The favorable impacts of silibinin polyphenols as adjunctive therapy in reducing the complications of COVID-19: A review of research evidence and underlying mechanisms.

The proceeding pandemic of coronavirus disease 2019 is the latest global challenge. Like most other infectious diseases, inflammation, oxidative stress, and immune system dysfunctions play a pivotal role in the pathogenesis of COVID-19. Furthermore, the quest of finding a potential pharmaceutical therapy for preventing and treating COVID-19 is still ongoing. Silymarin, a mixture of flavonolignans extracted from the milk thistle, has exhibited numerous therapeutic benefits. We reviewed the beneficial effects of silymarin on oxidative stress, inflammation, and the immune system, as primary factors involved in the pathogenesis of COVID-19. We searched PubMed/Medline, Web of Science, Scopus, and Science Direct databases up to April 2022 using the relevant keywords. In summary, the current review indicates that silymarin might exert therapeutic effects against COVID-19 by improving the antioxidant system, attenuating inflammatory response and respiratory distress, and enhancing immune system function. Silymarin can also bind to target proteins of SARS-CoV-2, including main protease, spike glycoprotein, and RNA-dependent RNA-polymerase, leading to the inhibition of viral replication. Although multiple lines of evidence suggest the possible promising impacts of silymarin in COVID-19, further clinical trials are encouraged.

Evaluation of tuberculosis infection in COVID-19 patients: a case of tuberculosis and COVID-19 co-infection.

The COVID-19 pandemic affected millions of people worldwide, becoming a challenge of every nation. Since the COVID-19 can present wide spectrum of clinical signs and symptoms, patients with symptoms similar to that of COVID-19 may be misdiagnosed during the context of COVID-19 pandemic. In this regard, various co-infections may affect the outcome of COVID-19 patients if it lefts undiagnosed, especially during the administration of immunosuppressive drugs. Similar to COVID-19, TB affect the lungs and respiratory airways primarily. These two diseases have resembling symptoms, including dry cough, fever, and dyspnea. Due to the importance of early COVID-19 diagnosis, many other respiratory infectious diseases such as tuberculosis (TB) may be missed. Herein, a case of COVID-19 and tuberculosis co-infection is presented.

A Review of the Potential of Artificial Intelligence Approaches to Forecasting COVID-19 Spreading

The spread of SARS-CoV-2 can be considered one of the most complicated patterns with a large number of uncertainties and nonlinearities. Therefore, analysis and prediction of the distribution of this virus are one of the most challenging problems, affecting the planning and managing of its impacts. Although different vaccines and drugs have been proved, produced, and distributed one after another, several new fast-spreading SARS-CoV-2 variants have been detected. This is why numerous techniques based on artificial intelligence (AI) have been recently designed or redeveloped to forecast these variants more effectively. The focus of such methods is on deep learning (DL) and machine learning (ML), and they can forecast nonlinear trends in epidemiological issues appropriately. This short review aims to summarize and evaluate the trustworthiness and performance of some important AI-empowered approaches used for the prediction of the spread of COVID-19. Sixty-five preprints, peer-reviewed papers, conference proceedings, and book chapters published in 2020 were reviewed. Our criteria to include or exclude references were the performance of these methods reported in the documents. The results revealed that although methods under discussion in this review have suitable potential to predict the spread of COVID-19, there are still weaknesses and drawbacks that fall in the domain of future research and scientific endeavors.

Rectus sheath hematoma in COVID‐19 patient: The importance of abdominal pain in the course of COVID‐19

We report herein a case of a 58‐year‐old woman with COVID‐19. During the hospitalization, the patient complained of acute abdominal pain, and abdominal CT revealed the rectus sheath hematoma (RSH). Since corticosteroids and anti‐coagulation are commonly administered in COVID‐19 patients, physicians should consider RSH as a possible diagnosis for acute abdominal pain. Rectus Sheath Hematoma though infrequent should be considered as a possible cause of acute abdominal pain in COVID‐19 patients.

Rectus sheath hematoma in COVID-19 patient: The importance of abdominal pain in the course of COVID-19.

We report herein a case of a 58-year-old woman with COVID-19. During the hospitalization, the patient complained of acute abdominal pain, and abdominal CT revealed the rectus sheath hematoma (RSH). Since corticosteroids and anti-coagulation are commonly administered in COVID-19 patients, physicians should consider RSH as a possible diagnosis for acute abdominal pain.

