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Introduction: COVID-19 is a disease caused by the severe acute respiratory syndrome coronavirus 2 that has appeared as a global pandemic in recent times. Currently, the transmission rate has slowed down significantly, but the definite pathological reason behind this is still unknown. Therefore, the prevalence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody must be studied to establish the relation between the rate of transmission and antibody presence. Materials and Methods: A clinical assessment was performed to evaluate the seroprevalence of SARS-CoV-2 Immunoglobulin G (IgG) antibodies among 299 healthy volunteers in the period of February to May 2021. Serum samples were analyzed using chemiluminescent microparticle immunoassay (CMIA) technology to detect the presence of IgG antibodies. Result: It was observed that 21% of the participants were seropositive, and 78% of the population was seronegative across the different genders. This confirmed that the generation of antibodies is independent of gender. Simultaneously, a t-test was performed that further suggested no statistical correlation between gender and seroprevalence. Moreover, a comprehensive analysis was performed to establish the relation between age and blood group with the seroprevalence. However, there was no statistical relationship found among these parameters. Conclusion: This study assisted in examining the underlying causes of high or low seroprevalence among healthy volunteers.
RESUMO
Aim: The study aimed to identify quantitative parameters that increase the risk of rhino-orbito-cerebral mucormycosis, and subsequently developed a machine learning model that can anticipate susceptibility to developing this condition. Methods: Clinicopathological data from 124 patients were used to quantify their association with COVID-19-associated mucormycosis (CAM) and subsequently develop a machine learning model to predict its likelihood. Results: Diabetes mellitus, noninvasive ventilation and hypertension were found to have statistically significant associations with radiologically confirmed CAM cases. Conclusion: Machine learning models can be used to accurately predict the likelihood of development of CAM, and this methodology can be used in creating prediction algorithms of a wide variety of infections and complications.
Fungal infections caused by the Mucorales order of fungi usually target patients with a weakened immune system. They are usually also associated with abnormal blood sugar states, such as in diabetic patients. Recent work during the COVID-19 outbreak suggested that excessive steroid use and diabetes may be behind the rise in fungal infections caused by Mucorales, known as mucormycosis, in India, but little work has been done to see whether we can predict the risk of mucormycosis. This study found that these fungal infections need not necessarily be caused by Mucorales' species, but by a wide variety of fungi that target patients with weak immune systems. Secondly, we found that diabetes, breathing-assisting devices and high blood pressure states had associations with COVID-19-associated fungal infections. Finally, we were able to develop a machine learning model that showed high accuracy when predicting the risk of development of these fungal infections.
