Marjan Mansourian

Bio: Marjan Mansourian is an academic researcher from Isfahan University of Medical Sciences. The author has contributed to research in topics: Medicine & Cohort study. The author has an hindex of 25, co-authored 188 publications receiving 2212 citations. Previous affiliations of Marjan Mansourian include Polytechnic University of Catalonia & University of Isfahan.

Methodological Note: Neurofeedback: A Comprehensive Review on System Design, Methodology and Clinical Applications

Abstract: Neurofeedback is a kind of biofeedback, which teaches self-control of brain functions to subjects by measuring brain waves and providing a feedback signal. Neurofeedback usually provides the audio and or video feedback. Positive or negative feedback is produced for desirable or undesirable brain activities, respectively. In this review, we provided clinical and technical information about the following issues: (1) Various neurofeedback treatment protocols i.e. alpha, beta, alpha/theta, delta, gamma, and theta; (2) Different EEG electrode placements i.e. standard recording channels in the frontal, temporal, central, and occipital lobes; (3) Electrode montages (unipolar, bipolar); (4) Types of neurofeedback i.e. frequency, power, slow cortical potential, functional magnetic resonance imaging, and so on; (5) Clinical applications of neurofeedback i.e. treatment of attention deficit hyperactivity disorder, anxiety, depression, epilepsy, insomnia, drug addiction, schizophrenia, learning disabilities, dyslexia and dyscalculia, autistic spectrum disorders and so on as well as other applications such as pain management, and the improvement of musical and athletic performance; and (6) Neurofeedback softwares. To date, many studies have been conducted on the neurofeedback therapy and its effectiveness on the treatment of many diseases. Neurofeedback, like other treatments, has its own pros and cons. Although it is a non-invasive procedure, its validity has been questioned in terms of conclusive scientific evidence. For example, it is expensive, time-consuming and its benefits are not long-lasting. Also, it might take months to show the desired improvements. Nevertheless, neurofeedback is known as a complementary and alternative treatment of many brain dysfunctions. However, current research does not support conclusive results about its efficacy.

COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life

Abstract: 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 article, 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 coronavirus exposure risk.

A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) using optimized ensemble learning

Abstract: 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.

Systematic review and metaanalysis of air pollution exposure and risk of diabetes

Abstract: The present systematic review and metaanalysis of published observational studies was conducted to assess the health effects of exposure to air pollution on diabetes risk. Online databases were searched through January 2013, and the reference lists of pertinent articles reporting observational studies in humans were examined. Pooled relative risks and 95 % confidence intervals were calculated with a random-effects model. Exposure to air pollution was associated with slight increase in risk of diabetes and susceptibility of people with diabetes to air pollution. These results were consistent between time-series, case-crossover and cohort studies and between studies conducted in North America and Europe. The association between exposure to air pollution and diabetes was stronger for gaseous pollutants than for particulate matter. Our metaanalysis suggests that exposure to air pollution may be a risk factor for diabetes and increase susceptibility of people with diabetes to air pollution.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association

Abstract: WRITING GROUP MEMBERS Emelia J. Benjamin, MD, SCM, FAHA Michael J. Blaha, MD, MPH Stephanie E. Chiuve, ScD Mary Cushman, MD, MSc, FAHA Sandeep R. Das, MD, MPH, FAHA Rajat Deo, MD, MTR Sarah D. de Ferranti, MD, MPH James Floyd, MD, MS Myriam Fornage, PhD, FAHA Cathleen Gillespie, MS Carmen R. Isasi, MD, PhD, FAHA Monik C. Jiménez, ScD, SM Lori Chaffin Jordan, MD, PhD Suzanne E. Judd, PhD Daniel Lackland, DrPH, FAHA Judith H. Lichtman, PhD, MPH, FAHA Lynda Lisabeth, PhD, MPH, FAHA Simin Liu, MD, ScD, FAHA Chris T. Longenecker, MD Rachel H. Mackey, PhD, MPH, FAHA Kunihiro Matsushita, MD, PhD, FAHA Dariush Mozaffarian, MD, DrPH, FAHA Michael E. Mussolino, PhD, FAHA Khurram Nasir, MD, MPH, FAHA Robert W. Neumar, MD, PhD, FAHA Latha Palaniappan, MD, MS, FAHA Dilip K. Pandey, MBBS, MS, PhD, FAHA Ravi R. Thiagarajan, MD, MPH Mathew J. Reeves, PhD Matthew Ritchey, PT, DPT, OCS, MPH Carlos J. Rodriguez, MD, MPH, FAHA Gregory A. Roth, MD, MPH Wayne D. Rosamond, PhD, FAHA Comilla Sasson, MD, PhD, FAHA Amytis Towfighi, MD Connie W. Tsao, MD, MPH Melanie B. Turner, MPH Salim S. Virani, MD, PhD, FAHA Jenifer H. Voeks, PhD Joshua Z. Willey, MD, MS John T. Wilkins, MD Jason HY. Wu, MSc, PhD, FAHA Heather M. Alger, PhD Sally S. Wong, PhD, RD, CDN, FAHA Paul Muntner, PhD, MHSc On behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee Heart Disease and Stroke Statistics—2017 Update

Heart Disease and Stroke Statistics—2016 Update: A Report From the American Heart Association

