Evaluation of MicroRNAs expression pattern (miR-28, miR-181a, miR-34a, and miR-31) in Patients with COVID-19 Admitted to ICU, and Diabetic COVID-19 patients.

INTRODUCTION: MicroRNAs, or miRNAs, with regulatory performance in inflammatory responses and infection are the prevalent manifestations of severe Coronavirus disease (COVID-19). This study aimed to evaluate whether PBMC miRNAs are diagnostic biomarkers to screen the ICU COVID-19 and diabetic-COVID-19 subjects. METHODS: Candidate miRNAs were selected through previous studies, and then the PBMC levels of selected miRNAs (miR-28, miR-31, miR-34a, and miR-181a) were measured via quantitative reverse transcription PCR. The diagnostic value of miRNAs was determined by the receiver operating characteristic (ROC) curve. The bioinformatics analysis was utilized to predict the DEMs genes and relevant bio-functions. RESULTS: The COVID-19 patients admitted to the ICU had significantly greater levels of selected miRNAs compared to non-hospitalized COVID-19 and healthy people. Besides, the mean miR-28 and miR-34a expression levels in the diabetic-COVID-19 group were upregulated considerably when compared with the non-diabetic COVID-19 group. ROC analyses demonstrated the role of miR-28, -miR-34a, and -181a as new biomarkers to discriminate the non-hospitalized COVID-19 group from the COVID-19 patients admitted to ICU samples, and also miR-34a can probably act as a useful biomarker for screening diabetic COVID-19 patients. Using bioinformatics analyses, we found the performance of target transcripts in many bio-processes and diverse metabolic routes such as regulating multiple inflammatory parameters. DISCUSSION: The difference in miRNA expression patterns between the studied groups suggested that miR-28, miR-34a, and miR181a could be helpful as potent biomarkers for diagnosing and controlling COVID-19.

Expression profiling of inflammation-related genes including IFI-16, NOTCH2, CXCL8, THBS1 in COVID-19 patients.

The present study aimed to scrutinize the expression profile of inflammatory-related genes (IFI-16, NOTCH2, CXCL8, and THBS1) from acute to post-acute stage of this infectious epidemic. The current cross-sectional study consisted of 53 acute-phase COVID-19 patients and 53 healthy individuals between February and March 2021. The extraction of total RNA was performed from PBMC specimens and also expression level of selected genes (IFI-16, NOTCH2, CXCL8, and THBS1) was evaluated by real-time PCR. Subsequently, levels of these factors were re-measured six weeks after the acute phase to determine if the levels of chosen genes returned to normal after the acute phase of COVID-19. Receiver operating characteristic (ROC) curve was plotted to test potential of genes as a diagnostic biomarker. The expression levels of inflammatory-related genes were significantly different between healthy and COVID-19 subjects. Besides, a significant higher CXCL8 level was found in the acute-phase COVID-19 compared to post-acute-phase infection which may be able to be considered as a potential biomarker for distinguishing between the acute phases from the post-acute-phase status. Deregulation of the inflammatory-related genes in COVID-19 patients, especially CXCL-8, can be serving as potent biomarkers to manage the COVID-19 infection.

Software Architecture for Automated Assessment of Prescription Writing.

Prescribing skills are a crucial competency in medical practice considering the increasing numbers of medications available and the increasingly complex patients with multiple diseases faced in clinical practice. Medical students need to become proficient in these skills during training, as required by medical licensing colleges. Not only is teaching the fundamentals of safe and cost-effective prescribing to medical students challenging but evaluating their prescribing skills by faculty members is difficult and time consuming. The COVID-19 pandemic has accelerated the interest in clinically relevant online exams, including automated assessment of short answer style questions. The goal of this project was to design a software to automate the assessment of learners' prescriptions written during low stakes formative assessments. After establishing the components of a legal prescription with multiple medications, and identifying the sources of errors in prescribing and prescribing assessment, we designed and validated an architecture and developed a prototype for automated parsing of learner prescriptions.

The expression patterns of MALAT-1, NEAT-1, THRIL, and miR-155-5p in the acute to the post-acute phase of COVID-19 disease.

