ABSTRACT
Background: A good understanding of the possible risk factors for coronavirus disease 19 (COVID-19) severity could help clinicians in identifying patients who need prioritized treatment to prevent disease progression and adverse outcome. In the present study, we aimed to correlate clinical and laboratory characteristics of hospitalized COVID-19 patients to disease outcome in Saudi Arabia. Materials and Methods: The present study included 199 COVID-19 patients admitted to King Fahd Specialist Hospital, Buraydah, Qassim, Saudi Arabia, from April to December 2020. Patients were followed-up until discharge either for recovery or death. Demographic data, clinical data and laboratory results were retrieved from electronic patient records. Results: Critical COVID-19 cases showed higher mean of age and higher prevalence of co-morbid conditions. Fifty-five patients died during the observation period. Risk factors for in hospital death for COVID 19 patients were leukocytosis (OR 1.89, 95% CI 1.008-3.548, p = 0.081), lymphocytopenia (OR 2.152, 95% CI 1.079-4.295, p = 0.020), neutrophilia (OR 1.839, 95% CI 0.951-3.55, p = 0.047), thrombocytopenia (OR 2.152, 95% CI 0.852-5.430, p = 0.085), liver injury (OR 2.689, 95% CI 1.373-4.944, p = 0.003), acute kidney injury (OR 1.248, 95% CI 0.631-2.467 p = 0.319), pancreatic injury (OR 1.973, 95% CI 0.939-4.144, p = 0.056) and high D dimer (OR 2.635, 95% CI 0.747-9.287, p = 0.091). Conclusion: Clinical and laboratory data of COVID-19 patients may help understanding the pathogenesis of the disease and subsequently improve of the outcome of patients by determination of the associated risk factors and recognition of high risk group who are more liable for complications and in hospital death. The present study put an eye on some parameters (laboratory and clinical) that should be alarming signs that the patient is at high risk bad prognosis.
ABSTRACT
To eradicate most infectious diseases, mathematical modelling of contagious diseases has revealed that a combination of quarantine, vaccination, and cure is frequently required. However, eradicating the disease will remain a difficult task if they aren't provided at the appropriate time and in the right quantity. Control analysis has been shown to be an effective way for discovering the best approaches to preventing the spread of contagious diseases through the development of disease preventive interventions. The method comprises reducing the cost of infection, implementing control measures, or both. In order to gain a better understanding of COVID-19's future dynamics, this study presents a compartmental mathematical model. The problem is modelled as a highly nonlinear coupled system of classical order ODEs, which is then generalised using the Mittag-Leffler kernel's fractal-fractional derivative. The uniqueness of the fractional model under discussion has also been demonstrated. The boundedness and non-negativity of the considered model are also established. The next generation technique is used to examine basic reproduction, anddisease free and endemic equilibrium. We used reported cases from Australia in this investigation due to the high risk of infection. The reported cases are considered between 1st July 2021 and 20th August 2021. On the basis of previous data, the spread of infection is predicted for the next 600 days which is shown through different graphs. The graphical solution of the considered nonlinear model is obtained via numerical scheme by implementing the MATLAB software. Based on the fitted values of parameters, the basic reproduction number R0 is calculated as R0≈1.58276. Furthermore, the impact of fractional and fractal parameter on the disease spread among different classes is demonstrated. In addition, the impact of quarantine and vaccination on infected people is dramatically depicted. It's been argued that public awareness of the quarantine and effective vaccination can drastically reduce infection rates in the population.
ABSTRACT
The prevalence of allergic diseases is regarded as one of the key challenges in health worldwide. Although the precise mechanisms underlying this rapid increase in prevalence are unknown, emerging evidence suggests that genetic and environmental factors play a significant role. The immune system, microbiota, viruses, and bacteria have all been linked to the onset of allergy disorders in recent years. Avoiding allergen exposure is the best treatment option; however, steroids, antihistamines, and other symptom-relieving drugs are also used. Allergen bioinformatics encompasses both computational tools/methods and allergen-related data resources for managing, archiving, and analyzing allergological data. This study highlights allergy-promoting mechanisms, algorithms, and concepts in allergen bioinformatics, as well as major areas for future research in the field of allergology.