Background: Data on the epidemiological characteristics and clinical features of COVID-19 in patients of different ages and sex are limited. Existing studies have mainly focused on the pediatric and elderly population.Objective: Assess whether age and sex interact with other risk factors to influence the severity of SARS-CoV-2 infection.Material and Methods: The study sample included all consecutive patients who satisfied the inclusion criteria and who were treated from 24 February to 1 July 2020 in Dubai Mediclinic Parkview (560 cases) and Al Ain Hospital (605 cases), United Arab Emirates. We compared disease severity estimated from the radiological findings among patients of different age groups and sex. To analyze factors associated with an increased risk of severe disease, we conducted uni- and multivariate regression analyses. Specifically, age, sex, laboratory findings, and personal risk factors were used to predict moderate and severe COVID-19 with conventional machine learning methods.Results: Need for O₂ supplementation was positively correlated with age. Intensive care was required more often for men of all ages (p < 0.01). Males were more likely to have at least moderate disease severity (p = 0.0083). These findings were aligned with the results of biochemical findings and suggest a direct correlation between older age and male sex with a severe course of the disease. In young males (18–39 years), the percentage of the lung parenchyma covered with consolidation and the density characteristics of lesions were higher than those of other age groups; however, there was no marked sex difference in middle-aged (40–64 years) and older adults (≥65 years). From the univariate analysis, the risk of the non-mild COVID-19 was significantly higher (p < 0.05) in midlife adults and older adults compared to young adults. The multivariate analysis provided similar findings.Conclusion: Age and sex were important predictors of disease severity in the set of data typically collected on admission. Sexual dissimilarities reduced with age. Age disparities were more pronounced if studied with the clinical markers of disease severity than with the radiological markers. The impact of sex on the clinical markers was more evident than that of age in our study.

Tuberculosis of the heart is relatively rare and presents a significant diagnostic difficulty for physicians. It is the leading cause of death from infectious illness. It is one of the top 10 leading causes of death worldwide, with a disproportionate impact in low- and middle-income nations. The radiologist plays a pivotal role as CMR is a non-invasive radiological method that can aid in identifying potential overlap and differential diagnosis between tuberculosis, mass lesions, pericarditis, and myocarditis. Regardless of similarities or overlap in observations, the combination of clinical and certain particular radiological features, which are also detected by comparison to earlier and follow-up CMR scans, may aid in the differential diagnosis. CMR offers a significant advantage over echocardiography for detecting, characterizing, and assessing cardiovascular abnormalities. In conjunction with clinical presentation, knowledge of LGE, feature tracking, and parametric imaging in CMR may help in the early detection of tuberculous myopericarditis and serve as a surrogate for endomyocardial biopsy resulting in a quicker diagnosis and therapy. This article aims to explain the current state of cardiac tuberculosis, the diagnostic utility of CMR in tuberculosis (TB) patients, and offer an overview of the various imaging and laboratory procedures used to detect cardiac tuberculosis.

Background: Recently, rapid fluctuations of ambient temperature were found to be associated with hospital admission for cardiovascular diseases in general and the ischemic stroke in particular. Objective: to test if climatic factors predict the incidence of stroke reliably and to study the predictive potential of risk factors for a stroke. Materials and methods: In a retrospective design, we studied 566 patients admitted to the stroke unit in 2016-2019. A distributed lag nonlinear model was used to explore immediate and delayed effects of weather and clinicodemographic risk factors on the stroke incidence. Supervised machine learning was used to build models predictive of the mRS score. We assessed model performance by calculating $\text{R}^{2}$ , mean absolute error and root-mean-square error. Results and conclusions: We found a non-correlation between the weather parameters and statistics on stroke. The disparities in their trends lead us to investigate behind time effects of the environment with distributed lag models and a concordant impact of all the settings - with machine learning models. If categorized into two classes by severity and functional outcomes, the cases have few disparities in the weather parameters within a week before the stroke onset. In contrast to the groups classified by severity, the ones grouped by outcomes have a significant difference in age, nationality, the presence of background diseases and smoking status. We ranked environmental and individual risk factors by the information gain that they provide to the models. Inclusion of the weather parameters into the machine learning model predicting the mRS score provides a slight boost in performance.

