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gait, balance performance, and ADLs.The brain electrical activity, recorded and materialized as electroencephalogram (EEG) signals, is known to be very useful in the diagnosis of brain-related pathology. However, manual examination of these EEG signals has various limitations, including time-consuming inspections, the need for highly trained neurologists, and the subjectiveness of the evaluation. Thus, an automated EEG pathology detection system would be helpful to assist neurologists to enhance the treatment procedure by making a quicker diagnosis and reducing error due to the human element. This work proposes the application of a time-frequency spectrum to convert the EEG signals onto the image domain. The spectrum images are then applied to the Convolutional Neural Network (CNN) to learn robust features that can aid the automatic detection of pathology and normal EEG signals. Three popular CNN in the form of the DenseNet, Inception-ResNet v2, and SeizureNet were employed. The extracted deep-learned features from the spectrum images are then passed onto the support vector machine (SVM) classifier. The effectiveness of the proposed approach was assessed using the publicly available Temple University Hospital (TUH) abnormal EEG corpus dataset, which is demographically balanced. The proposed SeizureNet-SVM-based system achieved state-of-the-art performance accuracy, sensitivity, and specificity of 96.65%, 90.48%, and 100%, respectively. The results show that the proposed framework may serve as a diagnostic tool to assist clinicians in the detection of EEG pathology for early treatment.<br /><br /> Multiple sclerosis (MS) is a neurodegenerative disease that affects the central nervous system, especially the brain, spinal cord, and optic nerve. Diagnosis of this disease is a very complex process and generally requires a lot of time. In addition, treatments are applied without any information on the disability course in each MS patient. For these two reasons, the objective of this study was to improve the MS diagnosis and predict the long-term course of disability in MS patients based on clinical data and retinal nerve fiber layer (RNFL) thickness, measured by optical coherence tomography (OCT).<br /><br /> A total of 104 healthy controls and 108 MS patients, 82 of whom had a 10-year follow-up, were enrolled. Classification algorithms such as multiple linear regression (MLR), support vector machines (SVM), decision tree (DT), k-nearest neighbours (k-NN), Naïve Bayes (NB), ensemble classifier (EC) and long short-term memory (LSTM) recurrent neural network were tested to develop two predictive models MS diagnosis mand predict the course of MS. This advance could help clinicians select more specific treatments for each MS patient. Therefore, our findings underscore the potential of RNFL thickness as a reliable MS biomarker.This study investigated the effects of l-arginine and l-lysine on the water holding capacity, shear force, color, and protein denaturation of frozen porcine Longissimus lumborum. Four batches were prepared, each corresponding to samples of an experimental treatment without a cryoprotective solution, injecting a 0.3% sodium tripolyphosphate and 0.5% NaCl solution, a 0.5% l-arginine solution, or a 0.5% l-lysine solution. [https://www.selleckchem.com/products/abbv-cls-484.html learn more] The results showed that both l-arginine and l-lysine decreased thawing loss, cooking loss, shear force, L⁎ values, b⁎ values, and surface hydrophobicity, but they increased pH values, a⁎ values, percentages of peak areas for T21 relaxation times, and Ca2+-ATPase activity. Additionally, both histological and transmission electron microscopy images showed that l-lysine, and especially l-arginine could inhibit the formation of gaps between fiber bundles, alleviate the disruption of intracellular spaces, and maintain the structural integrity of sarcomeres. Overall, the results showed that both l-arginine and l-lysine hindered the structural damage of muscle fibers during freezing and protected myofibrillar proteins from denaturation, ultimately contributing to superior quality attributes.Brain tumors, a group of heterogeneous diseases, are the second most common cancer and the leading cause of cancer-related deaths in children. Insight into the prognosis of pediatric brain tumor survival has led to improved outcomes and could be further advanced through precision in prognosis. We analyzed the United States SEER population-based dataset of 15,723 pediatric brain tumor patients diagnosed and followed between 1975 and 2016 using a stratified Cox proportional hazards model. Mortality risk declined with increased age at diagnosis, the adjusted hazard ratio (aHR) (95 % confidence interval) was 0.60 (0.55, 0.67) and 0.47 (0.42, 0.52) for ages at diagnosis 1-10 years and 10-19 years, respectively, when compared with infants. Non-Hispanic Caucasian patients showed a lower risk of mortality than non-Hispanic African Americans (1.21 (1.11, 1.32)) and Hispanics (1.21 (1.11, 1.32)). Primary tumor sites, grades, and histology showed substantial heterogeneity in mortality risk. Brainstem (2.62 (2.41, 2.85)) and Cerebrum (1.63 (1.46, 1.81)) had an elevated risk of mortality than lobes. Similarly, Grade II (1.32 (1.07, 1.62)), Grade III (3.39 (2.74, 4.19)), and Grade IV (2.18 (1.80, 2.64)) showed an inflated risk of mortality than Grade I. Compared to low-grade glioma, high-grade glioma (7.92 (7.09, 8.85)), Primitive neuroectodermal tumors (4.72 (4.15, 5.37)), Medulloblastoma (3.11 (2.79, 3.47)), and Ependymal-tumors (2.20 (1.95, 2.48)) had increased risk of mortality. County-level poverty and geographic region showed substantial variation in survival. This large population-based comprehensive study confirmed identified prognostic factors of pediatric brain tumor survival and provided estimates as epidemiologic evidence with greater generalization.<br /><br /> To determine the role of progesterone, pessary and cervical cerclage in reducing the risk of (preterm birth) PTB in twin pregnancies and compare these interventions using pairwise and network meta-analysis.<br /><br /> Medline, Embase, CINAHL and Cochrane databases were explored. The inclusion criteria were studies in which twin pregnancies were randomized to an intervention for the prevention of PTB (any type of progesterone, cervical cerclage, cervical pessary, or any combination of these) or to a control group (e.g. placebo or treatment as usual). Interventions of interest were either progesterone [vaginal or oral natural progesterone or intramuscular 17a-hydroxyprogesterone caproate (17-OHPC)], cerclage (McDonald or Shirodkar), or cervical pessary. The primary outcome was PTB &lt; 34 weeks of gestation. Both primary and secondary outcomes were explored in an unselected population of twin pregnancies and in women at higher risk of PTB (defined as those with cervical length &lt;25 mm). Random-effect head-to-head and a multiple-treatment meta-analyses were used to analyze the data and results expressed as risk ratios.