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1.
Asthma Allergy Immunology ; 19(3):174-182, 2021.
Article in English | EMBASE | ID: covidwho-1856522

ABSTRACT

Objective: The clinical features of COVID-19 range from asymptomatic disease to severe pneumonia or even death. Therefore, many researchers have investigated the factors that could affect the severity of COVID-19. We aimed to assess the impact of aero-allergen sensitization and allergic diseases on the severity of COVID-19. Materials and Methods: We included 60 adult patients with symptomatic COVID-19 and allocated them into two groups equal in number as having severe and non-severe COVID-19. We evaluated the demographic features and allergic diseases in addition to clinical, laboratory and radiological findings of COVID-19. Skin prick tests (SPTs) with common aero-allergens, serum total IgE levels and blood eosinophil counts were evaluated 3 months after the patient's recovery from COVID-19. Results: The mean age of the patients was 52 ± 11 years and 73.3% of the patients were male. There was no significant difference between the two groups in terms of age, gender, smoking habits, obesity and comorbidities. Although the frequency of sensitization to aeroallergens and the allergic diseases were similar, the history of allergic diseases in the family was higher in the severe group (p<0.001). The polysensitization in SPTs was associated with the presence of a cytokine storm during the infection (p=0.02). Total IgE levels and blood eosinophil counts were not significantly different between the two groups. Conclusion: The presence of atopy or allergic diseases does not seem to be related to the severity of COVID-19. However, polysensitization and a family history of allergic diseases are more prominent in those having a cytokine storm and severe COVID-19, respectively.

2.
European Journal of Immunology ; 51:362-362, 2021.
Article in English | Web of Science | ID: covidwho-1716882
3.
Concurrency and Computation: Practice and Experience ; 2022.
Article in English | Scopus | ID: covidwho-1653209

ABSTRACT

The rapid growth in the airline industry, which started in 2009, continued until the COVID-19 era, with the annual number of passengers almost doubling in 10 years. This situation has led to increased competition between airline companies, whose profitability has decreased considerably. They aimed to increase their profitability by making services like seat selection, excess baggage, Wi-Fi access optional under the name of ancillary services. To the best of our knowledge, there is no recommendation system for recommending ancillary services for airline companies. Also, to the best of our knowledge, there is no testing framework to compare recommendation algorithms considering their scalabilities and running times. In this paper, we propose a framework based on Lambda architecture for recommendation systems that run on a big data processing platform. The proposed method utilizes association rule and sequential pattern mining algorithms that are designed for big data processing platforms. To facilitate testing of the proposed method, we implement a prototype application. We conduct an experimental study on the prototype to investigate the performance of the proposed methodology using accuracy, scalability, and latency related performance metrics. The results indicate that the proposed method proves to be useful and has negligible processing overheads. © 2022 John Wiley & Sons Ltd.

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