Introduction The phenomenon of coronavirus disease 2019 (COVID-19)-related candidiasis is gaining increased attention and acknowledgment as an integral component of the severe consequences of COVID-19. The aim of the present study was to assess the association between Candida albicans and COVID-19 in complete denture wearers. Materials and methods An observational study was conducted on 45 complete denture wearers, who were divided into three groups as follows: Group 1, 15 subjects with mild to moderate COVID-19 infection; Group 2, 15 subjects with severe COVID-19 infection; and Group 3, 15 subjects without COVID-19 infection. Mean colony forming units (CFU) were observed on agar plates containing Sabouraud dextrose in the salivary samples of the participants. Analysis of variance, followed by post-hoc analysis by Tukey\'s test, was used to compare CFU between the groups. Pearson\'s correlation coefficient was used to study the correlation between variables. Results The highest average colony-forming units of Candida albicans were observed in Group 2, followed by Group 1, compared to the control group, and a significant (p<0.001) difference was found. A weak positive correlation was found between the age of the patients and the duration of denture usage, as well as between age and the counts of Candida albicans in Groups 1 and 3. This correlation was more pronounced in Group 3. A strong positive correlation was observed in all groups between the Candida albicans count and the duration of denture usage by the patients. Conclusion The association between Candida albicans and denture wear was compounded by the presence of COVID-19. Consequently, the timely identification of Candida albicans infection in patients with COVID-19 is important to establish more efficacious approaches for antifungal treatment and prophylactic interventions.

Instinctive detection of infections by carefully inspecting the signs on the plant leaves is an easier and economic way to diagnose different plant leaf diseases. This defines a way in which symptoms of diseased plants are detected utilizing the concept of feature learning (Sulistyo et al., 2020). The physical method of detecting and analyzing diseases takes a lot of time and has chances of making many errors (Sulistyo et al., 2020). So a method has been developed to identify the symptoms by just acquiring the chili plant leaf image. The methodology used involves image database, extracting the region of interest, training and testing images, symptoms/features extraction of the plant image using moments, building of the symptom vector feature dataset, and finding the correlation and similarity between different symptoms of the plant (Sulistyo et al., 2020). This will detect different diseases of the plant.