In late December 2019, strange pneumonia was detected in a seafood market in Wuhan, China which was later termed COVID-19 by the World Health Organization. At present, the virus has spread across 232 countries worldwide killing 2,409,011 as of 17 February 2021 (9:37 CET). Motivated by a recent dataset, knowledge gaps, surge in global cases, and the need to combat the virus spread, this study examined the relationship between COVID-19 confirmed cases and attributable deaths at the global and regional levels. We used a panel of 232 countries (further disaggregated into Africa-49, Americas-54, Eastern Mediterranean-23, Europe-61, Southeast Asia-10, and Western Pacific-35) from 03 January 2020 to 28 November 2020, and the instrumental variable generalized method of moment's model (IV-GMM) for analysing the datasets. The results showed that COVID-19 confirmed cases at both the global and regional levels have a strong positive effect on deaths. Thus, the confirmed cases significantly increase attributable deaths at the global and regional levels. At the global level, a 1% increase in confirmed cases increases attributable deaths by 0.78%. Regionally, a 1% increase in confirmed cases increases attributable deaths by 0.65% in Africa, 0.90% in the Americas, 0.67% in the Eastern Mediterranean, 0.72% in Europe, 0.88% in Southeast Asia, and 0.52% in the Western Pacific. This study expands the understanding of the relationship between COVID-19 cases and deaths by using a global dataset and the instrumental variable generalized method of moment's model (IV-GMM) for the analysis that addresses endogeneity and omitted variable issues.

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Modelling spatial variations of novel coronavirus disease (COVID-19): evidence from a global perspective.

The COVID-19 pandemic, within a short time span, has had a significant impact on every aspect of life in almost every country on the planet. As it evolved from a local epidemic isolated to certain regions of China, to the deadliest pandemic since the influenza outbreak of 1918, scientists all over the world have only amplified their efforts to combat it. In that battle, Artificial Intelligence, or AI, with its wide ranging capabilities and versatility, has played a vital role and thus has had a sizable impact. In this review, we present a comprehensive analysis of the use of AI techniques for spatio-temporal modeling and forecasting and impact modeling on diverse populations as it relates to COVID-19. Furthermore, we catalogue the articles in these areas based on spatio-temporal modeling, intrinsic parameters, extrinsic parameters, dynamic parameters and multivariate inputs (to ascertain the penetration of AI usage in each sub area). The manner in which AI is used and the associated techniques utilized vary for each body of work. Majority of articles use deep learning models, compartment models, stochastic methods and numerous statistical methods. We conclude by listing potential paths of research for which AI based techniques can be used for greater impact in tackling the pandemic.

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Use of Artificial Intelligence on spatio-temporal data to generate insights during COVID-19 pandemic: A Review

Neuroblastoma is the most common inheritable, solid neoplasm in children found under the age of 7 and accounts for approximately 7% of childhood cancers A common treatment that has been prescribed for over a decade is retinoid therapy [using all‑trans retinoic acid (RA)] Treatment with this differentiating agent has been revealed to progress the cells from their stem‑cell state to a mature neuronal state gaining classical neuronal characteristics, including the suppression of proliferation However, the molecular mechanism underlying the action of RA treatment remains to be elucidated In the present study, a novel mechanism of RA‑induced differentiation via regulation of receptor tyrosine kinase‑like orphan receptor 1 (ROR1) is reported ROR1 is overexpressed in neuroblastoma but significantly downregulated in mature differentiated neurons Hence, it was hypothesized that RA may modulate ROR1 leading to differentiation and termination of cancerous properties Immunoblotting revealed that following RA treatment, ROR1 levels initially increased then sharply decreased by 96 h This was paired with synaptophysin, a mature neuron marker, sharply increasing concurrently, providing evidence of differentiation by 96 h Investigation of the ROR1 pathway confirmed ROR1‑dependent downstream activation of the PI3K/AKT signaling axis, a growth pathway previously demonstrated to promote differentiation Chromatin immunoprecipitation revealed an increase in RAR binding to the promoters of ROR1 and its endogenous ligand, Wnt5a This research provided compelling evidence that RA is able to modulate the expression of ROR1 and Wnt5a to promote differentiation through the expression of synaptophysin This data combined with the overarching data from the scientific community regarding proliferation and other proliferative factors in early‑stage neurons provides a more in‑depth model of the process of differentiation in neurons

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Retinoic acid induces differentiation in neuroblastoma via ROR1 by modulating retinoic acid response elements.

Artificial Intelligence: Potential Tool to Subside SARS-CoV-2 Pandemic

This paper explores recent roles of artificial intelligence and extended reality development during the coronavirus pandemic and then predicts their significant roles in the post-COVID-19 era in an interdisciplinary manner. To begin with, we investigate roles of artificial intelligence in tackling coronavirus during the outbreak since 2020 until today. It has been effectively used for many ways, such as forecasting the spread of COVID-19 on multimodal data using data analytics, preliminary diagnosis the virus disease from specific symptoms using machine learning, and analyzing big data from social media platforms to accurately prevent the spread of virus. At the same time, due to rapid advancement in recent immersive technology and extended reality is a very popular research topic in computer science, we discuss roles of extended reality which has been extensively used during the virus outbreak for various purposes, such as supporting for businesses and education and helping the medical and health care workers. For instance, it can used for supporting psychological recovery from medical treatment for virus patients, reducing the face-to-face interactivity of physicians with the symptomatic patients, and helping people with the use of telemedicine. Next, we present a new summary of integrated roles of artificial intelligence and extended reality development in the post-COVID-19 era in an interdisciplinary perspective. Moreover, we suggest possible directions of artificial intelligence in extended reality which can be used to guide the design of the next-generation human-computer interaction applications in the future.

