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Accurate stroke assessment and consequent favorable clinical outcomes rely on the early identification and quantification of aneurysmal subarachnoid hemorrhage (aSAH) in non-contrast computed tomography (NCCT) images. However, hemorrhagic lesions can be complex and difficult to distinguish manually. To solve these problems, here we propose a novel Hybrid 2D/3D UNet deep-learning framework for automatic aSAH identification and quantification in NCCT images. We evaluated 1824 consecutive patients admitted with aSAH to four hospitals in China between June 2018 and May 2022. Accuracy and precision, Dice scores and intersection over union (IoU), and interclass correlation coefficients (ICC) were calculated to assess model performance, segmentation performance, and correlations between automatic and manual segmentation, respectively. A total of 1355 patients with aSAH were enrolled: 931, 101, 179, and 144 in four datasets, of whom 326 were scanned with Siemens, 640 with Philips, and 389 with GE Medical Systems scanners. Our proposed deep-learning method accurately identified (accuracies 0.993-0.999) and segmented (Dice scores 0.550-0.897) hemorrhage in both the internal and external datasets, even combinations of hemorrhage subtypes. We further developed a convenient AI-assisted platform based on our algorithm to assist clinical workflows, whose performance was comparable to manual measurements by experienced neurosurgeons (ICCs 0.815-0.957) but with greater efficiency and reduced cost. While this tool has not yet been prospectively tested in clinical practice, our innovative hybrid network algorithm and platform can accurately identify and quantify aSAH, paving the way for fast and cheap NCCT interpretation and a reliable AI-based approach to expedite clinical decision-making for aSAH patients., Competing Interests: Declaration of Competing Interest All authors declare no financial or non-financial competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

As in many other cancers, highly malignant proliferation and disordered cell division play irreplaceable roles in the exceedingly easy recurrence and complex progression of glioblastoma multiforme (GBM); however, mechanistic studies of the numerous regulators involved in this process are still insufficiently thorough. The role of BCAS3 has been studied in other cancers, but its role in GBM is unclear. Here, our goal was to investigate the expression pattern of BCAS3 in GBM and its potential mechanism of action. Using TCGA database and human GBM samples, we found that BCAS3 expression was up-regulated in GBM, and its high expression predicted poor prognosis. To further investigate the relationship between BCAS3 and GBM characteristics, we up-regulated and down-regulated BCAS3 expression in GBM to detect its effect on cell proliferation and cell cycle. At the same time, we established U87 cells stably overexpressing BCAS3 and generated an intracranial xenograft model to investigate the Potential role of BCAS3 in vivo. Finally, based on in vitro cell experiments and in vivo GBM xenograft models, we observed that BCAS3 significantly regulates GBM cell proliferation and cell cycle and that this regulation is associated with p53/GADD45α Signaling pathway. Taken together, our findings suggest that BCAS3 is inextricably linked to the progression of GBM and that targeting BCAS3 may have therapeutic effects in GBM patients., (Copyright © 2022 Elsevier Inc. All rights reserved.)