Identification of vasospasm biomarkers for cerebral hemorrhage via bio-informatics analysis.

BACKGROUND: The incidence of cerebral hemorrhage has rapidly increased over time, and vascular dysfunction has a significant influence on the pathogenesis and outcome of these patients. This is also the case for vasospasm in cerebral hemorrhage, but there is no method to assess this. We conducted this study to find molecular biomarkers of vasospasm in cerebral hemorrhage patients. METHODS: Raw data of GSE37924 was downloaded from the Gene Expression Omnibus (GEO) database, including 66 samples with cerebral vasospasm and 62 samples without cerebral vasospasm. Differentially expressed genes (DEGs) between samples with cerebral vasospasm and those without cerebral vasospasm were analyzed using the limma package in R software. To determine the functions of DEGs, we conducted functional enrichment analysis of DEGs through the clusterProfiler package in R. The protein-protein interaction (PPI) network of DEGs was constructed through STRING (https://string-db.org/) and generated via Cytoscape software. To understand the correlation between DEGs and immune-related genes, immune-related cerebral vasospasm genes were obtained via intersecting immune-related genes and cerebral vasospasm DEGs. We also compared the infiltration of 28 immune cells between cases with cerebral vasospasm and those without cerebral vasospasm. Finally, we constructed a model to perform the validation experiments. RESULTS: Of the DEGs, there were 24 upregulated and 21 downregulated genes in the vasospasm samples compared to the no-vasospasm samples. Functional enrichment analysis showed that these genes play key roles in several biological processes and signaling pathways such as the bone morphogenetic protein (BMP) signaling pathway, cellular response to BMP stimulus, natural killer cell chemotaxis, negative regulation of transmembrane receptor protein serine/threonine kinase signaling pathway, MHC protein complex binding, and receptor ligand activity, among others. CCL4, HLA-DQA1, IGF2, NTS, and so on were the significant immune-related genes. Furthermore, the immune cell infiltration results showed that there were differences between patients with vasospasm and those without vasospasm. Finally, we found that CCL4 had significantly higher expression in patients with vasospasm than those without vasospasm. CONCLUSIONS: CCL4 is an important regulator of vascular dysfunction in cerebral hemorrhage.

Diagnosis of Middle Cerebral Artery Stenosis Using Transcranial Doppler Images Based on Convolutional Neural Network.

BACKGROUND: The purpose of this study was to explore the diagnostic value of convolutional neural networks (CNNs) in middle cerebral artery (MCA) stenosis by analyzing transcranial Doppler (TCD) images. METHODS: Overall, 278 patients who underwent cerebral vascular TCD and cerebral angiography were enrolled and classified into stenosis and non-stenosis groups based on cerebral angiography findings. Manual measurements were performed on TCD images. The patients were divided into a training set and a test set, and the CNN architecture was used to classify TCD images. The diagnostic accuracies of manual measurements, CNNs, and TCD parameters for MCA stenosis were calculated and compared. RESULTS: Overall, 203 patients without stenosis and 75 patients with stenosis were evaluated. The sensitivity, specificity, and area under the curve (AUC) for manual measurements of MCA stenosis were 0.80, 0.83, and 0.81, respectively. After 24 iterations of the running model in the training set, the sensitivity, specificity, and AUC of the CNNs in the test set were 0.84, 0.86, and 0.80, respectively. The diagnostic value of CNNs differed minimally from that of manual measurements. Two parameters of TCD, peak systolic velocity and mean flow velocity, were higher in patients with stenosis than in those without stenosis; however, their diagnostic values were significantly lower than those of CNNs (P < 0.05). CONCLUSIONS: The diagnostic value of CNNs for MCA stenosis based on TCD images paralleled that of manual measurements. CNNs could be used as an auxiliary diagnostic tool to improve the diagnosis of MCA stenosis.

[Diagnostic value of 64-slice spiral computed tomographic angiography in vertebral artery stenosis: a meta-analysis].

OBJECTIVE: To evaluate the diagnostic value of 64-slice spiral computed tomographic angiography (CTA) in vertebral artery stenosis through a meta-analysis of the relevant data. METHODS: A database search of Cochrane Library, PubMed, EBSCO, CBM-disc and CNKI was performed to identify the relevant English and Chinese language articles with such keywords as 64-slice computer tomography, angiography and vertebral artery stenosis. Quality evaluation, heterogeneity test and sensitivity and specificity to the qualified original data were conducted. Summary receiver operating characteristic (SROC) curve, the area under curve (AUC) and diagnostic odds ratio (DROC) were also calculated. RESULTS: A total of 4 studies were eligible for meta-analysis. Among them, 1 was graded as A and 3 were graded as B. No heterogeneity was found based upon a fixed effect model. For vertebral artery stenosis > or = 50%, the pooled weighted sensitivity, specificity, DROC and SROC AUC was 0.98 (0.94 -1.00), 0.93 (0.89 -0.96), 526.33 and 0.9899 respectively; while for vertebral artery stenosis > or = 50%, the parameters were 0.98 (0.91 - 1.00), 0.97 (0.94 -0.99), 838.40 and 0.9932 respectively. CONCLUSIONS: 64-slice spiral CTA has such a high level of accuracy, sensitivity and specificity in the non-invasive diagnosis of vertebral artery stenosis.