Roles of Artificial Intelligence and Extended Reality Development in the Post-COVID-19 Era

The prediction of diseases caused by viral infections is a complex medical task where many real data that consists of different variables must be employed. As known, COVID-19 is the most dangerous disease worldwide; nowhere, an effective drug has been found yet. To limit its spread, it is essential to find a rational method that shows the spread of this virus by relying on many infected people’s data. A model consisting of three artificial neural networks’ (ANN) functions was developed to predict COVID-19 separation in Iraq based on real infection data supplied by the public health department at the Iraqi Ministry of Health. The performance efficiency of this model was evaluated, where its performance efficiency reached 81.6% when employed four statistical error criteria as mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination (R2), and Nash-Sutcliffe coefficient (NC). The severity of the virus’s spread across Iraq was assessed in a short term (in the next 6 months), where the results show that the spread severity will intensify in this short term by 17.1%, and the average death cases will increase by 8.3%. These results clarified by creating spatial distribution maps for virus spread are simulated by employing a Geographic Information System (GIS) environment to be used as a useful database for developing plans for combating viruses in Iraq.

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COVID-19 prediction analysis using artificial intelligence procedures and GIS spatial analyst: a case study for Iraq

Neuroblastoma is an embryonic tumor of the sympathetic nervous system originating from neural crest cell remnants, which may be found in the adrenal glands or sympathetic chain. We report a case of a three-year-old girl who presented with flaccid paraparesis, and acute respiratory distress that required mechanical ventilation after a multiple-day history of gradual-onset dyspnea and right upper-lung opacity that was initially misdiagnosed as pneumonia. Chest and spinal magnetic resonance imaging (MRI) revealed a tumor in the posterior mediastinum with intra-spinal canal extension. The patient underwent thoracotomy and surgical en bloc resection with spinal canal decompression. Histopathology revealed neuroblastoma. The postoperative period was uneventful. The patient was administered a course of chemotherapy but died due to advanced stage tumor, severe pancytopenia, and septicemia with renal failure. The aim of this report is to consider posterior mediastinal neuroblastoma in the differential diagnosis of dyspnea and acute lower limb weakness in young children.

https://nsj.org.sa/content/nsj/24/4/320.full.pdf

Posterior mediastinal neuroblastoma masked as flaccid paraparesis in a 3 year child.

The Journal is the primary organ of Continuing Paediatric Medical Education in Sri Lanka. The journal also has a website. Free full text access is available for all readers.The Sri Lanka Journal of Child Health is now indexed in SciVerse Scopus (Source Record ID 19900193609), Index Medicus for South-East Asia Region (IMSEAR), CABI (Centre for Agriculture and Bioscience International Global Health Database), DOAJ and is available in Google, as well as Google Scholar.The policies of the journal are modelled on the Committee on Publication Ethics (COPE) Guidelines on Principles of Transparency and Best Practice in Scholarly Publishing. Sri Lanka Journal of Child Health is recognised by the International Committee of Medical Journal Editors (ICMJE) as a publication following the ICMJE Recommendations. 

Socio-demographic characteristics and risk factors of functional constipation in children: A case-control study

Abstract Introduction  Clinical teaching is essential for the continuity of education of health care professionals. Developing teaching skills is highly required to communicate efficiently and transfer experience and knowledge to others. To our knowledge, medical students across Iraq did not give their perception of clinical teaching in a comprehensive way. Therefore, the aim of this study is to understand our students' perception of clinical teaching and to learn from their responses what subtypes of clinical teaching they may desire the most. Methods  An online survey was distributed among medical students in Iraqi universities between February and April 2022. Results  In all, 2,562 students (1,567 females and 995 males) responded from 24 of 26 colleges of medicine in Iraq. The majority of responders were senior students. In total, 73.3% of our students had no previous experience in teaching. Those willing to work in a teaching or academic hospital comprised 84.2% of the students, and 1,563 students were interested in pursuing “clinical teaching” for medical students/residents upon completion of residency. Collective confidence in teaching abilities was rated as 3.4 out of 5. In total, 1,505 medical students felt they would be interested to have an elective period in medical education, and the majority voted for a 1-month period. Male students perceived more confidence in their variable teaching abilities compared to females ( p  < 0.05). Conclusion  This is the first study among Iraqi medical students that revealed a desire to avail of a 4-week elective in clinical teaching and become incorporated within the undergraduate curriculum of medical schools in Iraq.

Medical Students' Attitudes and Awareness toward Teaching and Participation in Formal Clinical Teaching in Iraq

Ewing’s sarcoma is a highly malignant bone tumor commonly affecting the diaphysis of long bones and pelvic bones. It belongs to a spectrum of neoplastic diseases known as the Ewing sarcoma family of tumors (EFT) with the same histologic, immunohistochemical characteristics and chromosomal translocation. It commonly affects children between the ages of 5-15 years. Spinal involvement might be primary or metastatic. Primary spinal involvement is infrequent. We report a 7-year female with acute paraplegia caused by dorsal osseous Ewing's sarcoma that her presentation mimics other causes of acute lower limb weakness in children. Early detection of this rare case and prompt treatment will ensure better outcomes for the patient.

Farah Samier Yahya

Papers

COVID-19 prediction analysis using artificial intelligence procedures and GIS spatial analyst: a case study for Iraq

Posterior mediastinal neuroblastoma masked as flaccid paraparesis in a 3 year child.

Socio-demographic characteristics and risk factors of functional constipation in children: A case-control study

Medical Students' Attitudes and Awareness toward Teaching and Participation in Formal Clinical Teaching in Iraq

A primary dorsal osseous Ewing’s sarcoma hidden as acute paraplegia in a 7-year child: A Case report