Smell disorders associated with COVID-19 infection

Background: We performed a search in the PubMed databases, Web of Science, LILACS, MEDLINE, SciELO, and Cochrane Library using the keywords COVID-19, Novel coronavirus, corona, 2019-nCoV, SARS-CoV-2, ENT, nose, anosmia, hyposmia, smell, olfactory, ORL, different ENT related symptoms. We reviewed published and peer-reviewed studies that reported the ENT manifestations in COVID-19 laboratory-confirmed positive patients. Main text: Within the included 2549 COVID-19 laboratory-confirmed positive patients, smell affection was reported in 1453 patients (57%). The other reported ENT manifestations were taste disorder (49.2%), headache (42.8%), nasal blockage (26.3%), sore throat (25.7%), runny nose or rhinorrhea (21.3%), upper respiratory tract infection (URTI) (7.9%), and frequent sneezing (3.6%). Conclusion(s): Smell affection in COVID-19 is common and could be one of the red flag signs in COVID-19 infection. With a sensitivity of utilized questionnaire in smell identification, a homogenous universal well-defined COVID-19 questionnaire is needed to make the COVID-19 data collection more sensible.Copyright © 2021, The Author(s).

Characterization, in-silico, and in-vitro study of a new steroid derivative from Ophiocoma dentata as a potential treatment for COVID-19.

The medicinal potential of marine invertebrates' bioactive components that may act as anti-COVID-19 demonstrated promising results. Ophiocoma dentata, which is common in the Red Sea, is one such source. Therefore, this study aimed to isolate a new compound from the brittle star, Ophiocoma dentata, and evaluate its efficacy as anti-COVID-19 in-silico and in-vitro. Standard procedures were followed in order to assess the isolated compound's preliminary toxicity and anti-inflammatory properties. Computer virtual screening technology through molecular docking and ADMET studies was conducted as well as a new steroid derivative was isolated for the first time, named 5α-cholesta-4(27), 24-dien-3ß, 23 ß-diol. Investigation of the Anti-Covid-19 activity of the isolated compound using a Plaque reduction assay revealed 95% inhibition at a concentration of 5 ng/µl (12.48 µM). Moreover, this compound showed an IC50 of 11,350 ± 1500 ng/ml against the normal fibroblast cells, indicating its safety. Interestingly, this compound exhibited anti-inflammatory activity with an IC50 of 51.92 ± 0.03 µg/ml compared to a reference drug's IC50 of 53.64 ± 0.01 µg/ml, indicating that this compound is a potent anti-inflammatory. In silico data have proved that the isolated compound is a promising viral inhibitor against SARS-CoV2 and is thus recommended as a future nature preventive and curative antiviral drug.

MODELING COVID-19 DAILY CASES IN EGYPT

The present COVID-19 virus outbreak has started in the Wuhan area of China and is expanding worldwide. This lethal epidemic had an immense impact on millions of citizens and provided some political and social response. The outcome of any research investigation in this area remains to be decided. However, it is essential to assess the epidemic dynamics to correctly predict the quarantine operations and safeguard individuals at least at an appropriate stage. In big data science and other relevant fields, it is essential to have the best possible overview of data. To accomplish this, we researched the COVID-19 pandemic dynamics in Egypt to include a structure for effective quarantines. Guidelines for studying pandemic processes have been clarified and can be used to arrive at hypotheses. The statistical model is a significant development in describing the COVID-19 daily cases in Egypt.

FPGA Realizations of Chaotic Epidemic and Disease Models Including Covid-19.

The spread of epidemics and diseases is known to exhibit chaotic dynamics; a fact confirmed by many developed mathematical models. However, to the best of our knowledge, no attempt to realize any of these chaotic models in analog or digital electronic form has been reported in the literature. In this work, we report on the efficient FPGA implementations of three different virus spreading models and one disease progress model. In particular, the Ebola, Influenza, and COVID-19 virus spreading models in addition to a Cancer disease progress model are first numerically analyzed for parameter sensitivity via bifurcation diagrams. Subsequently and despite the large number of parameters and large number of multiplication (or division) operations, these models are efficiently implemented on FPGA platforms using fixed-point architectures. Detailed FPGA design process, hardware architecture and timing analysis are provided for three of the studied models (Ebola, Influenza, and Cancer) on an Altera Cyclone IV EP4CE115F29C7 FPGA chip. All models are also implemented on a high performance Xilinx Artix-7 XC7A100TCSG324 FPGA for comparison of the needed hardware resources. Experimental results showing real-time control of the chaotic dynamics are presented.