Quantitative Analysis of Hemodynamic Changes in Branch Arteries Covered by Flow Diverters.

BACKGROUND AND OBJECTIVES: Understanding post-treatment hemodynamic alterations and their association with the patency of covered branch arteries is limited. This study aims to identify hemodynamic changes after flow diverter stenting and investigate their correlation with the patency status of covered branch arteries. METHODS: All patients treated with pipeline embolization device for anterior cerebral artery aneurysms at our center between 2016 and 2020 were screened for inclusion. Quantitative digital subtraction angiography was used to analyze changes in hemodynamic parameters pre- and post-stenting. The patency status of covered branch arteries after stenting was categorized as either patent or flow impairment (defined as artery stenosis or occlusion). RESULTS: A total of 71 patients, encompassing 89 covered branch arteries, were enrolled. Flow impairment was observed in 11.2% (10/89) of the branches. The mean transit time and full width at half maximum (FWHM) in covered branches were significantly prolonged post-stenting (P = .004 and .023, respectively). Flow-impaired branch arteries exhibited hemodynamic shifts contrary to those in patent branch arteries. Specifically, flow-impaired branches showed marked reductions in time to peak, FWHM, and mean transit time (decreases of 32.8%, 32.6%, and 29%, respectively; P = .006, .002, and .002, respectively). Further multivariate analysis revealed that reductions in FWHM in the branches (odds ratio = 0.97, 95% CI: 0.95-0.99, P = .007) and smoking (odds ratio = 14.5, 95% CI: 1.39-151.76, P = .026) were independent predictors of flow impairment of covered branches. CONCLUSION: Pipeline embolization device stenting can cause a reduction in blood flow in branch arteries. Compared with patent branches, flow-impaired branches exhibit an increase in blood flow velocity after stenting. Smoking and ΔFWHM in the covered branches indicate flow impairment.

Diagnosis of intracranial aneurysms by computed tomography angiography using deep learning-based detection and segmentation.

BACKGROUND: Detecting and segmenting intracranial aneurysms (IAs) from angiographic images is a laborious task. OBJECTIVE: To evaluates a novel deep-learning algorithm, named vessel attention (VA)-Unet, for the efficient detection and segmentation of IAs. METHODS: This retrospective study was conducted using head CT angiography (CTA) examinations depicting IAs from two hospitals in China between 2010 and 2021. Training included cases with subarachnoid hemorrhage (SAH) and arterial stenosis, common accompanying vascular abnormalities. Testing was performed in cohorts with reference-standard digital subtraction angiography (cohort 1), with SAH (cohort 2), acquired outside the time interval of training data (cohort 3), and an external dataset (cohort 4). The algorithm's performance was evaluated using sensitivity, recall, false positives per case (FPs/case), and Dice coefficient, with manual segmentation as the reference standard. RESULTS: The study included 3190 CTA scans with 4124 IAs. Sensitivity, recall, and FPs/case for detection of IAs were, respectively, 98.58%, 96.17%, and 2.08 in cohort 1; 95.00%, 88.8%, and 3.62 in cohort 2; 96.00%, 93.77%, and 2.60 in cohort 3; and, 96.17%, 94.05%, and 3.60 in external cohort 4. The segmentation accuracy, as measured by the Dice coefficient, was 0.78, 0.71, 0.71, and 0.66 for cohorts 1-4, respectively. VA-Unet detection recall and FPs/case and segmentation accuracy were affected by several clinical factors, including aneurysm size, bifurcation aneurysms, and the presence of arterial stenosis and SAH. CONCLUSIONS: VA-Unet accurately detected and segmented IAs in head CTA comparably to expert interpretation. The proposed algorithm has significant potential to assist radiologists in efficiently detecting and segmenting IAs from CTA images.

sRNAminer: A multifunctional toolkit for next-generation sequencing small RNA data mining in plants.

Small RNAs (sRNAs), found extensively in plants, play an essential role in plant growth and development. Although various sRNA analysis tools have been developed for plants, the use of most of them depends on programming and command-line environments, which is a challenge for many wet-lab biologists. Furthermore, current sRNA analysis tools mostly focus on the analysis of certain type of sRNAs and are resource-intensive, normally demanding an immense amount of time and effort to learn the use of numerous tools or scripts and assemble them into a workable pipeline to get the final results. Here, we present sRNAminer, a powerful stand-alone toolkit with a user-friendly interface that integrates all common functions for the analysis of three major types of plant sRNAs: microRNAs (miRNAs), phased small interfering RNAs (phasiRNAs), and heterochromatic siRNAs (hc-siRNAs). We constructed a curated or "golden" set of MIRNA and PHAS loci, which was used to assess the performance of sRNAminer in comparison to other existing tools. The results showed that sRNAminer outperformed these tools in multiple aspects, highlighting its functionality. In addition, to enable an efficient evaluation of sRNA annotation results, we developed Integrative Genomics Viewer (IGV)-sRNA, a modified genome browser optimized from IGV and we incorporated it as a functional module in sRNAminer. IGV-sRNA can display a wealth of sRNA-specific features, enabling a more comprehensive understanding of sRNA data. sRNAminer and IGV-sRNA are both platform-independent software that can be run under all operating systems. They are now freely available at https://github.com/kli28/sRNAminer and https://gitee.com/CJchen/IGV-sRNA.