Transmission of Cough Aerosols in a Hospital Room Related to COVID-19

The COVID-19 coronavirus outbreak in recent years has become a pandemic across the globe. Which has a significant risk on human health causing nearly 5 million deaths worldwide. Recent research has shown that aerosol transmission is the main cause of COVID-19 virus spread. Sanitization of hands or the 6 feet at a recommended distance by WHO can reduce the virus but people with greater occupancy are needed to be addressed. A vulnerable place for disease infection is a hospital room where many patients have been infected with COVID-19. Transmission of the virus is the main cause of COVID-19 spread. Thus, understanding the flow physics of virus transmission through CFD analysis is necessary. A Euler-Lagrangian model is used to investigate aerosol transmission from an infected person inside a hospital room under the effect of ventilation. Ventilation plays a great role in the removal of aerosols from an enclosed environment. UDF was applied to turn the ventilation on after the coughing. Results have shown that without ventilation the aerosols are reaching the outlets of the room faster, while due to the ventilation effect the same aerosols reach slower due to recirculation. Aerosols travel according to the dominant ventilation flow. If the ventilation is turned off and there is no steady downward movement of air, the particles do not fall to the ground but will evaporate with time. Evaporation of the particles depends on the ambient temperature and relative humidity. The existence of running ventilation is required to prevent the circulation of aerosols. © 2022 IEEE.

Higher Pre-infection HbA1c level in diabetic individuals as a marker of severity of COVID-19

Objective: Diabetes is one of the major risk factor responsible for poor outcomes of corona virus disease (COVID-19). Association between pre infection HbA1c level in diabetic individuals and severity of COVID-19 based on the requirement for hospitalization, will be helpful in controlling and defeating COVID-19. Methods: This was cross sectional study carried out in Khalifa Gul Nawaz, (KGN) hospital, Bannu, Pakistan. 160 diabetic individuals with COVID-19 were registered in this study. All the registered subjects were divided into Hospitalized COVID-19 Daibetic, (HCD group) and Non Hospitalized COVID-19 Daibetic (NHCD groups). In HCD group 32 diabetic individual with a deadly disease and severe complications of COVID-19 were considered while in NHCD group 128 diabetic individuals with mild complications of COVID-19 were present. Data analysis was performed by using IBM SPSS version 26. Results: Mean age of HCD group was significantly higher (p< 0.05) as compared to NHCD group. Among the different co-morbidities hypertension and chronic kidney diseases were significantly associated with hospitalization of COVID-19 individuals. But, after adjusting for several preexisting clinical factors, HbA1c ≥9% was the only predictor linked with a substantially higher risk of hospitalization in a multivariate analysis. Conclusion: Higher pre infection levels of HbA1c are clear markers for the hospitalization of COVID-19. Using HbA1c levels ≥ 9, we can highlight the high risk population and can successfully control and defeat COVID-19.

Auxiliary Diagnosis of COVID-19 Based on 5G-Enabled Federated Learning

The development of 5G and artificial intelligence technologies have brought novel ideas to the prevention, control, and diagnosis of disease. Due to the limitation of the privacy protection of medical big data, releasing the data of patients is not allowed. However, as COVID-19 spreads globally, it is urgent to develop a robust diagnostic model to serve as many institutions as possible. Therefore, we propose a 5G-enabled architecture of auxiliary diagnosis based on federated learning for multiple institutions and central cloud collaboration to realize the sharing of diagnosis models with high generalization performance. In order to exchange model and parameters between the central and distributed nodes, a framework of diagnosis model cognition is constructed for sharing and updating the model adaptively. The severity classification experiments of COVID-19 were carried out on the central cloud and three edge cloud servers to verify the effectiveness of the proposed architecture and model cognition strategy. At the same time, the aggregated model shows good performance with accuracy rates of 95.3, 79.4, and 97.7 percent on distributed nodes, and the recognition results can assist doctors in executing auxiliary diagnosis. Finally, the open issues of model fusion of multimodal data in the 5G network architecture are discussed.

A comparison of emergency department presentations for medically unexplained symptoms in frequent attenders during COVID-19.

Medically unexplained symptoms (MUS) are those with no identified organic aetiology. Our emergency department (ED) perceived an increase in MUS frequency during COVID-19. The primary aim was to compare MUS incidence in frequent attenders (FAs) during COVID-19 and a control period.A retrospective list of FA-MUS presenting to our ED from March to June 2019 (control) and March to June 2020 (during COVID-19) was compared. Fisher's exact test was used to compare binomial proportions; this presented as relative risk (RR) with 95% confidence intervals (95%CI).During COVID-19, ED attendances reduced by 32.7%, with a significant increase in the incidence of FA-MUS and FA-MUS ED visits compared to control; RR 1.5 (95%CI 1.1-1.8) p=0.0006, and RR 1.8 (95%CI 1.6-2.0), p<0.0001, respectively.Despite reduced ED attendances during COVID-19, there was a significant increase in the incidence of FA-MUS patients and corresponding ED visits by this cohort. This presents a challenge to ED clinicians who may feel underprepared to manage these patients effectively.