BCN is the official Publication of Neuroscience Departments of Tehran University of Medical Sciences and Shahid Beheshti University of Medical Sciences. BCN is an international multidisciplinary journal that publishes editorials, original full‐length research articles, short communications, reviews, methodological papers, commentaries, perspectives and “news and reports” in the broad fields of developmental, molecular, cellular, systems, computational, behavioral, cognitive, and clinical neurosciences. Manuscripts submitted to BCN should describe novel results generated by experiments that were guided by clearly defined aims or hypotheses and should not have appeared or submitted to any other journals. BCN’s aim is to provide serious ties in interdisciplinary communication, accessibility to a broad readership internationally, effective peer review process, and independence from all possible non scientific interests. BCN also tries to empower national, regional and international collaborative networks in the field of neuroscience and to be the voice of the Iranian and regional neuroscience community in the world of neuroscientists. BCN is a new journal dedicated to neuroscience field which intends to become an indexed and cited peer reviewed journal and to meet its quality standards in near future. To achieve this aim, BCN asks for support from all Iranian Neuroscientists doing research around the word in submitting manuscripts in all aspects of neuroscience research. We need your comments and valuable inputs. We are also asking Iranian Neuroscientists who are principle investigators to nominate as reviewers and/or editors in the different areas of neuroscience in BCN.

In the early months of the COVID-19 pandemic with no designated cure or vaccine, the only way to break the infection chain is self-isolation and maintaining the physical distancing. In this paper, we present a potential application of the Internet of Things (IoT) in healthcare and physical distance monitoring for pandemic situations. The proposed framework consists of three parts: a lightweight and low-cost IoT node, a smartphone application (app), and fog-based Machine Learning (ML) tools for data analysis and diagnosis. The IoT node tracks health parameters, including body temperature, cough rate, respiratory rate, and blood oxygen saturation, then updates the smartphone app to display the user health conditions. The app notifies the user to maintain a physical distance of 2 m (or 6 ft), which is a key factor in controlling virus spread. In addition, a Fuzzy Mamdani system (running at the fog server) considers the environmental risk and user health conditions to predict the risk of spreading infection in real time. The environmental risk conveys from the virtual zone concept and provides updated information for different places. Two scenarios are considered for the communication between the IoT node and fog server, 4G/5G/WiFi, or LoRa, which can be selected based on environmental constraints. The required energy usage and bandwidth (BW) are compared for various event scenarios. The COVID-SAFE framework can assist in minimizing the corona-virus exposure risk.

Cancer is a collection of diseases that involves growing abnormal cells with the potential to invade or spread to the body. Breast cancer is the second leading cause of cancer death among women. A method for 5-year breast cancer recurrence prediction is presented in this manuscript. Clinicopathologic characteristics of 579 breast cancer patients (recurrence prevalence of 19.3%) were analyzed and discriminative features were selected using statistical feature selection methods. They were further refined by Particle Swarm Optimization (PSO) as the inputs of the classification system with ensemble learning (Bagged Decision Tree: BDT). The proper combination of selected categorical features and also the weight (importance) of the selected interval-measurement-scale features were identified by the PSO algorithm. The performance of HPBCR (hybrid predictor of breast cancer recurrence) was assessed using the holdout and 4-fold cross-validation. Three other classifiers namely as supported vector machines, DT, and multilayer perceptron neural network were used for comparison. The selected features were diagnosis age, tumor size, lymph node involvement ratio, number of involved axillary lymph nodes, progesterone receptor expression, having hormone therapy and type of surgery. The minimum sensitivity, specificity, precision and accuracy of HPBCR were 77%, 93%, 95% and 85%, respectively in the entire cross-validation folds and the hold-out test fold. HPBCR outperformed the other tested classifiers. It showed excellent agreement with the gold standard (i.e. the oncologist opinion after blood tumor marker and imaging tests, and tissue biopsy). This algorithm is thus a promising online tool for the prediction of breast cancer recurrence.