Zabramski classification in predicting the occurrence of symptomatic intracerebral hemorrhage in sporadic cerebral cavernous malformations.

OBJECTIVE: The authors aimed to investigate the evolutionary characteristics of the Zabramski classification of cerebral cavernous malformations (CCMs) and the value of the Zabramski classification in predicting clinical outcome in patients with sporadic CCM. METHODS: This retrospective study consecutively included cases of sporadic CCM that had been untreated from January 2001 through December 2021. Baseline and follow-up patient information was recorded. The evolution of the Zabramski classification of a sporadic CCM was defined as the initial lesion type changing into another type for the first time on MRI follow-up. The primary outcome was the occurrence of a hemorrhage event, which was defined as a symptomatic event with radiological evidence of overt intracerebral hemorrhage. RESULTS: Among the 255 included cases, 55 (21.6%) were classified as type I CCM, 129 (50.6%) as type II CCM, and 71 (27.8%) as type III CCM, based on initial MRI. During a mean follow-up of 58.8 ± 33.6 months, 51 (20.0%) patients had lesion classification transformation, whereas 204 (80.0%) patients maintained their initial type. Among the 51 transformed lesions, 29 (56.9%) were type I, 11 (21.6%) were type II, and 11 (21.6%) were type III. Based on all follow-up imaging, of the initial 55 type I lesions, 26 (47.3%) remained type I and 27 (49.1%) regressed to type III because of hematoma absorption; 91.5% of type II and 84.5% of type III lesions maintained their initial type during MRI follow-up. The classification change rate of type I lesions was statistically significantly higher than those of type II and III lesions. After a total follow-up of 1157.7 patient-years, new clinical hemorrhage events occurred in 40 (15.7%) patients. The annual cumulative incidence rate for symptomatic hemorrhage in all patients was 3.4 (95% CI 2.5-4.7) per 100 person-years. Kaplan-Meier survival analysis showed that the annual cumulative incidence rate for symptomatic hemorrhage of type I CCM (15.3 per 100 patient-years) was significantly higher than those of type II (0.6 per 100 patient-years) and type III (2.3 per 100 patient-years). CONCLUSIONS: This study suggests that the Zabramski classification is helpful in estimating clinical outcome and can assist with surgical decision-making in patients with sporadic CCM.

Unruptured brain arteriovenous malformations causing seizures localize to one common brain network.

Seizures are a frequent symptom of unruptured brain arteriovenous malformations (bAVMs). However, the brain regions responsible for these seizures remain unclear. To identify the brain regions causally involved in bAVM-related seizures, we retrospectively reviewed 220 patients with unruptured bAVMs. Using voxel-based lesion-symptom mapping (VLSM) analyses, we tested whether individual brain regions were associated with unruptured bAVM-related seizures. The result revealed that unruptured bAVMs causing seizures are anatomically heterogeneous at the voxel level. Subsequently, lesion network mapping (LNM) analyses was performed to determine whether bAVMs causing seizures belonged to a distributed brain network. LNM analyses indicated that these lesions were located in a functional network characterized by connectivity to the left caudate and precuneus. Moreover, the discrimination performance of the identified seizure network was evaluated in discovery set by calculating the individualized network damage score and was tested in validation set. Based on the calculated network damage scores, patients were divided into low-, medium-, and high-risk groups. The prevalence of seizures significantly differed among the three risk categories in both discovery (p = .003) and validation set (p = .004). Finally, we calculated the percentage of voxels in the canonical resting-state networks that overlapped with the seizure-susceptible brain regions to investigate the involvement of resting-state networks. With an involvement percentage over 50%, the frontoparietal control (82.9%), limbic function (76.7%), and default mode network (69.3%) were considered to be impacted in bAVM-related seizures. Our study identified the seizure-susceptible brain regions for unruptured bAVMs, which could be a plausible neuroimaging biomarker in predicting possible seizures.

Simplex cerebral cavernous malformations with MAP3K3 mutation have distinct clinical characteristics.

