Advancements and challenges in antifungal therapeutic development
Bibliographic record
Abstract
Over recent decades, the global burden of fungal disease has expanded dramatically. It is estimated that fungal disease kills approximately 1.5 million individuals annually; however, the true worldwide burden of fungal infection is thought to be higher due to existing gaps in diagnostics and clinical understanding of mycotic disease. The development of resistance to antifungals across diverse pathogenic fungal genera is an increasingly common and devastating phenomenon due to the dearth of available antifungal classes. These factors necessitate a coordinated response by researchers, clinicians, public health agencies, and the pharmaceutical industry to develop new antifungal strategies, as the burden of fungal disease continues to grow. This review provides a comprehensive overview of the new antifungal therapeutics currently in clinical trials, highlighting their spectra of activity and progress toward clinical implementation. We also profile up-and-coming intracellular proteins and pathways primed for the development of novel antifungals targeting their activity. Ultimately, we aim to emphasize the importance of increased investment into antifungal therapeutics in the current continually evolving landscape of infectious disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".