In a search for potential drug candidates for combating COVID-19: computational study revealed salvianolic acid B as a potential therapeutic targeting 3CLpro and spike proteins.

The global prevalence of COVID-19 disease and the overwhelming increase in death toll urge scientists to discover new effective drugs. Although the drug discovery process is a challenging and time-consuming, fortunately, the plant kingdom was found to have many active therapeutics possessing broad-spectrum antiviral activity including those candidates active against severe acute respiratory syndrome coronaviruses (SARS-CoV). Herein, nine traditional Chinese medicinal plant constituents from different origins (Glycyrrhizin 1, Lycorine 2, Puerarin 3, Daidzein 4, Daidzin 5, Salvianolic acid B 6, Dihydrotanshinone I 7, Tanshinone I 8, Tanshinone IIa 9) previously reported to exhibit antiviral activity against SARS-CoV were virtually screened in silico (molecular docking) as potential inhibitors of SARS-CoV-2 target proteins. The tested medicinal plant compounds were in silico screened for their activity against two key SARS-CoV-2 target proteins; 3CLpro, and Spike binding-domain proteins. Among the tested medicinal plant compounds, Salvianolic acid B 6 (Sal-B) showed promising binding affinities against the two specified SARS-CoV-2 target proteins compared to the reference standards used. Hence molecular dynamics simulations followed by calculating the free-binding energy were carried out for Sal-B providing information on its affinity, stability, and thermodynamic behavior within the two SARS-CoV-2 target proteins as well as key ligand-protein binding aspects. Besides, the quantum mechanical calculations showed that Sal-B can adopt different conformations due to the existence of various rotatable bonds. Therefore, the enhanced antiviral activity of Sal-B among other studied compounds can be also attributed to the structural flexibility of Sal-B. Our study gives an explanation of the structure activity relationship required for targeting SARS-CoV-2 3CLpro and Spike proteins and also facilitates the future design and synthesis of new potential drugs exhibiting better affinity and specificity. Besides, an ADME study was carried out on screened compounds and reference controls revealing their pharmacokinetics properties.Communicated by Ramaswamy H. Sarma.

Revisiting activity of some glucocorticoids as a potential inhibitor of SARS-CoV-2 main protease: theoretical study† † Electronic supplementary information (ESI) available. See DOI: 10.1039/d0ra10674g

The global breakout of COVID-19 and raised death toll has prompted scientists to develop novel drugs capable of inhibiting SARS-CoV-2. Conducting studies on repurposing some FDA-approved glucocorticoids can be a promising prospective for finding a treatment for COVID-19. In addition, the use of anti-inflammatory drugs, such as glucocorticoids, is a pivotal step in the treatment of critical cases of COVID-19, as they can provoke an inflammatory cytokine storm, damaging lungs. In this study, 22 FDA-approved glucocorticoids were identified through in silico (molecular docking) studies as the potential inhibitors of COVID-19's main protease. From tested compounds, ciclesonide 11, dexamethasone 2, betamethasone 1, hydrocortisone 4, fludrocortisone 3, and triamcinolone 8 are suggested as the most potent glucocorticoids active against COVID-19's main protease. Moreover, molecular dynamics simulations followed by the calculations of the binding free energy using MM-GBSA were carried out for the aforementioned promising candidate-screened glucocorticoids. In addition, quantum chemical calculations revealed two electron-rich sites on ciclesonide where binding interactions with the main protease and cleavage of the prodrug to the active metabolite take place. Our results have ramifications for conducting preclinical and clinical studies on promising glucocorticoids to hasten the development of effective therapeutics against COVID-19. Another advantage is that some glucocorticoids can be prioritized over others for the treatment of inflammation accompanying COVID-19. The global breakout of COVID-19 and raised death toll has prompted scientists to develop novel drugs capable of inhibiting SARS-CoV-2.

A Geolocated Dataset of COVID-19 Pandemic: Tweets with Location Information

The emergence of the damn Coronavirus inspired many researchers to seek information and to collect datasets aiming to conduct different analyses using text tweets from the Twitter platform. Twitter offers not only tweets but, retweets mentions, replies, quotes, direct messages, and many others to be pulled when making a request. While this research intends to gather tweets only and neglect the rest, a small program applied during scraping Twitter and before filtration to skip retweets and replies. The type of needed data obliges researchers to think about which keyword should be chosen. Fetching infected people direct this study to use sentences connected with logical operators. This step succeeded in obtaining the desired data. Although this data is filled with noise that makes it unsuitable to preserve, several steps are applied to isolate fake and duplicate accounts. A recent API was used to classify accounts to overcome automated accounts to achieve and ensure the originality of the dataset. This research will state gradually and step by step how to bypass any nuisance noise, also using available metadata to locate tweets and to present an acceptable and logical geolocated dataset suitable for further operations. This dataset has been extracted according to the geospatial information provided with the tweets. Statistics will be demonstrated according to the availability of such information. The study is trying to submit a reasonable geolocated dataset to approve its contribution by providing the most important information. The collected dataset reflects its importance on societies and how they interact with this crisis. Also, diagnosing weaknesses and strengths to gain benefits for future decision making, and hoping to avoid worst possibilities. © 2021 IEEE.

