An overview of opportunistic fungal infections associated with COVID-19.

The COVID-19 survivors and long-term steroid administered patients exhibit a variety of fungal co-infections. The lives of COVID-19 patients and survivors are hampered by fungal species of the genera Candida, Aspergillus, and Mucor. There have been cases of mucormycosis, aspergillosis, and candidiasis in COVID-19 patients. The treatments given to these opportunistic fungal infections include polyene like amphotericin B, azoles including imidazoles like ketoconazole, miconazole, and triazoles like fluconazole, voriconazole, itraconazole, Echinocandin derivatives like- caspofungin, micafungin, immunomodulatory therapy, granulocyte transfusion, etc. A successful recovery and the reduction of fatalities depend on prompt diagnosis and treatment. To reduce mortality, advanced techniques to identify such uncommon infections at a very early stage are necessary. This review's goal is to provide a summary of the systemic and superficial opportunistic fungal infections that the COVID-19 survivors were dealing with, including information on illness incidence, pathogenicity, and treatment.

The challenges of the genome-based identification of antifungal resistance in the clinical routine.

The increasing number of chronic and life-threatening infections caused by antimicrobial resistant fungal isolates is of critical concern. Low DNA sequencing cost may facilitate the identification of the genomic profile leading to resistance, the resistome, to rationally optimize the design of antifungal therapies. However, compared to bacteria, initiatives for resistome detection in eukaryotic pathogens are underdeveloped. Firstly, reported mutations in antifungal targets leading to reduced susceptibility must be extensively collected from the literature to generate comprehensive databases. This information should be complemented with specific laboratory screenings to detect the highest number possible of relevant genetic changes in primary targets and associations between resistance and other genomic markers. Strikingly, some drug resistant strains experience high-level genetic changes such as ploidy variation as much as duplications and reorganizations of specific chromosomes. Such variations involve allelic dominance, gene dosage increments and target expression regime effects that should be explicitly parameterized in antifungal resistome prediction algorithms. Clinical data indicate that predictors need to consider the precise pathogen species and drug levels of detail, instead of just genus and drug class. The concomitant needs for mutation accuracy and assembly quality assurance suggest hybrid sequencing approaches involving third-generation methods will be utilized. Moreover, fatal fast infections, like fungemia and meningitis, will further require both sequencing and analysis facilities are available in-house. Altogether, the complex nature of antifungal resistance demands extensive sequencing, data acquisition and processing, bioinformatic analysis pipelines, and standard protocols to be accomplished prior to genome-based protocols are applied in the clinical setting.