Puerarin was shown to exert anti-oxidative and anti-ferroptosis effects in multiple diseases. The goal of this study was to explore the neuroprotective effect of puerarin on early brain injury (EBI) after subarachnoid hemorrhage (SAH) in rats. A total of 177 adult male Sprague Dawley rats were used. SAH was included via endovascular perforation. Intranasal puerarin or intracerebroventricular dorsomorphin (AMPK inhibitor) and SR18292 (PGC1α inhibitor) were administered. The protein levels of pAMPK, PGC1α, Nrf2, 4HNE, HO1, MDA, ACSL4, GSSG, and iron concentration in the ipsilateral hemisphere were significantly increased, whereas SOD, GPX4, and GSH were decreased at 24 h after SAH. Moreover, puerarin treatment significantly increased the protein levels of pAMPK, PGC1α, Nrf2, HO1, SOD, GPX4, and GSH, but decreased the levels of 4HNE, MDA, ACSL4, GSSG, and iron concentration in the ipsilateral hemisphere at 24 h after SAH. Dorsomorphin or SR18292 partially abolished the beneficial effects of puerarin exerted on neurological dysfunction, oxidative stress injury, and ferroptosis. In conclusion, puerarin improved neurobehavioral impairments and attenuated oxidative-stress-induced brain ferroptosis after SAH in rats. The neuroprotection acted through the activation of the AMPK/PGC1α/Nrf2-signaling pathway. Thus, puerarin may serve as new therapeutics against EBI in SAH patients.

/pdf/puerarin-attenuates-oxidative-stress-and-ferroptosis-via-2htjxuvu.pdf

Puerarin Attenuates Oxidative Stress and Ferroptosis via AMPK/PGC1α/Nrf2 Pathway after Subarachnoid Hemorrhage in Rats

Intracranial aneurysm (IA), a local outpouching of cerebral arteries, is present in 3 to 5% of the population. Once formed, an IA can remain stable, grow or rupture. Determining the evolution of an IA is almost impossible. Rupture of an IA leads to subarachnoid haemorrhage (SAH). SAH which mostly affects young people with a median age of onset between 50 and 60 years has heavy consequences in term of life, disabilities and socio-economic burden. Thus, even if the large majority of IAs will never rupture it is of high importance to determine which IA is at risk of rupture. IA (in)stability is dependent of its vascular wall composition. The biology of the IA wall is complex and not completely understood. Nowadays, in clinic, the risk of rupture of an IA can be estimated by using scores based on the characteristics of the IA itself such as location, size or presence of shape irregularities, and on the anamnesis of the patient including ethnicity, age, hypertension or smoking status. Such scores are not completely satisfying and more information concerning IA (in)stability are needed to safely determine if a patient should be observed or treated. In the present review, the effects of known risk factors for rupture, as well as the effects of biomechanical forces on the IA wall composition will be summarized. Moreover, recent advances in high resolution vessel wall magnetic resonance imaging which are promising tools to help in the discrimination between stable and unstable IA wall will be described. Common data elements recently defined to improve current state of knowledge in IA disease and disease management will be presented. Finally, recent findings in genetics will be introduced and future directions in the field of IA will be exposed.

/pdf/intracranial-aneurysm-wall-in-stability-current-state-of-4an5lhka4p.pdf

Intracranial aneurysm wall (in)stability-current state of knowledge and clinical perspectives.

Aneurysmal subarachnoid hemorrhage (aSAH) is one of the most severe neurological disorders, with a high mortality rate and severe disabling functional sequelae. Systemic inflammation following hemorrhagic stroke may play an important role in mediating intracranial and extracranial tissue damage. Previous studies showed that various systemic inflammatory biomarkers might be useful in predicting clinical outcomes. Anti-inflammatory treatment might be a promising therapeutic approach for improving the prognosis of patients with aSAH. This review summarizes the complicated interactions between the nervous system and the immune system.

/pdf/systemic-inflammation-after-aneurysmal-subarachnoid-3nc5sn7a.pdf

Systemic Inflammation after Aneurysmal Subarachnoid Hemorrhage

The manual identification and segmentation of intracranial aneurysms (IAs) involved in the 3D reconstruction procedure are labor-intensive and prone to human errors. To meet the demands for routine clinical management and large cohort studies of IAs, fast and accurate patient-specific IA reconstruction becomes a research Frontier. In this study, a deep-learning-based framework for IA identification and segmentation was developed, and the impacts of image pre-processing and convolutional neural network (CNN) architectures on the framework’s performance were investigated. Three-dimensional (3D) segmentation-dedicated architectures, including 3D UNet, VNet, and 3D Res-UNet were evaluated. The dataset used in this study included 101 sets of anonymized cranial computed tomography angiography (CTA) images with 140 IA cases. After the labeling and image pre-processing, a training set and test set containing 112 and 28 IA lesions were used to train and evaluate the convolutional neural network mentioned above. The performances of three convolutional neural networks were compared in terms of training performance, segmentation performance, and segmentation efficiency using multiple quantitative metrics. All the convolutional neural networks showed a non-zero voxel-wise recall (V-Recall) at the case level. Among them, 3D UNet exhibited a better overall segmentation performance under the relatively small sample size. The automatic segmentation results based on 3D UNet reached an average V-Recall of 0.797 ± 0.140 (3.5% and 17.3% higher than that of VNet and 3D Res-UNet), as well as an average dice similarity coefficient (DSC) of 0.818 ± 0.100, which was 4.1%, and 11.7% higher than VNet and 3D Res-UNet. Moreover, the average Hausdorff distance (HD) of the 3D UNet was 3.323 ± 3.212 voxels, which was 8.3% and 17.3% lower than that of VNet and 3D Res-UNet. The three-dimensional deviation analysis results also showed that the segmentations of 3D UNet had the smallest deviation with a max distance of +1.4760/−2.3854 mm, an average distance of 0.3480 mm, a standard deviation (STD) of 0.5978 mm, a root mean square (RMS) of 0.7269 mm. In addition, the average segmentation time (AST) of the 3D UNet was 0.053s, equal to that of 3D Res-UNet and 8.62% shorter than VNet. The results from this study suggested that the proposed deep learning framework integrated with 3D UNet can provide fast and accurate IA identification and segmentation.

Deep learning-based recognition and segmentation of intracranial aneurysms under small sample size

/pdf/plasma-metabolic-signatures-for-intracranial-aneurysm-and-35rp4888.pdf

Plasma metabolic signatures for intracranial aneurysm and its rupture identified by pseudotargeted metabolomics.

Background and Purpose: The purpose of this study was to assess nationwide incidence and outcomes of aneurysmal subarachnoid hemorrhage (aSAH). The Swiss SOS (Swiss Study on Subarachnoid Hemorrhage...

Incidence and Outcome of Aneurysmal Subarachnoid Hemorrhage: The Swiss Study on Subarachnoid Hemorrhage (Swiss SOS).

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Incidence and Outcome of Aneurysmal Subarachnoid Hemorrhage: The Swiss Study on Subarachnoid Hemorrhage (Swiss SOS).