INTRODUCTION: One of the hallmarks of COVID-19 is overwhelming inflammation, which plays a very important role in the pathogenesis of COVID-19. Thus, identification of inflammatory factors that interact with the SARS-CoV-2 can be very important to control and diagnose the severity of COVID-19. The aim of this study was to investigate the expression patterns of inflammation-related non-coding RNAs (ncRNAs) including MALAT-1, NEAT-1, THRIL, and miR-155-5p from the acute phase to the recovery phase of COVID-19. METHODS: Total RNA was extracted from Peripheral Blood Mononuclear Cell (PBMC) samples of 20 patients with acute COVID-19 infection and 20 healthy individuals and the expression levels of MALAT-1, NEAT-1, THRIL, and miR-155-5p were evaluated by real-time PCR assay. Besides, in order to monitor the expression pattern of selected ncRNAs from the acute phase to the recovery phase of COVID-19 disease, the levels of ncRNAs were re-measured 6‒7 weeks after the acute phase. RESULT: The mean expression levels of MALAT-1, THRIL, and miR-155-5p were significantly increased in the acute phase of COVID-19 compared with a healthy control group. In addition, the expression levels of MALAT-1 and THRIL in the post-acute phase of COVID-19 were significantly lower than in the acute phase of COVID-19. According to the ROC curve analysis, these ncRNAs could be considered useful biomarkers for COVID-19 diagnosis and for discriminating between acute and post-acute phase of COVID-19. DISCUSSION: Inflammation-related ncRNAs (MALAT-1, THRIL, and miR-150-5p) can act as hopeful biomarkers for the monitoring and diagnosis of COVID-19 disease.

Effectiveness of COVID-19 Vaccines against Delta (B.1.617.2) Variant: A Systematic Review and Meta-Analysis of Clinical Studies.

The high transmissibility, mortality, and morbidity rate of the SARS-CoV-2 Delta (B.1.617.2) variant have raised concerns regarding vaccine effectiveness (VE). To address this issue, all publications relevant to the effectiveness of vaccines against the Delta variant were searched in the Web of Science, Scopus, EMBASE, and Medline (via PubMed) databases up to 15 October 2021. A total of 15 studies (36 datasets) were included in the meta-analysis. After the first dose, the VE against the Delta variant for each vaccine was 0.567 (95% CI 0.520-0.613) for Pfizer-BioNTech, 0.72 (95% CI 0.589-0.822) for Moderna, 0.44 (95% CI 0.301-0.588) for AstraZeneca, and 0.138 (95% CI 0.076-0.237) for CoronaVac. Meta-analysis of 2,375,957 vaccinated cases showed that the Pfizer-BioNTech vaccine had the highest VE against the infection after the second dose, at 0.837 (95% CI 0.672-0.928), and third dose, at 0.972 (95% CI 0.96-0.978), as well as the highest VE for the prevention of severe infection or death, at 0.985 (95% CI 0.95-0.99), amongst all COVID-19 vaccines. The short-term effectiveness of vaccines, especially mRNA-based vaccines, for the prevention of the Delta variant infection, hospitalization, severe infection, and death is supported by this study. Limitations include a lack of long-term efficacy data, and under-reporting of COVID-19 infection cases in observational studies, which has the potential to falsely skew VE rates. Overall, this study supports the decisions by public health decision makers to promote the population vaccination rate to control the Delta variant infection and the emergence of further variants.

Acute and post-acute phase of COVID-19: Analyzing expression patterns of miRNA-29a-3p, 146a-3p, 155-5p, and let-7b-3p in PBMC.

BACKGROUND: When a new pathogen, such as severe acute respiratory syndrome coronavirus 2, appears all novel information can aid in the process of monitoring and in the diagnosis of the coronavirus disease (COVID-19). The aim of the current study is to elucidate the specific miRNA profile which can act as new biomarkers for distinguishing acute COVID-19 disease from the healthy group and those in the post-acute phase of the COVID-19 disease. METHODS: The expression level of selected miRNAs including let-7b-3p, miR-29a-3p, miR-146a-3p and miR-155-5p were evaluated in peripheral blood mononuclear cells (PBMCs) of COVID-19 patients, in both the acute and post-acute COVID-19 phase of the disease and healthy groups, by real-time PCR assays. Specificity and sensitivity of miRNAs was tested by receiver operating characteristic (ROC) analysis in COVID-19 patients. RESULTS: The expression level of all miRNAs in COVID-19 patients was significantly higher than in the healthy group. Therefore, the expression pattern of miR-29a-3p, miR-146a-3p and let-7b-3p in the post-acute COVID-19 phase was significantly different from the acute COVID-19 phase. ROC analyses demonstrated that miR-29a-3p, -155-5p and -146a-3p may serve as the novel biomarker for COVID-19 diagnosis with high specificity and sensitivity. In addition, miR-29a-3p, and -146a-3p can maybe act as novel biomarkers for distinguishing acute from post-acute phase of COVID-19 disease. DISCUSSION: The difference in miRNA expression pattern between COVID-19 patients and those in the healthy group, and between acute COVID-19 with post-acute COVID-19, suggested that cellular miRNAs could be used as promising biomarkers for diagnosis and monitoring of COVID-19.

Comparison of influenza type A and B with COVID-19: A global systematic review and meta-analysis on clinical, laboratory and radiographic findings.

