抗真菌
资源(消歧)
计算机科学
计算生物学
生物
微生物学
计算机网络
作者
Richard Kwizera,Alireza Abdolrasouli,Guillermo García‐Effrón,David W. Denning
标识
DOI:10.1016/s1473-3099(24)00429-8
摘要
Patients infected with antifungal-resistant fungi often do not respond to therapy, substantially increasing mortality risk. Some fungi are inherently resistant to particular antifungals, underscoring the importance of rapid genus identification or, ideally, rapid species identification. The past decade has seen an increase in variable antifungal resistance rates among human fungal pathogens, necessitating individual isolate testing. Various antifungal susceptibility testing (AFST) methods are most suitable for resource-constrained settings, including agar diffusion, gradient diffusion, broth microdilution, and automated tests, which all differ in speed, reliability, and cost; yet AFST remains largely unavailable in resource-constrained settings. This Personal View explores the feasibility of AFST implementation in resource-constrained settings and addresses broader accessibility concerns. We outline seven steps for implementation of AFST with an initial focus on accurate species identification (to predict intrinsic resistance) of Candida albicans, Candida parapsilosis, Candida glabrata, and Aspergillus fumigatus. New funding, laboratory and clinical training, clear protocols, access to media and reagents, acquisition and maintenance of quality control strains, and regular participation in an external quality assurance programme are all essential for sustainable AFST services. AFST is fundamental for patient care guidance, surveillance data generation, and strengthening antifungal stewardship programmes. Political commitment and international collaborations are crucial for enhanced AFST service delivery.
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