Ischemic stroke is a leading cause of death and disability worldwide. Several reports suggest that acute inflammation after ischemia-reperfusion exacerbates brain damage; however, molecular mechanisms underlying this effect remain unclear. Here, we report that MAC-3-positive immune cells, including infiltrating bone marrow-derived macrophages and activated microglia, express abundant angiopoietin-like protein (ANGPTL) 2 in ischemic mouse brain in a transient middle cerebral artery occlusion (MCAO) model. Both neurological deficits and infarct volume decreased in transient MCAO model mice established in Angptl2 knockout (KO) relative to wild-type mice. Acute brain inflammation after ischemia-reperfusion, as estimated by expression levels of pro-inflammatory cytokines such as interleukin (IL)-1β and tumor necrosis factor alpha (TNF)-α, was significantly suppressed in Angptl2 KO compared to control mice. Moreover, analysis employing bone marrow chimeric models using Angptl2 KO and wild-type mice revealed that infiltrated bone marrow-derived macrophages secreting ANGPTL2 significantly contribute to acute brain injury seen after ischemia-reperfusion. These studies demonstrate that infiltrating bone marrow-derived macrophages promote inflammation and injury in affected brain areas after ischemia-reperfusion, likely via ANGPTL2 secretion in the acute phase of ischemic stroke.

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Background: Evaluating cerebrovascular reserve (CVR) is important for patients with moyamoya disease (MMD). ¹²³I-iodoamphetamine single-photon emission CT (SPECT) with acetazolamide (ACZ) challenge is widely carried out, but using ACZ becomes problematic owing to its off-label use and its adverse effects. Here, we report the efficacy of dynamic susceptibility contrast MRI (DSC-MRI) for the evaluation of CVR in MMD patients. Methods: All 33 MMD patients underwent both SPECT and DSC-MRI at an interval of <10 days from each other (mean age 38.3 years). The region of interest (ROI) was the anterior cerebral artery (ACA) territory, middle cerebral artery (MCA) territory, basal ganglia and cerebellum hemisphere for cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) images. The ratios of the ROIs to the ipsilateral cerebellum were calculated for each parameter and evaluated. The CVR was calculated using images acquired by SPECT before and after ACZ administration. The ratios of DSC-MRI parameters and CVR were compared and evaluated for each ROI. Results: The MTT of the ACA and MCA territories significantly correlated with CVR (p < 0.0001). However, CBF and CBV had no correlation with CVR. The MTT ratio had a threshold of 1.966, with a sensitivity of 68.4% and a specificity of 91.5% for predicting decreased CVR (<10%). Conclusion: MTT had a negative correlation with CVR. DSC-MRI is easy, safe and useful for detecting decreased CVR and can be used as a standard examination in MMD patient's care.

Subarachnoid hemorrhage (SAH) is a serious cerebrovascular disease with a high mortality rate and is known as a disease that is hard to diagnose because it may be overlooked by noncontrast computed tomography (NCCT) examinations that are most frequently used for diagnosis. To create a system preventing this oversight of SAH, we trained artificial intelligence (AI) with NCCT images obtained from 419 patients with nontraumatic SAH and 338 healthy subjects and created an AI system capable of diagnosing the presence and location of SAH. Then, we conducted experiments in which five neurosurgery specialists, five nonspecialists, and the AI system interpreted NCCT images obtained from 135 patients with SAH and 196 normal subjects. The AI system was capable of performing a diagnosis of SAH with equal accuracy to that of five neurosurgery specialists, and the accuracy was higher than that of nonspecialists. Furthermore, the diagnostic accuracy of four out of five nonspecialists improved by interpreting NCCT images using the diagnostic results of the AI system as a reference, and the number of oversight cases was significantly reduced by the support of the AI system. This is the first report demonstrating that an AI system improved the diagnostic accuracy of SAH by nonspecialists.

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