Antibody-dependent enhancement (ADE) has been associated with severe disease outcomes in several viral infections, including respiratory infections. In vitro and in vivo studies showed that antibody-response to SARS-CoV and MERS-CoV could exacerbate infection via ADE. Recently in SARS CoV-2, the in vitro studies and structural analysis shows a risk of disease severity via ADE. This phenomenon is partially attributed to non-neutralizing antibodies or antibodies at sub-neutralizing levels. These antibodies result in antigen-antibody complexes' deposition and propagation of a chronic inflammatory process that destroys affected tissues. Further, antigen-antibody complexes may enhance the internalization of the virus into cells through the Fc gamma receptor (FcγR) and lead to further virus replication. Thus, ADE occur via two mechanisms; 1. Antibody mediated replication and 2. Enhanced immune activation. Antibody-mediated effector functions are mainly driven by complement activation, and the first complement in the cascade is complement 1q (C1q) which binds to the virus-antibody complex. Reports say that deficiency in circulating plasma levels of C1q, an independent predictor of mortality in high-risk patients, including diabetes, is associated with severe viral infections. Complement mediated ADE is reported in several viral infections such as dengue, West Nile virus, measles, RSV, Human immunodeficiency virus (HIV), and Ebola virus. This review discusses ADE in viral infections and the in vitro evidence of ADE in coronaviruses. We outline the mechanisms of ADE, emphasizing the role of complements, especially C1q in the outcome of the enhanced disease.

Tuberculosis (TB) is a chronic infectious lung disease, which caused the death of about 1.5 million people in 2020 alone. Therefore, it is important to detect TB accurately at an early stage to prevent the infection and associated deaths. Chest X-ray (CXR) is the most popularly used method for TB diagnosis. However, it is difficult to identify TB from CXR images in the early stage, which leads to time-consuming and expensive treatments. Moreover, due to the increase of drug-resistant tuberculosis, the disease becomes more challenging in recent years. In this work, a novel deep learning-based framework is proposed to reliably and automatically distinguish TB, non-TB (other lung infections), and healthy patients using a dataset of 40,000 CXR images. Moreover, a stacking machine learning-based diagnosis of drug-resistant TB using 3037 CXR images of TB patients is implemented. The largest drug-resistant TB dataset will be released to develop a machine learning model for drug-resistant TB detection and stratification. Besides, Score-CAM-based visualization technique was used to make the model interpretable to see where the best performing model learns from in classifying the image. The proposed approach shows an accuracy of 93.32% for the classification of TB, non-TB, and healthy patients on the largest dataset while around 87.48% and 79.59% accuracy for binary classification (drug-resistant vs drug-sensitive TB), and three-class classification (multi-drug resistant (MDR), extreme drug-resistant (XDR), and sensitive TB), respectively, which is the best reported result compared to the literature. The proposed solution can make fast and reliable detection of TB and drug-resistant TB from chest X-rays, which can help in reducing disease complications and spread.

There is a scarcity of data regarding the effect of Type 2 diabetes mellitus (T2DM) and associated comorbidities on the clinical presentation and outcome of symptomatic patients with -COVID-19 infection in comparison with non-diabetic patients. We described and compared the clinical presentation and radiological and hematological data of a cohort of symptomatic COVID19 positive T2DM diabetic patients (n = 59) versus another cohort of non-diabetic symptomatic COVID19 positive patients (n =244) diagnosed at the same time from January 2020 to May 2020. Associated comorbidities were -assessed, and the Charlson Comorbidity Index was calculated. The outcomes including duration of hospitalization, duration of Intensive Care Unit (ICU) stay, duration of mechanical ventilation, and duration of O2 -supplementation were assessed. Prevalence of T2DM in symptomatic COVID19 positive patients was 59/303 (=19.5%). Diabetic patients had higher prevalence of hypertension, chronic kidney disease (CKD) and cardiac dysfunction [coronary heart disease (CHD)], and congestive heart failure (CHF). Charlson Comorbidity score was significantly higher in the T2DM patients (2.4± 1.6) versus the non-diabetic -patients (0.28 ± 0.8; p: < 0.001). Clinically and radiologically, T2DM patients had significantly higher percentage of pneumonia, severe pneumonia and ARDS versus the non-diabetic patients. Hematologically, diabetic patients had significantly higher C-reactive protein (CRP), higher absolute neutrophilic count (ANC) and lower counts of lymphocytes and eosinophils compared to non-diabetic patients. They had significantly higher systolic and diastolic blood pressures, longer duration of hospitalization, ICU stay, mechanical ventilation and oxygen therapy. CRP was correlated significantly with the duration of stay in the ICU and the duration for oxygen supplementation (r = 0.37 and 0.42 respectively; p: <0.01). T2DM patients showed higher inflammatory response to COVID 19 with higher absolute neutrophilic count (ANC) and CRP with lower lymphocytic and eosinophilic counts. Diabetic patients had more comorbidities and more aggressive course of the disease with higher rate of ICU admission and longer need for hospitalization and oxygen use.