Objectives: To investigate the clinical characteristics of cerebral cavernous malformations (CCMs) with MAP3K3 somatic mutation. Methods: We performed a retrospective review of our CCMs database between May 2017 and December 2019. The patients with simplex CCMs identified to harbor a MAP3K3 or CCM gene somatic mutation were included. Clinical characteristics were recorded. Univariate and multivariate logistic analyses were used to assess the risk factors associated with hemorrhage events of CCMs. To explore the underlying mechanism, we transfected MEKK3-I441M-overexpressing and CCM2-knockdown lentiviruses into human umbilical vein endothelial cells (HUVECs) and investigated thrombomodulin (TM) and tight junctions (TJs) protein expression by western blotting and immunofluorescence. Finally, immunohistochemistry was used to validate TM and TJs protein expression in surgical samples. Results: Fifty simplex CCMs patients were included, comprising 38 MAP3K3 mutations and 12 CCM gene mutations. Nine (23.7%) patients with MAP3K3 mutations and 11(91.7%) patients with CCM gene mutations exhibited overt hemorrhage, respectively. Multivariate logistic analyses revealed that MAP3K3 mutation was associated with a lower risk of hemorrhage events. In the vitro experiments, ZO-1 expression was not reduced in MEKK3-I441M-overexpressing HUVECs compared with wild type, whereas it was significantly decreased in CCM2-knockdown HUVECs compared with control. In the MEKK3-I441M-overexpressing HUVECs, TM expression was increased, and the NF-κB pathway was significantly activated. After treatment with an NF-κB signaling inhibitor, TM expression was further upregulated. Meanwhile, TM expression was increased, but the NF-κB pathway was not activated in CCM2-knockdown HUVECs. Accordingly, immunohistochemistry showed that ZO-1 expression in the MAP3K3-mutant samples was significantly higher than that in the CCM-mutant samples. TM expression in the MAP3K3-mutant lesions was significantly lower than that in the CCM-mutant samples. Conclusion: Simplex CCMs with MAP3K3 mutation occasionally present with overt hemorrhage, which is associated with the biological function of MAP3K3 mutation.

Machine Learning-Enabled Determination of Diffuseness of Brain Arteriovenous Malformations from Magnetic Resonance Angiography.

The diffuseness of brain arteriovenous malformations (bAVMs) is a significant factor in surgical outcome evaluation and hemorrhagic risk prediction. However, there are still predicaments in identifying diffuseness, such as the judging variety resulting from different experience and difficulties in quantification. The purpose of this study was to develop a machine learning (ML) model to automatically identify the diffuseness of bAVM niduses using three-dimensional (3D) time-of-flight magnetic resonance angiography (TOF-MRA) images. A total of 635 patients with bAVMs who underwent TOF-MRA imaging were enrolled. Three experienced neuroradiologists delineated the bAVM lesions and identified the diffuseness on TOF-MRA images, which were considered the ground-truth reference. The U-Net-based segmentation model was trained to segment lesion areas. Eight mainstream ML models were trained through the radiomic features of segmented lesions to identify diffuseness, based on which an integrated model was built and yielded the best performance. In the test set, the Dice score, F2 score, precision, and recall for the segmentation model were 0.80 [0.72-0.84], 0.80 [0.71-0.86], 0.84 [0.77-0.93], and 0.82 [0.69-0.89], respectively. For the diffuseness identification model, the ensemble-based model was applied with an area under the Receiver-operating characteristic curves (AUC) of 0.93 (95% CI 0.87-0.99) in the training set. The AUC, accuracy, precision, recall, and F1 score for the diffuseness identification model were 0.95, 0.90, 0.81, 0.84, and 0.83, respectively, in the test set. The ML models showed good performance in automatically detecting bAVM lesions and identifying diffuseness. The method may help to judge the diffuseness of bAVMs objectively, quantificationally, and efficiently.

[Identification and expression analysis of Macaca mulatta piwil4 gene].

To investigate the structure and expression pattern of rhesus monkey PIWIL4 protein, homologous comparison and reverse transcription PCR (RT-PCR) were carried out to identify rhesus monkey piwil4. The expression of piwil4 mRNA was tested in rhesus monkey heart, brain, colon, epididymis and testis, and the result showed that piwil4 mRNA was expressed in these rhesus monkey tissues. Bioinformatic analysis suggested that the rhesus PIWIL4 protein shared 97% identity in amino acids and the same domains such as PAZ and Piwi with the human PIWIL4 (HIWI2) protein. The immunohistochemical result indicated that PIWIL4 proteins had the same localization in adult testes of the two species, but the distribution of these proteins was altered dynamically at different developmental stages in rhesus monkey testes. PIWIL4 protein was expressed in the nucleus of convoluted seminiferous tubules in infant monkey testes, whereas it was expressed in the cytoplasm of adult monkey testes. The results suggest that piwil4 gene play a similar role in rhesus and human, and different localizations of PIWIL4 protein in infant monkey and adult monkey testes suggest that it functions differently at different developmental stages.