Caffeine exacerbates seizure-induced death via postictal hypoxia.

Sudden unexpected death in epilepsy (SUDEP) is the leading epilepsy-related cause of premature mortality in people with intractable epilepsy, who are 27 times more likely to die than the general population. Impairment of the central control of breathing following a seizure has been identified as a putative cause of death, but the mechanisms underlying this seizure-induced breathing failure are largely unknown. Our laboratory has advanced a vascular theory of postictal behavioural dysfunction, including SUDEP. We have recently reported that seizure-induced death occurs after seizures invade brainstem breathing centres which then leads to local hypoxia causing breathing failure and death. Here we investigated the effects of caffeine and two adenosine receptors in two models of seizure-induced death. We recorded local oxygen levels in brainstem breathing centres as well as time to cessation of breathing and cardiac activity relative to seizure activity. The administration of the non-selective A1/A2A antagonist caffeine or the selective A1 agonist N6-cyclopentyladenosine reveals a detrimental effect on postictal hypoxia, providing support for caffeine modulating cerebral vasculature leading to brainstem hypoxia and cessation of breathing. Conversely, A2A activation with CGS-21680 was found to increase the lifespan of mice in both our models of seizure-induced death.

Femoral de-rotation osteotomy versus hip arthroscopy for management of femoroacetabular impingement in adult patients with decreased femoral anteversion: a matched retrospective cohort study.

Femoral de-rotation osteotomy (FDO) and hip arthroscopy are both recognized surgical options for the management of femoroacetabular impingement (FAI) in the setting of decreased femoral anteversion (<5°). Minimal comparative data exist regarding the difference in outcomes between these two techniques, and we believe this is the first study to provide that comparison. This retrospective cohort study included a total of 20 patients with such pathology, matched for age, gender and body mass index. A total of 10 patients were included in the FDO group [median anteversion -0.5° (true retroversion); average follow-up 17.9 months]. In total, 10 patients were included in the hip arthroscopy group [median anteversion -0.5° (true retroversion); average follow-up 28.5 months]. Both groups demonstrated statistically and clinically significant improvement in the post-operative International Hip Outcome Tool (iHOT-33) scores [median improvement: FDO group, 37.7 points (r 14-58.8; P < 0.041); hip arthroscopy group, 35.9 points (r 11.1-81; P < 0.05)], noting that the minimal clinically important difference for the iHOT-33 is 6.1 points. However, the study was not adequately powered to delineate a difference in improvement between the two groups. The findings suggest significant improvement in patient-reported outcomes, and clinical findings can be achieved with either FDO or hip arthroscopy for FAI in the setting of decreased femoral anteversion. However, selection of the most suitable surgical procedure using a patient-specific approach may optimize outcomes in this challenging population.

Semi-automatic measurement of intracranial hemorrhage growth on non-contrast CT.

BACKGROUND: Manual segmentations of intracranial hemorrhage on non-contrast CT images are the gold-standard in measuring hematoma growth but are prone to rater variability. AIMS: We demonstrate that a convex optimization-based interactive segmentation approach can accurately and reliably measure intracranial hemorrhage growth. METHODS: Baseline and 16-h follow-up head non-contrast CT images of 46 subjects presenting with intracranial hemorrhage were selected randomly from the ANNEXA-4 trial imaging database. Three users semi-automatically segmented intracranial hemorrhage to measure hematoma volume for each timepoint using our proposed method. Segmentation accuracy was quantitatively evaluated compared to manual segmentations by using Dice similarity coefficient, Pearson correlation, and Bland-Altman analysis. Intra- and inter-rater reliability of the Dice similarity coefficient and intracranial hemorrhage volumes and volume change were assessed by the intraclass correlation coefficient and minimum detectable change. RESULTS: Among the three users, the mean Dice similarity coefficient, Pearson correlation, and mean difference ranged from 76.79% to 79.76%, 0.970 to 0.980 (p < 0.001), and -1.5 to -0.4 ml, respectively, for all intracranial hemorrhage segmentations. Inter-rater intraclass correlation coefficients between the three users for Dice similarity coefficient and intracranial hemorrhage volume were 0.846 and 0.962, respectively, and the corresponding minimum detectable change was 2.51 ml. Inter-rater intraclass correlation coefficient for intracranial hemorrhage volume change ranged from 0.915 to 0.958 for each user compared to manual measurements, resulting in an minimum detectable change range of 2.14 to 4.26 ml. CONCLUSIONS: We spatially and volumetrically validate a novel interactive segmentation method for delineating intracranial hemorrhage on head non-contrast CT images. Good spatial overlap, excellent volume correlation, and good repeatability suggest its usefulness for measuring intracranial hemorrhage volume and volume change on non-contrast CT images.

Automated brain extraction from head CT and CTA images using convex optimization with shape propagation.

BACKGROUND AND OBJECTIVE: Non-Contrast Computer Tomography (NCCT) and CT angiography (CTA) are the most used and widely acceptable imaging modalities in clinical practice for the diagnosis and treatment of acute ischemic stroke (AIS) patients. Brain extraction of CT/CTA images plays an essential role in stroke imaging research. There is no robust automated brain extraction method in the literature that is well established for both NCCT and CTA images. Thus, a validated and automated brain extraction tool for CT imaging would be of great value for both research and clinical practice. METHODS: The proposed brain extraction method is based on the contour evolution technique, which extracts brain tissues from acquired NCCT and CTA images in a slice-by-slice fashion. Specifically, the proposed approach makes use of a novel propagation framework, which is initialized by a localized slice with the largest brain section in axial views, followed by a geodesic level-set evolution for automatically extracting the brain section in each slice. In particular, the segmented contour propagated from the previous slice is reused to penalize the defined object function for contour evolution to enforce the shape continuity between any two adjacent contours. We show that the defined contour evolution function can be solved iteratively by globally optimal convex optimization. RESULTS: The proposed brain extraction approach is quantitatively evaluated using 40 NCCT and CTA images acquired from 20 AIS patients and drawn from 4 different vendors, compared to manual segmentations using Dice and Jaccard coefficient metrics. The quantitative results show that the proposed segmentation algorithm is consistently accurate for both NCCT and CTA images using Dice metric. The proposed method is further validated on 1736 NCCT and CTA images of 1331 AIS patients acquired from three multi-national multi-centric clinical trials. A visual check performed on these data demonstrates a low failure rate of 0.4% for 1331 NCCT images and a zero-failure rate for 405 CTA images. CONCLUSIONS: Both quantitative and qualitative evaluation suggest that the proposed brain extraction approach for NCCT and CTA images can be used for different clinical imaging settings, thus serving to improve current image analysis in the field of neuroimaging.