Background: Radiomical data are redundant but they might serve as a tool for lung quantitative assessment reflecting disease severity and actual physiological status of COVID-19 patients. Objective: Test the effectiveness of machine learning in eliminating data redundancy of radiomics and reflecting pathophysiologic changes in patients with COVID-19 pneumonia. Methods: We analyzed 605 cases admitted to Al Ain Hospital from 24 February to 1 July, 2020. They met the following inclusion criteria: age≥18 years; inpatient admission; PCR positive for SARS-CoV-2; lung CT available at PACS. We categorized cases into 4 classes: mild ≤25% of pulmonary parenchymal involvement, moderate - 25-50%, severe - 50-75%, and critical – over 75%. We used CT scans to build regression models predicting the oxygenation level, respiratory and cardiovascular functioning. Results: Radiomical findings are a reliable source of information to assess the functional status of patients with COVID-19. Machine learning models can predict the oxygenation level, respiratory and cardiovascular functioning from a set of demographics and radiomics data regardless of the settings of reconstruction kernels. The regression models can be used for scoring lung impairment and comparing disease severity in follow up studies. The most accurate prediction we achieved was 6.454±3.715% of mean absolute error/range for all the features and 7.069±4.17% for radiomics. Conclusion: The models may contribute to the proper risk evaluation and disease management especially when the oxygen therapy impacts the actual values of the functional findings. Still, the structural assessment of an acute lung injury reflects the severity of the disease.

RNA (mRNA) vaccines used to prevent COVID-19 infection may cause myocarditis. We describe a case of acute myocarditis in a 27-year-old male after receiving the second dose of a Pfizer immunization. Three days after receiving the second dose of vaccine, he had acute chest pain. Electrocardiographic examination revealed non-specific ST-T changes in the inferior leads. Troponin levels in his laboratory tests were 733 ng/L. No abnormalities were detected on his echocardiography or coronary angiography. The basal inferoseptal segment was hypokinetic. The LV EF was 50%, whereas the RV EF was 46%. Epicardial and mesocardial LGE were shown in the left ventricle’s basal and mid anterolateral, posterolateral, and inferoseptal segments. The native T1 was 1265 ± 54 ms, and the native T2 was 57 ± 10 ms. Myocardial strain indicated that the baseline values for LV GLS (−14.55), RV GLS (−15.8), and RVCS (−6.88) were considerably lower. The diagnosis of acute myocarditis was determined based on the clinical presentation and cardiac magnetic resonance (CMR) findings.

Background: Chest radiographs are frequently used to evaluate pediatric patients with COVID-19 infection during the current pandemic. Despite the minimal radiation dose associated with chest radiography, children are far more sensitive to ionizing radiation's carcinogenic effects than adults. This study aimed to examine whether serum biochemical markers could be potentially used as a surrogate for imaging findings to reduce radiation exposure. Methods: The retrospective posthoc analysis of 187 pediatric patients who underwent initial chest radiographs and serum biochemical parameters on the first day of emergency department admission. The cohort was separated into two groups according to whether or not the initial chest radiograph revealed evidence of pneumonia. Spearman's rank correlation was used to connect serum biochemical markers with observations on chest radiographs. The Student's t-test was employed for normally distributed data, and for non-normally distributed data, the Mann–Whitney U test was used. A simple binary logistic regression was used to determine the importance of LDH in predicting chest radiographs. The discriminating ability of LDH in predicting chest radiographs was determined using receiver operating characteristics (ROC) analysis. The cut-off value was determined using Youden's test. Interobserver agreement was quantified using the Cohen k coefficient. Results: 187 chest radiographs from 187 individual pediatric patients (95 boys and 92 girls; mean age ± SD, 10.1 ± 6.0 years; range, nine months–18 years) were evaluated. The first group has 103 patients who did not have pneumonia on chest radiographs, while the second group contains 84 patients who had evidence of pneumonia on chest radiographs. GGO, GGO with consolidation, consolidation, and peri-bronchial thickening were deemed radiographic evidence of pneumonia in group 2 patients. Individuals in group 2 with radiological indications of pneumonia had significantly higher LDH levels (p = 0.001) than patients in group 1. The Spearman's rank correlation coefficient between LDH and chest radiography score is 0.425, showing a significant link. With a p-value of < 0.001, the simple binary logistic regression analysis result validated the relevance of LDH in predicting chest radiography. An abnormal chest radiograph was related to LDH > 200.50 U/L (AUC = 0.75), according to the ROC method. Interobserver agreement between the two reviewers was almost perfect for chest radiography results in both groups (k = 0.96, p = 0.001). Conclusion: This study results show that, compared to other biochemical indicators, LDH has an 80.6% sensitivity and a 62% specificity for predicting abnormal chest radiographs in a pediatric patient with confirmed COVID-19 infection. It also emphasizes that biochemical measures, rather than chest radiological imaging, can detect the pathogenic response to COVID-19 infection in the chest earlier. As a result, we hypothesized LDH levels might be potentially used instead of chest radiography in children with COVID-19, reducing radiation exposure.