clinical applicability assessment of novel orthodontic mini-implant design.

OBJECTIVE: To compare the stability and clinical applicability of a novel orthodontic mini-implant design (N2) with the most widely used commercially available (CA) design. MATERIALS AND METHODS: Two groups of mini-implants were tested: a CA design (1.5-mm diameter, 6-mm length) and N2 (3-mm diameter, 2-mm length, tapered shape). Implants were inserted in bone blocks of cortical bone simulation with varying densities (20 pounds per cubic foot [pcf], 30 pcf, and 40 pcf). A torque test was used to measure maximum insertion torque (MIT) and maximum removal torque (MRT). Compression and tension force vectors were applied at angles of 10°, 20°, 30°, and 40° using customized load pins to determine primary stability. RESULTS: Mean MIT and MRT were higher in the N2 than the CA design at all three cortical bone densities except MRT in 20 pcf bone (not statistically significant). The mean compression force required to displace the N2 at all distances and angulations was greater for the N2 than the CA design. At all displacement distances, the highest mean tension force required for N2 displacement was at 10° angulation, whereas at 30° and 40°, the mean tension force required to displace the CA design was greater. CONCLUSIONS: The primary stability of the N2 is superior to that of the CA design and is promising for both orthodontic and orthopedic clinical applicability, especially under compression force. The short length of the N2 reduces risk of damage to anatomic structures and root proximity during placement and orthodontic treatment. The stability of the N2 may be compromised in areas of high bone density and highly angulated tension force.

Mechanical stability assessment of novel orthodontic mini-implant designs: Part 2.

OBJECTIVE: To assess the mechanical stability of a newly revised orthodontic mini-implant design (N2) compared with a design introduced in Part 1 of the study (N1) and the most widely-used commercially-available design (CA). To evaluate the mean buccal bone thickness of maxillary and mandibular posterior teeth using cone-beam computed tomography (CBCT). MATERIALS AND METHODS: From the CBCT scans of 20 patients, six tomographic cross-sections were generated for each tooth. Buccal bone thickness was measured from the most convex point on the bone to the root surface. CA (1.5 mm in diameter and 6 mm in length), N1, and N2 (shorter and narrower than N1) were inserted in simulated bone with cortical and trabecular bone layers. Mechanical stability was compared in vitro through torque and lateral displacement tests. RESULTS: The bone thickness ranged from 2.26 to 3.88 mm. Maximum insertion torque was decreased significantly in N2 compared to N1. However, force levels for all displacement distances and torque ratio were the highest in N2, followed by N1 and CA (α = .05). CONCLUSIONS: Both torque and lateral displacement tests highlighted the enhanced stability of N2 compared with CA. Design revisions to N1 effectively mitigated N1's high insertion torque and thus potentially reduced microdamage to the surrounding bone. The N2 design is promising as evidenced by enhanced stability and high mechanical efficiency. Moreover, N2 is not limited to placement in interradicular spaces and has the capacity to be placed in the buccal bone superficial to the root surface with diminished risk of endangering nearby anatomic structures during placement and treatment.