ObjectiveIntracranial atherosclerosis is a major cause of ischaemic stroke worldwide. A number of studies have shown the effects of statin treatment on coronary and carotid artery plaques, but there is little evidence on the effects of statin treatment on intracranial atherosclerotic plaques.MethodsThe Intensive Statin Treatment in Acute Ischaemic Stroke Patients with Intracranial Atherosclerosis - High-Resolution Magnetic Resonance Imaging (STAMINA-MRI) Trial is a single-arm, prospective, observational study monitoring imaging and clinical outcomes of high-dose statin treatment among statin-naive patients with acute ischaemic stroke caused by symptomatic intracranial atherosclerosis. The primary outcome was the change in vascular remodelling and plaque characteristics before and after 6 months (median: 179 days, IQR 163–189 days) of statin treatment measured by high-resolution MRI (HR-MRI).ResultsA total of 77 patients (mean age: 62.6±13.7 years, 61.0% women) were included in this study. Low-density lipoprotein cholesterol (LDL-C) levels (mg/dL) at initial and follow-up assessments were 125.81±35.69 and 60.95±19.28, respectively. Overall, statin treatment significantly decreased enhancement of plaque volume (mm3, 32.07±39.15 vs 17.06±34.53, p=0.013), the wall area index (7.50±4.28 vs 5.86±4.05, p=0.016) and stenosis degree (%, 76.47±20.23 vs 64.05±21.29, p<0.001), but not the remodelling index (p=0.195). However, 35% patients showed no change or increased enhancement volume and stenosis degree after statin treatment. Higher reduction of LDL-C and longer duration of statin treatment were associated with decreased enhancement volume after statin treatment.ConclusionsHigh-dose statin treatment effectively stabilised symptomatic intracranial atherosclerotic plaques as documented by HR-MRI. Further study is needed to determine laboratory and genetic factors associated with poor response to statins and alternative therapeutic options, such as proprotein convertase subtilisin-kexin type 9 inhibitors, for these patients.Trial registration numberClinicalTrials.gov NCT02458755.
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.
OBJECTIVE:This study aimed to identify novel GATA5 mutations that underlie familial atrial fibrillation.METHODS:A total of 110 unrelated patients with familial atrial fibrillation and 200 unrelated, ethnically matched healthy controls were recruited. The entire coding region of the GATA5 gene was sequenced in 110 atrial fibrillation probands. The available relatives of the mutation carriers and 200 controls were subsequently genotyped for the identified mutations. The functional effect of the mutated GATA5 was characterized using a luciferase reporter assay system.RESULTS:Two novel heterozygous GATA5 mutations (p.Y138F and p.C210G) were identified in two of the 110 unrelated atrial fibrillation families. These missense mutations cosegregated with AF in the families and were absent in the 400 control chromosomes. A cross-species alignment of GATA5 protein sequence showed that the altered amino acids were completely conserved evolutionarily. A functional analysis revealed that the mutant GATA5 proteins were associated with significantly decreased transcriptional activation when compared with their wild-type counterpart.CONCLUSION:The findings expand the spectrum of GATA5 mutations linked to AF and provide novel insights into the molecular mechanism involved in the pathogenesis of atrial fibrillation, suggesting potential implications for the early prophylaxis and personalized treatment of this common arrhythmia.
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