Effects of COVID-19 on Pediatric Cancer Care: A Multicenter Study of 11 Middle Eastern Countries.

During the COVID-19 pandemic, major challenges are facing pediatric cancer centers regarding access to cancer centers, continuity of the anti-cancer therapy, hospital admission, and infection protection precautions. Pediatric oncologists actively treating children with cancer from 29 cancer centers at 11 countries were asked to answer a survey from May 2020 to August 2020 either directly or through the internet. COVID-19 pandemic affected the access to pediatric cancer care in the form of difficulty in reaching the center in 22 (75.9%) centers and affection of patients' flow in 21 (72.4%) centers. Health care professionals (HCP) were infected with COVID-19 in 20 (69%) surveyed centers. Eighteen centers (62%) modified the treatment guidelines. Care of follow-up patients was provided in-hospital in 8(27.6%) centers, through telemedicine in 10 (34.5%) centers, and just delayed in 11 (38%) centers. Pediatric oncologists had different expectations about the future effects of COVID-19 on pediatric cancer care. Seventy-six percent of pediatric oncologists think the COVID-19 pandemic will increase the use of telemedicine. Fifty-five percent of pediatric oncologists think if the COVID-19 pandemic persists, we will need to change chemotherapy protocols to less myelosuppressive ones. Collaborative studies are required to prioritize pediatric cancer management during COVID-19 era.

Epileptic Seizure Detection for Imbalanced Datasets Using an Integrated Machine Learning Approach.

Epileptic Seizure (Epilepsy) is a neurological disorder that occurs due to abnormal brain activities. Epilepsy affects patients' health and lead to life-threatening situations. Early prediction of epilepsy is highly effective to avoid seizures. Machine Learning algorithms have been used to classify epilepsy from Electroencephalograms (EEG) data. These algorithms exhibited reduced performance when classes are imbalanced. This work presents an integrated machine learning approach for epilepsy detection, which can effectively learn from imbalanced data. This approach utilizes Principal Component Analysis (PCA) at the first stage to extract both high- and low- variant Principal Components (PCs), which are empirically customized for imbalanced data classification. Conventionally, PCA is used for dimension reduction of a dataset leveraging PCs with high variances. In this paper, we propose a model to show that PCs associated with low variances can capture the implicit pattern of minor class of a dataset. The selected PCs are then fed into different machine learning classifiers to predict seizures. We performed experiments on the Epileptic Seizure Recognition dataset to evaluate our model. The experimental results show the robustness and effectiveness of the proposed model.