- 作者: Wenjie Fang, Junqi Wu, Mingrong Cheng, Xinlin Zhu, Mingwei Du, Chang Chen, Wanqing Liao, Kangkang Zhi & Weihua Pan
- 作者服務機構: 1.Department of Anorectal Surgery, The Third Affiliated Hospital of Guizhou Medical University, Guizhou, 558000, China 2.Department of Dermatology, Shanghai Key Laboratory of Molecular Medical Mycology, Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China 3.Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China 4.Department of Vascular and Endovascular Surgery, Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China 5.Shanghai Engineering Research Center of Lung Transplantation, Shanghai, 200433, China
- 中文摘要:
- 英文摘要:
Background The global burden of invasive fungal infections (IFIs) has shown an upsurge in recent years due to the
higher load of immunocompromised patients sufering from various diseases. The role of early and accurate diagnosis
in the aggressive containment of the fungal infection at the initial stages becomes crucial thus, preventing the development of a life-threatening situation. With the changing demands of clinical mycology, the feld of fungal diagnostics has evolved and come a long way from traditional methods of microscopy and culturing to more advanced nonculture-based tools. With the advent of more powerful approaches such as novel PCR assays, T2 Candida, microfuidic
chip technology, next generation sequencing, new generation biosensors, nanotechnology-based tools, artifcial
intelligence-based models, the face of fungal diagnostics is constantly changing for the better. All these advances
have been reviewed here giving the latest update to our readers in the most orderly fow.
Main text A detailed literature survey was conducted by the team followed by data collection, pertinent data extraction, in-depth analysis, and composing the various sub-sections and the fnal review. The review is unique in its kind
as it discusses the advances in molecular methods; advances in serology-based methods; advances in biosensor
technology; and advances in machine learning-based models, all under one roof. To the best of our knowledge, there
has been no review covering all of these felds (especially biosensor technology and machine learning using artifcial
intelligence) with relevance to invasive fungal infections.
Conclusion The review will undoubtedly assist in updating the scientifc community’s understanding of the most
recent advancements that are on the horizon and that may be implemented as adjuncts to the traditional diagnostic
algorithms. - 中文關鍵字:
- 英文關鍵字: s Invasive fungal infections, Fungal diagnostics, Mycology, Detection, PCR, Candidiasis