In addition, to demonstrate the applicability of the CFPS systems, it was proved that the four CFPS systems all had the potential to produce therapeutic proteins, and they could produce the receptor-binding domain (RBD) protein of SARS-CoV-2 with functional activity. They not only could expand the potential options for in vitro protein production, but also could increase the application range of the system by expanding the cell-free protein synthesis platform. The online version contains supplementary material available at 10.1186/s40643-021-00413-2.The online version contains supplementary material available at 10.1186/s40643-021-00413-2. In order to eradicate the COVID-19 pandemic, scientists around the world have been working very hard for a year or more with the motto of designing effective drugs and vaccines against the severe acute respiratory coronavirus 2 (SARS-CoV-2). Along with the positive results with the antiviral drugs and a few commercialized vaccines, the unresponsiveness as well as some side effects of such therapies have also been noticed, possibly due to the emergence of the SARS-CoV-2 variants. Therefore, current review summarized the actual effectiveness of the antivirals and vaccines which are in current use for the treatment of the COVID-19 patients. So far, some drugs have been found with hopeful results among which remdesivir and arbidol are with momentous clinical progress. Besides drug designing, vaccine development has been a major effort whereby the mRNA-1273 (Moderna) and BNT162b2 (Pfizer-BioNTech) vaccines showed the required efficacy and have been approved by the US Food and Drug Administration (USFDA). Whifor future remedies. COVID-19 pandemic has been the major threat to the global public health for a year (last of 2019-till date); and unfortunately, there is still as no specific antiviral agent which can be effectively used against this disease curation. Present review focused on the application of the convalescent plasma (CP) therapy as a quick remediation of the disease severity. While several drugs have been repurposed based on a number of completed clinical trials together with a huge ongoing effort to develop appropriate vaccine against the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the therapeutic approach of the CP therapy appears to be one of the effective methods to rescue the severely affected COVID-19 patients. Such a therapy based on passive immunity evolved from the SARS-CoV-2-infected patients who have fully recovered from COVID-19; and hence these individuals are quite likely to possess high titers of the SARS-CoV-2-neutralizing immunoglobulins (antibodies). However, there are some risks such therapy, and its effectivity also appeared doubtful in some cases. Thus, the current review discussed the issues raised by the administration of such plasma into the SARS-CoV-2-infected individuals. Application of CP therapy has been conducted since long time; and for the mitigation of COVID-19 severity, such pharmaceutical strategy is also being employed in spite of several risks which actually can be monitored as well as optimized in order to combat the SARS-CoV-2 infection.Application of CP therapy has been conducted since long time; and for the mitigation of COVID-19 severity, such pharmaceutical strategy is also being employed in spite of several risks which actually can be monitored as well as optimized in order to combat the SARS-CoV-2 infection.The early detection of the coronavirus disease 2019 (COVID-19) outbreak is important to save people's lives and restart the economy quickly and safely. People's social behavior, reflected in their mobility data, plays a major role in spreading the disease. Therefore, we used the daily mobility data aggregated at the county level beside COVID-19 statistics and demographic information for short-term forecasting of COVID-19 outbreaks in the United States. The daily data are fed to a deep learning model based on Long Short-Term Memory (LSTM) to predict the accumulated number of COVID-19 cases in the next two weeks. A significant average correlation was achieved (r=0.83 (p = 0.005)) between the model predicted and actual accumulated cases in the interval from August 1, 2020 until January 22, 2021. The model predictions had r > 0.7 for 87% of the counties across the United States. selleck chemicals llc A lower correlation was reported for the counties with total cases of The online version contains supplementary material available at 10.1186/s40537-021-00491-1.The online version contains supplementary material available at 10.1186/s40537-021-00491-1.This paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.