COVID-19 patients may present with myocarditis: A case report emphasizing the cardiac involvement of SARS-CoV-2.

The Coronavirus disease 2019 (COVID-19), which was declared to be pandemic on March 12, 2020, is the latest health concern worldwide. COVID-19 patients may develop cerebrovascular complications either during the course of COVID-19 or even as an initial presentation of the disease. Herein, a case of myocarditis in a COVID-19 patient without any respiratory signs and symptoms is presented.

Assessment of Three Mathematical Prediction Models for Forecasting the COVID-19 Outbreak in Iran and Turkey.

COVID-19 pandemic has become a concern of every nation, and it is crucial to apply an estimation model with a favorably-high accuracy to provide an accurate perspective of the situation. In this study, three explicit mathematical prediction models were applied to forecast the COVID-19 outbreak in Iran and Turkey. These models include a recursive-based method, Boltzmann Function-based model and Beesham's prediction model. These models were exploited to analyze the confirmed and death cases of the first 106 and 87 days of the COVID-19 outbreak in Iran and Turkey, respectively. This application indicates that the three models fail to predict the first 10 to 20 days of data, depending on the prediction model. On the other hand, the results obtained for the rest of the data demonstrate that the three prediction models achieve high values for the determination coefficient, whereas they yielded to different average absolute relative errors. Based on the comparison, the recursive-based model performs the best, while it estimated the COVID-19 outbreak in Iran better than that of in Turkey. Impacts of applying or relaxing control measurements like curfew in Turkey and reopening the low-risk businesses in Iran were investigated through the recursive-based model. Finally, the results demonstrate the merit of the recursive-based model in analyzing various scenarios, which may provide suitable information for health politicians and public health decision-makers.

Application of artificial neural networks to predict the COVID-19 outbreak.

BACKGROUND: Millions of people have been infected worldwide in the COVID-19 pandemic. In this study, we aim to propose fourteen prediction models based on artificial neural networks (ANN) to predict the COVID-19 outbreak for policy makers. METHODS: The ANN-based models were utilized to estimate the confirmed cases of COVID-19 in China, Japan, Singapore, Iran, Italy, South Africa and United States of America. These models exploit historical records of confirmed cases, while their main difference is the number of days that they assume to have impact on the estimation process. The COVID-19 data were divided into a train part and a test part. The former was used to train the ANN models, while the latter was utilized to compare the purposes. The data analysis shows not only significant fluctuations in the daily confirmed cases but also different ranges of total confirmed cases observed in the time interval considered. RESULTS: Based on the obtained results, the ANN-based model that takes into account the previous 14 days outperforms the other ones. This comparison reveals the importance of considering the maximum incubation period in predicting the COVID-19 outbreak. Comparing the ranges of determination coefficients indicates that the estimated results for Italy are the best one. Moreover, the predicted results for Iran achieved the ranges of [0.09, 0.15] and [0.21, 0.36] for the mean absolute relative errors and normalized root mean square errors, respectively, which were the best ranges obtained for these criteria among different countries. CONCLUSION: Based on the achieved results, the ANN-based model that takes into account the previous fourteen days for prediction is suggested to predict daily confirmed cases, particularly in countries that have experienced the first peak of the COVID-19 outbreak. This study has not only proved the applicability of ANN-based model for prediction of the COVID-19 outbreak, but also showed that considering incubation period of SARS-COV-2 in prediction models may generate more accurate estimations.

The neurological manifestations of COVID-19: a review article.

RESULTS: Various neurological manifestations have been reported in the literature associated with COVID-19, which in the current study are classified into Central Nervous System (CNS) related manifestations including headache, dizziness, impaired consciousness, acute cerebrovascular disease, epilepsy, and Peripheral Nervous System (PNS) related manifestations such as hyposmia/anosmia, hypogeusia/ageusia, muscle pain, and Guillain-Barre syndrome. CONCLUSION: During the current context of COVID-19 pandemic, physicians should be aware of wide spectrum of neurological COVID-19 sign and symptoms for early diagnosis and isolation of patients. In this regard, COVID-19 has been associated with many neurological manifestations such as confusion, anosmia, and ageusia. Also, various evidences support the possible CNS roles in the COVID-19 pathophysiology. In this regard, further investigation of CNS involvement of SARS-COV-2 is suggested.