RESUMEN
The management of medulloblastoma in children has dramatically changed over the past four decades, with the development of chemotherapy protocols aiming at improving survival and reducing long-term toxicities of high-dose craniospinal radiotherapy. While the staging and treatment of medulloblastoma were until recently based on the modified Chang's system, recent advances in the molecular biology of medulloblastoma have revolutionized approaches in the management of this increasingly complex disease. The evolution of systemic therapies is described in this review.
RESUMEN
Down syndrome (DS) is the commonest chromosomal disorder and is considered to be the most common syndrome associated with acute leukaemia. The objective of this study was to determine the characteristics of acute leukaemia in children with DS in Pakistan. It was a retrospective, cohort study conducted over a 2-year period, and the data was analysed in SPSS 20.0 in terms of descriptive statistics. Nineteen DS patients with acute leukaemia were enrolled. The proportion of DS-acute leukaemia was found to be 1.84% among all cases of paediatric acute leukaemia. The mean age of presentation was 5.5 years ± 4.3 SD with a male to female ratio of 1.1:1. The precursor B-cell ALL was found in 13 (68.4%) and acute myeloid leukaemia was found in 6 (31.6%) patients of DS. Thirteen patients (68.4%) completed treatment, while 6 (31.6%) expired due to treatment-related toxicity. Mean overall survival was 38 months ± 5.34 SD. The status of diagnosis of DS before presentation with acute leukaemia was the only statistically significant factor associated with the outcome. Few distinct characteristics of DS-acute leukaemia have been found in our population. Treatment toxicity was the sole cause of treatment failure.
RESUMEN
[This corrects the article DOI: 10.3389/fnins.2021.755817.].
RESUMEN
Electroencephalogram (EEG) is widely used for the diagnosis of neurological conditions like epilepsy, neurodegenerative illnesses and sleep related disorders. Proper interpretation of EEG recordings requires the expertise of trained neurologists, a resource which is scarce in the developing world. Neurologists spend a significant portion of their time sifting through EEG recordings looking for abnormalities. Most recordings turn out to be completely normal, owing to the low yield of EEG tests. To minimize such wastage of time and effort, automatic algorithms could be used to provide pre-diagnostic screening to separate normal from abnormal EEG. Data driven machine learning offers a way forward however, design and verification of modern machine learning algorithms require properly curated labeled datasets. To avoid bias, deep learning based methods must be trained on large datasets from diverse sources. This work presents a new open-source dataset, named the NMT Scalp EEG Dataset, consisting of 2,417 recordings from unique participants spanning almost 625 h. Each recording is labeled as normal or abnormal by a team of qualified neurologists. Demographic information such as gender and age of the patient are also included. Our dataset focuses on the South Asian population. Several existing state-of-the-art deep learning architectures developed for pre-diagnostic screening of EEG are implemented and evaluated on the NMT, and referenced against baseline performance on the well-known Temple University Hospital EEG Abnormal Corpus. Generalization of deep learning based architectures across the NMT and the reference datasets is also investigated. The NMT dataset is being released to increase the diversity of EEG datasets and to overcome the scarcity of accurately annotated publicly available datasets for EEG research.