Abstract: Author(s): Writing Group Members; Mozaffarian, Dariush; Benjamin, Emelia J; Go, Alan S; Arnett, Donna K; Blaha, Michael J; Cushman, Mary; Das, Sandeep R; de Ferranti, Sarah; Despres, Jean-Pierre; Fullerton, Heather J; Howard, Virginia J; Huffman, Mark D; Isasi, Carmen R; Jimenez, Monik C; Judd, Suzanne E; Kissela, Brett M; Lichtman, Judith H; Lisabeth, Lynda D; Liu, Simin; Mackey, Rachel H; Magid, David J; McGuire, Darren K; Mohler, Emile R; Moy, Claudia S; Muntner, Paul; Mussolino, Michael E; Nasir, Khurram; Neumar, Robert W; Nichol, Graham; Palaniappan, Latha; Pandey, Dilip K; Reeves, Mathew J; Rodriguez, Carlos J; Rosamond, Wayne; Sorlie, Paul D; Stein, Joel; Towfighi, Amytis; Turan, Tanya N; Virani, Salim S; Woo, Daniel; Yeh, Robert W; Turner, Melanie B; American Heart Association Statistics Committee; Stroke Statistics Subcommittee

Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association

Abstract: March 5, 2019 e1 WRITING GROUP MEMBERS Emelia J. Benjamin, MD, ScM, FAHA, Chair Paul Muntner, PhD, MHS, FAHA, Vice Chair Alvaro Alonso, MD, PhD, FAHA Marcio S. Bittencourt, MD, PhD, MPH Clifton W. Callaway, MD, FAHA April P. Carson, PhD, MSPH, FAHA Alanna M. Chamberlain, PhD Alexander R. Chang, MD, MS Susan Cheng, MD, MMSc, MPH, FAHA Sandeep R. Das, MD, MPH, MBA, FAHA Francesca N. Delling, MD, MPH Luc Djousse, MD, ScD, MPH Mitchell S.V. Elkind, MD, MS, FAHA Jane F. Ferguson, PhD, FAHA Myriam Fornage, PhD, FAHA Lori Chaffin Jordan, MD, PhD, FAHA Sadiya S. Khan, MD, MSc Brett M. Kissela, MD, MS Kristen L. Knutson, PhD Tak W. Kwan, MD, FAHA Daniel T. Lackland, DrPH, FAHA Tené T. Lewis, PhD Judith H. Lichtman, PhD, MPH, FAHA Chris T. Longenecker, MD Matthew Shane Loop, PhD Pamela L. Lutsey, PhD, MPH, FAHA Seth S. Martin, MD, MHS, FAHA Kunihiro Matsushita, MD, PhD, FAHA Andrew E. Moran, MD, MPH, FAHA Michael E. Mussolino, PhD, FAHA Martin O’Flaherty, MD, MSc, PhD Ambarish Pandey, MD, MSCS Amanda M. Perak, MD, MS Wayne D. Rosamond, PhD, MS, FAHA Gregory A. Roth, MD, MPH, FAHA Uchechukwu K.A. Sampson, MD, MBA, MPH, FAHA Gary M. Satou, MD, FAHA Emily B. Schroeder, MD, PhD, FAHA Svati H. Shah, MD, MHS, FAHA Nicole L. Spartano, PhD Andrew Stokes, PhD David L. Tirschwell, MD, MS, MSc, FAHA Connie W. Tsao, MD, MPH, Vice Chair Elect Mintu P. Turakhia, MD, MAS, FAHA Lisa B. VanWagner, MD, MSc, FAST John T. Wilkins, MD, MS, FAHA Sally S. Wong, PhD, RD, CDN, FAHA Salim S. Virani, MD, PhD, FAHA, Chair Elect On behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee

Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury

Abstract: Acute kidney injury (AKI) is a complex disorder for which currently there is no accepted definition. Having a uniform standard for diagnosing and classifying AKI would enhance our ability to manage these patients. Future clinical and translational research in AKI will require collaborative networks of investigators drawn from various disciplines, dissemination of information via multidisciplinary joint conferences and publications, and improved translation of knowledge from pre-clinical research. We describe an initiative to develop uniform standards for defining and classifying AKI and to establish a forum for multidisciplinary interaction to improve care for patients with or at risk for AKI. Members representing key societies in critical care and nephrology along with additional experts in adult and pediatric AKI participated in a two day conference in Amsterdam, The Netherlands, in September 2005 and were assigned to one of three workgroups. Each group's discussions formed the basis for draft recommendations that were later refined and improved during discussion with the larger group. Dissenting opinions were also noted. The final draft recommendations were circulated to all participants and subsequently agreed upon as the consensus recommendations for this report. Participating societies endorsed the recommendations and agreed to help disseminate the results. The term AKI is proposed to represent the entire spectrum of acute renal failure. Diagnostic criteria for AKI are proposed based on acute alterations in serum creatinine or urine output. A staging system for AKI which reflects quantitative changes in serum creatinine and urine output has been developed. We describe the formation of a multidisciplinary collaborative network focused on AKI. We have proposed uniform standards for diagnosing and classifying AKI which will need to be validated in future studies. The Acute Kidney Injury Network offers a mechanism for proceeding with efforts to improve patient outcomes.