Lumbosacral plexopathy secondary to discectomy and fixation.

Lumbosacral plexopathy (LSP) is a rare entity characterized by acute onset of pain followed by sensory and motor deficits, reflex changes and muscle atrophy. The diagnosis is based on clinical and EMG findings. LSP can result from pelvic tumors, infections, trauma, abdominopelvic or spinal surgery, radiation, intravenous drug abuse, diabetic neuropathy, vasculitis or maybe idiopathic. We present a case report of LSP following spinal surgery treated by pulse steroid and immunotherapy.

A comparative study for glioma classification using deep convolutional neural networks.

Gliomas are a type of central nervous system (CNS) tumor that accounts for the most of malignant brain tumors. The World Health Organization (WHO) divides gliomas into four grades based on the degree of malignancy. Gliomas of grades I-II are considered low-grade gliomas (LGGs), whereas gliomas of grades III-IV are termed high-grade gliomas (HGGs). Accurate classification of HGGs and LGGs prior to malignant transformation plays a crucial role in treatment planning. Magnetic resonance imaging (MRI) is the cornerstone for glioma diagnosis. However, examination of MRI data is a time-consuming process and error prone due to human intervention. In this study we introduced a custom convolutional neural network (CNN) based deep learning model trained from scratch and compared the performance with pretrained AlexNet, GoogLeNet and SqueezeNet through transfer learning for an effective glioma grade prediction. We trained and tested the models based on pathology-proven 104 clinical cases with glioma (50 LGGs, 54 HGGs). A combination of data augmentation techniques was used to expand the training data. Five-fold cross-validation was applied to evaluate the performance of each model. We compared the models in terms of averaged values of sensitivity, specificity, F1 score, accuracy, and area under the receiver operating characteristic curve (AUC). According to the experimental results, our custom-design deep CNN model achieved comparable or even better performance than the pretrained models. Sensitivity, specificity, F1 score, accuracy and AUC values of the custom model were 0.980, 0.963, 0.970, 0.971 and 0.989, respectively. GoogLeNet showed better performance than AlexNet and SqueezeNet in terms of accuracy and AUC with a sensitivity, specificity, F1 score, accuracy, and AUC values of 0.980, 0.889, 0.933, 0.933, and 0.987, respectively. AlexNet yielded a sensitivity, specificity, F1 score, accuracy, and AUC values of 0.940, 0.907, 0.922, 0.923 and 0.970, respectively. As for SqueezeNet, the sensitivity, specificity, F1 score, accuracy, and AUC values were 0.920, 0.870, 0.893, 0.894, and 0.975, respectively. The results have shown the effectiveness and robustness of the proposed custom model in classifying gliomas into LGG and HGG. The findings suggest that the deep CNNs and transfer learning approaches can be very useful to solve classification problems in the medical domain.

Posterior Fossa Epidural Hydatid Cyst in the Adult.

ABSTRACT: Hydatid cyst is an important parasitic disease especially in endemic regions. Hydatid cysts are most commonly found in the liver and lungs and only 1% to 2% of the cysts reach the brain. Intracranial hydatid cysts are usually supratentorial and majority of cases are children and young adults. In this case report, a 55-year-old woman who lives in rural area in Turkey, admitted to our clinic with severe headache, neck pain, gait disturbance, and vomiting. Her neurological examination revealed cerebellar ataxia and left dysmetria. Radiological findings were compatible with hydatid cyst. On the 3rd of antihelmintic therapy, the patient underwent suboccipital craniotomy and epidural cyst was excised using Dowling technique without rupture of the cyst wall. Patient's symptoms improved postoperatively. Although hydatid cyst in posterior fossa is a very rare entity, it should be kept in mind in patients with intracranial cystic lesions who live in endemic regions.

The Effect of Intravenous L-Carnitine on the Vasospasm Process in the Experimental Subarachnoid Hemorrhage Model.

AIM: Cerebral vasospasm after subarachnoid hemorrhage (SAH) is an important cause of morbidity and mortality. In this study, we examined the effects of L-carnitine on the cerebral vasospasm process. MATERIAL AND METHODS: Twenty male New Zealand white rabbits were randomly divided into 4 groups. Group 1 served as control; group 2 was not subjected to SAH and received intravenous L-carnitine 3 times; group 3 was subjected to SAH and group 4 was subjected to SAH and treated with 100 mg/kg intravenous L-carnitine at 0, 24, and 48 hours after SAH. All animals were euthanized by perfusion-fixation 72 hours after SAH induction. The brains were then removed and stored in fixative +4°C overnight. The subjects" basilar arteries were sectioned from four separate zones. Basilar artery cross-sectional areas and thicknesses of vessels were measured by using the SPOT for Windows Version 4.1 computer programme. Statistical comparisons were performed by using the Kruskal-Wallis and Mann-Whitney U tests. RESULTS: Basilar artery wall thicknesses in group 4 were significantly lower than in group 3 (p=0.009). Basilar artery cross-sectional areas in group 4 were higher than in group 3 and the difference was statistically significant (p=0.008). CONCLUSION: L-carnitine was shown to be potentially beneficial on the resolution of cerebral vasospasm following SAH.