Plaque modification and stabilization after drug-coated balloon angioplasty for intracranial atherosclerotic lesions

Plaque features of the middle cerebral artery are associated with periprocedural complications of intracranial angioplasty and stenting

PURPOSE

The identification of plaque features in the middle cerebral artery (MCA) may help minimize periprocedural complications and select patients suitable for percutaneous transluminal angioplasty and stenting (PTAS). However, relevant research is lacking.

METHODS

We retrospectively included patients with symptomatic MCA stenosis who received PTAS. All patients underwent intracranial vessel wall MRI (VWMRI) before surgery. Periprocedural complications (PC) included ischemic and hemorrhagic stroke within 30 days. Stenosis location, MCA shape, plaque eccentricity and distribution, plaque thickness and length, and enhancement ratio were compared between patients with and without PC.

RESULTS

Sixty-six patients were included in the study, of which 12.1% (8/66) had PC. Of the eight patients with PC, seven (87.5%) had superior wall plaques. In the non-PC group (n = 58), nine (17%) patients had superior wall plaques. Compared with patients without PC, those with PC had more frequent superior wall plaques (17% vs 87.5%, p < 0.001) and s-shaped MCAs (19% vs 50%, p = 0.071), different stenosis locations (p = 0.012), thicker plaques (1.58 [1.35, 2.00] vs 1.98 [1.73, 2.43], p = 0.038), and less frequent inferior wall plaques (79.2% vs 12.5%, p < 0.001). Multivariate analysis showed that only the presence of superior wall plaques (OR = 41.54 [2.31, 747.54]) was independently associated with PC.

CONCLUSION

MCA plaque features were highly correlated with PC in patients with symptomatic MCA stenosis who underwent PTAS.

Emerging Concept of Intracranial Arterial Diseases: The Role of High Resolution Vessel Wall MRI

Intracranial arterial disease (ICAD) is a heterogeneous condition characterized by distinct pathologies, including atherosclerosis. Advances in magnetic resonance technology have enabled the visualization of intracranial arteries using high-resolution vessel wall imaging (HR-VWI). This review summarizes the anatomical, embryological, and histological differences between the intracranial and extracranial arteries. Next, we review the heterogeneous pathophysiology of ICAD, including atherosclerosis, moyamoya or RNF213 spectrum disease, intracranial dissection, and vasculitis. We also discuss how advances in HR-VWI can be used to differentiate ICAD etiologies. We emphasize that one should consider clinical presentation and timing of imaging in the absence of pathology-radiology correlation data. Future research should focus on understanding the temporal profile of HR-VWI findings and developing quantitative interpretative approaches to improve the decision-making and management of ICAD.

Identidication of novel biomarkers in non-small cell lung cancer using machine learning

Lung cancer is one of the leading causes of cancer-related deaths worldwide, and non-small cell lung cancer (NSCLC) accounts for a large proportion of lung cancer cases, with few diagnostic and therapeutic targets currently available for NSCLC. This study aimed to identify specific biomarkers for NSCLC. We obtained three gene-expression profiles from the Gene Expression Omnibus database (GSE18842, GSE21933, and GSE32863) and screened for differentially expressed genes (DEGs) between NSCLC and normal lung tissue. Enrichment analyses were performed using Gene Ontology, Disease Ontology, and the Kyoto Encyclopedia of Genes and Genomes. Machine learning methods were used to identify the optimal diagnostic biomarkers for NSCLC using least absolute shrinkage and selection operator logistic regression, and support vector machine recursive feature elimination. CIBERSORT was used to assess immune cell infiltration in NSCLC and the correlation between biomarkers and immune cells. Finally, using western blot, small interfering RNA, Cholecystokinin-8, and transwell assays, the biological functions of biomarkers with high predictive value were validated. A total of 371 DEGs (165 up-regulated genes and 206 down-regulated genes) were identified, and enrichment analysis revealed that these DEGs might be linked to the development and progression of NSCLC. ABCA8, ADAMTS8, ASPA, CEP55, FHL1, PYCR1, RAMP3, and TPX2 genes were identified as novel diagnostic biomarkers for NSCLC. Monocytes were the most visible activated immune cells in NSCLC. The knockdown of the TPX2 gene, a biomarker with a high predictive value, inhibited A549 cell proliferation and migration. This study identified eight potential diagnostic biomarkers for NSCLC. Further, the TPX2 gene may be a therapeutic target for NSCLC.