Deep Learning Models for Classification and Localization of COVID-19 Abnormalities on Chest Radiographs

COVID-19 pandemic is five times more deadly than flu and other disease. It causes serious morbidity and mortality across the world. Like other pneumonias, pulmonary infection with COVID-19 results in fluids in the lungs and inflammation. Equally, the disease looks very similar to other bacterial and viral pneumonias on chest radiographs;as such it is very difficult to be diagnosed. In this work, Convolutional Neural Network (CNN), Faster Region Based Convolutional Neural Network (Faster R-CNN) and Chest X-ray Network (CheXNet) deep learning algorithms were used to develop models for classification and localization of COVID-19 abnormalities on chest radiographs models for normal and opacity (typical, atypical, indeterminate) cases in order to help medical doctors, radiologists and other health workers to provide fast and confident diagnosis of the COVID-19. Hence, CheXNet based model has comparatively outperformed other models for being able to classify chest radiographs as negative for pneumonia or typical, indeterminate and atypical for COVID-19 pandemic with 97% accuracy and more so for its ability to correctly classify chest radiographs for typical, indeterminate and atypical COVID-19 pandemic cases the model has comparatively outperformed other models with 93% precision. However, for the ability to correctly classify the chest radiographs as negative for pneumonia, Faster R-CNN based model outperformed other models with 94% recall. © 2021 IEEE.

Deep Learning Models for Predicting COVID-19 Using Chest X-Ray Images

The outbreak of COVID-19 has cost the world a lot of lives and causes the shutdown of businesses which get most of the countries gone into economic recession. Despite the fact that some of the vaccines of the pandemic are now available, immediately after the first wave of the COVID-19 pandemic, the second wave of the pandemic has now started and causes a lot of lives and grounds a lot of businesses that have resumed. Therefore, in order to contain its further spread among humans, testing and screening of a large number of suspected COVID-19 cases for appropriate quarantine and treatment measures are of high priority to all governments around the world. However, most of the countries are facing inadequate and standard laboratories for testing a large number of suspected COVID-19 cases in their countries despite the fact that the virus is now endemic like other communicable diseases. Therefore, alternatives in non-medical diagnosis of COVID-19 techniques using artificial intelligence which include deep learning, data mining, machine learning, expert system, software agent, and other techniques are urgently needed in the cause of the diagnosis, containing and combatting the further spread of the pandemic. In this study, deep learning algorithms were used to develop models for predicting COVID-19 using chest x-ray images, and models were able to extract COVID-19 imagery features and provide clinical diagnosis ahead of the pathogenic test with a view to saving time, thereby complementing COVID-19 testing laboratories. ResNet50-based model was found to have the highest accuracy, sensitivity, and AUC score of 99%, 89%, and 96%, respectively. In contrast, EfficientNet B4-based model was found to have the highest specificity of 89%. Therefore, ResNet50-based model which has the highest sensitivity of 89% can be used for diagnosis of COVID-19 infection as well as an adjuvant tool in radiology department in hospitals. © 2022, Springer Nature Switzerland AG.

ß-Blockers bearing hydroxyethylamine and hydroxyethylene as potential SARS-CoV-2 Mpro inhibitors: rational based design, in silico, in vitro, and SAR studies for lead optimization.

The global COVID-19 pandemic became more threatening especially after the introduction of the second and third waves with the current large expectations for a fourth one as well. This urged scientists to rapidly develop a new effective therapy to combat SARS-CoV-2. Based on the structures of ß-adrenergic blockers having the same hydroxyethylamine and hydroxyethylene moieties present in the HIV-1 protease inhibitors which were found previously to inhibit the replication of SARS-CoV, we suggested that they may decrease the SARS-CoV-2 entry into the host cell through their ability to decrease the activity of RAAS and ACE2 as well. Herein, molecular docking of twenty FDA-approved ß-blockers was performed targeting SARS-CoV-2 Mpro. Results showed promising inhibitory activities especially for Carvedilol (CAR) and Nebivolol (NEB) members. Moreover, these two drugs together with Bisoprolol (BIS) as an example from the lower active ones were subjected to molecular dynamics simulations at 100 ns. Great stability across the whole 100 ns timeframe was observed for the top docked ligands, CAR and NEB, over BIS. Conformational analysis of the examined drugs and hydrogen bond investigation with the pocket's crucial residues confirm the great affinity and confinement of CAR and NEB within the Mpro binding site. Moreover, the binding-free energy analysis and residue-wise contribution analysis highlight the nature of ligand-protein interaction and provide guidance for lead development and optimization. Furthermore, the examined three drugs were tested for their in vitro inhibitory activities towards SARS-CoV-2. It is worth mentioning that NEB achieved the most potential anti-SARS-CoV-2 activity with an IC50 value of 0.030 µg ml-1. Besides, CAR was found to have a promising inhibitory activity with an IC50 of 0.350 µg ml-1. Also, the IC50 value of BIS was found to be as low as 15.917 µg ml-1. Finally, the SARS-CoV-2 Mpro assay was performed to evaluate and confirm the inhibitory effects of the tested compounds (BIS, CAR, and NEB) towards the SARS-CoV-2 Mpro enzyme. The obtained results showed very promising SARS-CoV-2 Mpro inhibitory activities of BIS, CAR, and NEB (IC50 = 118.50, 204.60, and 60.20 µg ml-1, respectively) compared to lopinavir (IC50 = 73.68 µg ml-1) as a reference standard.

