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.

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.

In December 2019, a new type of coronavirus was detected for the first time in Wuhan, Hubei Province, China. According to the reported data, the emerging coronavirus has spread worldwide, infecting more than fifty‐seven million individuals, leading to more than one million deaths. The current study aimed to review and discuss the hematological findings of COVID‐19. Laboratory changes and hematologic abnormalities have been reported repeatedly in COVID‐19 patients. WBC count and peripheral blood lymphocytes are normal or slightly reduced while these indicators may change with the progression of the disease. In addition, several studies demonstrated that decreased hemoglobin levels in COVID‐19 patients were associated with the severity of the disease. Moreover, thrombocytopenia, which is reported in 5%‐40% of patients, is known to be associated with poor prognosis of the disease. COVID‐19 can present with various hematologic manifestations. In this regard, accurate evaluation of laboratory indicators at the beginning and during COVID‐19 can help physicians to adjust appropriate treatment and provide special and prompt care for those in need.

Sepsis is a severe reaction and excessive immune response to infection, which can lead to organ dysfunction, and death. This study aimed to investigate the protective effect of nano-curcumin (NC) on inflammatory biomarkers, endothelial function, oxidative stress indices, biochemical factors, nutritional status, and clinical outcomes in patients with sepsis. In the present double-blind placebo-controlled randomized clinical trial, 40 ICU-admitted patients were randomly allocated into either NC or placebo group for 10 days. Both nano-curcumin (160 mg) and placebo were administered via a nasogastric tube twice a day. The mRNA expression of nuclear-related factor 2 (Nrf-2), BCL2 associated X (BAX), B-cell lymphoma 2 (BCL-2), and toll-like receptor 4 (TLR-4) genes in the peripheral blood mononuclear cells (PBMCs), and the serum levels of primary, secondary, tertiary, and exploratory outcomes were assessed before the baseline and on days 5 and 10. There were significant improvements in the primary outcomes, including inflammatory markers (IL-6, IL-18, IL-1β, IL-10, TLR-4, BCL-2 and BAX), markers of endothelial function (ICAM-1 and VCAM-1), and oxidative stress indices (malondialdehyde (MDA), nuclear-related factor 2 (Nrf-2), catalase, superoxide dismutase (SOD), and TAC) (p < 0.005) in the NC group compared to the placebo group after 10 days, while no significant increase was observed in the glutathione peroxidase (GPx) level between the two groups. However, no significant decrease was observed in the levels of secondary outcomes, including biochemical factors (creatinine, fasting blood sugar (FBS), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), total bilirubin, triglycerides (TG) and total cholesterol (TC)) (P > 0.05). Our results showed that in the tertiary outcome (nutritional status), there was no significant increase (P > 0.05) except for TLC (P = 0.003). NC supplementation also resulted in a significant decrease in the exploratory outcomes including the SOFA score and the duration of mechanical ventilation (P < 0.05). Supplementation with NC may be a promising treatment strategy for critically ill patients with sepsis. However, further experiments are suggested to investigate the effects of nano-curcumin on biochemical pathways involved in sepsis.

Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is a novel coronavirus that has infected more than 2,900,000 individuals worldwide. The widespread of coronavirus 2019 (COVID-19) brings about the need for a prediction model to adopt appropriate evidence-based strategies. In this study, multi-gene genetic programming (MGGP), as one of the artificial intelligence models, has been proposed for the first time for predicting the COVID-19 outbreak. Although this is a challenging task due to significant fluctuations of daily confirmed cases, the results achieved by MGGP are promising. To be more specific, the predicted confirmed cases are acceptably close to the observed values for seven countries considered in this study. Thus, MGGP is suggested for developing estimation models of COVID-19. Furthermore, similarities and differences between the proposed prediction models are presented. Finally, it is discussed why a country-based prediction model is recommended.