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Background: Numerous noncontrast computed tomography (NCCT) markers have been reported and validated as effective predictors of hematoma expansion (HE). Our objective was to develop and validate a score based on NCCT markers and clinical characteristics to predict risk of HE in acute intracerebral hemorrhage (ICH) patients.

Methods: We prospectively collected spontaneous ICH patients at the First Affiliated Hospital of Chongqing Medical University to form the development cohort (n = 395) and at the Third Affiliated Hospital of Chongqing Medical University to establish the validation cohort (n = 139).

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Background: Intracranial hemorrhage (ICH) is a devastating stroke subtype with a high rate of mortality and disability. Therapeutic options available are primarily limited to supportive care and blood pressure control, whereas the surgical approach remains controversial. In this study, we explored the effects of noninvasive vagus nerve stimulation (nVNS) on hematoma volume and outcome in a rat model of collagenase-induced ICH.

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Objective: This randomized controlled trial (RCT) aimed to compare the short-, mid-, and long-term outcomes in patients with malignant intracranial hypertension undergoing either decompressive craniectomy (DC) or hinge craniotomy (HC).

Methods: In this prospective RCT, 38 patients diagnosed with malignant intracranial hypertension due to ischemic infarction, traumatic brain injury, or non-lesional spontaneous intracerebral hemorrhage, who required cranial decompression, were randomly allocated to the DC and HC groups.

Results: The need for reoperation, particularly cranioplasty, in the DC group was significantly different from that in the HC group.

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Improving the Robustness of Deep-Learning Models in Predicting Hematoma Expansion from Admission Head CT.

AJNR Am J Neuroradiol

January 2025

From the Department of Radiology (A.T.T., D.Z., D.K., S. Payabvash) and Neurology (S. Park), NewYork-Presbyterian/Columbia University Irving Medical Center, Columbia University, New York, NY; Department of Radiology and Biomedical Imaging (G.A., A.M.) and Neurology (G.J.F., K.N.S.), Yale School of Medicine, New Haven, CT; Zeenat Qureshi Stroke Institute and Department of Neurology (A.I.Q.), University of Missouri, Columbia, MO; Department of Neurosurgery (S.M.), Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY; and Department of Neurology (S.B.M.), Weill Cornell Medical College, Cornell University, New York, NY.

Background And Purpose: Robustness against input data perturbations is essential for deploying deep-learning models in clinical practice. Adversarial attacks involve subtle, voxel-level manipulations of scans to increase deep-learning models' prediction errors. Testing deep-learning model performance on examples of adversarial images provides a measure of robustness, and including adversarial images in the training set can improve the model's robustness.

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Aβ40 Fibril Assembly on Human Cerebral Smooth Muscle Cells Impairs Cell Viability.

Biochemistry

January 2025

George and Anne Ryan Institute for Neuroscience, Department of Biomedical and Pharmacological Sciences, University of Rhode Island, Kingston, Rhode Island 02881, United States.

Cerebral vascular deposition of the amyloid-β (Aβ) peptide, a condition known as cerebral amyloid angiopathy (CAA), is associated with intracerebral hemorrhaging and contributes to disease progression in Alzheimer's disease (AD) and vascular cognitive impairment and dementia (VCID). Familial mutations at positions 22 and 23 within the Aβ peptide lead to early onset and severe CAA pathology. Here, we evaluate the effects of fibrillar Aβ peptides on the viability of primary-cultured human cerebral smooth muscle (HCSM) cells, which are the major site of amyloid deposition in cerebral blood vessel walls.

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