Acute Cervical Epidural Hematoma: Case Report.

A 74 year-old patient with a nocturnal onset of neck and chest pain was brought to an emergency clinic. Physical examination and cardiac assessment were normal. Three hours after the addmittance, a flaccid paralysis of the four limbs supervened. Suspecting of an unusual onset of central nervous system infection, a lumbar puncture was performed, yielding 20 ml of normal cerebrospinal fluid. Thirty oinutes after the puncture, the patient completely regained neurological funcion. He was then referred to a General Hospital where a computed tomography (CT) scan was done showing a large cervical epidural bleeding in the posterolateral region of C4/C5 extending to C7/Th1, along with a C6 vertebral body hemangioma. A magnetic resonance imaging revealed the same CT findings. A normal selective angiography of vertebral arteries, carotid arteries and thyreocervical trunk was carried out. Spontaneous spinal epidural hematoma (ASSEH) is a rare but dramatic cause of neurological impairment. In this article we report a fortunate case of complete recovery after an unusual spine cord decompression. We also review the current literature concerning diagnosis and treatment of ASSEH.58726-3

Mass-Luminosity Relation for White-Dwarf Stars

Recebido em 19 de Fevereiro de 1971 Improving the model of Milne in which the white-dwarf star is considered as consisting of an envelope of perfect gas and an interna1 part of degenerate electron gas, we investigated the relation between its mass and luminosity. In conclusion, we found thai, though the masses are the same, the luminosity of the white dwarfs which contain light elements in their interior is greater than that of those which contain heavier elements. Aperfeiçoando o modèlo de Milne em que a estrela anã branca é considerada como sendo constituida de um envelope de gás perfeito e de uma parte interna de gás de elétrons degenerado, investigamos a relação entre a sua massa e luminosidade. Como conclusão, achamos que, mesmo quando as massas sejam iguais, a luminosidade das anãs brancas que contenham elementos leves no seu interior é maior que a daquelas que contenham elementos pesados

The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.

ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance

Detection of Epileptogenic Cortical Malformations with Surface-Based MRI Morphometry

Magnetic resonance imaging has revolutionized the detection of structural abnormalities in patients with epilepsy. However, many focal abnormalities remain undetected in routine visual inspection. Here we use an automated, surface-based method for quantifying morphometric features related to epileptogenic cortical malformations to detect abnormal cortical thickness and blurred gray-white matter boundaries. Using MRI morphometry at 3T with surface-based spherical averaging techniques that precisely align anatomical structures between individual brains, we compared single patients with known lesions to a large normal control group to detect clusters of abnormal cortical thickness, gray-white matter contrast, local gyrification, sulcal depth, jacobian distance and curvature. To assess the effects of threshold and smoothing on detection sensitivity and specificity, we systematically varied these parameters with different thresholds and smoothing levels. To test the effectiveness of the technique to detect lesions of epileptogenic character, we compared the detected structural abnormalities to expert-tracings, intracranial EEG, pathology and surgical outcome in a homogeneous patient sample. With optimal parameters and by combining thickness and GWC, the surface-based detection method identified 92% of cortical lesions (sensitivity) with few false positives (96% specificity), successfully discriminating patients from controls 94% of the time. The detected structural abnormalities were related to the seizure onset zones, abnormal histology and positive outcome in all surgical patients. However, the method failed to adequately describe lesion extent in most cases. Automated surface-based MRI morphometry, if used with optimized parameters, may be a valuable additional clinical tool to improve the detection of subtle or previously occult malformations and therefore could improve identification of patients with intractable focal epilepsy who may benefit from surgery

'MRI-negative PET-positive' temporal lobe epilepsy (TLE) and mesial TLE differ with quantitative MRI and PET: a case control study

Background: \u27MRI negative PET positive temporal lobe epilepsy\u27 represents a substantial minority of temporal lobe epilepsy (TLE). Clinicopathological and qualitative imaging differences from mesial temporal lobe epilepsy are reported. We aimed to compare TLE with hippocampal sclerosis (HS+ve) and non lesional TLE without HS (HS-ve) on MRI, with respect to quantitative FDG-PET and MRI measures.Methods: 30 consecutive HS-ve patients with well-lateralised EEG were compared with 30 age- and sex-matched HS+ve patients with well-lateralised EEG. Cerebral, cortical lobar and hippocampal volumetric and co-registered FDG-PET metabolic analyses were performed.Results: There was no difference in whole brain, cerebral or cerebral cortical volumes. Both groups showed marginally smaller cerebral volumes ipsilateral to epileptogenic side (HS-ve 0.99, p = 0.02, HS+ve 0.98, p &lt; 0.001). In HS+ve, the ratio of epileptogenic cerebrum to whole brain volume was less (p = 0.02); the ratio of epileptogenic cerebral cortex to whole brain in the HS+ve group approached significance (p = 0.06). Relative volume deficits were seen in HS+ve in insular and temporal lobes. Both groups showed marked ipsilateral hypometabolism (p &lt; 0.001), most marked in temporal cortex. Mean hypointensity was more marked in epileptogenic-to-contralateral hippocampus in HS+ve (ratio: 0.86 vs 0.95, p &lt; 0.001). The mean FDG-PET ratio of ipsilateral to contralateral cerebral cortex however was low in both groups (ratio: HS-ve 0.97, p &lt; 0.0001; HS+ve 0.98, p = 0.003), and more marked in HS-ve across all lobes except insula.Conclusion: Overall, HS+ve patients showed more hippocampal, but also marginally more ipsilateral cerebral and cerebrocortical atrophy, greater ipsilateral hippocampal hypometabolism but similar ipsilateral cerebral cortical hypometabolism, confirming structural and functional differences between these groups.<br /

Low-surface energy surfactants with branched hydrocarbon architectures

International audienceSurface tensiometry and small-angle neutron scattering have been used to characterize a new class of low-surface energy surfactants (LSESs), "hedgehog" surfactants. These surfactants are based on highly branched hydrocarbon (HC) chains as replacements for environmentally hazardous fluorocarbon surfactants and polymers. Tensiometric analyses indicate that a subtle structural modification in the tails and headgroup results in significant effects on limiting surface tensions γcmc at the critical micelle concentration: a higher level of branching and an increased counterion size promote an effective reduction of surface tension to low values for HC surfactants (γcmc 24 mN m-1). These LSESs present a new class of potentially very important materials, which form lamellar aggregates in aqueous solutions independent of dilution

Texture classification of proteins using support vector machines and bio-inspired metaheuristics

6th International Joint Conference, BIOSTEC 2013, Barcelona, Spain, February 11-14, 2013[Abstract] In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94 %, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process