Research advances and public health strategies in China on WHO priority fungal pathogens
Bibliographic record
Abstract
Fungal pathogens pose significant and increasing threats to public health. Each year, over a billion people are infected by fungal pathogens, directly contributing to millions of deaths. To overcome the challenge of fungal threat, in 2022, World Health Organization (WHO) issued a Fungal Priority Pathogens List (FPPL) aimed at strengthening international response, promoting research, and enhancing policy intervention development. Over the past four decades, China has made tremendous progress in advancing our knowledge of fungal infections. Here, we review research trends and recent progress in China on fungal pathogens on the WHO FPPL, with an emphasis on four critical pathogens: Cryptococcus neoformans, Candidozyma auris, Aspergillus fumigatus, and Candida albicans since 2022. In addition, we describe national policies and strategic measures aimed at large-scale prevention and control of fungal infections. Our bibliometric analyses of articles published by Chinese researchers from 1983 to 2024 in the Web of Science Core Collection (WOSCC, English-language) and China National Knowledge Infrastructure (CNKI, Chinese-language) revealed increasing number of peer-reviewed publications on human fungal pathogens in both databases up to 2008 when the number in the CKNI database dropped and remained relatively flat since while that in the WOSCC database continued to increase, reflecting the strategic emphasis by Chinese institutions and funding agencies on achieving greater international visibility, academic impact, and integration within the global scientific community. In both databases, the four critical priority pathogens accounted for > 45% of the studies and the progresses made by Chinese researchers since 2022 on them are described here. A shared challenge for treating all fungal infections is the emergence and spread of antifungal resistance. We highlight antifungal resistance, tolerance, and persistence, and describe recent developments in antifungal drug pipelines, including those in China. Beyond scientific breakthroughs, China has been making coordinated prevention efforts and robust policy measures. However, significant challenges remain in understanding pathogen population dynamics and host-pathogen interactions; in developing and deploying rapid, sensitive, specific, and cost-effective diagnosis; in designing geographic region-specific and personalized prevention and treatments; and in alleviating the growing burden of antifungal resistance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.029 | 0.044 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".