Twitter APIs for Collecting Data of Influenza Viruses, A Systematic Review

Twitter has become famous due to its lenient policy for providing data to a variety of beneficiaries, as the retrieved data will be used according to the need and method chosen by the developer or researcher to accomplish their work. Unlike many social media networks, Twitter overcame limitations within its network and applied extra features making the usage of this platform much fun and determine. This paper will not explain Twitter's specifications and characteristics or usage instead, it will focus on describing the methods of aggregating data and demonstrate the main challenges during collecting the huge amount of data available on this platform. The Twitter Application Programming Interface (APIs) will be discussed, concentrating on the most common ones, especially those concerned with scraping and crawling Twitter data. These APIs are bounded and need solutions due to the various kinds of retrievals they make, yet, this paper will explain them and discuss appropriate matters to bypass these constraints. Furthermore, this review focuses on reviewing studies and researches collected from various journals using Twitter data to predict, detect and monitor the outbreak of influenza demonstrating all types of viruses caused it, stating the location of such spread. These flu viruses have been appeared since 2009 starting with Swin flu then H1N1, Avian flu, Flu, lastly and, still exist Covide-19, being discussed on Twitter platform, taking into consideration the launch of Twitter platform was in 2006. The importance of this review is that it shows the gradual emergence of the different dangerous flu viruses spread around the world, showing the history of two things;the appearance of the dangerous flu viruses (in the form of their collected datasets) and the developments and progresses applied to the APIs used for collecting these big datasets from Twitter platform, limitations and, constraints within these APIs are quite different during this period. Several tables abbreviate the most important information gained stating which method is the best and how advanced this process is over the last decade. The final result indicates the increasing number of researches recently and the use of very sophisticated methods to collect tweets, as a result of the emergence of the Coronavirus. © 2021 IEEE.

Role of vital parameters and biomarkers in predicting the outcome of patients in COVID ICU: A comparative study

Aim: The aim of the present study to compare the vital parameters and biomarkers in predicting the outcome of patients in Covid ICU. Methods: 200 patient were divided in to two groups, Group A of those who expired and Group B of the survivors. The mean for each parameter was calculated and compared among the two groups and based on which p value was calculated for each parameter undertaken in clinical evaluation. Blood reports of investigations assessing the levels of biomarkers like Procalcitonin (PCT), C- Reactive Protein (CRP), D-dimer, Ferritin, Lactate dehydrogenase (LDH) and Interleukin-6 (IL-6) sent on first day and last day of hospitalisation in covid ICU were collected for Group A (Expired) and Group B (Survived) and master chart was prepared. Results: The study population comprised of 200 confirmed Covid-19 cases, among which those expired (Group A) were 103, and those who survived (Group B) were 97. The mean age difference was statistically significant (p value = 0.003). The mean Heart Rate on day of admission was statistically significant (p value = 0.005). The mean Heart Rate on last day of hospitalisation difference was statistically significant (p value = 0.001). The Median PCT levels along with along with its interquartile range in Group A (Expired) versus Group B (Survivors) on day of admission the difference was statistically significant (p value < 0.001). The Median Ferritin levels along with its interquartile range in Group A (Expired) versus Group B (Survivors) on day of admission the difference was statistically significant (p value < 0.0003). The Median Ferritin levels along with its interquartile range in Group A (Expired) versus Group B (Survivors) on last day of hospitalisation the difference was statistically significant (p value < 0.001). Conclusion We concluded that the comparison of the two arms of study, clinical parameters such as Heart Rate, Systolic Blood Pressure, Respiratory Rate and Oxygen Saturation on the day of admission had significant difference among those who expired (Group A) versus the Survivors (Group B), further the raised levels in Expired group corresponded with the severity, poor outcome and higher mortality in critically ill patients in ICU. In addition to clinical parameters, the raised levels of biomarkers such as PCT, CRP, D-dimer Ferritin, LDH and IL-6 in the expired patients in comparison to the survivors on the day of admission and subsequently compared with those of the last day of hospitalisation are reliable indicator of progression towards severity, poor prognosis and outcome.

