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Type 1 Interferons (IFNs) have been associated with positive effects on Coronaviruses. Previous studies point towards the superior potency of IFNβ compared to IFNα against viral infections. We conducted a three-armed, individually-randomized, open-label, controlled trial of IFNβ1a and IFNβ1b, comparing them against each other and a control group. Patients were randomly assigned in a 1:1:1 ratio to IFNβ1a (subcutaneous injections of 12,000 IU on days 1, 3, 6), IFNβ1b (subcutaneous injections of 8,000,000 IU on days 1, 3, 6), or the control group. All three arms orally received Lopinavir/Ritonavir (400mg/100 mg twice a day for ten days) and a single dose of Hydroxychloroquine 400 mg on the first day. Our utilized primary outcome measure was Time To Clinical Improvement (TTCI) defined as the time from enrollment to discharge or a decline of two steps on the clinical seven-step ordinal scale, whichsoever came first. A total of 60 severely ill patients with positive RT-PCR and Chest CT scans underwent randomization (20 patients to each arm). In the Intention-To-Treat population, IFNβ1a was associated with a significant difference against the control group, in the TTCI; (HR; 2.36, 95% CI=1.10-5.17, P-value=0.031) while the IFNβ1b indicated no significant difference compared with the control; HR; 1.42, (95% CI=0•63-3•16, P-value=0•395). The median TTCI for both of the intervention groups was five days vs. seven days for the control group. The mortality was numerically lower in both of the intervention groups (20% in the IFNβ1a group and 30% in the IFNβ1b group vs. 45% in the control group). There were no significant differences between the three arms regarding the adverse events. In patients with laboratory-confirmed SARS-CoV-2 infection, as compared with the base therapeutic regiment, the benefit of a significant reduction in TTCI was observed in the IFNβ1a arm. This finding needs further confirmation in larger studies. Trial Registration Number: ClinicalTrials.gov, NCT04343768. (Submitted: 08/04/2020; First Online: 13/04/2020) (Registration Number: NCT04343768)

Background: COVID-19 has been associated with several neurological complications. One of these complications is transverse myelitis. Several cases of acute transverse myelitis are reported in association with this disease among the world. As there is lack of knowledge about the association of COVID-19 and myelitis and the clinical features of this complication are still ambiguous, we report two patients with transverse myelitis following COVID-19 infection. Patients: This study was performed in a referral center of COVID-19 in Iran(Shohada Tajrish hospital) and two patients with paraparesis and diagnosis of transverse myelitis were enrolled. Both patients had longitudinally extensive transverse myelitis that resulted in paraparesis. One of the patients had favorable outcome after treatment with plasma exchange but the other had no improvement following treatment.Conclusion: Transverse myelitis could be a complication of COVID-19 and infarction and inflammation could be suggested as probable mechanisms for this condition.

Background This study was conducted with the intension of providing a more detailed view about the dynamics of COVID-19 pandemic. To this aim, characteristics, implemented public health measures, and health outcome of COVID-19 patients during five consecutive waves of the disease were assessed. Methods This study was a population-based cross-sectional analysis of data on adult patients who were diagnosed with COVID-19 during five waves of the disease in Iran. Chi-squared test, One-way ANOVA, and Logistic Regression analysis were applied. A detailed literature review on implemented public health policies was performed by studying published documents and official websites responsible for conveying information about COVID-19. Results Data on 328,410 adult patients was analyzed. Main findings indicated that the probability of dying with COVID-19 has increased as the pandemic wore on, showing its highest odd during the third wave (odds ratio: 1.34, CI: 1.283–1.395) and has gradually decreased during the next two waves. The same pattern was observed in the proportion of patients requiring ICU admission (P < 0.001). First wave presented mainly with respiratory symptoms, gastrointestinal complaints were added during the second wave, neurological manifestations with peripheral involvement replaced the gastrointestinal complaints during the third wave, and central nervous system manifestations were added during the fourth and fifth waves. A significant difference in mean age of patients was revealed between the five waves (P < 0.001). Moreover, results showed a significant difference between men and women infected with COVID-19, with men having higher rates of the disease at the beginning. However, as the pandemic progressed the proportion of women gradually increased, and ultimately more women were diagnosed with COVID-19 during the fifth wave. Our observations pointed to the probability that complete lockdowns were the key measures that helped to mitigate the virus spread during the first twenty months of the pandemic in the country. Conclusion A changing pattern in demographic characteristics, clinical manifestations, and severity of the disease has been revealed as the pandemic unfolded. Reviewing COVID-19-related public health interventions highlighted the importance of immunization and early implementation of restrictive measures as effective strategies for reducing the acute burden of the disease.

Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies.Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission.Methods: We retrospectively studied 797 patients diagnosed with COVID-19 in Iran and the United Kingdom (U.K.). To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Several machine learning algorithms, including Random Forest (RF), logistic regression, gradient boosting classifier, support vector machine classifier, and artificial neural network algorithms were utilized to build classification models. The impact of each marker on the RF model predictions was studied by implementing the local interpretable model-agnostic explanation technique (LIME-SP).Results: Among 66 documented parameters, 15 factors with the highest predictive values were identified as follows: gender, age, blood urea nitrogen (BUN), creatinine, international normalized ratio (INR), albumin, mean corpuscular volume (MCV), white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin (MCH) along with a history of neurological, cardiovascular, and respiratory disorders. Our RF model can predict patient outcomes with a sensitivity of 70% and a specificity of 75%. The performance of the models was confirmed by blindly testing the models in an external dataset.Conclusions: Using two independent patient datasets, we designed a machine-learning-based model that could predict the risk of mortality from severe COVID-19 with high accuracy. The most decisive variables in our model were increased levels of BUN, lowered albumin levels, increased creatinine, INR, and RDW, along with gender and age. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 ICU decision-making.