Background: There are limited data on Coronavirus Disease 2019 (COVID-19) outcomes at a national level, and none after 60 days of follow up. The aim of this study was to describe national, 60-day all-cause mortality associated with COVID-19, and to identify risk factors associated with admission to an intensive care unit (ICU). Methods: This was a retrospective cohort study including the first consecutive 5000 patients with COVID-19 in Qatar who completed 60 days of follow up by June 17, 2020. The primary outcome was all-cause mortality at 60 days after COVID-19 diagnosis. In addition, we explored risk factors for admission to ICU. Results: Included patients were diagnosed with COVID-19 between February 28 and April 17, 2020. The majority (4436, 88.7%) were males and the median age was 35 years [interquartile range (IQR) 28–43]. By 60 days after COVID-19 diagnosis, 14 patients (0.28%) had died, 10 (0.2%) were still in hospital, and two (0.04%) were still in ICU. Fatal COVID-19 cases had a median age of 59.5 years (IQR 55.8–68), and were mostly males (13, 92.9%). All included pregnant women (26, 0.5%), children (131, 2.6%), and healthcare workers (135, 2.7%) were alive and not hospitalized at the end of follow up. A total of 1424 patients (28.5%) required hospitalization, out of which 108 (7.6%) were admitted to ICU. Most frequent co-morbidities in hospitalized adults were diabetes (23.2%), and hypertension (20.7%). Multivariable logistic regression showed that older age [adjusted odds ratio (aOR) 1.041, 95% confidence interval (CI) 1.022–1.061 per year increase; P < 0.001], male sex (aOR 4.375, 95% CI 1.964–9.744; P < 0.001), diabetes (aOR 1.698, 95% CI 1.050–2.746; P 0.031), chronic kidney disease (aOR 3.590, 95% CI 1.596–8.079, P 0.002), and higher BMI (aOR 1.067, 95% CI 1.027–1.108 per unit increase; P 0.001), were all independently associated with increased risk of ICU admission. Conclusions: In a relatively younger national cohort with a low co-morbidity burden, COVID-19 was associated with low all-cause mortality. Independent risk factors for ICU admission included older age, male sex, higher BMI, and co-existing diabetes or chronic kidney disease.

Studies on the etiology of respiratory infections among children in Qatar and surrounding countries are limited. To describe the prevalence and seasonality of RSV, influenza, and other respiratory pathogens among children in Qatar. We retrospectively collected and analyzed data of 33,404 children (<15 years) presented with influenza-like illness from 2012 to 2017. At least one respiratory pathogen was detected in 26,138 (78%) of patients. Together, human rhinoviruses (HRV), respiratory syncytial virus (RSV), and influenza viruses comprised nearly two-thirds of all cases, affecting 24%, 19.7%, and 18.5%, respectively. A prevalence of 5-10% was recorded for adenovirus, parainfluenza viruses (PIVs), human bocavirus (HboV), and human coronaviruses (HCoVs). Human metapneumovirus (HMPV), enteroviruses, M. pneumonia, and parechovirus had prevalences below 5%. While RSV, influenza, and HMPV exhibited strong seasonal activity in the winter, HRV was active during low RSV and influenza circulation. The burden of RSV exceeds that of influenza among young age groups, whereas influenza correlated positively with age. Further, HRV, adenovirus, influenza, and RSV infection rates varied significantly between male and females. This comprehensive multi-year study provides insights into the etiology of ILI among children in Qatar, which represents the Gulf region. Our results reinforce the significance of active surveillance of respiratory pathogens to improve infection prevention and control strategies, particularly among children.

Little is known about the association between respiratory viral infections and their impact on intestinal microbiota. Here, we compared the effect of influenza types, A and B, and influenza shedding in patients’ stools on the gut microbiota diversity and composition. Deep sequencing analysis was performed for the V4 region of the 16S rRNA gene. Fecal samples were collected from 38 adults with active respiratory influenza infection and 11 age-matched healthy controls. Influenza infection resulted in variations in intestinal bacterial community composition rather than in overall diversity. Overall, infected patients experienced an increased abundance of Bacteroidetes and a corresponding decrease in Firmicutes. Differential abundance testing illustrated that differences in gut microbiota composition were influenza type-dependent, identifying ten differentially abundant operational taxonomic units (OTUs) between influenza A- and influenza B-infected patients. Notably, virus shedding in fecal samples of some patients had significantly reduced gut bacterial diversity (p = 0.023). Further taxonomic analysis revealed that the abundance of Bacteroides fragilis was significantly higher among shedders compared to non-shedders (p = 0.037). These results provide fundamental evidence of the direct effect of influenza infection on gut microbiota diversity, as reported in patients shedding the virus.