Computational Insights on the Potential of Some NSAIDs for Treating COVID-19: Priority Set and Lead Optimization.

The discovery of drugs capable of inhibiting SARS-CoV-2 is a priority for human beings due to the severity of the global health pandemic caused by COVID-19. To this end, repurposing of FDA-approved drugs such as NSAIDs against COVID-19 can provide therapeutic alternatives that could be utilized as an effective safe treatment for COVID-19. The anti-inflammatory activity of NSAIDs is also advantageous in the treatment of COVID-19, as it was found that SARS-CoV-2 is responsible for provoking inflammatory cytokine storms resulting in lung damage. In this study, 40 FDA-approved NSAIDs were evaluated through molecular docking against the main protease of SARS-CoV-2. Among the tested compounds, sulfinpyrazone 2, indomethacin 3, and auranofin 4 were proposed as potential antagonists of COVID-19 main protease. Molecular dynamics simulations were also carried out for the most promising members of the screened NSAID candidates (2, 3, and 4) to unravel the dynamic properties of NSAIDs at the target receptor. The conducted quantum mechanical study revealed that the hybrid functional B3PW91 provides a good description of the spatial parameters of auranofin 4. Interestingly, a promising structure-activity relationship (SAR) was concluded from our study that could help in the future design of potential SARS-CoV-2 main protease inhibitors with expected anti-inflammatory effects as well. NSAIDs may be used by medicinal chemists as lead compounds for the development of potent SARS-CoV-2 (Mpro) inhibitors. In addition, some NSAIDs can be selectively designated for treatment of inflammation resulting from COVID-19.

Revisiting activity of some glucocorticoids as a potential inhibitor of SARS-CoV-2 main protease: theoretical study.

The global breakout of COVID-19 and raised death toll has prompted scientists to develop novel drugs capable of inhibiting SARS-CoV-2. Conducting studies on repurposing some FDA-approved glucocorticoids can be a promising prospective for finding a treatment for COVID-19. In addition, the use of anti-inflammatory drugs, such as glucocorticoids, is a pivotal step in the treatment of critical cases of COVID-19, as they can provoke an inflammatory cytokine storm, damaging lungs. In this study, 22 FDA-approved glucocorticoids were identified through in silico (molecular docking) studies as the potential inhibitors of COVID-19's main protease. From tested compounds, ciclesonide 11, dexamethasone 2, betamethasone 1, hydrocortisone 4, fludrocortisone 3, and triamcinolone 8 are suggested as the most potent glucocorticoids active against COVID-19's main protease. Moreover, molecular dynamics simulations followed by the calculations of the binding free energy using MM-GBSA were carried out for the aforementioned promising candidate-screened glucocorticoids. In addition, quantum chemical calculations revealed two electron-rich sites on ciclesonide where binding interactions with the main protease and cleavage of the prodrug to the active metabolite take place. Our results have ramifications for conducting preclinical and clinical studies on promising glucocorticoids to hasten the development of effective therapeutics against COVID-19. Another advantage is that some glucocorticoids can be prioritized over others for the treatment of inflammation accompanying COVID-19.

Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset.

COVID-19 or 2019-nCoV is no longer pandemic but rather endemic, with more than 651,247 people around world having lost their lives after contracting the disease. Currently, there is no specific treatment or cure for COVID-19, and thus living with the disease and its symptoms is inevitable. This reality has placed a massive burden on limited healthcare systems worldwide especially in the developing nations. Although neither an effective, clinically proven antiviral agents' strategy nor an approved vaccine exist to eradicate the COVID-19 pandemic, there are alternatives that may reduce the huge burden on not only limited healthcare systems but also the economic sector; the most promising include harnessing non-clinical techniques such as machine learning, data mining, deep learning and other artificial intelligence. These alternatives would facilitate diagnosis and prognosis for 2019-nCoV pandemic patients. Supervised machine learning models for COVID-19 infection were developed in this work with learning algorithms which include logistic regression, decision tree, support vector machine, naive Bayes, and artificial neutral network using epidemiology labeled dataset for positive and negative COVID-19 cases of Mexico. The correlation coefficient analysis between various dependent and independent features was carried out to determine a strength relationship between each dependent feature and independent feature of the dataset prior to developing the models. The 80% of the training dataset were used for training the models while the remaining 20% were used for testing the models. The result of the performance evaluation of the models showed that decision tree model has the highest accuracy of 94.99% while the Support Vector Machine Model has the highest sensitivity of 93.34% and Naïve Bayes Model has the highest specificity of 94.30%.