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COVID-19 has produced a global pandemic affecting all over of the world. Prediction of the rate of COVID-19 spread and modeling of its course have critical impact on both health system and policy makers. Indeed, policy making depends on judgments formed by the prediction models to propose new strategies and to measure the efficiency of the imposed policies. Based on the nonlinear and complex nature of this disorder and difficulties in estimation of virus transmission features using traditional epidemic models, artificial intelligence methods have been applied for prediction of its spread. Based on the importance of machine and deep learning approaches in the estimation of COVID-19 spreading trend, in the present study, we review studies which used these strategies to predict the number of new cases of COVID-19. Adaptive neuro-fuzzy inference system, long short-term memory, recurrent neural network and multilayer perceptron are among the mostly used strategies in this regard. We compared the performance of several machine learning methods in prediction of COVID-19 spread. Root means squared error (RMSE), mean absolute error (MAE), R 2 coefficient of determination (R 2 ), and mean absolute percentage error (MAPE) parameters were selected as performance measures for comparison of the accuracy of models. R 2 values have ranged from 0.64 to 1 for artificial neural network (ANN) and Bidirectional long short-term memory (LSTM), respectively. Adaptive neuro-fuzzy inference system (ANFIS), Autoregressive Integrated Moving Average (ARIMA) and Multilayer perceptron (MLP) have also have R 2 values near 1. ARIMA and LSTM had the highest MAPE values. Collectively, these models are capable of identification of learning parameters that affect dissimilarities in COVID-19 spread across various regions or populations, combining numerous intervention methods and implementing what-if scenarios by integrating data from diseases having analogous trends with COVID-19. Therefore, application of these methods would help in precise policy making to design the most appropriate interventions and avoid non-efficient restrictions.

Purpose: Glioma cell infiltration, in which the glioma tumor cells spread long distances from the primary location using white matter (WM) or blood vessels, is known as a significant challenge for surgery or localized chemotherapy and radiation therapy. Following the World Health Organization (WHO), the glioma grading system ranges from stages I to IV, in which lower-grade gliomas represent benign tumors, and higher grade gliomas are considered the most malignant. Materials and Methods: We gathered magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) data for seven patients with right precentral gyrus-located tumors and six age-and sex-matched healthy subjects for analysis. Tract-Based Spatial Statistics (TBSS) was utilized to evaluate whole-brain WM implication due to probable tumor infiltration. Also, along-tract statistics were used in order to trace the implicated WM tracts. Finally, for cortical evaluation of probable tumor cell migration, voxel-based morphometry (VBM) was utilized, which allowed us to do whole-brain cortical estimation. Results: The TBSS results revealed significantly higher fractional anisotropy (FA) and lower mean diffusivity (MD) in the left side superior corona radiata. Also, higher FA was observed in the right corticostriatal tract. Along-tract statistics were also compiled on the corpus callosum (CC), which is anatomically known as a hub between hemispheres. The body of the CC, which connected with the superior corona radiata anatomically, showed significantly higher FA values relative to healthy subjects, which are in line with the TBSS results. Consistent with these results, whole-brain gray matter changes were analyzed via VBM, which showed significant hypertrophy of both sides of the brainstem. Conclusion: In future investigations, focusing on the genetic basis of the glioma patients in line with imaging studies on a larger sample size, which is known as genetics imaging, would be a suitable approach for tracing this process.

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Context: One of the main objectives in neurosurgical procedures is the prevention of cerebral ischemia and hypoxia leading to secondary brain injury. Different methods for early detection of intraoperative cerebral ischemia and hypoxia have been used. Near-infrared spectroscopy (NIRS) is a simple, non-invasive method for monitoring cerebral oxygenation increasingly used today. Objectives: The aim of this study was to systematically review the brain monitoring with NIRS in neurosurgery. Data Sources: The search process resulted in the detection of 324 articles using valid keywords on the electronic databases, including Embase, PubMed, Scopus, Web of Science, and Cochrane Library. Study Selection: Subsequently, the full texts of 34 studies were reviewed, and finally 11 articles (seven prospective studies, three retrospective studies, and one randomized controlled trial) published from 2005 to 2020 were identified as eligible for systematic review. Data Extraction: Meta-analysis was not possible due to high heterogeneity in neurological and neurosurgical conditions of patients, expression of different clinical outcomes, and different standard reference tests in the studies reviewed. Results: The results showed that NIRS is a non-invasive cerebral oximetry that provides continuous and measurable cerebral oxygenation information and can be used in a variety of clinical settings.

Background: Nowadays, it has been suggested that the care of neurocritically ill patients in the Neurocritical Care Unit can outcome, hospitalization time and ICU stay. Therefore, the aim of this study was to evaluate the clinical condition and outcomes of these patients in our setting. Methods: We conducted a cross-sectional study in patients in the neurocritical care unit (NCCU) of Loghman Hakim hospital. The medical findings and outcome (discharge/death) were gathered in the data collection form. We used SPSS version 18 for statistical analysis with significant level < 0.05. Results: A total of 432 patients, including 237 (56.2%) male and 185 (43.8%) female (P = 0.01) were enrolled. There was statistically no

Objective: This study aims to explore a public volunteer’s hospital response model in natural disasters in Iran. Methods: This study employed grounded theory using the Strauss and Corbin 2008 method and data analysis was carried out in three steps, namely open, axial, and selective coding. The present qualitative study was done using semi-structured interviews with 36 participants who were on two levels and with different experiences in responding to emergencies and disasters as “public volunteers” and “experts”. National and local experts were comprised of professors in the field of disaster management, hospital managers, Red Crescent experts, staff and managers of Iran Ministry of Health and Medical Education. Results: The main concept of the paradigm model was “policy gap and inefficiency” in the management of public volunteers, which was rooted in political factions, ethnicity, regulations, and elites. The policy gap and inefficiency led to chaos and “crises over crises.” Overcoming the policy gap will result in hospital disaster resilience. Meanwhile, the model covered the causal, contextual, and intervening conditions, strategies, and consequences in relation to the public volunteers’ hospital response phase. Conclusions: The current public volunteers’ hospital in Iran suffered from the lack of a coherent, comprehensive, and forward-looking plan for their response. The most important beneficiaries of this paradigm model will be for health policy-makers, to clarify the main culprits of creating policy gap and inefficiency in Iran and other countries with a similar context. It can guide the decision-makings in upstream documents on the public volunteers. Further research should carried out to improve the understanding of the supportive legal framework, building the culture of volunteering, and enhancing volunteers’ retention rate.