The Policy Gap and Inefficiency in Public Volunteers' Response to Assist the Hospitals After Natural Disasters in Iran: A Grounded Theory Methodology.

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.

The Predictive Power of Near-Infrared Spectroscopy in Improving Cognitive Problems in Patients Undergoing Brain Surgeries: A Systematic Review.

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. The aim of this study was to systematically review the brain monitoring with NIRS in neurosurgery. 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. 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. 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. 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.

Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review.

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), R2 coefficient of determination (R2), and mean absolute percentage error (MAPE) parameters were selected as performance measures for comparison of the accuracy of models. R2 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 R2 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.

Determining factors on volunteers' presence in hospital response to disasters and emergencies: a qualitative study.

BACKGROUND: Volunteers' presence, as a critical issue in hospital response to disasters and emergencies, helps to readiness and quick response to the phenomena, preventing deaths caused by such incidences. This study aimed to determine the main factors affecting popular volunteers' presence in hospital response to disasters and emergencies in Iran. METHODS: This qualitative study, conducted on 31 semi-structured interviews during the years 2019 and 2020, concerning emergency specialist working in various health organization nationwide between June 2019 and April 2020. The non-structured and semi-structured interviews were adopted to gather concept code and analyzed using Graneheim recommendation method. RESULTS: Four main categories, identified as effective factors on volunteers' presence in hospital response to disasters and emergencies, including nine subcategories: (1) organizing and managing volunteers (with two sub-categories: (a) calling and registering volunteers, and (b) identifying volunteers' ability); (2) organizational structure (with two sub-categories: (a) operational planning and (b) coordination and communication); (3) training (with two sub-categories: (a) training in disasters and (b) volunteer training); and (4) volunteer challenges (with three sub-categories: (a) volunteer interaction with organizations, (b) volunteer logistics, and (c) volunteering culture). CONCLUSIONS: Exploring effective factors as regards volunteers' presence in hospital response phase in disasters to adopt a policy based on the experiences of managers and heads of health can help planners to provide effective design and implementation. It can scientifically contribute to disaster risk management and hospital emergency response in Iran.

Case Report: Chemotherapy Indication in a Case of Neurofibromatosis Type 1 Presenting Optic Pathway Glioma: A One-Year Clinical Case Study Using Differential Tractography Approach.

Neurofibromatosis type 1 (NF1) is associated with peripheral and central nervous system tumors. It is noteworthy that the regions in which these tumors frequently arise are the optic pathways (OPs) and the brainstem. Thus, we decided to trace the procedure of diffusion Magnetic Resonance Imaging (dMRI) alterations along with Short-Wavelength Automated Perimetry (SWAP) examinations of the OPs after surgery and chemotherapy over 1 year, which enabled us to evaluate chemotherapy's efficacy in an NF1 patient with an OP tumor. In this study, a 25-year-old woman with NF1 and left optic radiation (OR) glioma underwent surgery to remove the glioma. Immunohistochemistry (IHC) revealed a Pilocytic Astrocytoma (PA) WHO grade I. Post-operation chemotherapy done using nine treatment cycles of administering Temozolomide (TMZ) for 5 days every 4 weeks. Applying the region of interest (ROI) differential tractography method and SWAP four times every 3 months allowed us to follow the patient's visual acuity alterations longitudinally. The differential deterministic tractography method and statistical analyses enabled us to discover the white matter (WM) tracts anisotropy alterations over time. Furthermore, statistical analyses on the SWAP results along time illustrated possible alterations in visual acuity. Then, we could compare and associate the findings with the SWAP examinations and patient symptoms longitudinally. Statistical analyses of SWAP tests revealed a significant improvement in visual fields, and longitudinal differential tractography showed myelination and dense axonal packing in the left OR after 1 year of treatment. In this study, we examined an old hypothesis suggesting that chemotherapy is more effective than radiotherapy for NF1 patients with OP gliomas (OPGs) because of the radiation side effects on the visual field, cognition, and cerebrovascular complications. Our longitudinal clinical case study involving dMRI and SWAP on a single NF1-OPG patient showed that chemotherapy did not suppress the OP myelination over time. However, it should be noted that this is a clinical case study, and, therefore, the generalization of results is limited. Future investigations might focus on genetic-based imaging, particularly in more cases. Further, meta-analyses are recommended for giving a proper Field Of View (FOV) to researchers as a subtle clue regarding precision medicine.

Emerging role of microRNAs in the pathogenesis of amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a deadly motor neuron disease (MND) and the most frequent MND in adults. ALS is recognized by degenerative alterations in both upper and lower motor neurons. This disorder is classified to familial and sporadic classes. Disease-causing mutations in SOD1, C9ORF72, FUS, and TARDBP have been recognized in familial ALS cases. However, in spite of conduction of several genetic association studies, heritable genetic risk elements in sporadic have not been identified completely. Several miRNAs have been dysregulated in the serum samples or brain tissues of ALS patients. Moreover, a number of miRNAs have been suggested as putative biomarkers for sporadic ALS. In the current manuscript, we review of miRNAs in the development of ALS.

Assessment of the role of non-coding RNAs in the pathophysiology of Parkinson's disease.

Parkinson's disease (PD) is the second main neurodegenerative disease causing motor abnormalities in the middle-aged and old individuals. In some cases, cognitive dysfunction also occurs. The clinical signs of PD are bradykinesia, rigidity and resting tremor. As these signs might be detected in other neurological conditions such as multiple systems atrophy and corticobasal degeneration, it is necessary to find specific and sensitive markers for this disorder. Non-coding RNAs are implicated in the different PD-associated features such as α-synuclein expression and Lewy body construction, mitochondrial dysfunction, apoptosis, neuroinflammation and defects in glial cell-derived neurotrophic factor. Several researches have confirmed dysregulation of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in brain tissues, plasma exosomes and leukocytes of affected individuals or animal models of PD. A number of these transcripts directly regulate the neurodegenerative process in PD. In the current study, we review the current data about dysregulation of ncRNAs and the role of their genomic variants in the pathogenesis of PD.

miRNA signature in glioblastoma: Potential biomarkers and therapeutic targets.

MicroRNAs (miRNAs) are transcripts with sizes of about 22 nucleotides, which are produced through a multistep process in the nucleus and cytoplasm. These transcripts modulate the expression of their target genes through binding with certain target regions, particularly 3' suntranslated regions. They are involved in the pathogenesis of several kinds of cancers, such as glioblastoma. Several miRNAs, including miR-10b, miR-21, miR-17-92-cluster, and miR-93, have been up-regulated in glioblastoma cell lines and clinical samples. On the other hand, expression of miR-7, miR-29b, miR-32, miR-34, miR-181 family members, and a number of other miRNAs have been decreased in this type of cancer. In the current review, we explain the role of miRNAs in the pathogenesis of glioblastoma through providing a summary of studies that reported dysregulation of these epigenetic effectors in this kind of brain cancer.

Glioma Migration Through the Corpus Callosum and the Brainstem Detected by Diffusion and Magnetic Resonance Imaging: Initial Findings.

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.