We compared clinical symptoms, laboratory findings, radiographic signs and outcomes of COVID-19 and influenza to identify unique features. Depending on the heterogeneity test, we used either random or fixed-effect models to analyse the appropriateness of the pooled results. Overall, 540 articles included in this study; 75,164 cases of COVID-19 (157 studies), 113,818 influenza type A (251 studies) and 9266 influenza type B patients (47 studies) were included. Runny nose, dyspnoea, sore throat and rhinorrhoea were less frequent symptoms in COVID-19 cases (14%, 15%, 11.5% and 9.5%, respectively) in comparison to influenza type A (70%, 45.5%, 49% and 44.5%, respectively) and type B (74%, 33%, 38% and 49%, respectively). Most of the patients with COVID-19 had abnormal chest radiology (84%, p < 0.001) in comparison to influenza type A (57%, p < 0.001) and B (33%, p < 0.001). The incubation period in COVID-19 (6.4 days estimated) was longer than influenza type A (3.4 days). Likewise, the duration of hospitalization in COVID-19 patients (14 days) was longer than influenza type A (6.5 days) and influenza type B (6.7 days). Case fatality rate of hospitalized patients in COVID-19 (6.5%, p < 0.001), influenza type A (6%, p < 0.001) and influenza type B was 3%(p < 0.001). The results showed that COVID-19 and influenza had many differences in clinical manifestations and radiographic findings. Due to the lack of effective medication or vaccine for COVID-19, timely detection of this viral infection and distinguishing from influenza are very important.

Clinical characteristics, laboratory findings, radiographic signs and outcomes of 61,742 patients with confirmed COVID-19 infection: A systematic review and meta-analysis.

INTRODUCTION: In the current time where we face a COVID-19 pandemic, there is no vaccine or effective treatment at this time. Therefore, the prevention of COVID-19 and the rapid diagnosis of infected patients is crucial. METHOD: We searched all relevant literature published up to February 28, 2020. We used Random-effect models to analyze the appropriateness of the pooled results. RESULT: Eighty studies were included in the meta-analysis, including 61,742 patients with confirmed COVID-19 infection. 62.5% (95% CI 54.5-79, p < 0.001) of patients had a history of recent travel endemic area or contact with them. The most common symptoms among COVID-19 infected patients were fever 87% (95% CI 73-93, p < 0.001), and cough 68% (95% CI 55.5-74, p < 0.001)), respectively. The laboratory analysis showed that thrombocytosis was present in 61% (95% CI 41-78, p < 0.001) CRP was elevated in 79% (95% CI 65-91, p < 0.001), and lymphopenia in 57.5% (95% CI 42-79, p < 0.001). The most common radiographic signs were bilateral involvement in 81% (95% CI 62.5-87, p < 0.001), consolidation in 73.5% (95% CI 50.5-91, p < 0.001), and ground-glass opacity 73.5% (95% CI 40-90, p < 0.001) of patients. Case fatality rate (CFR) in <15 years old was 0.6%, in >50 years old was 39.5%, and in all range group was 6%. CONCLUSIONS: Fever and cough are the most common symptoms of COVID-19 infection in the literature published to date. Thombocytosis, lymphopenia, and increased CRP were common lab findings although most patients included in the overall analysis did not have laboratory values reported. Among Chinese patients with COVID-19, rates of hospitalization, critical condition, and hospitalization were high in this study, but these findings may be biased by reporting only confirmed cases.

Comparison of confirmed COVID-19 with SARS and MERS cases - Clinical characteristics, laboratory findings, radiographic signs and outcomes: A systematic review and meta-analysis.

INTRODUCTION: Within this large-scale study, we compared clinical symptoms, laboratory findings, radiographic signs, and outcomes of COVID-19, SARS, and MERS to find unique features. METHOD: We searched all relevant literature published up to February 28, 2020. Depending on the heterogeneity test, we used either random or fixed-effect models to analyze the appropriateness of the pooled results. Study has been registered in the PROSPERO database (ID 176106). RESULT: Overall 114 articles included in this study; 52 251 COVID-19 confirmed patients (20 studies), 10 037 SARS (51 studies), and 8139 MERS patients (43 studies) were included. The most common symptom was fever; COVID-19 (85.6%, P < .001), SARS (96%, P < .001), and MERS (74%, P < .001), respectively. Analysis showed that 84% of Covid-19 patients, 86% of SARS patients, and 74.7% of MERS patients had an abnormal chest X-ray. The mortality rate in COVID-19 (5.6%, P < .001) was lower than SARS (13%, P < .001) and MERS (35%, P < .001) between all confirmed patients. CONCLUSIONS: At the time of submission, the mortality rate in COVID-19 confirmed cases is lower than in SARS- and MERS-infected patients. Clinical outcomes and findings would be biased by reporting only confirmed cases, and this should be considered when interpreting the data.