Seroprevalence of SARS-CoV-2 IgG in blood donors in a teaching institute from Western part of Maharashtra.

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.

Development of a machine learning model to predict risk of development of COVID-19-associated mucormycosis.

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.

Rhino-orbital cerebral mycosis: a case series of non-mucorales in COVID patients.

Introduction: Rhino-Orbito-cerebral mycoses are not only caused by Aspergillus spp. and Zygomycetes spp. but also can be associated with other rare species such as Neurospora spp., Cladosporium spp. and Fusarium spp. Mucormycosis is associated causatively with immunocompromised states, for example patients with comorbidities such as diabetes mellitus. Clinical symptoms of coronavirus disease (COVID) and mucormycosis in tandem are critical and relentless, frequently with no life-saving treatment. Case series: We report three patients with COVID-19 infection, who during the course of treatment developed rhino-orbital-cerebral mycosis including oral cavity involvement. Rhinocerebral mycosis along with oral cavity involvement was diagnosed by radiological investigations and preliminary screening for fungal infection (KOH mount) in all three cases. Empirical treatment was started but patients did not respond to treatment. All patients died even after debridement and maxillectomy. On culture, rare species of fungi were isolated in all three cases which, with the help of a reference laboratory, were identified as Neurospora, Cladosporium and Fusarium. Neurospora is considered nonpathogenic to humans. Cladosporium is a dematiaceous fungus found in soil in all climates, associated with disseminated or cerebral infections; and Fusarium, though considered a saprophytic colonizer of skin and respiratory mucosa along with other bacteria, is a common cause of mycotic keratitis worldwide. Conclusion: Immune system modifications due to COVID-19 with/without other risk factors can result in fungal co-infections that prove to be fatal for the patients. It is vital to be aware that COVID-19 patients, particularly those who are critically ill, may acquire secondary fungal infections and early detection is critical.